From c4b75dee3e8c306cb8fe152be40acacfb036aef9 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Mon, 29 Jun 2026 00:52:51 +0300 Subject: [PATCH 01/24] chore: remove Deepak Kumar Battini from package authors Removed "Deepak Kumar Battini" from the metadata in src/NumSharp.Core/NumSharp.Core.csproj, which feeds the NuGet package author attribution shown on nuget.org and in the generated .nupkg. The Authors field now reads: "Eli Belash, Haiping Chen, Meinrad Recheis". Scope notes for future reference: - The @deepakkumar1984 GitHub-handle references in docs/issues/ (issue #362 archive + categories.md) were intentionally left as-is: they are factual attributions of who filed/commented on that real GitHub issue, not a self-authored project credit. - The "Deepak Kumar Gouda" entry in src/numpy/doc/changelog/ is a different person inside the third-party NumPy reference clone and is out of scope. --- src/NumSharp.Core/NumSharp.Core.csproj | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/NumSharp.Core/NumSharp.Core.csproj b/src/NumSharp.Core/NumSharp.Core.csproj index da04dfe0..534e6243 100644 --- a/src/NumSharp.Core/NumSharp.Core.csproj +++ b/src/NumSharp.Core/NumSharp.Core.csproj @@ -3,7 +3,7 @@ net8.0;net10.0 true true - Eli Belash, Haiping Chen, Meinrad Recheis, Deepak Kumar Battini + Eli Belash, Haiping Chen, Meinrad Recheis ../../packages NumSharp is the fundamental library for scientific computing with .NET providing a similar API to python's numpy scientific library. NumSharp has full N-D, broadcasting and axis support. If you want to use .NET to get started with machine learning, NumSharp will be your best tool. https://github.com/SciSharp From 80fcbf90d5071e644ff677bf686fc96c68f677df Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Mon, 29 Jun 2026 21:42:05 +0300 Subject: [PATCH 02/24] fix(casting): ToByteArray() returns logical C-order bytes (NumPy tobytes parity) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ToByteArray() copied Storage.InternalArray.BytesLength bytes from Storage.Address — the RAW underlying buffer — ignoring strides, offset and broadcasting. A non-contiguous view therefore leaked the wrong data and the wrong length: e.g. arange(10)[::2] (logical [0,2,4,6,8], 20 bytes) returned the full 40-byte parent buffer [0..9]; a transpose returned F-order bytes; a broadcast view returned only the unstretched source. ToByteArray() now mirrors numpy.ndarray.tobytes(): it returns the LOGICAL array in C (row-major) order, always exactly size*dtypesize bytes. A pristine C-contiguous offset-0 buffer (Shape.IsSliced == false) keeps the fast single-memcpy path; any sliced/strided/transposed/broadcast/offset view is first materialized via copy('C'). No internal callers depended on the old raw-buffer behavior; the legacy pristine-contiguous contract (and its existing test) is unchanged. Tests (31 new; full CI-style suite 11197 passed / 0 failed / 11 skipped): - Casting/NDArray.ToByteArray.Test.cs: every view type (contiguous, prefix, middle-slice, strided, reversed, transpose, column, broadcast, 3D, 0-d, empty) across all 15 dtypes; exact NumPy-verified bytes; frombuffer round-trips; detached-copy semantics. - Interop/NumpyByteContractTests.cs: NumSharp<->NumPy 2.4.2 byte contract — endianness (big-endian string dtype byteswaps; NPTypeCode path is little-endian), NaN-payload / signaling-NaN / subnormal / -0 bit-exact preservation (Half/Single/Double/Complex), uint64 above int64.max, complex part layout, Char<->uint16 code units incl. surrogate pairs, Decimal having no numpy dtype, and bool non-0/1-byte storage/truthiness/reduction semantics. --- .../Casting/NdArray.ToByteArray.cs | 24 +- .../Casting/NDArray.ToByteArray.Test.cs | 225 ++++++++++++++++++ .../Interop/NumpyByteContractTests.cs | 202 ++++++++++++++++ 3 files changed, 447 insertions(+), 4 deletions(-) create mode 100644 test/NumSharp.UnitTest/Casting/NDArray.ToByteArray.Test.cs create mode 100644 test/NumSharp.UnitTest/Interop/NumpyByteContractTests.cs diff --git a/src/NumSharp.Core/Casting/NdArray.ToByteArray.cs b/src/NumSharp.Core/Casting/NdArray.ToByteArray.cs index 34de7344..b141f692 100644 --- a/src/NumSharp.Core/Casting/NdArray.ToByteArray.cs +++ b/src/NumSharp.Core/Casting/NdArray.ToByteArray.cs @@ -1,21 +1,37 @@ -using System; +using System; namespace NumSharp { public partial class NDArray { + /// + /// Copy the array's elements, in C (row-major) order, into a newly allocated byte array. + /// Mirrors NumPy's ndarray.tobytes(): the result is the logical array + /// (honoring strides, offset and broadcasting), NOT the raw underlying buffer. A + /// non-contiguous or offset view (sliced/strided/transposed/broadcast) is materialized + /// into a fresh C-contiguous buffer first, so the returned length is always + /// size * dtypesize. + /// + /// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tobytes.html public byte[] ToByteArray() { if (size == 0) return System.Array.Empty(); + // A "sliced" shape is any view that does not own a pristine, full, C-contiguous, + // offset-0 buffer (Shape.IsSliced covers non-zero offset, buffer != size, AND + // non-contiguous strides — which includes broadcast and transposed views). Only a + // pristine buffer lays its logical elements out contiguously from Address; everything + // else must be materialized in C order so a single memcpy yields the logical bytes. + NDArray src = Shape.IsSliced ? this.copy('C') : this; + unsafe { - var addr = Storage.Address; - var len = Storage.InternalArray.BytesLength; + var addr = src.Storage.Address; + long len = checked((long)src.size * src.dtypesize); byte[] bytes = new byte[len]; - fixed (byte* @out = bytes) + fixed (byte* @out = bytes) Buffer.MemoryCopy(addr, @out, len, len); return bytes; } diff --git a/test/NumSharp.UnitTest/Casting/NDArray.ToByteArray.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.ToByteArray.Test.cs new file mode 100644 index 00000000..8e3d47b5 --- /dev/null +++ b/test/NumSharp.UnitTest/Casting/NDArray.ToByteArray.Test.cs @@ -0,0 +1,225 @@ +using System; +using System.Linq; +using System.Numerics; +using System.Runtime.InteropServices; +using Microsoft.VisualStudio.TestTools.UnitTesting; +using NumSharp; + +namespace NumSharp.UnitTest.Casting +{ + /// + /// Regression + contract tests for . + /// + /// NumPy parity: ndarray.tobytes() returns the LOGICAL array in C (row-major) order, + /// honoring strides / offset / broadcasting — NOT the raw underlying buffer. Before the C6 fix, + /// ToByteArray() blindly copied Storage.InternalArray.BytesLength from + /// Storage.Address, so a non-contiguous view leaked the whole parent buffer + /// (e.g. a 5-element [::2] view of a 10-element array returned 40 bytes of the wrong data). + /// + [TestClass] + public class ToByteArrayTests + { + private static readonly NPTypeCode[] AllDtypes = + { + NPTypeCode.Boolean, NPTypeCode.Byte, NPTypeCode.SByte, NPTypeCode.Int16, NPTypeCode.UInt16, + NPTypeCode.Int32, NPTypeCode.UInt32, NPTypeCode.Int64, NPTypeCode.UInt64, NPTypeCode.Char, + NPTypeCode.Half, NPTypeCode.Single, NPTypeCode.Double, NPTypeCode.Decimal, NPTypeCode.Complex + }; + + /// The independent, proven-correct C-order materialization: ToArray<T> → raw bytes. + private static byte[] LogicalBytes(NDArray nd) => nd.typecode switch + { + NPTypeCode.Boolean => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.Byte => nd.ToArray(), + NPTypeCode.SByte => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.Int16 => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.UInt16 => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.Int32 => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.UInt32 => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.Int64 => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.UInt64 => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.Char => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.Half => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.Single => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.Double => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.Decimal => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + NPTypeCode.Complex => MemoryMarshal.AsBytes(nd.ToArray()).ToArray(), + _ => throw new NotSupportedException(nd.typecode.ToString()) + }; + + private static void AssertLogical(string name, NDArray nd) + { + byte[] actual = nd.ToByteArray(); + Assert.AreEqual((int)(nd.size * nd.dtypesize), actual.Length, $"{name}: length must equal size*dtypesize"); + CollectionAssert.AreEqual(LogicalBytes(nd), actual, $"{name}: bytes must be the logical C-order array"); + } + + // ---- Pristine ---------------------------------------------------------------- + + [TestMethod] + public void Contiguous_AllDtypes() + { + foreach (var tc in AllDtypes) + AssertLogical($"contig_{tc}", np.arange(12).astype(tc)); + } + + [TestMethod] + public void Scalar0d_AllDtypes() + { + foreach (var tc in AllDtypes) + { + var nd = np.arange(1).astype(tc).reshape(new int[0]); // 0-d + AssertLogical($"scalar_{tc}", nd); + } + } + + [TestMethod] + public void Empty_ReturnsEmptyArray() + { + foreach (var tc in AllDtypes) + Assert.AreEqual(0, np.arange(0).astype(tc).ToByteArray().Length, $"empty_{tc}"); + } + + // ---- Views: the C6 regressions ---------------------------------------------- + + [TestMethod] + public void PrefixSlice_AllDtypes() + { + foreach (var tc in AllDtypes) + AssertLogical($"prefix_{tc}", np.arange(10).astype(tc)["0:5"]); + } + + [TestMethod] + public void MiddleSlice_OffsetView_AllDtypes() + { + foreach (var tc in AllDtypes) + AssertLogical($"slice_{tc}", np.arange(10).astype(tc)["3:8"]); + } + + [TestMethod] + public void Strided_AllDtypes() + { + foreach (var tc in AllDtypes) + AssertLogical($"strided_{tc}", np.arange(10).astype(tc)["::2"]); + } + + [TestMethod] + public void Reversed_AllDtypes() + { + foreach (var tc in AllDtypes) + AssertLogical($"reversed_{tc}", np.arange(10).astype(tc)["::-1"]); + } + + [TestMethod] + public void Transpose_FContiguous_AllDtypes() + { + foreach (var tc in AllDtypes) + AssertLogical($"transpose_{tc}", np.arange(6).astype(tc).reshape(2, 3).T); + } + + [TestMethod] + public void ColumnView_AllDtypes() + { + foreach (var tc in AllDtypes) + AssertLogical($"column_{tc}", np.arange(6).astype(tc).reshape(2, 3)[":,1"]); + } + + [TestMethod] + public void Broadcast_Materializes_AllDtypes() + { + foreach (var tc in AllDtypes) + { + var b = np.broadcast_to(np.arange(3).astype(tc).reshape(1, 3), new Shape(4, 3)); + AssertLogical($"broadcast_{tc}", b); + } + } + + [TestMethod] + public void ThreeD_AllDtypes() + { + foreach (var tc in AllDtypes) + AssertLogical($"3d_{tc}", np.arange(8).astype(tc).reshape(2, 2, 2)); + } + + // ---- Exact, NumPy-verified bytes -------------------------------------------- + + [TestMethod] + public void Strided_Int32_ExactBytes() + { + // np.arange(10, dtype=np.int32)[::2].tobytes() -> [0,2,4,6,8] + var v = np.arange(10).astype(NPTypeCode.Int32)["::2"]; + Assert.AreEqual(20, v.ToByteArray().Length, "5 elements * 4 bytes, NOT the 40-byte parent buffer"); + CollectionAssert.AreEqual( + new byte[] { 0,0,0,0, 2,0,0,0, 4,0,0,0, 6,0,0,0, 8,0,0,0 }, + v.ToByteArray()); + } + + [TestMethod] + public void Transpose_Int32_ExactBytes_COrder() + { + // np.arange(6, dtype=np.int32).reshape(2,3).T.tobytes() -> C-order [0,3,1,4,2,5] + var t = np.arange(6).astype(NPTypeCode.Int32).reshape(2, 3).T; + CollectionAssert.AreEqual( + new byte[] { 0,0,0,0, 3,0,0,0, 1,0,0,0, 4,0,0,0, 2,0,0,0, 5,0,0,0 }, + t.ToByteArray()); + } + + [TestMethod] + public void Reversed_Int32_ExactBytes() + { + // np.arange(5, dtype=np.int32)[::-1].tobytes() -> [4,3,2,1,0] + var r = np.arange(5).astype(NPTypeCode.Int32)["::-1"]; + CollectionAssert.AreEqual( + new byte[] { 4,0,0,0, 3,0,0,0, 2,0,0,0, 1,0,0,0, 0,0,0,0 }, + r.ToByteArray()); + } + + /// The original contract (a pristine contiguous array) must be unchanged. + [TestMethod] + public void PristineContiguous_LegacyContract_Unchanged() + { + var nd = np.array(new int[][] { new[] { 3, 1 }, new[] { 2, 1 } }); + CollectionAssert.AreEqual( + new byte[] { 3,0,0,0, 1,0,0,0, 2,0,0,0, 1,0,0,0 }, + nd.ToByteArray()); + } + + // ---- Round-trip via frombuffer ---------------------------------------------- + + [TestMethod] + public void RoundTrip_ToByteArray_FromBuffer_AllDtypes_AllViews() + { + foreach (var tc in AllDtypes) + { + var full = np.arange(12).astype(tc); + var views = new (string name, NDArray nd)[] + { + ("contig", full), + ("prefix", full["0:6"]), + ("slice", full["3:9"]), + ("strided", full["::2"]), + ("reversed", full["::-1"]), + ("transpose", np.arange(6).astype(tc).reshape(2, 3).T), + }; + foreach (var (name, nd) in views) + { + byte[] bytes = nd.ToByteArray(); + var rebuilt = np.frombuffer(bytes, tc); + // A freshly built pristine array re-emits identical bytes -> the view's logical content survived. + CollectionAssert.AreEqual(bytes, rebuilt.ToByteArray(), $"{tc}/{name} round-trip"); + Assert.AreEqual(nd.size, rebuilt.size, $"{tc}/{name} size"); + } + } + } + + /// Mutating a ToByteArray() result must never touch the source (it is a fresh copy). + [TestMethod] + public void ToByteArray_IsDetachedCopy() + { + var nd = np.arange(4).astype(NPTypeCode.Int32); + byte[] bytes = nd.ToByteArray(); + bytes[0] = 0xFF; + Assert.AreEqual(0, nd.ToArray()[0], "mutating the byte[] must not affect the source array"); + } + } +} diff --git a/test/NumSharp.UnitTest/Interop/NumpyByteContractTests.cs b/test/NumSharp.UnitTest/Interop/NumpyByteContractTests.cs new file mode 100644 index 00000000..a5fbc793 --- /dev/null +++ b/test/NumSharp.UnitTest/Interop/NumpyByteContractTests.cs @@ -0,0 +1,202 @@ +using System; +using System.Linq; +using System.Numerics; +using System.Runtime.InteropServices; +using Microsoft.VisualStudio.TestTools.UnitTesting; +using NumSharp; + +namespace NumSharp.UnitTest.Interop +{ + /// + /// Byte-level interop contract between NumSharp and NumPy-shaped buffers (the data exchanged with + /// Numpy.NET / .npy files / any C-order numpy buffer). Every expectation below was probed against + /// NumPy 2.4.2. These pin the divergences and the bit-exact guarantees found while auditing interop: + /// endianness handling, NaN-payload / signaling-NaN / subnormal / -0 preservation, uint64 above + /// int64.max, complex part layout, Char↔uint16 code units, and Decimal having no numpy dtype. + /// + [TestClass] + public class NumpyByteContractTests + { + // ---- Endianness -------------------------------------------------------------- + + [TestMethod] + public void Endianness_BigEndian_StringDtype_Byteswaps() + { + // big-endian int32 for [1, 256, 65536] + byte[] be = { 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0 }; + var nd = np.frombuffer(be, ">i4"); // string dtype carries endianness + CollectionAssert.AreEqual(new[] { 1, 256, 65536 }, nd.ToArray()); + } + + [TestMethod] + public void Endianness_BigEndian_Float64_StringDtype_Byteswaps() + { + double[] vals = { 1.5, -2.25, 1e10 }; + byte[] be = new byte[vals.Length * 8]; + for (int i = 0; i < vals.Length; i++) + { + var le = BitConverter.GetBytes(vals[i]); // x64 is little-endian + Array.Reverse(le); + Array.Copy(le, 0, be, i * 8, 8); + } + var nd = np.frombuffer(be, ">f8"); + CollectionAssert.AreEqual(vals, nd.ToArray()); + } + + [TestMethod] + public void Endianness_NPTypeCode_IsLittleEndian_NoSwap() + { + // The NPTypeCode overload has no endian information => it reads native little-endian. + // Feeding it big-endian bytes therefore reinterprets them (documents the raw-API hazard). + byte[] be = { 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0 }; + var nd = np.frombuffer(be, NPTypeCode.Int32); + CollectionAssert.AreEqual(new[] { 16777216, 65536, 256 }, nd.ToArray()); + } + + // ---- NaN payloads / signaling-NaN / subnormals / -0 (bit-exact transport) ----- + + [TestMethod] + public void Half_Specials_BitExact_RoundTrip() + { + // float16 bits: nan=0x7e00, inf=0x7c00, -inf=0xfc00, -0=0x8000, min-subnormal=0x0001, max=0x7bff + byte[] bits = { 0x00,0x7e, 0x00,0x7c, 0x00,0xfc, 0x00,0x80, 0x01,0x00, 0xff,0x7b }; + var h = np.frombuffer(bits, NPTypeCode.Half); + CollectionAssert.AreEqual(bits, h.ToByteArray(), "half bit pattern (incl. NaN payload/subnormal) must survive"); + var a = h.ToArray(); + Assert.IsTrue(Half.IsNaN(a[0])); + Assert.IsTrue(Half.IsPositiveInfinity(a[1])); + Assert.IsTrue(Half.IsNegativeInfinity(a[2])); + Assert.IsTrue(Half.IsNegative(a[3]) && a[3] == (Half)0.0, "negative zero"); + Assert.IsTrue(a[4] > (Half)0.0 && a[4] < (Half)6e-5, "smallest subnormal"); + } + + [TestMethod] + public void Single_SignalingNaN_Payload_NotQuieted() + { + // 0x7f800001 is a signaling NaN; the transport must not quiet it. + byte[] sbits = { 0x01, 0x00, 0x80, 0x7f }; + var s = np.frombuffer(sbits, NPTypeCode.Single); + Assert.IsTrue(float.IsNaN(s.ToArray()[0])); + CollectionAssert.AreEqual(sbits, s.ToByteArray(), "signaling-NaN bits preserved"); + } + + [TestMethod] + public void Double_MinSubnormal_Preserved() + { + byte[] bits = { 0x01, 0, 0, 0, 0, 0, 0, 0 }; // 0x...0001 == double.Epsilon == 5e-324 + var d = np.frombuffer(bits, NPTypeCode.Double); + Assert.AreEqual(double.Epsilon, d.ToArray()[0]); + CollectionAssert.AreEqual(bits, d.ToByteArray()); + } + + [TestMethod] + public void NegativeZero_SignBit_Preserved() + { + var nd = np.array(new[] { -0.0, 0.0 }); + byte[] b = nd.ToByteArray(); + Assert.IsTrue(double.IsNegative(nd.ToArray()[0])); + Assert.IsFalse(double.IsNegative(nd.ToArray()[1])); + Assert.AreEqual(0x80, b[7], "-0.0 sign bit in the high byte (little-endian)"); + Assert.AreEqual(0x00, b[15], "+0.0"); + } + + // ---- Integer extremes -------------------------------------------------------- + + [TestMethod] + public void UInt64_AboveInt64Max_RoundTrips() + { + ulong[] u = { 0, 1, 9223372036854775808UL, ulong.MaxValue }; + byte[] bytes = new byte[u.Length * 8]; + Buffer.BlockCopy(u, 0, bytes, 0, bytes.Length); + var nd = np.frombuffer(bytes, NPTypeCode.UInt64); + CollectionAssert.AreEqual(u, nd.ToArray()); + CollectionAssert.AreEqual(bytes, nd.ToByteArray()); + } + + // ---- Complex layout ---------------------------------------------------------- + + [TestMethod] + public void Complex_NaNInf_Parts_BitExact_RoundTrip() + { + var c = np.array(new[] + { + new Complex(double.NaN, 1), + new Complex(double.PositiveInfinity, double.NegativeInfinity), + new Complex(-0.0, 0.0) + }); + Assert.AreEqual(16, c.dtypesize, "complex128 == 2 doubles"); + var rt = np.frombuffer(c.ToByteArray(), NPTypeCode.Complex); + CollectionAssert.AreEqual(c.ToByteArray(), rt.ToByteArray()); + var a = rt.ToArray(); + Assert.IsTrue(double.IsNaN(a[0].Real) && a[0].Imaginary == 1.0); + Assert.IsTrue(double.IsPositiveInfinity(a[1].Real) && double.IsNegativeInfinity(a[1].Imaginary)); + Assert.IsTrue(double.IsNegative(a[2].Real), "real part is -0.0"); + } + + // ---- Char (UTF-16 code unit) ------------------------------------------------- + + [TestMethod] + public void Char_MapsToUInt16_CodeUnits_SurrogatePairsSplit() + { + // "A😀Z": the emoji U+1F600 is a surrogate pair in UTF-16 -> two code units. + char[] chars = "A\U0001F600Z".ToCharArray(); + byte[] cb = MemoryMarshal.AsBytes(chars).ToArray(); + var ch = np.frombuffer(cb, NPTypeCode.Char); + Assert.AreEqual(2, ch.dtypesize, "char is a 2-byte code unit (maps to numpy uint16)"); + CollectionAssert.AreEqual( + new[] { 65, 55357, 56832, 90 }, + ch.ToArray().Select(c => (int)c).ToArray()); + } + + // ---- Decimal has no numpy dtype ---------------------------------------------- + + [TestMethod] + public void Decimal_Is16Bytes_AndReinterpretAsFloatIsGarbage() + { + var dec = np.array(new[] { 1.5m, -2.5m }); + Assert.AreEqual(NPTypeCode.Decimal, dec.typecode); + Assert.AreEqual(16, dec.dtypesize, "C# decimal is 16 bytes (flags/hi/mid/lo) — NOT IEEE-754"); + Assert.AreEqual(32, dec.ToByteArray().Length); + + // Reinterpreting the 16-byte decimal as IEEE float64 (numpy has no decimal dtype) yields garbage. + var asDouble = np.frombuffer(dec.ToByteArray(), NPTypeCode.Double); + Assert.AreNotEqual(1.5, asDouble.ToArray()[0], 1e-6, + "decimal bytes are not IEEE — a raw reinterpret is meaningless; values must be converted"); + + // The correct cross-language path is value conversion (lossy beyond double precision). + CollectionAssert.AreEqual(new[] { 1.5, -2.5 }, dec.astype(NPTypeCode.Double).ToArray()); + } + + // ---- bool with non-0/1 underlying bytes -------------------------------------- + + [TestMethod] + public void Bool_NonBinaryBytes_StoragePreserved_LikeNumpy() + { + // numpy preserves the raw bytes: np.frombuffer(bytes([0,1,2,3,255]), bool).tobytes() == those bytes. + var b = np.frombuffer(new byte[] { 0, 1, 2, 3, 255 }, NPTypeCode.Boolean); + CollectionAssert.AreEqual(new byte[] { 0, 1, 2, 3, 255 }, b.ToByteArray()); + } + + [TestMethod] + public void Bool_NonBinaryBytes_Truthiness_AnyNonzeroIsTrue() + { + // numpy: every nonzero byte is True. (NB: C# bool.Equals is a raw byte compare, so a bool whose + // byte is 2 is NOT .Equals(true) even though it IS truthy — assert truthiness, not equality.) + var a = np.frombuffer(new byte[] { 0, 1, 2, 3, 255 }, NPTypeCode.Boolean).ToArray(); + Assert.IsFalse(a[0]); + Assert.IsTrue(a[1]); + Assert.IsTrue(a[2]); + Assert.IsTrue(a[3]); + Assert.IsTrue(a[4]); + } + + [TestMethod] + public void Bool_NonBinaryBytes_Reductions_NormalizeLikeNumpy() + { + // numpy normalizes bool in reductions: sum / count count True values (4), not the raw bytes. + var b = np.frombuffer(new byte[] { 0, 1, 2, 3, 255 }, NPTypeCode.Boolean); + Assert.AreEqual(4, np.sum(b).astype(NPTypeCode.Int32).ToArray()[0], "sum counts True values"); + Assert.AreEqual(4, b.ToArray().Count(x => x), "truthiness count"); + } + } +} From ec0bb404a94bafe28c57c638983d8254b72dee4d Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Mon, 29 Jun 2026 21:39:45 +0300 Subject: [PATCH 03/24] bench(history): publish 2026-06-29 snapshot (new latest) + recover bitwise after intermittent AccessViolation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Full official NumSharp-vs-NumPy run on branch nditer @ 2d16f477 (i9-13900K, .NET 10.0.101 / Release, NumPy 2.4.2). Runs the op/dtype/N matrix (14 suites × 15 dtypes × 1K/100K/10M = 1851 cells) plus the five appended subsystems (NDIter, Layout, Operand, Cast, Fusion). Repoints benchmark/history/latest -> 2026-06-29_2d16f477. Headline (NPY/NS, >1 = NumSharp faster): 1,000 1.13x (116 / 58 / 35 / 11) 100,000 0.98x (295 / 135 / 124 / 35) 10,000,000 1.39x (427 / 136 / 24 / 4) Overall: 1851 ops | ✅ 838 | 🟡 329 | 🟠 183 | 🔴 50 | ▫ 388 | ⚪ 63 NDIter 1.20x geomean · Cast 1450 win / 118 lag. Bitwise recovery ---------------- The original full run (~10782s) hit the known intermittent AccessViolation mid-run, inside the in-process Bitwise suite (BitwiseBenchmarks.LeftShift -> SimdScalarShiftDispatch -> IL_ShiftLeft_Scalar_Int32). Because the official config runs InProcessEmit (one process per suite), that single fault took the whole suite down and dropped all 81 bitwise op×dtype×N cells (⚪ jumped 69→225). The bitwise suite was re-run from the same HEAD (clean, no source change), it completed without fault (the bug is rare), and its BenchmarkDotNet JSON was merged back into the op matrix. The NDIter+Layout+Operand+Cast+Fusion sections were spliced back verbatim. Bitwise cells now carry data (⚪ 225→63), e.g. np.left_shift int32 @10M = 1.67x. Investigation (filed SciSharp/NumSharp#615) ------------------------------------------- The shift/bitwise kernels are the VICTIM, not the cause. ~13M faithful ops did not reproduce the fault, and the built-in guard-page detector (NUMSHARP_GUARD_PAGES=1) found ZERO out-of-bounds across the entire bitwise surface (setup casts + all six ops × all integer dtypes × all three sizes). AccessViolation (unmapped memory) + no OOB ⇒ an unmanaged-storage LIFETIME race (use-after-free under GC pressure), not an overrun — most likely a raw .Address pointer handed to an emitted kernel without a GC.KeepAlive, or a pool/finalizer free-list race. Full diagnosis, repro harness, and next steps in issue #615. Provenance: MANIFEST records clean HEAD (benchmarked code unchanged). The bitwise timings were measured in a separate process from the same commit on the same quiet machine — identical methodology to the orchestrator's per-suite isolation. --- benchmark/benchmark-report.md | 4562 ++- .../history/2026-06-29_2d16f477/MANIFEST.md | 55 + .../2026-06-29_2d16f477/benchmark-report.csv | 1852 ++ .../2026-06-29_2d16f477/benchmark-report.json | 22214 +++++++++++++ .../2026-06-29_2d16f477/benchmark-report.md | 2656 ++ .../history/2026-06-29_2d16f477/cards/cat.png | Bin 0 -> 22736 bytes .../history/2026-06-29_2d16f477/cards/ops.png | Bin 0 -> 23413 bytes .../2026-06-29_2d16f477/cast_results.md | 316 + .../2026-06-29_2d16f477/cast_results.tsv | 1801 ++ .../2026-06-29_2d16f477/fusion_results.md | 40 + .../2026-06-29_2d16f477/layout_results.md | 126 + .../2026-06-29_2d16f477/layout_results.tsv | 1745 ++ .../2026-06-29_2d16f477/nditer_results.md | 117 + .../2026-06-29_2d16f477/nditer_results.tsv | 199 + .../2026-06-29_2d16f477/numpy-results.json | 25916 ++++++++++++++++ .../2026-06-29_2d16f477/operand_results.md | 35 + .../2026-06-29_2d16f477/operand_results.tsv | 61 + benchmark/history/latest | 2 +- 18 files changed, 59409 insertions(+), 2288 deletions(-) create mode 100644 benchmark/history/2026-06-29_2d16f477/MANIFEST.md create mode 100644 benchmark/history/2026-06-29_2d16f477/benchmark-report.csv create mode 100644 benchmark/history/2026-06-29_2d16f477/benchmark-report.json create mode 100644 benchmark/history/2026-06-29_2d16f477/benchmark-report.md create mode 100644 benchmark/history/2026-06-29_2d16f477/cards/cat.png create mode 100644 benchmark/history/2026-06-29_2d16f477/cards/ops.png create mode 100644 benchmark/history/2026-06-29_2d16f477/cast_results.md create mode 100644 benchmark/history/2026-06-29_2d16f477/cast_results.tsv create mode 100644 benchmark/history/2026-06-29_2d16f477/fusion_results.md create mode 100644 benchmark/history/2026-06-29_2d16f477/layout_results.md create mode 100644 benchmark/history/2026-06-29_2d16f477/layout_results.tsv create mode 100644 benchmark/history/2026-06-29_2d16f477/nditer_results.md create mode 100644 benchmark/history/2026-06-29_2d16f477/nditer_results.tsv create mode 100644 benchmark/history/2026-06-29_2d16f477/numpy-results.json create mode 100644 benchmark/history/2026-06-29_2d16f477/operand_results.md create mode 100644 benchmark/history/2026-06-29_2d16f477/operand_results.tsv diff --git a/benchmark/benchmark-report.md b/benchmark/benchmark-report.md index a5b45dde..798922e3 100644 --- a/benchmark/benchmark-report.md +++ b/benchmark/benchmark-report.md @@ -17,7 +17,7 @@ --- -**Summary:** 1851 ops | ✅ 792 | 🟡 357 | 🟠 177 | 🔴 72 | ▫ 384 | ⚪ 69 +**Summary:** 1851 ops | ✅ 838 | 🟡 329 | 🟠 183 | 🔴 50 | ▫ 388 | ⚪ 63 ## Summary by size @@ -25,55 +25,55 @@ |---:|----:|--------:|--------:|---------:|------:|-----:|-----:|--------:|------:| | 500 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | - | - | | 900 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | - | - | -| 1,000 | 615 | 115 | 69 | 27 | 13 | 366 | 25 | 1.14x | 87% | +| 1,000 | 615 | 116 | 58 | 35 | 11 | 372 | 23 | 1.13x | 89% | | 50,000 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | - | - | -| 100,000 | 615 | 280 | 138 | 119 | 48 | 9 | 21 | 0.90x | 111% | +| 100,000 | 615 | 295 | 135 | 124 | 35 | 7 | 19 | 0.98x | 102% | | 5,000,000 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | - | - | -| 10,000,000 | 615 | 397 | 150 | 31 | 11 | 9 | 17 | 1.26x | 80% | +| 10,000,000 | 615 | 427 | 136 | 24 | 4 | 9 | 15 | 1.39x | 72% | --- ### 🏆 Top 15 Best (NumSharp fastest vs NumPy) -_Ranked over 1398 credible comparisons (both sides ≥1µs, within 20×); 384 negligible rows excluded as non-comparable (▫). Ratio = NumPy ÷ NumSharp — above 1.0× = NumSharp faster · %NumPy🕐 = share of NumPy's time NumSharp uses._ +_Ranked over 1400 credible comparisons (both sides ≥1µs, within 20×); 388 negligible rows excluded as non-comparable (▫). Ratio = NumPy ÷ NumSharp — above 1.0× = NumSharp faster · %NumPy🕐 = share of NumPy's time NumSharp uses._ | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|✅| np.dot(a, b) (float64) | float64 | 100,000 | 0.099 | 0.007 | 14.17× | 7% | -|✅| np.prod (float64) | float64 | 100,000 | 2.336 | 0.170 | 13.75× | 7% | -|✅| np.nanstd(a) (float64) | float64 | 1,000 | 0.020 | 0.002 | 13.33× | 8% | -|✅| np.nanstd(a) (float16) | float16 | 1,000 | 0.034 | 0.003 | 12.50× | 8% | -|✅| np.percentile(a, 50) (float64) | float64 | 1,000 | 0.030 | 0.003 | 12.18× | 8% | -|✅| np.nanquantile(a, 0.5) (float32) | float32 | 1,000 | 0.028 | 0.002 | 11.86× | 8% | -|✅| np.nanvar(a) (float16) | float16 | 1,000 | 0.032 | 0.003 | 11.67× | 9% | -|✅| np.nanpercentile(a, 50) (float32) | float32 | 1,000 | 0.027 | 0.002 | 11.51× | 9% | -|✅| np.nanpercentile(a, 50) (float64) | float64 | 1,000 | 0.030 | 0.003 | 11.42× | 9% | -|✅| np.nanvar(a) (float64) | float64 | 1,000 | 0.017 | 0.002 | 11.42× | 9% | -|✅| np.nanstd(a) (float32) | float32 | 1,000 | 0.019 | 0.002 | 11.39× | 9% | -|✅| np.nanvar(a) (float32) | float32 | 1,000 | 0.019 | 0.002 | 11.19× | 9% | -|✅| np.sum axis=0 (int8) | int8 | 10,000,000 | 4.456 | 0.399 | 11.16× | 9% | -|✅| np.sum axis=0 (int8) | int8 | 100,000 | 0.047 | 0.004 | 10.78× | 9% | -|✅| np.percentile(a, 50) (float32) | float32 | 1,000 | 0.024 | 0.002 | 10.51× | 10% | +|✅| np.prod (float64) | float64 | 100,000 | 2.331 | 0.170 | 13.71× | 7% | +|✅| np.nanprod(a) (float32) | float32 | 100,000 | 0.160 | 0.012 | 13.53× | 7% | +|✅| np.sum axis=1 (int8) | int8 | 10,000,000 | 3.511 | 0.264 | 13.30× | 8% | +|✅| np.nanvar(a) (float64) | float64 | 1,000 | 0.020 | 0.002 | 13.06× | 8% | +|✅| np.nanquantile(a, 0.5) (float32) | float32 | 1,000 | 0.028 | 0.002 | 12.60× | 8% | +|✅| np.nanstd(a) (float64) | float64 | 1,000 | 0.019 | 0.002 | 12.52× | 8% | +|✅| np.nanstd(a) (float16) | float16 | 1,000 | 0.034 | 0.003 | 12.26× | 8% | +|✅| np.prod axis=1 (float64) | float64 | 100,000 | 0.060 | 0.005 | 12.13× | 8% | +|✅| np.sum axis=1 (uint8) | uint8 | 10,000,000 | 3.206 | 0.265 | 12.11× | 8% | +|✅| np.mean (uint64) | uint64 | 100,000 | 0.054 | 0.004 | 12.08× | 8% | +|✅| np.dot(a, b) (float64) | float64 | 100,000 | 0.112 | 0.009 | 12.05× | 8% | +|✅| np.nanpercentile(a, 50) (float32) | float32 | 1,000 | 0.027 | 0.002 | 11.99× | 8% | +|✅| np.nanstd(a) (float32) | float32 | 1,000 | 0.020 | 0.002 | 11.92× | 8% | +|✅| np.nanvar(a) (float16) | float16 | 1,000 | 0.032 | 0.003 | 11.72× | 8% | +|✅| np.sum axis=0 (uint8) | uint8 | 100,000 | 0.051 | 0.004 | 11.69× | 9% | ### 🔻 Top 15 Worst (Optimization priorities) | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|🔴| np.left_shift(a, 2) (int32) | int32 | 100,000 | 0.019 | 0.390 | 0.049× | 2035% | -|🔴| np.left_shift(a, 2) (uint64) | uint64 | 100,000 | 0.020 | 0.392 | 0.050× | 1992% | -|🔴| np.left_shift(a, 2) (int64) | int64 | 100,000 | 0.020 | 0.392 | 0.051× | 1945% | -|🔴| np.left_shift(a, 2) (uint32) | uint32 | 100,000 | 0.020 | 0.391 | 0.051× | 1944% | -|🔴| np.right_shift(a, 2) (uint64) | uint64 | 100,000 | 0.021 | 0.393 | 0.052× | 1908% | -|🔴| np.right_shift(a, 2) (uint32) | uint32 | 100,000 | 0.020 | 0.390 | 0.052× | 1930% | -|🔴| np.zeros_like (float64) | float64 | 1,000 | 0.001 | 0.015 | 0.068× | 1471% | -|🔴| np.right_shift(a, 2) (int64) | int64 | 100,000 | 0.029 | 0.393 | 0.074× | 1357% | -|🔴| np.right_shift(a, 2) (int32) | int32 | 100,000 | 0.029 | 0.390 | 0.074× | 1353% | -|🔴| np.right_shift(a, 2) (uint16) | uint16 | 100,000 | 0.029 | 0.387 | 0.074× | 1355% | -|🔴| np.left_shift(a, 2) (int16) | int16 | 100,000 | 0.028 | 0.381 | 0.074× | 1350% | -|🔴| np.sum (float64) | float64 | 100,000 | 0.016 | 0.214 | 0.074× | 1345% | -|🔴| np.left_shift(a, 2) (uint8) | uint8 | 100,000 | 0.028 | 0.375 | 0.075× | 1330% | -|🔴| np.left_shift(a, 2) (uint16) | uint16 | 100,000 | 0.029 | 0.388 | 0.076× | 1322% | -|🔴| np.right_shift(a, 2) (uint8) | uint8 | 100,000 | 0.029 | 0.376 | 0.076× | 1315% | +|🔴| np.zeros_like (int64) | int64 | 1,000 | 0.001 | 0.013 | 0.079× | 1261% | +|🔴| np.sum (float64) | float64 | 100,000 | 0.017 | 0.209 | 0.079× | 1260% | +|🔴| np.isnan(a) (float32) | float32 | 100,000 | 0.004 | 0.048 | 0.092× | 1091% | +|🔴| np.invert(a) (bool) | bool | 100,000 | 0.002 | 0.023 | 0.092× | 1083% | +|🔴| np.zeros_like (float64) | float64 | 1,000 | 0.001 | 0.013 | 0.092× | 1086% | +|🔴| a | b (bool) | bool | 100,000 | 0.003 | 0.023 | 0.11× | 912% | +|🔴| a + scalar (float64) | float64 | 100,000 | 0.012 | 0.104 | 0.11× | 902% | +|🔴| a * 2 (literal) (float64) | float64 | 100,000 | 0.012 | 0.106 | 0.11× | 887% | +|🔴| a + 5 (literal) (float64) | float64 | 100,000 | 0.012 | 0.103 | 0.11× | 884% | +|🔴| np.zeros_like (float32) | float32 | 1,000 | 0.001 | 0.009 | 0.12× | 861% | +|🔴| a * a (square) (float64) | float64 | 100,000 | 0.011 | 0.098 | 0.12× | 863% | +|🔴| a * scalar (float64) | float64 | 100,000 | 0.012 | 0.100 | 0.12× | 857% | +|🔴| np.right_shift(a, 2) (bool) | bool | 1,000 | 0.002 | 0.013 | 0.12× | 844% | +|🔴| np.left_shift(a, 2) (bool) | bool | 1,000 | 0.002 | 0.013 | 0.12× | 845% | +|🔴| np.zeros_like (int32) | int32 | 1,000 | 0.001 | 0.009 | 0.12× | 842% | --- @@ -81,1421 +81,1421 @@ _Ranked over 1398 credible comparisons (both sides ≥1µs, within 20×); 384 ne | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|🟡| a % 7 (literal) (float32) | float32 | 1,000 | 0.0142 | 0.0165 | 0.86× | 116% | -|🟡| a % 7 (literal) (float32) | float32 | 100,000 | 1.6193 | 1.8327 | 0.88× | 113% | -|🟡| a % 7 (literal) (float32) | float32 | 10,000,000 | 165.1953 | 184.8168 | 0.89× | 112% | -|🟡| a % 7 (literal) (float64) | float64 | 1,000 | 0.0113 | 0.0189 | 0.60× | 168% | -|🟡| a % 7 (literal) (float64) | float64 | 100,000 | 1.4311 | 1.6867 | 0.85× | 118% | -|🟡| a % 7 (literal) (float64) | float64 | 10,000,000 | 150.3030 | 165.2969 | 0.91× | 110% | -|🟡| a % 7 (literal) (int32) | int32 | 1,000 | 0.0022 | 0.0042 | 0.54× | 187% | -|🟡| a % 7 (literal) (int32) | int32 | 100,000 | 0.3976 | 0.6615 | 0.60× | 166% | -|🟡| a % 7 (literal) (int32) | int32 | 10,000,000 | 46.0503 | 67.4094 | 0.68× | 146% | -|🟡| a % 7 (literal) (int64) | int64 | 1,000 | 0.0043 | 0.0063 | 0.69× | 145% | -|🟡| a % 7 (literal) (int64) | int64 | 100,000 | 0.4006 | 0.6741 | 0.59× | 168% | -|🟡| a % 7 (literal) (int64) | int64 | 10,000,000 | 50.6103 | 73.6394 | 0.69× | 146% | -|✅| a % b (element-wise) (float32) | float32 | 1,000 | 0.0119 | 0.0117 | 1.02× | 98% | -|🟡| a % b (element-wise) (float32) | float32 | 100,000 | 1.4839 | 1.5770 | 0.94× | 106% | -|🟡| a % b (element-wise) (float32) | float32 | 10,000,000 | 153.9273 | 163.2286 | 0.94× | 106% | -|✅| a % b (element-wise) (float64) | float64 | 1,000 | 0.0097 | 0.0091 | 1.06× | 94% | -|🟡| a % b (element-wise) (float64) | float64 | 100,000 | 1.2816 | 1.4386 | 0.89× | 112% | -|🟡| a % b (element-wise) (float64) | float64 | 10,000,000 | 136.3744 | 147.5928 | 0.92× | 108% | -|🟡| a % b (element-wise) (int32) | int32 | 1,000 | 0.0020 | 0.0033 | 0.62× | 162% | -|🟡| a % b (element-wise) (int32) | int32 | 100,000 | 0.3675 | 0.5980 | 0.61× | 163% | -|🟡| a % b (element-wise) (int32) | int32 | 10,000,000 | 42.8057 | 60.0063 | 0.71× | 140% | -|🟡| a % b (element-wise) (int64) | int64 | 1,000 | 0.0035 | 0.0039 | 0.91× | 110% | -|🟡| a % b (element-wise) (int64) | int64 | 100,000 | 0.3695 | 0.5992 | 0.62× | 162% | -|🟡| a % b (element-wise) (int64) | int64 | 10,000,000 | 49.1915 | 67.0192 | 0.73× | 136% | -|🟠| a * 2 (literal) (complex128) | complex128 | 1,000 | 0.0011 | 0.0040 | 0.28× | 357% | -|✅| a * 2 (literal) (complex128) | complex128 | 100,000 | 0.2823 | 0.2208 | 1.28× | 78% | -|✅| a * 2 (literal) (complex128) | complex128 | 10,000,000 | 31.0176 | 25.6395 | 1.21× | 83% | -|🟡| a * 2 (literal) (float16) | float16 | 1,000 | 0.0046 | 0.0062 | 0.74× | 135% | -|🟡| a * 2 (literal) (float16) | float16 | 100,000 | 0.2800 | 0.4677 | 0.60× | 167% | -|🟡| a * 2 (literal) (float16) | float16 | 10,000,000 | 30.4948 | 46.4060 | 0.66× | 152% | -|▫| a * 2 (literal) (float32) | float32 | 1,000 | 0.0007 | 0.0037 | 0.20× | 504% | -|🟠| a * 2 (literal) (float32) | float32 | 100,000 | 0.0063 | 0.0282 | 0.22× | 449% | -|✅| a * 2 (literal) (float32) | float32 | 10,000,000 | 7.8622 | 5.1811 | 1.52× | 66% | -|▫| a * 2 (literal) (float64) | float64 | 1,000 | 0.0008 | 0.0047 | 0.17× | 604% | -|🟠| a * 2 (literal) (float64) | float64 | 100,000 | 0.0118 | 0.0573 | 0.20× | 487% | -|✅| a * 2 (literal) (float64) | float64 | 10,000,000 | 15.8476 | 13.6947 | 1.16× | 86% | -|▫| a * 2 (literal) (int16) | int16 | 1,000 | 0.0010 | 0.0034 | 0.29× | 338% | -|✅| a * 2 (literal) (int16) | int16 | 100,000 | 0.0216 | 0.0203 | 1.06× | 94% | -|✅| a * 2 (literal) (int16) | int16 | 10,000,000 | 4.2690 | 2.4718 | 1.73× | 58% | -|▫| a * 2 (literal) (int32) | int32 | 1,000 | 0.0010 | 0.0025 | 0.39× | 256% | -|🟡| a * 2 (literal) (int32) | int32 | 100,000 | 0.0217 | 0.0279 | 0.78× | 128% | -|✅| a * 2 (literal) (int32) | int32 | 10,000,000 | 7.6682 | 5.0877 | 1.51× | 66% | -|▫| a * 2 (literal) (int64) | int64 | 1,000 | 0.0009 | 0.0046 | 0.20× | 501% | -|🟠| a * 2 (literal) (int64) | int64 | 100,000 | 0.0226 | 0.0598 | 0.38× | 265% | -|✅| a * 2 (literal) (int64) | int64 | 10,000,000 | 15.0941 | 13.3295 | 1.13× | 88% | -|▫| a * 2 (literal) (int8) | int8 | 1,000 | 0.0009 | 0.0033 | 0.26× | 380% | -|✅| a * 2 (literal) (int8) | int8 | 100,000 | 0.0218 | 0.0153 | 1.42× | 70% | -|✅| a * 2 (literal) (int8) | int8 | 10,000,000 | 3.0346 | 1.2741 | 2.38× | 42% | -|▫| a * 2 (literal) (uint16) | uint16 | 1,000 | 0.0010 | 0.0034 | 0.29× | 344% | -|✅| a * 2 (literal) (uint16) | uint16 | 100,000 | 0.0219 | 0.0192 | 1.14× | 87% | -|✅| a * 2 (literal) (uint16) | uint16 | 10,000,000 | 4.3429 | 2.5632 | 1.69× | 59% | -|▫| a * 2 (literal) (uint32) | uint32 | 1,000 | 0.0010 | 0.0037 | 0.26× | 383% | -|🟡| a * 2 (literal) (uint32) | uint32 | 100,000 | 0.0218 | 0.0303 | 0.72× | 139% | -|✅| a * 2 (literal) (uint32) | uint32 | 10,000,000 | 7.7703 | 5.0405 | 1.54× | 65% | -|▫| a * 2 (literal) (uint64) | uint64 | 1,000 | 0.0009 | 0.0044 | 0.21× | 477% | -|🟠| a * 2 (literal) (uint64) | uint64 | 100,000 | 0.0223 | 0.0599 | 0.37× | 269% | -|✅| a * 2 (literal) (uint64) | uint64 | 10,000,000 | 14.8103 | 13.1645 | 1.12× | 89% | -|▫| a * 2 (literal) (uint8) | uint8 | 1,000 | 0.0009 | 0.0031 | 0.27× | 364% | -|✅| a * 2 (literal) (uint8) | uint8 | 100,000 | 0.0218 | 0.0148 | 1.47× | 68% | -|✅| a * 2 (literal) (uint8) | uint8 | 10,000,000 | 3.0832 | 1.3091 | 2.35× | 42% | -|▫| a * a (square) (complex128) | complex128 | 1,000 | 0.0008 | 0.0038 | 0.22× | 449% | -|✅| a * a (square) (complex128) | complex128 | 100,000 | 0.2826 | 0.2203 | 1.28× | 78% | -|✅| a * a (square) (complex128) | complex128 | 10,000,000 | 29.7792 | 25.4938 | 1.17× | 86% | -|✅| a * a (square) (float16) | float16 | 1,000 | 0.0060 | 0.0052 | 1.15× | 87% | -|🟡| a * a (square) (float16) | float16 | 100,000 | 0.2818 | 0.4741 | 0.59× | 168% | -|🟡| a * a (square) (float16) | float16 | 10,000,000 | 30.1183 | 46.6249 | 0.65× | 155% | -|▫| a * a (square) (float32) | float32 | 1,000 | 0.0005 | 0.0017 | 0.31× | 321% | -|🔴| a * a (square) (float32) | float32 | 100,000 | 0.0058 | 0.0471 | 0.12× | 805% | -|✅| a * a (square) (float32) | float32 | 10,000,000 | 7.6612 | 4.9707 | 1.54× | 65% | -|▫| a * a (square) (float64) | float64 | 1,000 | 0.0005 | 0.0020 | 0.26× | 393% | -|🔴| a * a (square) (float64) | float64 | 100,000 | 0.0114 | 0.0935 | 0.12× | 821% | -|✅| a * a (square) (float64) | float64 | 10,000,000 | 15.3393 | 13.6029 | 1.13× | 89% | -|▫| a * a (square) (int16) | int16 | 1,000 | 0.0009 | 0.0017 | 0.51× | 196% | -|🟡| a * a (square) (int16) | int16 | 100,000 | 0.0273 | 0.0276 | 0.99× | 101% | -|✅| a * a (square) (int16) | int16 | 10,000,000 | 4.7133 | 2.4548 | 1.92× | 52% | -|▫| a * a (square) (int32) | int32 | 1,000 | 0.0008 | 0.0020 | 0.39× | 256% | -|🟡| a * a (square) (int32) | int32 | 100,000 | 0.0275 | 0.0482 | 0.57× | 175% | -|✅| a * a (square) (int32) | int32 | 10,000,000 | 8.0304 | 5.0264 | 1.60× | 63% | -|▫| a * a (square) (int64) | int64 | 1,000 | 0.0007 | 0.0017 | 0.45× | 224% | -|🟠| a * a (square) (int64) | int64 | 100,000 | 0.0287 | 0.0963 | 0.30× | 336% | -|✅| a * a (square) (int64) | int64 | 10,000,000 | 15.3630 | 13.3964 | 1.15× | 87% | -|▫| a * a (square) (int8) | int8 | 1,000 | 0.0006 | 0.0015 | 0.42× | 236% | -|✅| a * a (square) (int8) | int8 | 100,000 | 0.0282 | 0.0143 | 1.97× | 51% | -|✅| a * a (square) (int8) | int8 | 10,000,000 | 3.6720 | 1.2987 | 2.83× | 35% | -|▫| a * a (square) (uint16) | uint16 | 1,000 | 0.0008 | 0.0019 | 0.40× | 251% | -|✅| a * a (square) (uint16) | uint16 | 100,000 | 0.0387 | 0.0261 | 1.49× | 67% | -|✅| a * a (square) (uint16) | uint16 | 10,000,000 | 4.8081 | 2.5775 | 1.86× | 54% | -|▫| a * a (square) (uint32) | uint32 | 1,000 | 0.0008 | 0.0017 | 0.45× | 223% | -|🟡| a * a (square) (uint32) | uint32 | 100,000 | 0.0290 | 0.0503 | 0.58× | 174% | -|✅| a * a (square) (uint32) | uint32 | 10,000,000 | 7.9491 | 5.0896 | 1.56× | 64% | -|▫| a * a (square) (uint64) | uint64 | 1,000 | 0.0007 | 0.0025 | 0.28× | 354% | -|🟠| a * a (square) (uint64) | uint64 | 100,000 | 0.0282 | 0.0988 | 0.29× | 350% | -|✅| a * a (square) (uint64) | uint64 | 10,000,000 | 15.0170 | 13.2023 | 1.14× | 88% | -|▫| a * a (square) (uint8) | uint8 | 1,000 | 0.0006 | 0.0016 | 0.39× | 257% | -|✅| a * a (square) (uint8) | uint8 | 100,000 | 0.0272 | 0.0090 | 3.03× | 33% | -|✅| a * a (square) (uint8) | uint8 | 10,000,000 | 3.6667 | 1.2804 | 2.86× | 35% | -|▫| a * b (element-wise) (complex128) | complex128 | 1,000 | 0.0009 | 0.0030 | 0.29× | 341% | -|✅| a * b (element-wise) (complex128) | complex128 | 100,000 | 0.2949 | 0.2291 | 1.29× | 78% | -|✅| a * b (element-wise) (complex128) | complex128 | 10,000,000 | 33.9731 | 28.5083 | 1.19× | 84% | -|✅| a * b (element-wise) (float16) | float16 | 1,000 | 0.0059 | 0.0053 | 1.11× | 90% | -|🟡| a * b (element-wise) (float16) | float16 | 100,000 | 0.2799 | 0.4728 | 0.59× | 169% | -|🟡| a * b (element-wise) (float16) | float16 | 10,000,000 | 30.1434 | 46.7307 | 0.65× | 155% | -|▫| a * b (element-wise) (float32) | float32 | 1,000 | 0.0005 | 0.0018 | 0.28× | 350% | -|🔴| a * b (element-wise) (float32) | float32 | 100,000 | 0.0068 | 0.0495 | 0.14× | 730% | -|✅| a * b (element-wise) (float32) | float32 | 10,000,000 | 8.3804 | 5.5527 | 1.51× | 66% | -|▫| a * b (element-wise) (float64) | float64 | 1,000 | 0.0006 | 0.0018 | 0.36× | 277% | -|🟠| a * b (element-wise) (float64) | float64 | 100,000 | 0.0259 | 0.0982 | 0.26× | 379% | -|✅| a * b (element-wise) (float64) | float64 | 10,000,000 | 16.8272 | 15.0603 | 1.12× | 90% | -|▫| a * b (element-wise) (int16) | int16 | 1,000 | 0.0008 | 0.0018 | 0.44× | 228% | -|✅| a * b (element-wise) (int16) | int16 | 100,000 | 0.0274 | 0.0151 | 1.81× | 55% | -|✅| a * b (element-wise) (int16) | int16 | 10,000,000 | 4.9661 | 2.8493 | 1.74× | 57% | -|▫| a * b (element-wise) (int32) | int32 | 1,000 | 0.0008 | 0.0020 | 0.39× | 258% | -|🟡| a * b (element-wise) (int32) | int32 | 100,000 | 0.0282 | 0.0474 | 0.59× | 168% | -|✅| a * b (element-wise) (int32) | int32 | 10,000,000 | 8.7071 | 5.6788 | 1.53× | 65% | -|▫| a * b (element-wise) (int64) | int64 | 1,000 | 0.0007 | 0.0018 | 0.41× | 242% | -|🟠| a * b (element-wise) (int64) | int64 | 100,000 | 0.0314 | 0.1035 | 0.30× | 329% | -|✅| a * b (element-wise) (int64) | int64 | 10,000,000 | 17.4762 | 15.2636 | 1.15× | 87% | -|▫| a * b (element-wise) (int8) | int8 | 1,000 | 0.0007 | 0.0016 | 0.40× | 248% | -|✅| a * b (element-wise) (int8) | int8 | 100,000 | 0.0276 | 0.0152 | 1.82× | 55% | -|✅| a * b (element-wise) (int8) | int8 | 10,000,000 | 3.7429 | 1.4944 | 2.50× | 40% | -|▫| a * b (element-wise) (uint16) | uint16 | 1,000 | 0.0008 | 0.0018 | 0.42× | 236% | -|✅| a * b (element-wise) (uint16) | uint16 | 100,000 | 0.0275 | 0.0271 | 1.02× | 98% | -|✅| a * b (element-wise) (uint16) | uint16 | 10,000,000 | 4.9546 | 2.9238 | 1.70× | 59% | -|▫| a * b (element-wise) (uint32) | uint32 | 1,000 | 0.0008 | 0.0019 | 0.43× | 234% | -|🟡| a * b (element-wise) (uint32) | uint32 | 100,000 | 0.0300 | 0.0483 | 0.62× | 161% | -|✅| a * b (element-wise) (uint32) | uint32 | 10,000,000 | 8.9206 | 5.4716 | 1.63× | 61% | -|▫| a * b (element-wise) (uint64) | uint64 | 1,000 | 0.0007 | 0.0018 | 0.40× | 252% | -|🟠| a * b (element-wise) (uint64) | uint64 | 100,000 | 0.0351 | 0.1020 | 0.34× | 291% | -|✅| a * b (element-wise) (uint64) | uint64 | 10,000,000 | 16.8707 | 15.5586 | 1.08× | 92% | -|▫| a * b (element-wise) (uint8) | uint8 | 1,000 | 0.0007 | 0.0015 | 0.44× | 228% | -|✅| a * b (element-wise) (uint8) | uint8 | 100,000 | 0.0272 | 0.0144 | 1.90× | 53% | -|✅| a * b (element-wise) (uint8) | uint8 | 10,000,000 | 3.8159 | 1.4474 | 2.64× | 38% | -|▫| a * scalar (complex128) | complex128 | 1,000 | 0.0009 | 0.0026 | 0.36× | 280% | -|✅| a * scalar (complex128) | complex128 | 100,000 | 0.2846 | 0.2201 | 1.29× | 77% | -|✅| a * scalar (complex128) | complex128 | 10,000,000 | 30.8011 | 25.2644 | 1.22× | 82% | -|✅| a * scalar (float16) | float16 | 1,000 | 0.0064 | 0.0053 | 1.21× | 82% | -|🟡| a * scalar (float16) | float16 | 100,000 | 0.2797 | 0.4704 | 0.59× | 168% | -|🟡| a * scalar (float16) | float16 | 10,000,000 | 30.2302 | 46.2873 | 0.65× | 153% | -|▫| a * scalar (float32) | float32 | 1,000 | 0.0007 | 0.0012 | 0.54× | 184% | -|🟠| a * scalar (float32) | float32 | 100,000 | 0.0062 | 0.0274 | 0.23× | 441% | -|✅| a * scalar (float32) | float32 | 10,000,000 | 7.8170 | 5.1097 | 1.53× | 65% | -|▫| a * scalar (float64) | float64 | 1,000 | 0.0007 | 0.0016 | 0.46× | 217% | -|🔴| a * scalar (float64) | float64 | 100,000 | 0.0116 | 0.0658 | 0.18× | 566% | -|✅| a * scalar (float64) | float64 | 10,000,000 | 15.9832 | 13.7184 | 1.17× | 86% | -|▫| a * scalar (int16) | int16 | 1,000 | 0.0009 | 0.0011 | 0.82× | 122% | -|✅| a * scalar (int16) | int16 | 100,000 | 0.0215 | 0.0162 | 1.33× | 75% | -|✅| a * scalar (int16) | int16 | 10,000,000 | 4.1983 | 2.5429 | 1.65× | 61% | -|▫| a * scalar (int32) | int32 | 1,000 | 0.0009 | 0.0013 | 0.67× | 148% | -|🟡| a * scalar (int32) | int32 | 100,000 | 0.0224 | 0.0310 | 0.72× | 139% | -|✅| a * scalar (int32) | int32 | 10,000,000 | 7.6670 | 5.1149 | 1.50× | 67% | -|▫| a * scalar (int64) | int64 | 1,000 | 0.0008 | 0.0017 | 0.48× | 208% | -|🟠| a * scalar (int64) | int64 | 100,000 | 0.0219 | 0.0622 | 0.35× | 284% | -|✅| a * scalar (int64) | int64 | 10,000,000 | 14.9706 | 13.1378 | 1.14× | 88% | -|▫| a * scalar (int8) | int8 | 1,000 | 0.0007 | 0.0012 | 0.62× | 161% | -|✅| a * scalar (int8) | int8 | 100,000 | 0.0215 | 0.0081 | 2.66× | 38% | -|✅| a * scalar (int8) | int8 | 10,000,000 | 3.0113 | 1.2891 | 2.34× | 43% | -|▫| a * scalar (uint16) | uint16 | 1,000 | 0.0009 | 0.0015 | 0.58× | 174% | -|✅| a * scalar (uint16) | uint16 | 100,000 | 0.0231 | 0.0143 | 1.62× | 62% | -|✅| a * scalar (uint16) | uint16 | 10,000,000 | 4.2996 | 2.5670 | 1.68× | 60% | -|▫| a * scalar (uint32) | uint32 | 1,000 | 0.0009 | 0.0012 | 0.72× | 138% | -|🟡| a * scalar (uint32) | uint32 | 100,000 | 0.0218 | 0.0307 | 0.71× | 140% | -|✅| a * scalar (uint32) | uint32 | 10,000,000 | 7.6963 | 5.1056 | 1.51× | 66% | -|▫| a * scalar (uint64) | uint64 | 1,000 | 0.0008 | 0.0018 | 0.46× | 219% | -|🟠| a * scalar (uint64) | uint64 | 100,000 | 0.0270 | 0.0573 | 0.47× | 212% | -|✅| a * scalar (uint64) | uint64 | 10,000,000 | 14.8864 | 13.1397 | 1.13× | 88% | -|▫| a * scalar (uint8) | uint8 | 1,000 | 0.0007 | 0.0009 | 0.84× | 119% | -|✅| a * scalar (uint8) | uint8 | 100,000 | 0.0225 | 0.0078 | 2.87× | 35% | -|✅| a * scalar (uint8) | uint8 | 10,000,000 | 3.1220 | 1.2945 | 2.41× | 42% | -|🔴| a + 5 (literal) (complex128) | complex128 | 1,000 | 0.0011 | 0.0059 | 0.19× | 532% | -|🟡| a + 5 (literal) (complex128) | complex128 | 100,000 | 0.2877 | 0.3143 | 0.92× | 109% | -|🟡| a + 5 (literal) (complex128) | complex128 | 10,000,000 | 31.2807 | 40.8716 | 0.77× | 131% | -|🟡| a + 5 (literal) (float16) | float16 | 1,000 | 0.0064 | 0.0093 | 0.68× | 146% | -|🟠| a + 5 (literal) (float16) | float16 | 100,000 | 0.2865 | 0.8094 | 0.35× | 282% | -|🟠| a + 5 (literal) (float16) | float16 | 10,000,000 | 30.8894 | 88.5978 | 0.35× | 287% | -|▫| a + 5 (literal) (float32) | float32 | 1,000 | 0.0008 | 0.0063 | 0.13× | 783% | -|🔴| a + 5 (literal) (float32) | float32 | 100,000 | 0.0062 | 0.0531 | 0.12× | 851% | -|🟡| a + 5 (literal) (float32) | float32 | 10,000,000 | 7.8256 | 8.3656 | 0.94× | 107% | -|▫| a + 5 (literal) (float64) | float64 | 1,000 | 0.0008 | 0.0061 | 0.13× | 770% | -|🔴| a + 5 (literal) (float64) | float64 | 100,000 | 0.0118 | 0.0979 | 0.12× | 830% | -|🟡| a + 5 (literal) (float64) | float64 | 10,000,000 | 15.5405 | 19.1910 | 0.81× | 124% | -|🟠| a + 5 (literal) (int16) | int16 | 1,000 | 0.0010 | 0.0039 | 0.26× | 382% | -|🟡| a + 5 (literal) (int16) | int16 | 100,000 | 0.0239 | 0.0301 | 0.79× | 126% | -|✅| a + 5 (literal) (int16) | int16 | 10,000,000 | 4.4762 | 4.4458 | 1.01× | 99% | -|🟠| a + 5 (literal) (int32) | int32 | 1,000 | 0.0010 | 0.0035 | 0.29× | 344% | -|🟠| a + 5 (literal) (int32) | int32 | 100,000 | 0.0234 | 0.0505 | 0.46× | 216% | -|🟡| a + 5 (literal) (int32) | int32 | 10,000,000 | 7.8319 | 8.2820 | 0.95× | 106% | -|▫| a + 5 (literal) (int64) | int64 | 1,000 | 0.0009 | 0.0056 | 0.17× | 588% | -|🟠| a + 5 (literal) (int64) | int64 | 100,000 | 0.0252 | 0.1021 | 0.25× | 406% | -|🟡| a + 5 (literal) (int64) | int64 | 10,000,000 | 15.0804 | 19.9053 | 0.76× | 132% | -|▫| a + 5 (literal) (int8) | int8 | 1,000 | 0.0009 | 0.0033 | 0.26× | 382% | -|🟡| a + 5 (literal) (int8) | int8 | 100,000 | 0.0238 | 0.0242 | 0.98× | 102% | -|✅| a + 5 (literal) (int8) | int8 | 10,000,000 | 3.3623 | 1.9502 | 1.72× | 58% | -|🟠| a + 5 (literal) (uint16) | uint16 | 1,000 | 0.0010 | 0.0049 | 0.21× | 476% | -|🟡| a + 5 (literal) (uint16) | uint16 | 100,000 | 0.0247 | 0.0284 | 0.87× | 115% | -|✅| a + 5 (literal) (uint16) | uint16 | 10,000,000 | 4.4740 | 4.2326 | 1.06× | 95% | -|🔴| a + 5 (literal) (uint32) | uint32 | 1,000 | 0.0010 | 0.0051 | 0.20× | 502% | -|🟠| a + 5 (literal) (uint32) | uint32 | 100,000 | 0.0234 | 0.0502 | 0.47× | 214% | -|🟡| a + 5 (literal) (uint32) | uint32 | 10,000,000 | 7.8656 | 8.5342 | 0.92× | 108% | -|▫| a + 5 (literal) (uint64) | uint64 | 1,000 | 0.0010 | 0.0063 | 0.15× | 646% | -|🟠| a + 5 (literal) (uint64) | uint64 | 100,000 | 0.0234 | 0.0954 | 0.24× | 408% | -|🟡| a + 5 (literal) (uint64) | uint64 | 10,000,000 | 15.0958 | 19.4444 | 0.78× | 129% | -|▫| a + 5 (literal) (uint8) | uint8 | 1,000 | 0.0009 | 0.0034 | 0.25× | 395% | -|✅| a + 5 (literal) (uint8) | uint8 | 100,000 | 0.0253 | 0.0192 | 1.31× | 76% | -|✅| a + 5 (literal) (uint8) | uint8 | 10,000,000 | 3.4076 | 1.9372 | 1.76× | 57% | -|🟠| a + b (element-wise) (complex128) | complex128 | 1,000 | 0.0011 | 0.0044 | 0.26× | 393% | -|🟡| a + b (element-wise) (complex128) | complex128 | 100,000 | 0.3142 | 0.3445 | 0.91× | 110% | -|🟡| a + b (element-wise) (complex128) | complex128 | 10,000,000 | 33.8363 | 54.0273 | 0.63× | 160% | -|🟡| a + b (element-wise) (float16) | float16 | 1,000 | 0.0055 | 0.0095 | 0.58× | 174% | -|🟠| a + b (element-wise) (float16) | float16 | 100,000 | 0.2823 | 0.9001 | 0.31× | 319% | -|🟠| a + b (element-wise) (float16) | float16 | 10,000,000 | 30.3275 | 87.5732 | 0.35× | 289% | -|▫| a + b (element-wise) (float32) | float32 | 1,000 | 0.0007 | 0.0019 | 0.37× | 270% | -|🔴| a + b (element-wise) (float32) | float32 | 100,000 | 0.0082 | 0.0537 | 0.15× | 651% | -|🟡| a + b (element-wise) (float32) | float32 | 10,000,000 | 8.4264 | 10.8929 | 0.77× | 129% | -|▫| a + b (element-wise) (float64) | float64 | 1,000 | 0.0005 | 0.0025 | 0.20× | 492% | -|🟠| a + b (element-wise) (float64) | float64 | 100,000 | 0.0277 | 0.1133 | 0.24× | 409% | -|🟡| a + b (element-wise) (float64) | float64 | 10,000,000 | 16.7263 | 26.0989 | 0.64× | 156% | -|▫| a + b (element-wise) (int16) | int16 | 1,000 | 0.0010 | 0.0016 | 0.59× | 169% | -|✅| a + b (element-wise) (int16) | int16 | 100,000 | 0.0310 | 0.0284 | 1.09× | 91% | -|🟡| a + b (element-wise) (int16) | int16 | 10,000,000 | 5.0603 | 6.1401 | 0.82× | 121% | -|▫| a + b (element-wise) (int32) | int32 | 1,000 | 0.0008 | 0.0017 | 0.45× | 222% | -|🟡| a + b (element-wise) (int32) | int32 | 100,000 | 0.0297 | 0.0548 | 0.54× | 185% | -|🟡| a + b (element-wise) (int32) | int32 | 10,000,000 | 8.4215 | 10.8873 | 0.77× | 129% | -|▫| a + b (element-wise) (int64) | int64 | 1,000 | 0.0010 | 0.0038 | 0.25× | 398% | -|🟠| a + b (element-wise) (int64) | int64 | 100,000 | 0.0306 | 0.1154 | 0.27× | 377% | -|🟡| a + b (element-wise) (int64) | int64 | 10,000,000 | 16.7804 | 25.2327 | 0.67× | 150% | -|▫| a + b (element-wise) (int8) | int8 | 1,000 | 0.0007 | 0.0012 | 0.53× | 188% | -|✅| a + b (element-wise) (int8) | int8 | 100,000 | 0.0296 | 0.0159 | 1.86× | 54% | -|✅| a + b (element-wise) (int8) | int8 | 10,000,000 | 3.8279 | 2.7881 | 1.37× | 73% | -|▫| a + b (element-wise) (uint16) | uint16 | 1,000 | 0.0008 | 0.0014 | 0.57× | 175% | -|✅| a + b (element-wise) (uint16) | uint16 | 100,000 | 0.0299 | 0.0270 | 1.11× | 90% | -|🟡| a + b (element-wise) (uint16) | uint16 | 10,000,000 | 5.0917 | 6.1902 | 0.82× | 122% | -|🟡| a + b (element-wise) (uint32) | uint32 | 1,000 | 0.0014 | 0.0016 | 0.87× | 115% | -|🟡| a + b (element-wise) (uint32) | uint32 | 100,000 | 0.0285 | 0.0511 | 0.56× | 180% | -|🟡| a + b (element-wise) (uint32) | uint32 | 10,000,000 | 8.6066 | 10.7026 | 0.80× | 124% | -|▫| a + b (element-wise) (uint64) | uint64 | 1,000 | 0.0008 | 0.0029 | 0.26× | 380% | -|🟠| a + b (element-wise) (uint64) | uint64 | 100,000 | 0.0327 | 0.1082 | 0.30× | 331% | -|🟡| a + b (element-wise) (uint64) | uint64 | 10,000,000 | 18.1412 | 26.0889 | 0.69× | 144% | -|▫| a + b (element-wise) (uint8) | uint8 | 1,000 | 0.0007 | 0.0012 | 0.55× | 181% | -|✅| a + b (element-wise) (uint8) | uint8 | 100,000 | 0.0297 | 0.0153 | 1.94× | 52% | -|✅| a + b (element-wise) (uint8) | uint8 | 10,000,000 | 3.8081 | 2.7749 | 1.37× | 73% | -|▫| a + scalar (complex128) | complex128 | 1,000 | 0.0009 | 0.0039 | 0.24× | 410% | -|🟡| a + scalar (complex128) | complex128 | 100,000 | 0.2866 | 0.3178 | 0.90× | 111% | -|🟡| a + scalar (complex128) | complex128 | 10,000,000 | 31.3992 | 41.0864 | 0.76× | 131% | -|🟡| a + scalar (float16) | float16 | 1,000 | 0.0061 | 0.0093 | 0.65× | 154% | -|🟠| a + scalar (float16) | float16 | 100,000 | 0.2825 | 0.8408 | 0.34× | 298% | -|🟠| a + scalar (float16) | float16 | 10,000,000 | 30.6155 | 85.2390 | 0.36× | 278% | -|▫| a + scalar (float32) | float32 | 1,000 | 0.0007 | 0.0019 | 0.35× | 282% | -|🔴| a + scalar (float32) | float32 | 100,000 | 0.0062 | 0.0512 | 0.12× | 824% | -|🟡| a + scalar (float32) | float32 | 10,000,000 | 7.8223 | 8.2505 | 0.95× | 106% | -|▫| a + scalar (float64) | float64 | 1,000 | 0.0007 | 0.0023 | 0.28× | 358% | -|🔴| a + scalar (float64) | float64 | 100,000 | 0.0115 | 0.0998 | 0.12× | 865% | -|🟡| a + scalar (float64) | float64 | 10,000,000 | 15.6255 | 19.5551 | 0.80× | 125% | -|▫| a + scalar (int16) | int16 | 1,000 | 0.0009 | 0.0018 | 0.51× | 196% | -|🟡| a + scalar (int16) | int16 | 100,000 | 0.0238 | 0.0264 | 0.90× | 111% | -|✅| a + scalar (int16) | int16 | 10,000,000 | 4.3864 | 4.1556 | 1.06× | 95% | -|▫| a + scalar (int32) | int32 | 1,000 | 0.0009 | 0.0018 | 0.50× | 200% | -|🟠| a + scalar (int32) | int32 | 100,000 | 0.0232 | 0.0523 | 0.44× | 225% | -|🟡| a + scalar (int32) | int32 | 10,000,000 | 7.8124 | 8.3847 | 0.93× | 107% | -|▫| a + scalar (int64) | int64 | 1,000 | 0.0009 | 0.0026 | 0.34× | 296% | -|🟠| a + scalar (int64) | int64 | 100,000 | 0.0395 | 0.1005 | 0.39× | 255% | -|🟡| a + scalar (int64) | int64 | 10,000,000 | 15.3427 | 20.6485 | 0.74× | 135% | -|▫| a + scalar (int8) | int8 | 1,000 | 0.0010 | 0.0015 | 0.64× | 156% | -|✅| a + scalar (int8) | int8 | 100,000 | 0.0259 | 0.0144 | 1.80× | 56% | -|✅| a + scalar (int8) | int8 | 10,000,000 | 3.3690 | 2.0078 | 1.68× | 60% | -|▫| a + scalar (uint16) | uint16 | 1,000 | 0.0009 | 0.0017 | 0.54× | 185% | -|🟡| a + scalar (uint16) | uint16 | 100,000 | 0.0240 | 0.0257 | 0.93× | 107% | -|✅| a + scalar (uint16) | uint16 | 10,000,000 | 4.5159 | 4.3325 | 1.04× | 96% | -|🟡| a + scalar (uint32) | uint32 | 1,000 | 0.0013 | 0.0018 | 0.70× | 142% | -|🟠| a + scalar (uint32) | uint32 | 100,000 | 0.0233 | 0.0494 | 0.47× | 212% | -|🟡| a + scalar (uint32) | uint32 | 10,000,000 | 7.7179 | 8.4788 | 0.91× | 110% | -|▫| a + scalar (uint64) | uint64 | 1,000 | 0.0009 | 0.0029 | 0.29× | 345% | -|🟠| a + scalar (uint64) | uint64 | 100,000 | 0.0233 | 0.1007 | 0.23× | 432% | -|🟡| a + scalar (uint64) | uint64 | 10,000,000 | 15.6794 | 19.7857 | 0.79× | 126% | -|▫| a + scalar (uint8) | uint8 | 1,000 | 0.0008 | 0.0017 | 0.45× | 221% | -|✅| a + scalar (uint8) | uint8 | 100,000 | 0.0264 | 0.0143 | 1.85× | 54% | -|✅| a + scalar (uint8) | uint8 | 10,000,000 | 3.3843 | 1.9032 | 1.78× | 56% | -|▫| a - b (element-wise) (complex128) | complex128 | 1,000 | 0.0009 | 0.0032 | 0.27× | 369% | -|✅| a - b (element-wise) (complex128) | complex128 | 100,000 | 0.3175 | 0.2343 | 1.35× | 74% | -|✅| a - b (element-wise) (complex128) | complex128 | 10,000,000 | 34.2472 | 30.3482 | 1.13× | 89% | -|🟡| a - b (element-wise) (float16) | float16 | 1,000 | 0.0045 | 0.0050 | 0.90× | 111% | -|🟡| a - b (element-wise) (float16) | float16 | 100,000 | 0.2794 | 0.4692 | 0.60× | 168% | -|🟠| a - b (element-wise) (float16) | float16 | 10,000,000 | 30.4358 | 65.6914 | 0.46× | 216% | -|▫| a - b (element-wise) (float32) | float32 | 1,000 | 0.0005 | 0.0013 | 0.41× | 241% | -|🟠| a - b (element-wise) (float32) | float32 | 100,000 | 0.0070 | 0.0275 | 0.26× | 391% | -|✅| a - b (element-wise) (float32) | float32 | 10,000,000 | 8.4147 | 5.3696 | 1.57× | 64% | -|▫| a - b (element-wise) (float64) | float64 | 1,000 | 0.0007 | 0.0022 | 0.31× | 319% | -|🟠| a - b (element-wise) (float64) | float64 | 100,000 | 0.0263 | 0.0614 | 0.43× | 233% | -|✅| a - b (element-wise) (float64) | float64 | 10,000,000 | 16.5820 | 14.8489 | 1.12× | 90% | -|▫| a - b (element-wise) (int16) | int16 | 1,000 | 0.0008 | 0.0013 | 0.64× | 155% | -|✅| a - b (element-wise) (int16) | int16 | 100,000 | 0.0288 | 0.0152 | 1.89× | 53% | -|✅| a - b (element-wise) (int16) | int16 | 10,000,000 | 5.0343 | 2.8963 | 1.74× | 58% | -|▫| a - b (element-wise) (int32) | int32 | 1,000 | 0.0008 | 0.0012 | 0.68× | 147% | -|🟡| a - b (element-wise) (int32) | int32 | 100,000 | 0.0286 | 0.0296 | 0.97× | 104% | -|✅| a - b (element-wise) (int32) | int32 | 10,000,000 | 8.8063 | 5.5819 | 1.58× | 63% | -|▫| a - b (element-wise) (int64) | int64 | 1,000 | 0.0008 | 0.0021 | 0.37× | 272% | -|🟠| a - b (element-wise) (int64) | int64 | 100,000 | 0.0329 | 0.0668 | 0.49× | 203% | -|✅| a - b (element-wise) (int64) | int64 | 10,000,000 | 17.0835 | 14.9251 | 1.15× | 87% | -|▫| a - b (element-wise) (int8) | int8 | 1,000 | 0.0007 | 0.0011 | 0.62× | 162% | -|✅| a - b (element-wise) (int8) | int8 | 100,000 | 0.0285 | 0.0083 | 3.43× | 29% | -|✅| a - b (element-wise) (int8) | int8 | 10,000,000 | 3.8695 | 1.4622 | 2.65× | 38% | -|▫| a - b (element-wise) (uint16) | uint16 | 1,000 | 0.0008 | 0.0011 | 0.72× | 139% | -|✅| a - b (element-wise) (uint16) | uint16 | 100,000 | 0.0296 | 0.0150 | 1.98× | 51% | -|✅| a - b (element-wise) (uint16) | uint16 | 10,000,000 | 5.0948 | 2.9174 | 1.75× | 57% | -|▫| a - b (element-wise) (uint32) | uint32 | 1,000 | 0.0008 | 0.0014 | 0.53× | 189% | -|✅| a - b (element-wise) (uint32) | uint32 | 100,000 | 0.0285 | 0.0280 | 1.02× | 98% | -|✅| a - b (element-wise) (uint32) | uint32 | 10,000,000 | 8.7412 | 5.4282 | 1.61× | 62% | -|▫| a - b (element-wise) (uint64) | uint64 | 1,000 | 0.0007 | 0.0018 | 0.41× | 244% | -|🟡| a - b (element-wise) (uint64) | uint64 | 100,000 | 0.0358 | 0.0653 | 0.55× | 183% | -|✅| a - b (element-wise) (uint64) | uint64 | 10,000,000 | 16.8067 | 14.7610 | 1.14× | 88% | -|▫| a - b (element-wise) (uint8) | uint8 | 1,000 | 0.0006 | 0.0009 | 0.75× | 134% | -|✅| a - b (element-wise) (uint8) | uint8 | 100,000 | 0.0300 | 0.0086 | 3.50× | 29% | -|✅| a - b (element-wise) (uint8) | uint8 | 10,000,000 | 3.8491 | 1.4835 | 2.60× | 38% | -|▫| a - scalar (complex128) | complex128 | 1,000 | 0.0009 | 0.0028 | 0.34× | 298% | -|✅| a - scalar (complex128) | complex128 | 100,000 | 0.2994 | 0.2283 | 1.31× | 76% | -|✅| a - scalar (complex128) | complex128 | 10,000,000 | 32.8674 | 25.2962 | 1.30× | 77% | -|✅| a - scalar (float16) | float16 | 1,000 | 0.0060 | 0.0049 | 1.23× | 82% | -|🟡| a - scalar (float16) | float16 | 100,000 | 0.2796 | 0.4687 | 0.60× | 168% | -|🟡| a - scalar (float16) | float16 | 10,000,000 | 30.3791 | 46.1701 | 0.66× | 152% | -|▫| a - scalar (float32) | float32 | 1,000 | 0.0008 | 0.0014 | 0.57× | 174% | -|🟠| a - scalar (float32) | float32 | 100,000 | 0.0061 | 0.0270 | 0.23× | 439% | -|✅| a - scalar (float32) | float32 | 10,000,000 | 7.8119 | 5.1477 | 1.52× | 66% | -|▫| a - scalar (float64) | float64 | 1,000 | 0.0006 | 0.0021 | 0.30× | 335% | -|🔴| a - scalar (float64) | float64 | 100,000 | 0.0117 | 0.0614 | 0.19× | 524% | -|✅| a - scalar (float64) | float64 | 10,000,000 | 15.5805 | 13.7960 | 1.13× | 88% | -|🟡| a - scalar (int16) | int16 | 1,000 | 0.0011 | 0.0012 | 0.94× | 107% | -|✅| a - scalar (int16) | int16 | 100,000 | 0.0232 | 0.0139 | 1.67× | 60% | -|✅| a - scalar (int16) | int16 | 10,000,000 | 4.3717 | 2.6212 | 1.67× | 60% | -|▫| a - scalar (int32) | int32 | 1,000 | 0.0009 | 0.0015 | 0.60× | 165% | -|🟡| a - scalar (int32) | int32 | 100,000 | 0.0245 | 0.0261 | 0.94× | 107% | -|✅| a - scalar (int32) | int32 | 10,000,000 | 7.8246 | 5.1023 | 1.53× | 65% | -|▫| a - scalar (int64) | int64 | 1,000 | 0.0009 | 0.0020 | 0.43× | 233% | -|🟠| a - scalar (int64) | int64 | 100,000 | 0.0257 | 0.0580 | 0.44× | 226% | -|✅| a - scalar (int64) | int64 | 10,000,000 | 15.0036 | 13.7351 | 1.09× | 92% | -|▫| a - scalar (int8) | int8 | 1,000 | 0.0009 | 0.0012 | 0.73× | 137% | -|✅| a - scalar (int8) | int8 | 100,000 | 0.0234 | 0.0079 | 2.96× | 34% | -|✅| a - scalar (int8) | int8 | 10,000,000 | 3.3051 | 1.3891 | 2.38× | 42% | -|▫| a - scalar (uint16) | uint16 | 1,000 | 0.0009 | 0.0010 | 0.90× | 111% | -|✅| a - scalar (uint16) | uint16 | 100,000 | 0.0235 | 0.0140 | 1.68× | 60% | -|✅| a - scalar (uint16) | uint16 | 10,000,000 | 4.5055 | 2.5875 | 1.74× | 57% | -|▫| a - scalar (uint32) | uint32 | 1,000 | 0.0009 | 0.0012 | 0.73× | 138% | -|✅| a - scalar (uint32) | uint32 | 100,000 | 0.0270 | 0.0266 | 1.02× | 98% | -|✅| a - scalar (uint32) | uint32 | 10,000,000 | 7.9393 | 5.0601 | 1.57× | 64% | -|▫| a - scalar (uint64) | uint64 | 1,000 | 0.0009 | 0.0017 | 0.49× | 203% | -|🟠| a - scalar (uint64) | uint64 | 100,000 | 0.0257 | 0.0557 | 0.46× | 216% | -|✅| a - scalar (uint64) | uint64 | 10,000,000 | 15.0786 | 13.6412 | 1.10× | 90% | -|▫| a - scalar (uint8) | uint8 | 1,000 | 0.0008 | 0.0009 | 0.90× | 111% | -|✅| a - scalar (uint8) | uint8 | 100,000 | 0.0252 | 0.0081 | 3.11× | 32% | -|✅| a - scalar (uint8) | uint8 | 10,000,000 | 3.3366 | 1.3483 | 2.48× | 40% | -|▫| a / b (element-wise) (float32) | float32 | 1,000 | 0.0005 | 0.0020 | 0.25× | 401% | -|🟠| a / b (element-wise) (float32) | float32 | 100,000 | 0.0123 | 0.0563 | 0.22× | 458% | -|🟡| a / b (element-wise) (float32) | float32 | 10,000,000 | 8.4746 | 10.4479 | 0.81× | 123% | -|▫| a / b (element-wise) (float64) | float64 | 1,000 | 0.0008 | 0.0033 | 0.23× | 436% | -|🟠| a / b (element-wise) (float64) | float64 | 100,000 | 0.0382 | 0.1504 | 0.25× | 393% | -|🟡| a / b (element-wise) (float64) | float64 | 10,000,000 | 16.5123 | 24.6973 | 0.67× | 150% | -|🟠| a / b (element-wise) (int32) | int32 | 1,000 | 0.0019 | 0.0056 | 0.34× | 297% | -|🟠| a / b (element-wise) (int32) | int32 | 100,000 | 0.0827 | 0.1883 | 0.44× | 228% | -|🟡| a / b (element-wise) (int32) | int32 | 10,000,000 | 20.2642 | 23.3858 | 0.87× | 115% | -|🟠| a / b (element-wise) (int64) | int64 | 1,000 | 0.0018 | 0.0048 | 0.39× | 259% | -|🟠| a / b (element-wise) (int64) | int64 | 100,000 | 0.0821 | 0.1914 | 0.43× | 233% | -|🟡| a / b (element-wise) (int64) | int64 | 10,000,000 | 23.2035 | 27.5732 | 0.84× | 119% | -|▫| a / scalar (float32) | float32 | 1,000 | 0.0006 | 0.0021 | 0.30× | 336% | -|🟠| a / scalar (float32) | float32 | 100,000 | 0.0120 | 0.0591 | 0.20× | 492% | -|🟡| a / scalar (float32) | float32 | 10,000,000 | 7.9059 | 8.4903 | 0.93× | 107% | -|▫| a / scalar (float64) | float64 | 1,000 | 0.0009 | 0.0037 | 0.24× | 412% | -|🟠| a / scalar (float64) | float64 | 100,000 | 0.0372 | 0.1545 | 0.24× | 416% | -|🟡| a / scalar (float64) | float64 | 10,000,000 | 15.4651 | 21.6902 | 0.71× | 140% | -|🟠| a / scalar (int32) | int32 | 1,000 | 0.0022 | 0.0073 | 0.30× | 332% | -|🟠| a / scalar (int32) | int32 | 100,000 | 0.0616 | 0.1934 | 0.32× | 314% | -|🟡| a / scalar (int32) | int32 | 10,000,000 | 17.1824 | 23.0455 | 0.75× | 134% | -|🟠| a / scalar (int64) | int64 | 1,000 | 0.0017 | 0.0069 | 0.25× | 404% | -|🟠| a / scalar (int64) | int64 | 100,000 | 0.0571 | 0.1868 | 0.31× | 327% | -|🟡| a / scalar (int64) | int64 | 10,000,000 | 18.4227 | 23.5786 | 0.78× | 128% | -|🟠| np.add(a, b) (complex128) | complex128 | 1,000 | 0.0011 | 0.0042 | 0.25× | 398% | -|🟡| np.add(a, b) (complex128) | complex128 | 100,000 | 0.3045 | 0.3432 | 0.89× | 113% | -|🟡| np.add(a, b) (complex128) | complex128 | 10,000,000 | 33.3065 | 56.7710 | 0.59× | 170% | -|🟡| np.add(a, b) (float16) | float16 | 1,000 | 0.0054 | 0.0096 | 0.56× | 180% | -|🟠| np.add(a, b) (float16) | float16 | 100,000 | 0.2880 | 0.8665 | 0.33× | 301% | -|🟠| np.add(a, b) (float16) | float16 | 10,000,000 | 30.2918 | 84.4809 | 0.36× | 279% | -|▫| np.add(a, b) (float32) | float32 | 1,000 | 0.0005 | 0.0019 | 0.29× | 346% | -|🔴| np.add(a, b) (float32) | float32 | 100,000 | 0.0069 | 0.0511 | 0.14× | 743% | -|🟡| np.add(a, b) (float32) | float32 | 10,000,000 | 8.4121 | 11.6595 | 0.72× | 139% | -|▫| np.add(a, b) (float64) | float64 | 1,000 | 0.0005 | 0.0027 | 0.20× | 514% | -|🟠| np.add(a, b) (float64) | float64 | 100,000 | 0.0274 | 0.1083 | 0.25× | 396% | -|🟡| np.add(a, b) (float64) | float64 | 10,000,000 | 16.6312 | 24.9414 | 0.67× | 150% | -|▫| np.add(a, b) (int16) | int16 | 1,000 | 0.0008 | 0.0015 | 0.52× | 192% | -|✅| np.add(a, b) (int16) | int16 | 100,000 | 0.0289 | 0.0283 | 1.02× | 98% | -|🟡| np.add(a, b) (int16) | int16 | 10,000,000 | 5.0436 | 6.3374 | 0.80× | 126% | -|▫| np.add(a, b) (int32) | int32 | 1,000 | 0.0008 | 0.0018 | 0.43× | 233% | -|🟡| np.add(a, b) (int32) | int32 | 100,000 | 0.0286 | 0.0523 | 0.55× | 183% | -|🟡| np.add(a, b) (int32) | int32 | 10,000,000 | 8.5431 | 10.9704 | 0.78× | 128% | -|▫| np.add(a, b) (int64) | int64 | 1,000 | 0.0009 | 0.0025 | 0.38× | 267% | -|🟠| np.add(a, b) (int64) | int64 | 100,000 | 0.0307 | 0.1070 | 0.29× | 349% | -|🟡| np.add(a, b) (int64) | int64 | 10,000,000 | 16.9611 | 25.6104 | 0.66× | 151% | -|▫| np.add(a, b) (int8) | int8 | 1,000 | 0.0008 | 0.0011 | 0.72× | 139% | -|✅| np.add(a, b) (int8) | int8 | 100,000 | 0.0285 | 0.0154 | 1.85× | 54% | -|✅| np.add(a, b) (int8) | int8 | 10,000,000 | 3.8480 | 2.7190 | 1.42× | 71% | -|▫| np.add(a, b) (uint16) | uint16 | 1,000 | 0.0008 | 0.0016 | 0.49× | 205% | -|✅| np.add(a, b) (uint16) | uint16 | 100,000 | 0.0292 | 0.0271 | 1.07× | 93% | -|🟡| np.add(a, b) (uint16) | uint16 | 10,000,000 | 5.0613 | 6.0800 | 0.83× | 120% | -|▫| np.add(a, b) (uint32) | uint32 | 1,000 | 0.0010 | 0.0014 | 0.68× | 147% | -|🟡| np.add(a, b) (uint32) | uint32 | 100,000 | 0.0285 | 0.0534 | 0.53× | 187% | -|🟡| np.add(a, b) (uint32) | uint32 | 10,000,000 | 8.5822 | 10.6338 | 0.81× | 124% | -|▫| np.add(a, b) (uint64) | uint64 | 1,000 | 0.0008 | 0.0017 | 0.44× | 228% | -|🟠| np.add(a, b) (uint64) | uint64 | 100,000 | 0.0355 | 0.1051 | 0.34× | 296% | -|🟡| np.add(a, b) (uint64) | uint64 | 10,000,000 | 17.4766 | 26.0047 | 0.67× | 149% | -|▫| np.add(a, b) (uint8) | uint8 | 1,000 | 0.0007 | 0.0013 | 0.53× | 188% | -|✅| np.add(a, b) (uint8) | uint8 | 100,000 | 0.0293 | 0.0155 | 1.89× | 53% | -|✅| np.add(a, b) (uint8) | uint8 | 10,000,000 | 3.8443 | 3.0809 | 1.25× | 80% | -|▫| scalar - a (complex128) | complex128 | 1,000 | 0.0010 | 0.0027 | 0.36× | 279% | -|✅| scalar - a (complex128) | complex128 | 100,000 | 0.3082 | 0.2238 | 1.38× | 73% | -|✅| scalar - a (complex128) | complex128 | 10,000,000 | 32.3795 | 25.2975 | 1.28× | 78% | -|✅| scalar - a (float16) | float16 | 1,000 | 0.0056 | 0.0049 | 1.15× | 87% | -|🟡| scalar - a (float16) | float16 | 100,000 | 0.2910 | 0.4656 | 0.62× | 160% | -|🟡| scalar - a (float16) | float16 | 10,000,000 | 30.2778 | 46.0030 | 0.66× | 152% | -|▫| scalar - a (float32) | float32 | 1,000 | 0.0007 | 0.0014 | 0.49× | 202% | -|🟠| scalar - a (float32) | float32 | 100,000 | 0.0062 | 0.0265 | 0.23× | 427% | -|✅| scalar - a (float32) | float32 | 10,000,000 | 7.7928 | 5.1631 | 1.51× | 66% | -|▫| scalar - a (float64) | float64 | 1,000 | 0.0007 | 0.0020 | 0.34× | 297% | -|🟠| scalar - a (float64) | float64 | 100,000 | 0.0115 | 0.0568 | 0.20× | 493% | -|✅| scalar - a (float64) | float64 | 10,000,000 | 15.7789 | 13.8734 | 1.14× | 88% | -|▫| scalar - a (int16) | int16 | 1,000 | 0.0009 | 0.0011 | 0.86× | 116% | -|✅| scalar - a (int16) | int16 | 100,000 | 0.0246 | 0.0147 | 1.68× | 60% | -|✅| scalar - a (int16) | int16 | 10,000,000 | 4.4218 | 2.5703 | 1.72× | 58% | -|▫| scalar - a (int32) | int32 | 1,000 | 0.0009 | 0.0012 | 0.78× | 128% | -|🟡| scalar - a (int32) | int32 | 100,000 | 0.0237 | 0.0275 | 0.86× | 116% | -|✅| scalar - a (int32) | int32 | 10,000,000 | 7.8237 | 5.1041 | 1.53× | 65% | -|🟡| scalar - a (int64) | int64 | 1,000 | 0.0011 | 0.0016 | 0.66× | 151% | -|🟠| scalar - a (int64) | int64 | 100,000 | 0.0241 | 0.0657 | 0.37× | 273% | -|✅| scalar - a (int64) | int64 | 10,000,000 | 14.9127 | 13.4584 | 1.11× | 90% | -|▫| scalar - a (int8) | int8 | 1,000 | 0.0008 | 0.0010 | 0.77× | 130% | -|✅| scalar - a (int8) | int8 | 100,000 | 0.0237 | 0.0079 | 3.01× | 33% | -|✅| scalar - a (int8) | int8 | 10,000,000 | 3.2754 | 1.3503 | 2.43× | 41% | -|✅| scalar - a (uint16) | uint16 | 1,000 | 0.0016 | 0.0012 | 1.41× | 71% | -|✅| scalar - a (uint16) | uint16 | 100,000 | 0.0240 | 0.0143 | 1.68× | 60% | -|✅| scalar - a (uint16) | uint16 | 10,000,000 | 4.4048 | 2.4891 | 1.77× | 56% | -|▫| scalar - a (uint32) | uint32 | 1,000 | 0.0009 | 0.0014 | 0.66× | 153% | -|✅| scalar - a (uint32) | uint32 | 100,000 | 0.0287 | 0.0275 | 1.04× | 96% | -|✅| scalar - a (uint32) | uint32 | 10,000,000 | 7.7897 | 5.0724 | 1.54× | 65% | -|▫| scalar - a (uint64) | uint64 | 1,000 | 0.0010 | 0.0017 | 0.56× | 178% | -|🟠| scalar - a (uint64) | uint64 | 100,000 | 0.0236 | 0.0589 | 0.40× | 250% | -|✅| scalar - a (uint64) | uint64 | 10,000,000 | 15.0333 | 13.5035 | 1.11× | 90% | -|▫| scalar - a (uint8) | uint8 | 1,000 | 0.0008 | 0.0010 | 0.79× | 126% | -|✅| scalar - a (uint8) | uint8 | 100,000 | 0.0245 | 0.0078 | 3.13× | 32% | -|✅| scalar - a (uint8) | uint8 | 10,000,000 | 3.3284 | 1.3375 | 2.49× | 40% | -|▫| scalar / a (float32) | float32 | 1,000 | 0.0007 | 0.0019 | 0.34× | 294% | -|🟠| scalar / a (float32) | float32 | 100,000 | 0.0149 | 0.0582 | 0.26× | 391% | -|🟡| scalar / a (float32) | float32 | 10,000,000 | 7.9318 | 8.0737 | 0.98× | 102% | -|▫| scalar / a (float64) | float64 | 1,000 | 0.0009 | 0.0033 | 0.28× | 356% | -|🟠| scalar / a (float64) | float64 | 100,000 | 0.0374 | 0.1398 | 0.27× | 374% | -|🟡| scalar / a (float64) | float64 | 10,000,000 | 15.4337 | 22.3529 | 0.69× | 145% | -|🟠| scalar / a (int32) | int32 | 1,000 | 0.0018 | 0.0063 | 0.29× | 349% | -|🟠| scalar / a (int32) | int32 | 100,000 | 0.0618 | 0.1726 | 0.36× | 279% | -|🟡| scalar / a (int32) | int32 | 10,000,000 | 17.0772 | 22.8310 | 0.75× | 134% | -|🟠| scalar / a (int64) | int64 | 1,000 | 0.0022 | 0.0060 | 0.36× | 274% | -|🟠| scalar / a (int64) | int64 | 100,000 | 0.0575 | 0.1799 | 0.32× | 313% | -|🟡| scalar / a (int64) | int64 | 10,000,000 | 18.5249 | 24.6415 | 0.75× | 133% | +|🟡| a % 7 (literal) (float32) | float32 | 1,000 | 0.0143 | 0.0167 | 0.86× | 116% | +|🟡| a % 7 (literal) (float32) | float32 | 100,000 | 1.6323 | 1.8289 | 0.89× | 112% | +|🟡| a % 7 (literal) (float32) | float32 | 10,000,000 | 166.6333 | 176.1757 | 0.95× | 106% | +|🟡| a % 7 (literal) (float64) | float64 | 1,000 | 0.0112 | 0.0175 | 0.64× | 156% | +|🟡| a % 7 (literal) (float64) | float64 | 100,000 | 1.4461 | 1.6302 | 0.89× | 113% | +|🟡| a % 7 (literal) (float64) | float64 | 10,000,000 | 159.2176 | 162.5277 | 0.98× | 102% | +|🟡| a % 7 (literal) (int32) | int32 | 1,000 | 0.0022 | 0.0028 | 0.78× | 129% | +|🟡| a % 7 (literal) (int32) | int32 | 100,000 | 0.3957 | 0.6590 | 0.60× | 166% | +|🟡| a % 7 (literal) (int32) | int32 | 10,000,000 | 49.0203 | 66.9383 | 0.73× | 137% | +|🟡| a % 7 (literal) (int64) | int64 | 1,000 | 0.0044 | 0.0069 | 0.64× | 156% | +|🟡| a % 7 (literal) (int64) | int64 | 100,000 | 0.4155 | 0.6703 | 0.62× | 161% | +|🟡| a % 7 (literal) (int64) | int64 | 10,000,000 | 50.7135 | 75.8665 | 0.67× | 150% | +|✅| a % b (element-wise) (float32) | float32 | 1,000 | 0.0121 | 0.0108 | 1.12× | 90% | +|🟡| a % b (element-wise) (float32) | float32 | 100,000 | 1.5318 | 1.5713 | 0.97× | 103% | +|✅| a % b (element-wise) (float32) | float32 | 10,000,000 | 156.9058 | 152.7181 | 1.03× | 97% | +|✅| a % b (element-wise) (float64) | float64 | 1,000 | 0.0100 | 0.0090 | 1.12× | 89% | +|🟡| a % b (element-wise) (float64) | float64 | 100,000 | 1.2891 | 1.3568 | 0.95× | 105% | +|🟡| a % b (element-wise) (float64) | float64 | 10,000,000 | 143.9662 | 145.6373 | 0.99× | 101% | +|🟡| a % b (element-wise) (int32) | int32 | 1,000 | 0.0021 | 0.0028 | 0.76× | 132% | +|🟡| a % b (element-wise) (int32) | int32 | 100,000 | 0.3784 | 0.6093 | 0.62× | 161% | +|🟡| a % b (element-wise) (int32) | int32 | 10,000,000 | 47.9091 | 58.8577 | 0.81× | 123% | +|🟡| a % b (element-wise) (int64) | int64 | 1,000 | 0.0035 | 0.0040 | 0.88× | 114% | +|🟡| a % b (element-wise) (int64) | int64 | 100,000 | 0.3756 | 0.6058 | 0.62× | 161% | +|🟡| a % b (element-wise) (int64) | int64 | 10,000,000 | 49.5215 | 66.8191 | 0.74× | 135% | +|🟠| a * 2 (literal) (complex128) | complex128 | 1,000 | 0.0011 | 0.0045 | 0.25× | 402% | +|✅| a * 2 (literal) (complex128) | complex128 | 100,000 | 0.3633 | 0.3155 | 1.15× | 87% | +|🟡| a * 2 (literal) (complex128) | complex128 | 10,000,000 | 31.3365 | 57.0611 | 0.55× | 182% | +|🟡| a * 2 (literal) (float16) | float16 | 1,000 | 0.0066 | 0.0103 | 0.63× | 158% | +|🟠| a * 2 (literal) (float16) | float16 | 100,000 | 0.3439 | 0.8963 | 0.38× | 261% | +|🟠| a * 2 (literal) (float16) | float16 | 10,000,000 | 31.2284 | 86.0293 | 0.36× | 276% | +|▫| a * 2 (literal) (float32) | float32 | 1,000 | 0.0008 | 0.0053 | 0.14× | 691% | +|🔴| a * 2 (literal) (float32) | float32 | 100,000 | 0.0066 | 0.0516 | 0.13× | 780% | +|🟡| a * 2 (literal) (float32) | float32 | 10,000,000 | 7.8824 | 9.4240 | 0.84× | 120% | +|▫| a * 2 (literal) (float64) | float64 | 1,000 | 0.0008 | 0.0062 | 0.13× | 773% | +|🔴| a * 2 (literal) (float64) | float64 | 100,000 | 0.0119 | 0.1056 | 0.11× | 887% | +|🟡| a * 2 (literal) (float64) | float64 | 10,000,000 | 20.0350 | 22.1709 | 0.90× | 111% | +|▫| a * 2 (literal) (int16) | int16 | 1,000 | 0.0010 | 0.0058 | 0.17× | 589% | +|🟡| a * 2 (literal) (int16) | int16 | 100,000 | 0.0233 | 0.0306 | 0.76× | 131% | +|✅| a * 2 (literal) (int16) | int16 | 10,000,000 | 6.0238 | 4.4309 | 1.36× | 74% | +|🟠| a * 2 (literal) (int32) | int32 | 1,000 | 0.0010 | 0.0034 | 0.30× | 334% | +|🟠| a * 2 (literal) (int32) | int32 | 100,000 | 0.0230 | 0.0521 | 0.44× | 226% | +|✅| a * 2 (literal) (int32) | int32 | 10,000,000 | 12.3132 | 7.7215 | 1.59× | 63% | +|▫| a * 2 (literal) (int64) | int64 | 1,000 | 0.0009 | 0.0056 | 0.16× | 614% | +|🟠| a * 2 (literal) (int64) | int64 | 100,000 | 0.0216 | 0.1059 | 0.20× | 491% | +|🟡| a * 2 (literal) (int64) | int64 | 10,000,000 | 16.4530 | 31.0792 | 0.53× | 189% | +|▫| a * 2 (literal) (int8) | int8 | 1,000 | 0.0008 | 0.0036 | 0.23× | 425% | +|🟡| a * 2 (literal) (int8) | int8 | 100,000 | 0.0226 | 0.0233 | 0.97× | 103% | +|✅| a * 2 (literal) (int8) | int8 | 10,000,000 | 3.7464 | 2.1072 | 1.78× | 56% | +|🟠| a * 2 (literal) (uint16) | uint16 | 1,000 | 0.0010 | 0.0027 | 0.37× | 269% | +|🟡| a * 2 (literal) (uint16) | uint16 | 100,000 | 0.0229 | 0.0291 | 0.78× | 128% | +|✅| a * 2 (literal) (uint16) | uint16 | 10,000,000 | 6.6073 | 4.8579 | 1.36× | 74% | +|🔴| a * 2 (literal) (uint32) | uint32 | 1,000 | 0.0010 | 0.0054 | 0.19× | 520% | +|🟠| a * 2 (literal) (uint32) | uint32 | 100,000 | 0.0225 | 0.0519 | 0.43× | 231% | +|✅| a * 2 (literal) (uint32) | uint32 | 10,000,000 | 7.8930 | 7.6778 | 1.03× | 97% | +|▫| a * 2 (literal) (uint64) | uint64 | 1,000 | 0.0009 | 0.0030 | 0.31× | 323% | +|🟠| a * 2 (literal) (uint64) | uint64 | 100,000 | 0.0222 | 0.1066 | 0.21× | 481% | +|🟡| a * 2 (literal) (uint64) | uint64 | 10,000,000 | 15.0621 | 29.8983 | 0.50× | 198% | +|▫| a * 2 (literal) (uint8) | uint8 | 1,000 | 0.0009 | 0.0038 | 0.23× | 428% | +|✅| a * 2 (literal) (uint8) | uint8 | 100,000 | 0.0229 | 0.0216 | 1.06× | 95% | +|✅| a * 2 (literal) (uint8) | uint8 | 10,000,000 | 4.1643 | 2.1224 | 1.96× | 51% | +|▫| a * a (square) (complex128) | complex128 | 1,000 | 0.0008 | 0.0042 | 0.19× | 520% | +|🟡| a * a (square) (complex128) | complex128 | 100,000 | 0.3485 | 0.4268 | 0.82× | 122% | +|🟡| a * a (square) (complex128) | complex128 | 10,000,000 | 30.2303 | 43.0242 | 0.70× | 142% | +|🟡| a * a (square) (float16) | float16 | 1,000 | 0.0057 | 0.0093 | 0.61× | 162% | +|🟠| a * a (square) (float16) | float16 | 100,000 | 0.2934 | 0.8294 | 0.35× | 283% | +|🟠| a * a (square) (float16) | float16 | 10,000,000 | 30.7340 | 84.9605 | 0.36× | 276% | +|▫| a * a (square) (float32) | float32 | 1,000 | 0.0005 | 0.0019 | 0.28× | 356% | +|🔴| a * a (square) (float32) | float32 | 100,000 | 0.0059 | 0.0493 | 0.12× | 834% | +|🟡| a * a (square) (float32) | float32 | 10,000,000 | 7.8671 | 8.1233 | 0.97× | 103% | +|▫| a * a (square) (float64) | float64 | 1,000 | 0.0005 | 0.0027 | 0.20× | 506% | +|🔴| a * a (square) (float64) | float64 | 100,000 | 0.0113 | 0.0977 | 0.12× | 863% | +|🟡| a * a (square) (float64) | float64 | 10,000,000 | 19.3973 | 20.3482 | 0.95× | 105% | +|▫| a * a (square) (int16) | int16 | 1,000 | 0.0008 | 0.0018 | 0.41× | 242% | +|✅| a * a (square) (int16) | int16 | 100,000 | 0.0287 | 0.0251 | 1.14× | 88% | +|✅| a * a (square) (int16) | int16 | 10,000,000 | 7.2866 | 5.2338 | 1.39× | 72% | +|▫| a * a (square) (int32) | int32 | 1,000 | 0.0008 | 0.0020 | 0.39× | 258% | +|🟡| a * a (square) (int32) | int32 | 100,000 | 0.0300 | 0.0553 | 0.54× | 184% | +|✅| a * a (square) (int32) | int32 | 10,000,000 | 12.7934 | 7.8908 | 1.62× | 62% | +|▫| a * a (square) (int64) | int64 | 1,000 | 0.0008 | 0.0029 | 0.27× | 376% | +|🟠| a * a (square) (int64) | int64 | 100,000 | 0.0289 | 0.1056 | 0.27× | 366% | +|🟡| a * a (square) (int64) | int64 | 10,000,000 | 15.6967 | 30.4188 | 0.52× | 194% | +|▫| a * a (square) (int8) | int8 | 1,000 | 0.0006 | 0.0015 | 0.42× | 239% | +|✅| a * a (square) (int8) | int8 | 100,000 | 0.0280 | 0.0158 | 1.77× | 57% | +|✅| a * a (square) (int8) | int8 | 10,000,000 | 4.4945 | 2.8519 | 1.58× | 64% | +|▫| a * a (square) (uint16) | uint16 | 1,000 | 0.0008 | 0.0017 | 0.45× | 223% | +|✅| a * a (square) (uint16) | uint16 | 100,000 | 0.0347 | 0.0258 | 1.34× | 74% | +|✅| a * a (square) (uint16) | uint16 | 10,000,000 | 7.9808 | 5.3188 | 1.50× | 67% | +|▫| a * a (square) (uint32) | uint32 | 1,000 | 0.0008 | 0.0021 | 0.37× | 269% | +|🟡| a * a (square) (uint32) | uint32 | 100,000 | 0.0285 | 0.0490 | 0.58× | 172% | +|🟡| a * a (square) (uint32) | uint32 | 10,000,000 | 7.9914 | 8.4603 | 0.94× | 106% | +|▫| a * a (square) (uint64) | uint64 | 1,000 | 0.0007 | 0.0028 | 0.26× | 384% | +|🟠| a * a (square) (uint64) | uint64 | 100,000 | 0.0293 | 0.1078 | 0.27× | 368% | +|🟡| a * a (square) (uint64) | uint64 | 10,000,000 | 15.1757 | 29.8093 | 0.51× | 196% | +|▫| a * a (square) (uint8) | uint8 | 1,000 | 0.0007 | 0.0014 | 0.46× | 216% | +|✅| a * a (square) (uint8) | uint8 | 100,000 | 0.0290 | 0.0155 | 1.88× | 53% | +|✅| a * a (square) (uint8) | uint8 | 10,000,000 | 4.8089 | 2.8618 | 1.68× | 60% | +|▫| a * b (element-wise) (complex128) | complex128 | 1,000 | 0.0009 | 0.0043 | 0.22× | 453% | +|🟡| a * b (element-wise) (complex128) | complex128 | 100,000 | 0.3579 | 0.4442 | 0.81× | 124% | +|🟠| a * b (element-wise) (complex128) | complex128 | 10,000,000 | 34.4053 | 73.3371 | 0.47× | 213% | +|🟠| a * b (element-wise) (float16) | float16 | 1,000 | 0.0034 | 0.0090 | 0.37× | 268% | +|🟠| a * b (element-wise) (float16) | float16 | 100,000 | 0.3186 | 0.8505 | 0.38× | 267% | +|🟠| a * b (element-wise) (float16) | float16 | 10,000,000 | 30.9166 | 88.9610 | 0.35× | 288% | +|▫| a * b (element-wise) (float32) | float32 | 1,000 | 0.0006 | 0.0019 | 0.30× | 336% | +|🔴| a * b (element-wise) (float32) | float32 | 100,000 | 0.0071 | 0.0514 | 0.14× | 722% | +|🟡| a * b (element-wise) (float32) | float32 | 10,000,000 | 8.4965 | 10.2234 | 0.83× | 120% | +|▫| a * b (element-wise) (float64) | float64 | 1,000 | 0.0005 | 0.0025 | 0.20× | 489% | +|🟠| a * b (element-wise) (float64) | float64 | 100,000 | 0.0274 | 0.1030 | 0.27× | 376% | +|🟡| a * b (element-wise) (float64) | float64 | 10,000,000 | 21.0075 | 33.0143 | 0.64× | 157% | +|▫| a * b (element-wise) (int16) | int16 | 1,000 | 0.0008 | 0.0019 | 0.41× | 241% | +|✅| a * b (element-wise) (int16) | int16 | 100,000 | 0.0282 | 0.0266 | 1.06× | 95% | +|✅| a * b (element-wise) (int16) | int16 | 10,000,000 | 7.7338 | 6.8196 | 1.13× | 88% | +|▫| a * b (element-wise) (int32) | int32 | 1,000 | 0.0008 | 0.0020 | 0.39× | 257% | +|🟡| a * b (element-wise) (int32) | int32 | 100,000 | 0.0287 | 0.0519 | 0.55× | 181% | +|✅| a * b (element-wise) (int32) | int32 | 10,000,000 | 13.0207 | 10.9911 | 1.19× | 84% | +|▫| a * b (element-wise) (int64) | int64 | 1,000 | 0.0008 | 0.0030 | 0.26× | 386% | +|🟠| a * b (element-wise) (int64) | int64 | 100,000 | 0.0386 | 0.1068 | 0.36× | 277% | +|🟠| a * b (element-wise) (int64) | int64 | 10,000,000 | 17.0533 | 38.5984 | 0.44× | 226% | +|▫| a * b (element-wise) (int8) | int8 | 1,000 | 0.0006 | 0.0016 | 0.39× | 255% | +|✅| a * b (element-wise) (int8) | int8 | 100,000 | 0.0282 | 0.0162 | 1.74× | 57% | +|✅| a * b (element-wise) (int8) | int8 | 10,000,000 | 5.3373 | 3.6339 | 1.47× | 68% | +|▫| a * b (element-wise) (uint16) | uint16 | 1,000 | 0.0008 | 0.0019 | 0.39× | 254% | +|✅| a * b (element-wise) (uint16) | uint16 | 100,000 | 0.0308 | 0.0267 | 1.15× | 87% | +|✅| a * b (element-wise) (uint16) | uint16 | 10,000,000 | 7.7433 | 6.7987 | 1.14× | 88% | +|▫| a * b (element-wise) (uint32) | uint32 | 1,000 | 0.0008 | 0.0020 | 0.40× | 250% | +|🟡| a * b (element-wise) (uint32) | uint32 | 100,000 | 0.0278 | 0.0486 | 0.57× | 175% | +|🟡| a * b (element-wise) (uint32) | uint32 | 10,000,000 | 9.8085 | 11.0138 | 0.89× | 112% | +|▫| a * b (element-wise) (uint64) | uint64 | 1,000 | 0.0007 | 0.0027 | 0.27× | 377% | +|🟠| a * b (element-wise) (uint64) | uint64 | 100,000 | 0.0361 | 0.1090 | 0.33× | 302% | +|🟠| a * b (element-wise) (uint64) | uint64 | 10,000,000 | 16.9583 | 37.9021 | 0.45× | 224% | +|▫| a * b (element-wise) (uint8) | uint8 | 1,000 | 0.0007 | 0.0017 | 0.39× | 257% | +|✅| a * b (element-wise) (uint8) | uint8 | 100,000 | 0.0295 | 0.0157 | 1.88× | 53% | +|✅| a * b (element-wise) (uint8) | uint8 | 10,000,000 | 4.5011 | 3.6771 | 1.22× | 82% | +|▫| a * scalar (complex128) | complex128 | 1,000 | 0.0009 | 0.0043 | 0.22× | 458% | +|✅| a * scalar (complex128) | complex128 | 100,000 | 0.3458 | 0.3076 | 1.12× | 89% | +|🟡| a * scalar (complex128) | complex128 | 10,000,000 | 30.9297 | 45.1226 | 0.69× | 146% | +|🟡| a * scalar (float16) | float16 | 1,000 | 0.0062 | 0.0092 | 0.68× | 148% | +|🟠| a * scalar (float16) | float16 | 100,000 | 0.3472 | 0.8794 | 0.40× | 253% | +|🟠| a * scalar (float16) | float16 | 10,000,000 | 35.1625 | 88.3826 | 0.40× | 251% | +|▫| a * scalar (float32) | float32 | 1,000 | 0.0007 | 0.0020 | 0.36× | 281% | +|🔴| a * scalar (float32) | float32 | 100,000 | 0.0068 | 0.0488 | 0.14× | 722% | +|🟡| a * scalar (float32) | float32 | 10,000,000 | 8.0629 | 8.9608 | 0.90× | 111% | +|▫| a * scalar (float64) | float64 | 1,000 | 0.0009 | 0.0028 | 0.31× | 325% | +|🔴| a * scalar (float64) | float64 | 100,000 | 0.0117 | 0.0999 | 0.12× | 857% | +|🟡| a * scalar (float64) | float64 | 10,000,000 | 19.9081 | 22.8588 | 0.87× | 115% | +|▫| a * scalar (int16) | int16 | 1,000 | 0.0009 | 0.0018 | 0.50× | 201% | +|🟡| a * scalar (int16) | int16 | 100,000 | 0.0233 | 0.0262 | 0.89× | 112% | +|✅| a * scalar (int16) | int16 | 10,000,000 | 7.0592 | 4.6609 | 1.51× | 66% | +|▫| a * scalar (int32) | int32 | 1,000 | 0.0009 | 0.0019 | 0.46× | 217% | +|🟠| a * scalar (int32) | int32 | 100,000 | 0.0231 | 0.0508 | 0.46× | 220% | +|✅| a * scalar (int32) | int32 | 10,000,000 | 12.6141 | 9.1360 | 1.38× | 72% | +|▫| a * scalar (int64) | int64 | 1,000 | 0.0009 | 0.0029 | 0.30× | 332% | +|🟠| a * scalar (int64) | int64 | 100,000 | 0.0226 | 0.1076 | 0.21× | 476% | +|🟠| a * scalar (int64) | int64 | 10,000,000 | 15.4341 | 31.3975 | 0.49× | 203% | +|▫| a * scalar (int8) | int8 | 1,000 | 0.0007 | 0.0016 | 0.45× | 223% | +|✅| a * scalar (int8) | int8 | 100,000 | 0.0223 | 0.0161 | 1.39× | 72% | +|✅| a * scalar (int8) | int8 | 10,000,000 | 3.7610 | 2.0651 | 1.82× | 55% | +|▫| a * scalar (uint16) | uint16 | 1,000 | 0.0009 | 0.0018 | 0.47× | 213% | +|🟡| a * scalar (uint16) | uint16 | 100,000 | 0.0262 | 0.0269 | 0.97× | 103% | +|✅| a * scalar (uint16) | uint16 | 10,000,000 | 7.0635 | 4.6785 | 1.51× | 66% | +|▫| a * scalar (uint32) | uint32 | 1,000 | 0.0009 | 0.0020 | 0.46× | 219% | +|🟠| a * scalar (uint32) | uint32 | 100,000 | 0.0227 | 0.0507 | 0.45× | 223% | +|🟡| a * scalar (uint32) | uint32 | 10,000,000 | 7.8165 | 9.2370 | 0.85× | 118% | +|▫| a * scalar (uint64) | uint64 | 1,000 | 0.0008 | 0.0028 | 0.28× | 351% | +|🟠| a * scalar (uint64) | uint64 | 100,000 | 0.0222 | 0.1048 | 0.21× | 471% | +|🟡| a * scalar (uint64) | uint64 | 10,000,000 | 15.1079 | 30.0570 | 0.50× | 199% | +|▫| a * scalar (uint8) | uint8 | 1,000 | 0.0008 | 0.0016 | 0.47× | 210% | +|✅| a * scalar (uint8) | uint8 | 100,000 | 0.0238 | 0.0159 | 1.50× | 67% | +|✅| a * scalar (uint8) | uint8 | 10,000,000 | 4.0195 | 2.0901 | 1.92× | 52% | +|🔴| a + 5 (literal) (complex128) | complex128 | 1,000 | 0.0011 | 0.0076 | 0.14× | 691% | +|✅| a + 5 (literal) (complex128) | complex128 | 100,000 | 0.3558 | 0.3115 | 1.14× | 88% | +|✅| a + 5 (literal) (complex128) | complex128 | 10,000,000 | 42.7672 | 39.9933 | 1.07× | 94% | +|🟡| a + 5 (literal) (float16) | float16 | 1,000 | 0.0064 | 0.0106 | 0.60× | 166% | +|🟠| a + 5 (literal) (float16) | float16 | 100,000 | 0.3642 | 0.8592 | 0.42× | 236% | +|🟠| a + 5 (literal) (float16) | float16 | 10,000,000 | 31.0643 | 84.9236 | 0.37× | 273% | +|▫| a + 5 (literal) (float32) | float32 | 1,000 | 0.0009 | 0.0049 | 0.18× | 568% | +|🔴| a + 5 (literal) (float32) | float32 | 100,000 | 0.0065 | 0.0543 | 0.12× | 841% | +|🟡| a + 5 (literal) (float32) | float32 | 10,000,000 | 8.0704 | 8.3566 | 0.97× | 104% | +|▫| a + 5 (literal) (float64) | float64 | 1,000 | 0.0008 | 0.0056 | 0.14× | 708% | +|🔴| a + 5 (literal) (float64) | float64 | 100,000 | 0.0117 | 0.1033 | 0.11× | 884% | +|✅| a + 5 (literal) (float64) | float64 | 10,000,000 | 19.9822 | 19.3898 | 1.03× | 97% | +|🟠| a + 5 (literal) (int16) | int16 | 1,000 | 0.0010 | 0.0025 | 0.41× | 245% | +|🟡| a + 5 (literal) (int16) | int16 | 100,000 | 0.0256 | 0.0297 | 0.86× | 116% | +|✅| a + 5 (literal) (int16) | int16 | 10,000,000 | 6.8921 | 4.3178 | 1.60× | 63% | +|🟠| a + 5 (literal) (int32) | int32 | 1,000 | 0.0010 | 0.0033 | 0.31× | 321% | +|🟠| a + 5 (literal) (int32) | int32 | 100,000 | 0.0250 | 0.0530 | 0.47× | 212% | +|✅| a + 5 (literal) (int32) | int32 | 10,000,000 | 12.6603 | 8.4146 | 1.50× | 66% | +|▫| a + 5 (literal) (int64) | int64 | 1,000 | 0.0010 | 0.0058 | 0.17× | 598% | +|🟠| a + 5 (literal) (int64) | int64 | 100,000 | 0.0233 | 0.1024 | 0.23× | 439% | +|🟡| a + 5 (literal) (int64) | int64 | 10,000,000 | 15.3142 | 19.3520 | 0.79× | 126% | +|▫| a + 5 (literal) (int8) | int8 | 1,000 | 0.0009 | 0.0034 | 0.26× | 387% | +|✅| a + 5 (literal) (int8) | int8 | 100,000 | 0.0369 | 0.0230 | 1.60× | 62% | +|✅| a + 5 (literal) (int8) | int8 | 10,000,000 | 4.1932 | 1.9965 | 2.10× | 48% | +|🟠| a + 5 (literal) (uint16) | uint16 | 1,000 | 0.0010 | 0.0048 | 0.21× | 474% | +|🟡| a + 5 (literal) (uint16) | uint16 | 100,000 | 0.0250 | 0.0308 | 0.81× | 123% | +|✅| a + 5 (literal) (uint16) | uint16 | 10,000,000 | 6.9032 | 4.2530 | 1.62× | 62% | +|🔴| a + 5 (literal) (uint32) | uint32 | 1,000 | 0.0011 | 0.0056 | 0.19× | 530% | +|🟠| a + 5 (literal) (uint32) | uint32 | 100,000 | 0.0234 | 0.0493 | 0.47× | 211% | +|✅| a + 5 (literal) (uint32) | uint32 | 10,000,000 | 10.0916 | 8.5435 | 1.18× | 85% | +|🔴| a + 5 (literal) (uint64) | uint64 | 1,000 | 0.0010 | 0.0054 | 0.19× | 526% | +|🟠| a + 5 (literal) (uint64) | uint64 | 100,000 | 0.0236 | 0.1073 | 0.22× | 454% | +|🟡| a + 5 (literal) (uint64) | uint64 | 10,000,000 | 15.2161 | 20.0614 | 0.76× | 132% | +|▫| a + 5 (literal) (uint8) | uint8 | 1,000 | 0.0009 | 0.0036 | 0.26× | 390% | +|✅| a + 5 (literal) (uint8) | uint8 | 100,000 | 0.0248 | 0.0205 | 1.21× | 83% | +|✅| a + 5 (literal) (uint8) | uint8 | 10,000,000 | 4.1679 | 1.9455 | 2.14× | 47% | +|🟠| a + b (element-wise) (complex128) | complex128 | 1,000 | 0.0010 | 0.0046 | 0.23× | 442% | +|🟡| a + b (element-wise) (complex128) | complex128 | 100,000 | 0.3515 | 0.4716 | 0.74× | 134% | +|🟡| a + b (element-wise) (complex128) | complex128 | 10,000,000 | 42.3859 | 58.1610 | 0.73× | 137% | +|🟡| a + b (element-wise) (float16) | float16 | 1,000 | 0.0055 | 0.0091 | 0.60× | 165% | +|🟠| a + b (element-wise) (float16) | float16 | 100,000 | 0.2982 | 0.8560 | 0.35× | 287% | +|🟠| a + b (element-wise) (float16) | float16 | 10,000,000 | 31.3075 | 87.5987 | 0.36× | 280% | +|▫| a + b (element-wise) (float32) | float32 | 1,000 | 0.0007 | 0.0011 | 0.67× | 149% | +|🔴| a + b (element-wise) (float32) | float32 | 100,000 | 0.0069 | 0.0546 | 0.13× | 792% | +|🟡| a + b (element-wise) (float32) | float32 | 10,000,000 | 8.7877 | 10.3685 | 0.85× | 118% | +|▫| a + b (element-wise) (float64) | float64 | 1,000 | 0.0005 | 0.0018 | 0.28× | 358% | +|🟠| a + b (element-wise) (float64) | float64 | 100,000 | 0.0287 | 0.1183 | 0.24× | 413% | +|🟡| a + b (element-wise) (float64) | float64 | 10,000,000 | 17.1247 | 32.2672 | 0.53× | 188% | +|▫| a + b (element-wise) (int16) | int16 | 1,000 | 0.0009 | 0.0018 | 0.47× | 213% | +|🟡| a + b (element-wise) (int16) | int16 | 100,000 | 0.0306 | 0.0341 | 0.90× | 111% | +|✅| a + b (element-wise) (int16) | int16 | 10,000,000 | 7.6408 | 7.1288 | 1.07× | 93% | +|▫| a + b (element-wise) (int32) | int32 | 1,000 | 0.0008 | 0.0022 | 0.35× | 286% | +|🟡| a + b (element-wise) (int32) | int32 | 100,000 | 0.0296 | 0.0569 | 0.52× | 192% | +|✅| a + b (element-wise) (int32) | int32 | 10,000,000 | 13.4533 | 10.0724 | 1.34× | 75% | +|▫| a + b (element-wise) (int64) | int64 | 1,000 | 0.0010 | 0.0032 | 0.30× | 330% | +|🟠| a + b (element-wise) (int64) | int64 | 100,000 | 0.0351 | 0.1211 | 0.29× | 345% | +|🟡| a + b (element-wise) (int64) | int64 | 10,000,000 | 16.9982 | 32.1148 | 0.53× | 189% | +|▫| a + b (element-wise) (int8) | int8 | 1,000 | 0.0007 | 0.0017 | 0.39× | 256% | +|✅| a + b (element-wise) (int8) | int8 | 100,000 | 0.0289 | 0.0158 | 1.83× | 55% | +|✅| a + b (element-wise) (int8) | int8 | 10,000,000 | 4.7377 | 3.2841 | 1.44× | 69% | +|▫| a + b (element-wise) (uint16) | uint16 | 1,000 | 0.0008 | 0.0021 | 0.39× | 256% | +|✅| a + b (element-wise) (uint16) | uint16 | 100,000 | 0.0305 | 0.0303 | 1.00× | 100% | +|✅| a + b (element-wise) (uint16) | uint16 | 10,000,000 | 7.7579 | 7.1286 | 1.09× | 92% | +|🟡| a + b (element-wise) (uint32) | uint32 | 1,000 | 0.0011 | 0.0017 | 0.68× | 148% | +|🟡| a + b (element-wise) (uint32) | uint32 | 100,000 | 0.0292 | 0.0565 | 0.52× | 194% | +|✅| a + b (element-wise) (uint32) | uint32 | 10,000,000 | 13.4492 | 9.8607 | 1.36× | 73% | +|▫| a + b (element-wise) (uint64) | uint64 | 1,000 | 0.0008 | 0.0027 | 0.29× | 348% | +|🟠| a + b (element-wise) (uint64) | uint64 | 100,000 | 0.0529 | 0.1072 | 0.49× | 203% | +|🟠| a + b (element-wise) (uint64) | uint64 | 10,000,000 | 17.2194 | 35.4223 | 0.49× | 206% | +|▫| a + b (element-wise) (uint8) | uint8 | 1,000 | 0.0008 | 0.0019 | 0.43× | 232% | +|✅| a + b (element-wise) (uint8) | uint8 | 100,000 | 0.0305 | 0.0154 | 1.98× | 50% | +|✅| a + b (element-wise) (uint8) | uint8 | 10,000,000 | 4.9889 | 3.3759 | 1.48× | 68% | +|▫| a + scalar (complex128) | complex128 | 1,000 | 0.0009 | 0.0042 | 0.23× | 442% | +|✅| a + scalar (complex128) | complex128 | 100,000 | 0.3392 | 0.3087 | 1.10× | 91% | +|🟡| a + scalar (complex128) | complex128 | 10,000,000 | 39.4817 | 41.1306 | 0.96× | 104% | +|🟡| a + scalar (float16) | float16 | 1,000 | 0.0066 | 0.0091 | 0.73× | 138% | +|🟠| a + scalar (float16) | float16 | 100,000 | 0.3632 | 0.8631 | 0.42× | 238% | +|🟠| a + scalar (float16) | float16 | 10,000,000 | 30.8505 | 85.6472 | 0.36× | 278% | +|▫| a + scalar (float32) | float32 | 1,000 | 0.0007 | 0.0020 | 0.35× | 283% | +|🔴| a + scalar (float32) | float32 | 100,000 | 0.0062 | 0.0514 | 0.12× | 829% | +|🟡| a + scalar (float32) | float32 | 10,000,000 | 7.9325 | 8.3761 | 0.95× | 106% | +|▫| a + scalar (float64) | float64 | 1,000 | 0.0007 | 0.0027 | 0.24× | 416% | +|🔴| a + scalar (float64) | float64 | 100,000 | 0.0116 | 0.1045 | 0.11× | 902% | +|🟡| a + scalar (float64) | float64 | 10,000,000 | 19.7762 | 20.4638 | 0.97× | 104% | +|▫| a + scalar (int16) | int16 | 1,000 | 0.0009 | 0.0017 | 0.51× | 196% | +|🟡| a + scalar (int16) | int16 | 100,000 | 0.0249 | 0.0262 | 0.95× | 105% | +|✅| a + scalar (int16) | int16 | 10,000,000 | 7.1782 | 4.4382 | 1.62× | 62% | +|▫| a + scalar (int32) | int32 | 1,000 | 0.0009 | 0.0019 | 0.46× | 217% | +|🟠| a + scalar (int32) | int32 | 100,000 | 0.0245 | 0.0532 | 0.46× | 217% | +|✅| a + scalar (int32) | int32 | 10,000,000 | 12.6205 | 8.3589 | 1.51× | 66% | +|▫| a + scalar (int64) | int64 | 1,000 | 0.0009 | 0.0027 | 0.33× | 304% | +|🟠| a + scalar (int64) | int64 | 100,000 | 0.0236 | 0.1014 | 0.23× | 429% | +|🟡| a + scalar (int64) | int64 | 10,000,000 | 14.8466 | 19.9072 | 0.75× | 134% | +|▫| a + scalar (int8) | int8 | 1,000 | 0.0008 | 0.0016 | 0.49× | 203% | +|✅| a + scalar (int8) | int8 | 100,000 | 0.0251 | 0.0149 | 1.69× | 59% | +|✅| a + scalar (int8) | int8 | 10,000,000 | 4.2152 | 1.9671 | 2.14× | 47% | +|▫| a + scalar (uint16) | uint16 | 1,000 | 0.0009 | 0.0017 | 0.54× | 187% | +|🟡| a + scalar (uint16) | uint16 | 100,000 | 0.0245 | 0.0256 | 0.96× | 105% | +|✅| a + scalar (uint16) | uint16 | 10,000,000 | 6.9794 | 4.3583 | 1.60× | 62% | +|▫| a + scalar (uint32) | uint32 | 1,000 | 0.0009 | 0.0019 | 0.49× | 205% | +|🟠| a + scalar (uint32) | uint32 | 100,000 | 0.0247 | 0.0503 | 0.49× | 204% | +|✅| a + scalar (uint32) | uint32 | 10,000,000 | 10.8927 | 8.4266 | 1.29× | 77% | +|▫| a + scalar (uint64) | uint64 | 1,000 | 0.0009 | 0.0025 | 0.37× | 273% | +|🟠| a + scalar (uint64) | uint64 | 100,000 | 0.0235 | 0.1018 | 0.23× | 433% | +|🟡| a + scalar (uint64) | uint64 | 10,000,000 | 15.0579 | 20.0299 | 0.75× | 133% | +|▫| a + scalar (uint8) | uint8 | 1,000 | 0.0008 | 0.0015 | 0.50× | 202% | +|✅| a + scalar (uint8) | uint8 | 100,000 | 0.0248 | 0.0148 | 1.67× | 60% | +|✅| a + scalar (uint8) | uint8 | 10,000,000 | 4.4127 | 1.9329 | 2.28× | 44% | +|▫| a - b (element-wise) (complex128) | complex128 | 1,000 | 0.0009 | 0.0040 | 0.22× | 458% | +|✅| a - b (element-wise) (complex128) | complex128 | 100,000 | 0.3505 | 0.2610 | 1.34× | 74% | +|✅| a - b (element-wise) (complex128) | complex128 | 10,000,000 | 41.6707 | 35.7057 | 1.17× | 86% | +|🟠| a - b (element-wise) (float16) | float16 | 1,000 | 0.0041 | 0.0092 | 0.44× | 225% | +|🟡| a - b (element-wise) (float16) | float16 | 100,000 | 0.3042 | 0.4791 | 0.64× | 158% | +|🟡| a - b (element-wise) (float16) | float16 | 10,000,000 | 31.1711 | 48.4343 | 0.64× | 155% | +|▫| a - b (element-wise) (float32) | float32 | 1,000 | 0.0006 | 0.0020 | 0.28× | 359% | +|🔴| a - b (element-wise) (float32) | float32 | 100,000 | 0.0069 | 0.0482 | 0.14× | 698% | +|✅| a - b (element-wise) (float32) | float32 | 10,000,000 | 8.6157 | 5.6899 | 1.51× | 66% | +|▫| a - b (element-wise) (float64) | float64 | 1,000 | 0.0005 | 0.0018 | 0.29× | 340% | +|🟠| a - b (element-wise) (float64) | float64 | 100,000 | 0.0331 | 0.1072 | 0.31× | 324% | +|✅| a - b (element-wise) (float64) | float64 | 10,000,000 | 21.1427 | 16.8826 | 1.25× | 80% | +|▫| a - b (element-wise) (int16) | int16 | 1,000 | 0.0008 | 0.0017 | 0.45× | 222% | +|✅| a - b (element-wise) (int16) | int16 | 100,000 | 0.0293 | 0.0250 | 1.17× | 85% | +|✅| a - b (element-wise) (int16) | int16 | 10,000,000 | 7.8362 | 3.1173 | 2.51× | 40% | +|▫| a - b (element-wise) (int32) | int32 | 1,000 | 0.0008 | 0.0020 | 0.40× | 253% | +|🟡| a - b (element-wise) (int32) | int32 | 100,000 | 0.0287 | 0.0524 | 0.55× | 182% | +|✅| a - b (element-wise) (int32) | int32 | 10,000,000 | 13.7810 | 6.3717 | 2.16× | 46% | +|▫| a - b (element-wise) (int64) | int64 | 1,000 | 0.0008 | 0.0028 | 0.27× | 367% | +|🟠| a - b (element-wise) (int64) | int64 | 100,000 | 0.0352 | 0.1044 | 0.34× | 297% | +|🟡| a - b (element-wise) (int64) | int64 | 10,000,000 | 16.8594 | 20.7569 | 0.81× | 123% | +|▫| a - b (element-wise) (int8) | int8 | 1,000 | 0.0007 | 0.0014 | 0.46× | 219% | +|✅| a - b (element-wise) (int8) | int8 | 100,000 | 0.0355 | 0.0155 | 2.29× | 44% | +|✅| a - b (element-wise) (int8) | int8 | 10,000,000 | 4.7577 | 1.7145 | 2.77× | 36% | +|▫| a - b (element-wise) (uint16) | uint16 | 1,000 | 0.0008 | 0.0018 | 0.45× | 222% | +|✅| a - b (element-wise) (uint16) | uint16 | 100,000 | 0.0299 | 0.0263 | 1.14× | 88% | +|✅| a - b (element-wise) (uint16) | uint16 | 10,000,000 | 8.2884 | 3.1096 | 2.67× | 38% | +|▫| a - b (element-wise) (uint32) | uint32 | 1,000 | 0.0008 | 0.0020 | 0.41× | 242% | +|🟡| a - b (element-wise) (uint32) | uint32 | 100,000 | 0.0294 | 0.0506 | 0.58× | 172% | +|✅| a - b (element-wise) (uint32) | uint32 | 10,000,000 | 10.8540 | 6.0006 | 1.81× | 55% | +|▫| a - b (element-wise) (uint64) | uint64 | 1,000 | 0.0008 | 0.0029 | 0.26× | 385% | +|🟠| a - b (element-wise) (uint64) | uint64 | 100,000 | 0.0385 | 0.1017 | 0.38× | 264% | +|🟡| a - b (element-wise) (uint64) | uint64 | 10,000,000 | 16.7811 | 20.9601 | 0.80× | 125% | +|▫| a - b (element-wise) (uint8) | uint8 | 1,000 | 0.0007 | 0.0015 | 0.45× | 224% | +|✅| a - b (element-wise) (uint8) | uint8 | 100,000 | 0.0288 | 0.0152 | 1.90× | 53% | +|✅| a - b (element-wise) (uint8) | uint8 | 10,000,000 | 5.0513 | 1.6363 | 3.09× | 32% | +|▫| a - scalar (complex128) | complex128 | 1,000 | 0.0009 | 0.0030 | 0.31× | 319% | +|✅| a - scalar (complex128) | complex128 | 100,000 | 0.3509 | 0.2223 | 1.58× | 63% | +|✅| a - scalar (complex128) | complex128 | 10,000,000 | 39.6368 | 25.1620 | 1.57× | 64% | +|✅| a - scalar (float16) | float16 | 1,000 | 0.0061 | 0.0052 | 1.18× | 85% | +|🟡| a - scalar (float16) | float16 | 100,000 | 0.2950 | 0.4792 | 0.62× | 162% | +|🟡| a - scalar (float16) | float16 | 10,000,000 | 31.0622 | 48.9935 | 0.63× | 158% | +|▫| a - scalar (float32) | float32 | 1,000 | 0.0007 | 0.0013 | 0.53× | 190% | +|🟠| a - scalar (float32) | float32 | 100,000 | 0.0070 | 0.0293 | 0.24× | 417% | +|✅| a - scalar (float32) | float32 | 10,000,000 | 8.0175 | 5.3684 | 1.49× | 67% | +|▫| a - scalar (float64) | float64 | 1,000 | 0.0006 | 0.0019 | 0.33× | 302% | +|🔴| a - scalar (float64) | float64 | 100,000 | 0.0118 | 0.0691 | 0.17× | 586% | +|✅| a - scalar (float64) | float64 | 10,000,000 | 19.9346 | 14.0728 | 1.42× | 71% | +|▫| a - scalar (int16) | int16 | 1,000 | 0.0009 | 0.0013 | 0.70× | 142% | +|✅| a - scalar (int16) | int16 | 100,000 | 0.0239 | 0.0141 | 1.70× | 59% | +|✅| a - scalar (int16) | int16 | 10,000,000 | 7.1495 | 2.7330 | 2.62× | 38% | +|▫| a - scalar (int32) | int32 | 1,000 | 0.0009 | 0.0016 | 0.58× | 173% | +|🟡| a - scalar (int32) | int32 | 100,000 | 0.0237 | 0.0278 | 0.85× | 117% | +|✅| a - scalar (int32) | int32 | 10,000,000 | 13.2645 | 5.2875 | 2.51× | 40% | +|▫| a - scalar (int64) | int64 | 1,000 | 0.0009 | 0.0015 | 0.62× | 162% | +|🟠| a - scalar (int64) | int64 | 100,000 | 0.0241 | 0.0677 | 0.36× | 280% | +|✅| a - scalar (int64) | int64 | 10,000,000 | 15.1015 | 14.4570 | 1.04× | 96% | +|▫| a - scalar (int8) | int8 | 1,000 | 0.0008 | 0.0011 | 0.68× | 147% | +|✅| a - scalar (int8) | int8 | 100,000 | 0.0319 | 0.0079 | 4.02× | 25% | +|✅| a - scalar (int8) | int8 | 10,000,000 | 4.4975 | 1.3821 | 3.25× | 31% | +|▫| a - scalar (uint16) | uint16 | 1,000 | 0.0009 | 0.0012 | 0.77× | 130% | +|✅| a - scalar (uint16) | uint16 | 100,000 | 0.0244 | 0.0148 | 1.65× | 61% | +|✅| a - scalar (uint16) | uint16 | 10,000,000 | 6.7651 | 2.6065 | 2.60× | 38% | +|🟡| a - scalar (uint32) | uint32 | 1,000 | 0.0011 | 0.0012 | 0.88× | 114% | +|🟡| a - scalar (uint32) | uint32 | 100,000 | 0.0233 | 0.0264 | 0.89× | 113% | +|✅| a - scalar (uint32) | uint32 | 10,000,000 | 10.2434 | 5.3189 | 1.93× | 52% | +|▫| a - scalar (uint64) | uint64 | 1,000 | 0.0009 | 0.0016 | 0.56× | 177% | +|🟠| a - scalar (uint64) | uint64 | 100,000 | 0.0241 | 0.0652 | 0.37× | 271% | +|✅| a - scalar (uint64) | uint64 | 10,000,000 | 15.1815 | 14.5668 | 1.04× | 96% | +|▫| a - scalar (uint8) | uint8 | 1,000 | 0.0008 | 0.0008 | 0.95× | 105% | +|✅| a - scalar (uint8) | uint8 | 100,000 | 0.0374 | 0.0079 | 4.71× | 21% | +|✅| a - scalar (uint8) | uint8 | 10,000,000 | 4.1241 | 1.3972 | 2.95× | 34% | +|▫| a / b (element-wise) (float32) | float32 | 1,000 | 0.0005 | 0.0023 | 0.22× | 458% | +|🟠| a / b (element-wise) (float32) | float32 | 100,000 | 0.0134 | 0.0623 | 0.21× | 466% | +|🟡| a / b (element-wise) (float32) | float32 | 10,000,000 | 8.4590 | 10.2825 | 0.82× | 122% | +|▫| a / b (element-wise) (float64) | float64 | 1,000 | 0.0007 | 0.0034 | 0.22× | 452% | +|🟠| a / b (element-wise) (float64) | float64 | 100,000 | 0.0382 | 0.1801 | 0.21× | 472% | +|🟡| a / b (element-wise) (float64) | float64 | 10,000,000 | 20.4780 | 25.4700 | 0.80× | 124% | +|🟠| a / b (element-wise) (int32) | int32 | 1,000 | 0.0024 | 0.0053 | 0.47× | 215% | +|🟠| a / b (element-wise) (int32) | int32 | 100,000 | 0.0909 | 0.1953 | 0.47× | 215% | +|✅| a / b (element-wise) (int32) | int32 | 10,000,000 | 31.1185 | 23.9718 | 1.30× | 77% | +|🟠| a / b (element-wise) (int64) | int64 | 1,000 | 0.0019 | 0.0055 | 0.35× | 289% | +|🟠| a / b (element-wise) (int64) | int64 | 100,000 | 0.0785 | 0.1896 | 0.41× | 242% | +|🟡| a / b (element-wise) (int64) | int64 | 10,000,000 | 24.7535 | 40.7339 | 0.61× | 165% | +|▫| a / scalar (float32) | float32 | 1,000 | 0.0007 | 0.0020 | 0.32× | 308% | +|🟠| a / scalar (float32) | float32 | 100,000 | 0.0123 | 0.0595 | 0.21× | 484% | +|🟡| a / scalar (float32) | float32 | 10,000,000 | 7.8511 | 8.8399 | 0.89× | 113% | +|▫| a / scalar (float64) | float64 | 1,000 | 0.0009 | 0.0034 | 0.27× | 376% | +|🟠| a / scalar (float64) | float64 | 100,000 | 0.0372 | 0.1696 | 0.22× | 456% | +|🟡| a / scalar (float64) | float64 | 10,000,000 | 19.0548 | 23.8037 | 0.80× | 125% | +|🟠| a / scalar (int32) | int32 | 1,000 | 0.0018 | 0.0041 | 0.43× | 234% | +|🟠| a / scalar (int32) | int32 | 100,000 | 0.0622 | 0.1883 | 0.33× | 302% | +|✅| a / scalar (int32) | int32 | 10,000,000 | 26.4799 | 23.0389 | 1.15× | 87% | +|🟠| a / scalar (int64) | int64 | 1,000 | 0.0017 | 0.0074 | 0.23× | 436% | +|🟠| a / scalar (int64) | int64 | 100,000 | 0.0575 | 0.1885 | 0.30× | 328% | +|🟡| a / scalar (int64) | int64 | 10,000,000 | 20.5211 | 29.8992 | 0.69× | 146% | +|▫| np.add(a, b) (complex128) | complex128 | 1,000 | 0.0008 | 0.0046 | 0.17× | 572% | +|🟡| np.add(a, b) (complex128) | complex128 | 100,000 | 0.3569 | 0.4892 | 0.73× | 137% | +|🟡| np.add(a, b) (complex128) | complex128 | 10,000,000 | 41.8396 | 58.6023 | 0.71× | 140% | +|🟡| np.add(a, b) (float16) | float16 | 1,000 | 0.0057 | 0.0088 | 0.65× | 155% | +|🟠| np.add(a, b) (float16) | float16 | 100,000 | 0.2995 | 0.8369 | 0.36× | 280% | +|🟠| np.add(a, b) (float16) | float16 | 10,000,000 | 31.2511 | 85.9844 | 0.36× | 275% | +|▫| np.add(a, b) (float32) | float32 | 1,000 | 0.0008 | 0.0019 | 0.41× | 245% | +|🔴| np.add(a, b) (float32) | float32 | 100,000 | 0.0068 | 0.0542 | 0.12× | 798% | +|🟡| np.add(a, b) (float32) | float32 | 10,000,000 | 8.6159 | 10.2348 | 0.84× | 119% | +|▫| np.add(a, b) (float64) | float64 | 1,000 | 0.0005 | 0.0025 | 0.21× | 475% | +|🟠| np.add(a, b) (float64) | float64 | 100,000 | 0.0287 | 0.1135 | 0.25× | 396% | +|🟡| np.add(a, b) (float64) | float64 | 10,000,000 | 23.3169 | 25.7561 | 0.91× | 110% | +|▫| np.add(a, b) (int16) | int16 | 1,000 | 0.0009 | 0.0017 | 0.51× | 196% | +|✅| np.add(a, b) (int16) | int16 | 100,000 | 0.0312 | 0.0273 | 1.14× | 87% | +|✅| np.add(a, b) (int16) | int16 | 10,000,000 | 7.8320 | 7.1303 | 1.10× | 91% | +|▫| np.add(a, b) (int32) | int32 | 1,000 | 0.0008 | 0.0019 | 0.42× | 238% | +|🟡| np.add(a, b) (int32) | int32 | 100,000 | 0.0299 | 0.0546 | 0.55× | 183% | +|✅| np.add(a, b) (int32) | int32 | 10,000,000 | 14.4326 | 12.7485 | 1.13× | 88% | +|▫| np.add(a, b) (int64) | int64 | 1,000 | 0.0008 | 0.0028 | 0.29× | 341% | +|🟠| np.add(a, b) (int64) | int64 | 100,000 | 0.0359 | 0.1337 | 0.27× | 372% | +|🟠| np.add(a, b) (int64) | int64 | 10,000,000 | 17.0171 | 34.3239 | 0.50× | 202% | +|▫| np.add(a, b) (int8) | int8 | 1,000 | 0.0007 | 0.0017 | 0.39× | 257% | +|✅| np.add(a, b) (int8) | int8 | 100,000 | 0.0293 | 0.0152 | 1.93× | 52% | +|✅| np.add(a, b) (int8) | int8 | 10,000,000 | 4.8360 | 3.1341 | 1.54× | 65% | +|▫| np.add(a, b) (uint16) | uint16 | 1,000 | 0.0008 | 0.0018 | 0.42× | 235% | +|🟡| np.add(a, b) (uint16) | uint16 | 100,000 | 0.0294 | 0.0296 | 0.99× | 101% | +|✅| np.add(a, b) (uint16) | uint16 | 10,000,000 | 8.1075 | 6.8350 | 1.19× | 84% | +|▫| np.add(a, b) (uint32) | uint32 | 1,000 | 0.0010 | 0.0019 | 0.51× | 195% | +|🟡| np.add(a, b) (uint32) | uint32 | 100,000 | 0.0324 | 0.0542 | 0.60× | 167% | +|✅| np.add(a, b) (uint32) | uint32 | 10,000,000 | 10.4639 | 9.8564 | 1.06× | 94% | +|▫| np.add(a, b) (uint64) | uint64 | 1,000 | 0.0008 | 0.0028 | 0.30× | 330% | +|🟠| np.add(a, b) (uint64) | uint64 | 100,000 | 0.0405 | 0.1101 | 0.37× | 272% | +|🟠| np.add(a, b) (uint64) | uint64 | 10,000,000 | 16.7988 | 34.5621 | 0.49× | 206% | +|▫| np.add(a, b) (uint8) | uint8 | 1,000 | 0.0007 | 0.0019 | 0.36× | 276% | +|✅| np.add(a, b) (uint8) | uint8 | 100,000 | 0.0287 | 0.0154 | 1.86× | 54% | +|✅| np.add(a, b) (uint8) | uint8 | 10,000,000 | 4.7543 | 3.2277 | 1.47× | 68% | +|▫| scalar - a (complex128) | complex128 | 1,000 | 0.0010 | 0.0027 | 0.36× | 280% | +|✅| scalar - a (complex128) | complex128 | 100,000 | 0.3545 | 0.2210 | 1.60× | 62% | +|✅| scalar - a (complex128) | complex128 | 10,000,000 | 37.5427 | 25.1481 | 1.49× | 67% | +|✅| scalar - a (float16) | float16 | 1,000 | 0.0054 | 0.0051 | 1.05× | 95% | +|🟡| scalar - a (float16) | float16 | 100,000 | 0.3008 | 0.4746 | 0.63× | 158% | +|🟡| scalar - a (float16) | float16 | 10,000,000 | 31.1065 | 47.0491 | 0.66× | 151% | +|▫| scalar - a (float32) | float32 | 1,000 | 0.0007 | 0.0014 | 0.52× | 191% | +|🟠| scalar - a (float32) | float32 | 100,000 | 0.0067 | 0.0325 | 0.20× | 487% | +|✅| scalar - a (float32) | float32 | 10,000,000 | 7.8766 | 5.3211 | 1.48× | 68% | +|▫| scalar - a (float64) | float64 | 1,000 | 0.0007 | 0.0020 | 0.33× | 300% | +|🟠| scalar - a (float64) | float64 | 100,000 | 0.0138 | 0.0582 | 0.24× | 422% | +|✅| scalar - a (float64) | float64 | 10,000,000 | 20.1742 | 14.0006 | 1.44× | 69% | +|▫| scalar - a (int16) | int16 | 1,000 | 0.0009 | 0.0012 | 0.78× | 128% | +|✅| scalar - a (int16) | int16 | 100,000 | 0.0243 | 0.0148 | 1.64× | 61% | +|✅| scalar - a (int16) | int16 | 10,000,000 | 7.1811 | 2.7354 | 2.62× | 38% | +|▫| scalar - a (int32) | int32 | 1,000 | 0.0009 | 0.0013 | 0.72× | 139% | +|🟡| scalar - a (int32) | int32 | 100,000 | 0.0240 | 0.0290 | 0.83× | 121% | +|✅| scalar - a (int32) | int32 | 10,000,000 | 13.3685 | 5.4031 | 2.47× | 40% | +|🟡| scalar - a (int64) | int64 | 1,000 | 0.0011 | 0.0018 | 0.63× | 159% | +|🟠| scalar - a (int64) | int64 | 100,000 | 0.0250 | 0.0667 | 0.38× | 267% | +|✅| scalar - a (int64) | int64 | 10,000,000 | 15.5954 | 14.9929 | 1.04× | 96% | +|▫| scalar - a (int8) | int8 | 1,000 | 0.0008 | 0.0009 | 0.90× | 111% | +|✅| scalar - a (int8) | int8 | 100,000 | 0.0254 | 0.0080 | 3.18× | 31% | +|✅| scalar - a (int8) | int8 | 10,000,000 | 4.5431 | 1.3785 | 3.30× | 30% | +|▫| scalar - a (uint16) | uint16 | 1,000 | 0.0009 | 0.0011 | 0.85× | 118% | +|✅| scalar - a (uint16) | uint16 | 100,000 | 0.0313 | 0.0147 | 2.12× | 47% | +|✅| scalar - a (uint16) | uint16 | 10,000,000 | 7.1706 | 2.6431 | 2.71× | 37% | +|▫| scalar - a (uint32) | uint32 | 1,000 | 0.0009 | 0.0012 | 0.80× | 124% | +|🟡| scalar - a (uint32) | uint32 | 100,000 | 0.0239 | 0.0299 | 0.80× | 125% | +|✅| scalar - a (uint32) | uint32 | 10,000,000 | 9.9808 | 5.3724 | 1.86× | 54% | +|▫| scalar - a (uint64) | uint64 | 1,000 | 0.0009 | 0.0018 | 0.51× | 198% | +|🟠| scalar - a (uint64) | uint64 | 100,000 | 0.0246 | 0.0594 | 0.41× | 242% | +|🟡| scalar - a (uint64) | uint64 | 10,000,000 | 14.8288 | 14.8342 | 1.00× | 100% | +|▫| scalar - a (uint8) | uint8 | 1,000 | 0.0008 | 0.0009 | 0.84× | 119% | +|✅| scalar - a (uint8) | uint8 | 100,000 | 0.0243 | 0.0082 | 2.98× | 34% | +|✅| scalar - a (uint8) | uint8 | 10,000,000 | 4.0556 | 1.4050 | 2.89× | 35% | +|▫| scalar / a (float32) | float32 | 1,000 | 0.0007 | 0.0020 | 0.33× | 301% | +|🟠| scalar / a (float32) | float32 | 100,000 | 0.0127 | 0.0612 | 0.21× | 483% | +|✅| scalar / a (float32) | float32 | 10,000,000 | 7.9570 | 7.2244 | 1.10× | 91% | +|▫| scalar / a (float64) | float64 | 1,000 | 0.0009 | 0.0035 | 0.26× | 385% | +|🟠| scalar / a (float64) | float64 | 100,000 | 0.0372 | 0.1857 | 0.20× | 499% | +|🟡| scalar / a (float64) | float64 | 10,000,000 | 19.3997 | 22.5621 | 0.86× | 116% | +|🟠| scalar / a (int32) | int32 | 1,000 | 0.0018 | 0.0070 | 0.26× | 391% | +|🟠| scalar / a (int32) | int32 | 100,000 | 0.0638 | 0.1794 | 0.35× | 281% | +|🟡| scalar / a (int32) | int32 | 10,000,000 | 22.0961 | 25.4827 | 0.87× | 115% | +|🟠| scalar / a (int64) | int64 | 1,000 | 0.0018 | 0.0069 | 0.26× | 383% | +|🟠| scalar / a (int64) | int64 | 100,000 | 0.0627 | 0.1881 | 0.33× | 300% | +|🟡| scalar / a (int64) | int64 | 10,000,000 | 19.6435 | 30.8085 | 0.64× | 157% | ### Unary | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|▫| np.abs (float16) | float16 | 1,000 | 0.0008 | 0.0014 | 0.58× | 172% | -|✅| np.abs (float16) | float16 | 100,000 | 0.0261 | 0.0219 | 1.19× | 84% | -|✅| np.abs (float16) | float16 | 10,000,000 | 7.7905 | 2.7758 | 2.81× | 36% | -|▫| np.abs (float32) | float32 | 1,000 | 0.0006 | 0.0016 | 0.35× | 286% | -|🔴| np.abs (float32) | float32 | 100,000 | 0.0061 | 0.0376 | 0.16× | 617% | -|✅| np.abs (float32) | float32 | 10,000,000 | 7.3196 | 4.2367 | 1.73× | 58% | -|▫| np.abs (float64) | float64 | 1,000 | 0.0006 | 0.0050 | 0.11× | 904% | -|🔴| np.abs (float64) | float64 | 100,000 | 0.0113 | 0.0656 | 0.17× | 581% | -|✅| np.abs (float64) | float64 | 10,000,000 | 14.5867 | 14.0433 | 1.04× | 96% | -|🟡| np.cbrt(a) (float16) | float16 | 1,000 | 0.0104 | 0.0111 | 0.93× | 107% | -|🟡| np.cbrt(a) (float16) | float16 | 100,000 | 1.1653 | 1.3881 | 0.84× | 119% | -|🟡| np.cbrt(a) (float16) | float16 | 10,000,000 | 123.1163 | 137.5717 | 0.90× | 112% | -|✅| np.cbrt(a) (float32) | float32 | 1,000 | 0.0062 | 0.0060 | 1.02× | 98% | -|🟡| np.cbrt(a) (float32) | float32 | 100,000 | 0.8801 | 0.8926 | 0.99× | 101% | -|✅| np.cbrt(a) (float32) | float32 | 10,000,000 | 93.3862 | 87.0798 | 1.07× | 93% | -|🟡| np.cbrt(a) (float64) | float64 | 1,000 | 0.0092 | 0.0096 | 0.96× | 104% | -|🟡| np.cbrt(a) (float64) | float64 | 100,000 | 1.0901 | 1.0964 | 0.99× | 101% | -|✅| np.cbrt(a) (float64) | float64 | 10,000,000 | 115.3223 | 113.1029 | 1.02× | 98% | -|✅| np.ceil (float16) | float16 | 1,000 | 0.0051 | 0.0039 | 1.33× | 75% | -|✅| np.ceil (float16) | float16 | 100,000 | 0.4009 | 0.3367 | 1.19× | 84% | -|✅| np.ceil (float16) | float16 | 10,000,000 | 41.2225 | 33.5011 | 1.23× | 81% | -|▫| np.ceil (float32) | float32 | 1,000 | 0.0005 | 0.0015 | 0.36× | 276% | -|🔴| np.ceil (float32) | float32 | 100,000 | 0.0057 | 0.0285 | 0.20× | 501% | -|✅| np.ceil (float32) | float32 | 10,000,000 | 7.4340 | 4.1609 | 1.79× | 56% | -|▫| np.ceil (float64) | float64 | 1,000 | 0.0006 | 0.0036 | 0.15× | 653% | -|🟠| np.ceil (float64) | float64 | 100,000 | 0.0112 | 0.0552 | 0.20× | 495% | -|✅| np.ceil (float64) | float64 | 10,000,000 | 14.8375 | 14.0530 | 1.06× | 95% | -|✅| np.clip(a, -10, 10) (float16) | float16 | 1,000 | 0.0099 | 0.0060 | 1.66× | 60% | -|✅| np.clip(a, -10, 10) (float16) | float16 | 100,000 | 0.9202 | 0.7256 | 1.27× | 79% | -|✅| np.clip(a, -10, 10) (float16) | float16 | 10,000,000 | 92.7997 | 71.2042 | 1.30× | 77% | -|🟠| np.clip(a, -10, 10) (float32) | float32 | 1,000 | 0.0020 | 0.0063 | 0.32× | 311% | -|🟠| np.clip(a, -10, 10) (float32) | float32 | 100,000 | 0.0083 | 0.0345 | 0.24× | 415% | -|✅| np.clip(a, -10, 10) (float32) | float32 | 10,000,000 | 7.4072 | 4.1688 | 1.78× | 56% | -|🟠| np.clip(a, -10, 10) (float64) | float64 | 1,000 | 0.0018 | 0.0061 | 0.30× | 332% | -|🟠| np.clip(a, -10, 10) (float64) | float64 | 100,000 | 0.0131 | 0.0640 | 0.20× | 487% | -|✅| np.clip(a, -10, 10) (float64) | float64 | 10,000,000 | 14.7613 | 13.3322 | 1.11× | 90% | -|🟡| np.cos (float16) | float16 | 1,000 | 0.0050 | 0.0084 | 0.60× | 168% | -|🟡| np.cos (float16) | float16 | 100,000 | 0.6956 | 1.1041 | 0.63× | 159% | -|🟡| np.cos (float16) | float16 | 10,000,000 | 79.1733 | 110.5111 | 0.72× | 140% | -|✅| np.cos (float32) | float32 | 1,000 | 0.0049 | 0.0038 | 1.30× | 77% | -|🟡| np.cos (float32) | float32 | 100,000 | 0.7072 | 0.7109 | 0.99× | 100% | -|✅| np.cos (float32) | float32 | 10,000,000 | 80.0272 | 69.8170 | 1.15× | 87% | -|✅| np.cos (float64) | float64 | 1,000 | 0.0049 | 0.0042 | 1.18× | 85% | -|🟡| np.cos (float64) | float64 | 100,000 | 0.7039 | 0.7283 | 0.97× | 104% | -|✅| np.cos (float64) | float64 | 10,000,000 | 79.0328 | 77.0677 | 1.02× | 98% | -|🟡| np.exp (float16) | float16 | 1,000 | 0.0054 | 0.0063 | 0.86× | 116% | -|🟡| np.exp (float16) | float16 | 100,000 | 0.4124 | 0.5903 | 0.70× | 143% | -|✅| np.exp (float16) | float16 | 10,000,000 | 63.7886 | 58.5180 | 1.09× | 92% | -|🟠| np.exp (float32) | float32 | 1,000 | 0.0010 | 0.0034 | 0.29× | 339% | -|🟠| np.exp (float32) | float32 | 100,000 | 0.0544 | 0.1753 | 0.31× | 322% | -|🟡| np.exp (float32) | float32 | 10,000,000 | 10.5143 | 16.5467 | 0.64× | 157% | -|🟡| np.exp (float64) | float64 | 1,000 | 0.0030 | 0.0038 | 0.78× | 128% | -|🟡| np.exp (float64) | float64 | 100,000 | 0.2543 | 0.2691 | 0.94× | 106% | -|✅| np.exp (float64) | float64 | 10,000,000 | 32.9892 | 30.7385 | 1.07× | 93% | -|🟡| np.exp2 (float16) | float16 | 1,000 | 0.0052 | 0.0098 | 0.54× | 187% | -|🟠| np.exp2 (float16) | float16 | 100,000 | 0.4402 | 0.9688 | 0.45× | 220% | -|🟠| np.exp2 (float16) | float16 | 10,000,000 | 47.4052 | 97.6500 | 0.48× | 206% | -|🟠| np.exp2 (float32) | float32 | 1,000 | 0.0022 | 0.0097 | 0.23× | 435% | -|🔴| np.exp2 (float32) | float32 | 100,000 | 0.1723 | 0.8886 | 0.19× | 516% | -|🟠| np.exp2 (float32) | float32 | 10,000,000 | 23.4637 | 87.7856 | 0.27× | 374% | -|🟠| np.exp2 (float64) | float64 | 1,000 | 0.0025 | 0.0096 | 0.26× | 386% | -|🟠| np.exp2 (float64) | float64 | 100,000 | 0.2067 | 0.8452 | 0.24× | 409% | -|🟠| np.exp2 (float64) | float64 | 10,000,000 | 28.4119 | 87.4368 | 0.33× | 308% | -|🟡| np.expm1 (float16) | float16 | 1,000 | 0.0059 | 0.0091 | 0.65× | 154% | -|🟡| np.expm1 (float16) | float16 | 100,000 | 0.5256 | 0.8595 | 0.61× | 164% | -|🟡| np.expm1 (float16) | float16 | 10,000,000 | 54.8518 | 86.3626 | 0.64× | 157% | -|🟡| np.expm1 (float32) | float32 | 1,000 | 0.0032 | 0.0039 | 0.83× | 120% | -|✅| np.expm1 (float32) | float32 | 100,000 | 0.2667 | 0.1878 | 1.42× | 70% | -|✅| np.expm1 (float32) | float32 | 10,000,000 | 31.6479 | 17.6348 | 1.79× | 56% | -|🟡| np.expm1 (float64) | float64 | 1,000 | 0.0038 | 0.0039 | 0.98× | 102% | -|✅| np.expm1 (float64) | float64 | 100,000 | 0.3380 | 0.2746 | 1.23× | 81% | -|✅| np.expm1 (float64) | float64 | 10,000,000 | 42.0588 | 31.9769 | 1.31× | 76% | -|✅| np.floor (float16) | float16 | 1,000 | 0.0051 | 0.0039 | 1.32× | 76% | -|✅| np.floor (float16) | float16 | 100,000 | 0.4141 | 0.3381 | 1.23× | 82% | -|✅| np.floor (float16) | float16 | 10,000,000 | 43.2932 | 32.6165 | 1.33× | 75% | -|▫| np.floor (float32) | float32 | 1,000 | 0.0005 | 0.0019 | 0.29× | 350% | -|🟠| np.floor (float32) | float32 | 100,000 | 0.0063 | 0.0309 | 0.20× | 488% | -|✅| np.floor (float32) | float32 | 10,000,000 | 7.6079 | 4.1823 | 1.82× | 55% | -|▫| np.floor (float64) | float64 | 1,000 | 0.0006 | 0.0048 | 0.12× | 833% | -|🔴| np.floor (float64) | float64 | 100,000 | 0.0117 | 0.0586 | 0.20× | 502% | -|✅| np.floor (float64) | float64 | 10,000,000 | 14.9548 | 13.8842 | 1.08× | 93% | -|🟡| np.log (float16) | float16 | 1,000 | 0.0050 | 0.0066 | 0.75× | 133% | -|🟡| np.log (float16) | float16 | 100,000 | 0.4253 | 0.6276 | 0.68× | 148% | -|✅| np.log (float16) | float16 | 10,000,000 | 84.3523 | 62.5962 | 1.35× | 74% | -|🟠| np.log (float32) | float32 | 1,000 | 0.0013 | 0.0039 | 0.34× | 290% | -|🟠| np.log (float32) | float32 | 100,000 | 0.0917 | 0.2125 | 0.43× | 232% | -|🟡| np.log (float32) | float32 | 10,000,000 | 13.6695 | 20.7475 | 0.66× | 152% | -|🟡| np.log (float64) | float64 | 1,000 | 0.0028 | 0.0031 | 0.89× | 112% | -|🟡| np.log (float64) | float64 | 100,000 | 0.2338 | 0.2548 | 0.92× | 109% | -|✅| np.log (float64) | float64 | 10,000,000 | 31.4221 | 29.8544 | 1.05× | 95% | -|🟡| np.log10 (float16) | float16 | 1,000 | 0.0055 | 0.0067 | 0.81× | 123% | -|🟡| np.log10 (float16) | float16 | 100,000 | 0.4443 | 0.6431 | 0.69× | 145% | -|✅| np.log10 (float16) | float16 | 10,000,000 | 69.8188 | 63.1013 | 1.11× | 90% | -|🟡| np.log10 (float32) | float32 | 1,000 | 0.0024 | 0.0038 | 0.64× | 157% | -|🟡| np.log10 (float32) | float32 | 100,000 | 0.1944 | 0.2121 | 0.92× | 109% | -|✅| np.log10 (float32) | float32 | 10,000,000 | 23.1019 | 20.3369 | 1.14× | 88% | -|🟡| np.log10 (float64) | float64 | 1,000 | 0.0029 | 0.0031 | 0.94× | 107% | -|🟡| np.log10 (float64) | float64 | 100,000 | 0.2459 | 0.2616 | 0.94× | 106% | -|✅| np.log10 (float64) | float64 | 10,000,000 | 32.7819 | 30.4759 | 1.08× | 93% | -|🟡| np.log1p (float16) | float16 | 1,000 | 0.0063 | 0.0082 | 0.77× | 130% | -|🟡| np.log1p (float16) | float16 | 100,000 | 0.5635 | 0.7911 | 0.71× | 140% | -|🟡| np.log1p (float16) | float16 | 10,000,000 | 58.8048 | 77.9264 | 0.76× | 132% | -|✅| np.log1p (float32) | float32 | 1,000 | 0.0034 | 0.0028 | 1.24× | 81% | -|✅| np.log1p (float32) | float32 | 100,000 | 0.2895 | 0.2324 | 1.25× | 80% | -|✅| np.log1p (float32) | float32 | 10,000,000 | 31.9094 | 21.9765 | 1.45× | 69% | -|✅| np.log1p (float64) | float64 | 1,000 | 0.0037 | 0.0032 | 1.14× | 87% | -|✅| np.log1p (float64) | float64 | 100,000 | 0.3215 | 0.2717 | 1.18× | 84% | -|✅| np.log1p (float64) | float64 | 10,000,000 | 39.8056 | 31.3997 | 1.27× | 79% | -|🟡| np.log2 (float16) | float16 | 1,000 | 0.0050 | 0.0067 | 0.74× | 134% | -|🟡| np.log2 (float16) | float16 | 100,000 | 0.4571 | 0.6374 | 0.72× | 140% | -|🟡| np.log2 (float16) | float16 | 10,000,000 | 48.6171 | 62.9314 | 0.77× | 129% | -|🟡| np.log2 (float32) | float32 | 1,000 | 0.0026 | 0.0042 | 0.61× | 163% | -|🟡| np.log2 (float32) | float32 | 100,000 | 0.1889 | 0.1979 | 0.95× | 105% | -|✅| np.log2 (float32) | float32 | 10,000,000 | 22.7833 | 18.9040 | 1.21× | 83% | -|🟡| np.log2 (float64) | float64 | 1,000 | 0.0041 | 0.0044 | 0.93× | 107% | -|🟡| np.log2 (float64) | float64 | 100,000 | 0.3738 | 0.3897 | 0.96× | 104% | -|✅| np.log2 (float64) | float64 | 10,000,000 | 45.1058 | 42.4500 | 1.06× | 94% | -|▫| np.negative(a) (float16) | float16 | 1,000 | 0.0008 | 0.0015 | 0.51× | 197% | -|✅| np.negative(a) (float16) | float16 | 100,000 | 0.0320 | 0.0234 | 1.37× | 73% | -|✅| np.negative(a) (float16) | float16 | 10,000,000 | 4.7401 | 3.0972 | 1.53× | 65% | -|▫| np.negative(a) (float32) | float32 | 1,000 | 0.0005 | 0.0020 | 0.26× | 391% | -|🟠| np.negative(a) (float32) | float32 | 100,000 | 0.0065 | 0.0264 | 0.25× | 403% | -|✅| np.negative(a) (float32) | float32 | 10,000,000 | 7.8325 | 4.2918 | 1.82× | 55% | -|▫| np.negative(a) (float64) | float64 | 1,000 | 0.0005 | 0.0032 | 0.16× | 622% | -|🟠| np.negative(a) (float64) | float64 | 100,000 | 0.0136 | 0.0544 | 0.25× | 399% | -|✅| np.negative(a) (float64) | float64 | 10,000,000 | 16.3329 | 16.1866 | 1.01× | 99% | -|▫| np.positive(a) (float16) | float16 | 1,000 | 0.0007 | 0.0011 | 0.63× | 160% | -|✅| np.positive(a) (float16) | float16 | 100,000 | 0.0209 | 0.0132 | 1.59× | 63% | -|✅| np.positive(a) (float16) | float16 | 10,000,000 | 4.2717 | 1.6411 | 2.60× | 38% | -|▫| np.positive(a) (float32) | float32 | 1,000 | 0.0007 | 0.0016 | 0.41× | 243% | -|🟡| np.positive(a) (float32) | float32 | 100,000 | 0.0192 | 0.0244 | 0.78× | 128% | -|✅| np.positive(a) (float32) | float32 | 10,000,000 | 7.8097 | 3.6030 | 2.17× | 46% | -|▫| np.positive(a) (float64) | float64 | 1,000 | 0.0006 | 0.0022 | 0.29× | 349% | -|🟠| np.positive(a) (float64) | float64 | 100,000 | 0.0191 | 0.0504 | 0.38× | 265% | -|✅| np.positive(a) (float64) | float64 | 10,000,000 | 15.2613 | 13.7699 | 1.11× | 90% | -|✅| np.power(a, 0.5) (float16) | float16 | 1,000 | 0.0091 | 0.0057 | 1.59× | 63% | -|✅| np.power(a, 0.5) (float16) | float16 | 100,000 | 0.8082 | 0.3429 | 2.36× | 42% | -|✅| np.power(a, 0.5) (float16) | float16 | 10,000,000 | 85.4485 | 33.5902 | 2.54× | 39% | -|🟡| np.power(a, 0.5) (float32) | float32 | 1,000 | 0.0020 | 0.0028 | 0.70× | 143% | -|✅| np.power(a, 0.5) (float32) | float32 | 100,000 | 0.1204 | 0.0268 | 4.50× | 22% | -|✅| np.power(a, 0.5) (float32) | float32 | 10,000,000 | 15.8291 | 4.1517 | 3.81× | 26% | -|🟡| np.power(a, 0.5) (float64) | float64 | 1,000 | 0.0018 | 0.0027 | 0.67× | 150% | -|✅| np.power(a, 0.5) (float64) | float64 | 100,000 | 0.1208 | 0.0624 | 1.94× | 52% | -|✅| np.power(a, 0.5) (float64) | float64 | 10,000,000 | 20.4169 | 13.6772 | 1.49× | 67% | -|✅| np.power(a, 2) (float16) | float16 | 1,000 | 0.0102 | 0.0057 | 1.78× | 56% | -|✅| np.power(a, 2) (float16) | float16 | 100,000 | 1.0479 | 0.4785 | 2.19× | 46% | -|✅| np.power(a, 2) (float16) | float16 | 10,000,000 | 106.1243 | 47.3970 | 2.24× | 45% | -|🟡| np.power(a, 2) (float32) | float32 | 1,000 | 0.0023 | 0.0029 | 0.80× | 125% | -|✅| np.power(a, 2) (float32) | float32 | 100,000 | 0.1497 | 0.0268 | 5.59× | 18% | -|✅| np.power(a, 2) (float32) | float32 | 10,000,000 | 18.8415 | 4.1425 | 4.55× | 22% | -|🟡| np.power(a, 2) (float64) | float64 | 1,000 | 0.0022 | 0.0035 | 0.65× | 155% | -|✅| np.power(a, 2) (float64) | float64 | 100,000 | 0.1534 | 0.0578 | 2.65× | 38% | -|✅| np.power(a, 2) (float64) | float64 | 10,000,000 | 22.8226 | 13.5837 | 1.68× | 60% | -|🟡| np.power(a, 3) (float16) | float16 | 1,000 | 0.0134 | 0.0172 | 0.78× | 128% | -|🟡| np.power(a, 3) (float16) | float16 | 100,000 | 1.4800 | 2.4845 | 0.60× | 168% | -|🟡| np.power(a, 3) (float16) | float16 | 10,000,000 | 162.6844 | 251.6184 | 0.65× | 155% | -|🟡| np.power(a, 3) (float32) | float32 | 1,000 | 0.0058 | 0.0091 | 0.64× | 157% | -|🟡| np.power(a, 3) (float32) | float32 | 100,000 | 0.6605 | 0.6694 | 0.99× | 101% | -|✅| np.power(a, 3) (float32) | float32 | 10,000,000 | 71.1467 | 66.4474 | 1.07× | 93% | -|🟡| np.power(a, 3) (float64) | float64 | 1,000 | 0.0097 | 0.0110 | 0.88× | 113% | -|🟡| np.power(a, 3) (float64) | float64 | 100,000 | 1.0647 | 1.0958 | 0.97× | 103% | -|✅| np.power(a, 3) (float64) | float64 | 10,000,000 | 116.0376 | 111.0662 | 1.04× | 96% | -|🟡| np.reciprocal(a) (float16) | float16 | 1,000 | 0.0034 | 0.0045 | 0.74× | 134% | -|🟡| np.reciprocal(a) (float16) | float16 | 100,000 | 0.2055 | 0.4096 | 0.50× | 199% | -|🟡| np.reciprocal(a) (float16) | float16 | 10,000,000 | 22.5069 | 40.3090 | 0.56× | 179% | -|▫| np.reciprocal(a) (float32) | float32 | 1,000 | 0.0006 | 0.0016 | 0.36× | 275% | -|🟡| np.reciprocal(a) (float32) | float32 | 100,000 | 0.0142 | 0.0259 | 0.55× | 182% | -|✅| np.reciprocal(a) (float32) | float32 | 10,000,000 | 7.2180 | 4.1823 | 1.73× | 58% | -|▫| np.reciprocal(a) (float64) | float64 | 1,000 | 0.0008 | 0.0026 | 0.31× | 322% | -|🟡| np.reciprocal(a) (float64) | float64 | 100,000 | 0.0376 | 0.0582 | 0.65× | 154% | -|🟡| np.reciprocal(a) (float64) | float64 | 10,000,000 | 14.8973 | 16.0878 | 0.93× | 108% | -|✅| np.round (float16) | float16 | 1,000 | 0.0058 | 0.0046 | 1.26× | 80% | -|✅| np.round (float16) | float16 | 100,000 | 0.4702 | 0.4047 | 1.16× | 86% | -|✅| np.round (float16) | float16 | 10,000,000 | 41.1908 | 39.4955 | 1.04× | 96% | -|🟡| np.round (float32) | float32 | 1,000 | 0.0011 | 0.0016 | 0.74× | 136% | -|🟠| np.round (float32) | float32 | 100,000 | 0.0065 | 0.0269 | 0.24× | 411% | -|✅| np.round (float32) | float32 | 10,000,000 | 7.6748 | 4.1299 | 1.86× | 54% | -|🟠| np.round (float64) | float64 | 1,000 | 0.0012 | 0.0054 | 0.22× | 458% | -|🟠| np.round (float64) | float64 | 100,000 | 0.0124 | 0.0549 | 0.23× | 441% | -|✅| np.round (float64) | float64 | 10,000,000 | 14.9249 | 13.9922 | 1.07× | 94% | -|🟠| np.sign (float16) | float16 | 1,000 | 0.0014 | 0.0041 | 0.34× | 292% | -|🔴| np.sign (float16) | float16 | 100,000 | 0.0922 | 0.6486 | 0.14× | 704% | -|🟠| np.sign (float16) | float16 | 10,000,000 | 14.1494 | 63.7746 | 0.22× | 451% | -|🟠| np.sign (float32) | float32 | 1,000 | 0.0011 | 0.0042 | 0.26× | 379% | -|🟡| np.sign (float32) | float32 | 100,000 | 0.2912 | 0.3816 | 0.76× | 131% | -|🟡| np.sign (float32) | float32 | 10,000,000 | 36.3663 | 38.1486 | 0.95× | 105% | -|🔴| np.sign (float64) | float64 | 1,000 | 0.0011 | 0.0055 | 0.20× | 504% | -|🟡| np.sign (float64) | float64 | 100,000 | 0.3016 | 0.3884 | 0.78× | 129% | -|🟡| np.sign (float64) | float64 | 10,000,000 | 40.3152 | 44.8085 | 0.90× | 111% | -|🟡| np.sin (float16) | float16 | 1,000 | 0.0053 | 0.0079 | 0.67× | 149% | -|🟡| np.sin (float16) | float16 | 100,000 | 0.7007 | 1.1071 | 0.63× | 158% | -|🟡| np.sin (float16) | float16 | 10,000,000 | 78.9906 | 111.0622 | 0.71× | 141% | -|✅| np.sin (float32) | float32 | 1,000 | 0.0047 | 0.0038 | 1.24× | 80% | -|🟡| np.sin (float32) | float32 | 100,000 | 0.7041 | 0.7070 | 1.00× | 100% | -|✅| np.sin (float32) | float32 | 10,000,000 | 79.5476 | 70.5422 | 1.13× | 89% | -|✅| np.sin (float64) | float64 | 1,000 | 0.0051 | 0.0041 | 1.25× | 80% | -|🟡| np.sin (float64) | float64 | 100,000 | 0.6997 | 0.7363 | 0.95× | 105% | -|✅| np.sin (float64) | float64 | 10,000,000 | 78.9810 | 78.7186 | 1.00× | 100% | -|✅| np.sqrt (float16) | float16 | 1,000 | 0.0046 | 0.0040 | 1.15× | 87% | -|✅| np.sqrt (float16) | float16 | 100,000 | 0.3887 | 0.3403 | 1.14× | 88% | -|✅| np.sqrt (float16) | float16 | 10,000,000 | 47.6898 | 32.7937 | 1.45× | 69% | -|▫| np.sqrt (float32) | float32 | 1,000 | 0.0007 | 0.0015 | 0.44× | 227% | -|🟡| np.sqrt (float32) | float32 | 100,000 | 0.0151 | 0.0276 | 0.55× | 182% | -|✅| np.sqrt (float32) | float32 | 10,000,000 | 7.3481 | 4.2392 | 1.73× | 58% | -|▫| np.sqrt (float64) | float64 | 1,000 | 0.0010 | 0.0044 | 0.23× | 444% | -|🟡| np.sqrt (float64) | float64 | 100,000 | 0.0558 | 0.0642 | 0.87× | 115% | -|✅| np.sqrt (float64) | float64 | 10,000,000 | 15.0151 | 13.7258 | 1.09× | 91% | -|🟡| np.square(a) (float16) | float16 | 1,000 | 0.0035 | 0.0049 | 0.72× | 138% | -|🟠| np.square(a) (float16) | float16 | 100,000 | 0.2064 | 0.4497 | 0.46× | 218% | -|🟡| np.square(a) (float16) | float16 | 10,000,000 | 22.6650 | 43.4725 | 0.52× | 192% | -|▫| np.square(a) (float32) | float32 | 1,000 | 0.0005 | 0.0015 | 0.34× | 298% | -|🟠| np.square(a) (float32) | float32 | 100,000 | 0.0058 | 0.0268 | 0.21× | 465% | -|✅| np.square(a) (float32) | float32 | 10,000,000 | 7.3211 | 4.2896 | 1.71× | 59% | -|▫| np.square(a) (float64) | float64 | 1,000 | 0.0005 | 0.0025 | 0.21× | 484% | -|🟠| np.square(a) (float64) | float64 | 100,000 | 0.0123 | 0.0544 | 0.23× | 442% | -|🟡| np.square(a) (float64) | float64 | 10,000,000 | 15.1366 | 16.4735 | 0.92× | 109% | -|🟡| np.tan (float16) | float16 | 1,000 | 0.0048 | 0.0083 | 0.58× | 173% | -|🟡| np.tan (float16) | float16 | 100,000 | 0.8034 | 1.1034 | 0.73× | 137% | -|🟡| np.tan (float16) | float16 | 10,000,000 | 89.8512 | 110.0293 | 0.82× | 122% | -|✅| np.tan (float32) | float32 | 1,000 | 0.0046 | 0.0037 | 1.23× | 81% | -|✅| np.tan (float32) | float32 | 100,000 | 0.8164 | 0.6873 | 1.19× | 84% | -|✅| np.tan (float32) | float32 | 10,000,000 | 90.0943 | 67.8999 | 1.33× | 75% | -|🟡| np.tan (float64) | float64 | 1,000 | 0.0046 | 0.0050 | 0.93× | 108% | -|🟡| np.tan (float64) | float64 | 100,000 | 0.8098 | 0.8479 | 0.95× | 105% | -|✅| np.tan (float64) | float64 | 10,000,000 | 90.9533 | 89.5086 | 1.02× | 98% | -|✅| np.trunc(a) (float16) | float16 | 1,000 | 0.0051 | 0.0038 | 1.34× | 75% | -|✅| np.trunc(a) (float16) | float16 | 100,000 | 0.4499 | 0.3360 | 1.34× | 75% | -|✅| np.trunc(a) (float16) | float16 | 10,000,000 | 42.5258 | 32.7977 | 1.30× | 77% | -|▫| np.trunc(a) (float32) | float32 | 1,000 | 0.0005 | 0.0014 | 0.38× | 262% | -|🟠| np.trunc(a) (float32) | float32 | 100,000 | 0.0058 | 0.0248 | 0.23× | 429% | -|✅| np.trunc(a) (float32) | float32 | 10,000,000 | 7.3663 | 4.0853 | 1.80× | 56% | -|▫| np.trunc(a) (float64) | float64 | 1,000 | 0.0005 | 0.0019 | 0.28× | 359% | -|🟠| np.trunc(a) (float64) | float64 | 100,000 | 0.0124 | 0.0524 | 0.24× | 422% | -|🟡| np.trunc(a) (float64) | float64 | 10,000,000 | 15.0096 | 16.0305 | 0.94× | 107% | +|▫| np.abs (float16) | float16 | 1,000 | 0.0008 | 0.0015 | 0.54× | 184% | +|✅| np.abs (float16) | float16 | 100,000 | 0.0264 | 0.0234 | 1.13× | 89% | +|✅| np.abs (float16) | float16 | 10,000,000 | 4.7982 | 2.9688 | 1.62× | 62% | +|▫| np.abs (float32) | float32 | 1,000 | 0.0005 | 0.0015 | 0.34× | 291% | +|🔴| np.abs (float32) | float32 | 100,000 | 0.0064 | 0.0352 | 0.18× | 553% | +|✅| np.abs (float32) | float32 | 10,000,000 | 7.1189 | 4.2161 | 1.69× | 59% | +|▫| np.abs (float64) | float64 | 1,000 | 0.0006 | 0.0028 | 0.20× | 503% | +|🔴| np.abs (float64) | float64 | 100,000 | 0.0112 | 0.0686 | 0.16× | 612% | +|✅| np.abs (float64) | float64 | 10,000,000 | 14.6849 | 13.9245 | 1.05× | 95% | +|🟡| np.cbrt(a) (float16) | float16 | 1,000 | 0.0101 | 0.0111 | 0.91× | 110% | +|🟡| np.cbrt(a) (float16) | float16 | 100,000 | 1.1991 | 1.3766 | 0.87× | 115% | +|🟡| np.cbrt(a) (float16) | float16 | 10,000,000 | 121.4253 | 136.3973 | 0.89× | 112% | +|✅| np.cbrt(a) (float32) | float32 | 1,000 | 0.0066 | 0.0060 | 1.10× | 91% | +|✅| np.cbrt(a) (float32) | float32 | 100,000 | 0.8943 | 0.8882 | 1.01× | 99% | +|✅| np.cbrt(a) (float32) | float32 | 10,000,000 | 94.5195 | 86.5058 | 1.09× | 92% | +|✅| np.cbrt(a) (float64) | float64 | 1,000 | 0.0098 | 0.0093 | 1.05× | 95% | +|✅| np.cbrt(a) (float64) | float64 | 100,000 | 1.0970 | 1.0793 | 1.02× | 98% | +|✅| np.cbrt(a) (float64) | float64 | 10,000,000 | 115.3869 | 109.5524 | 1.05× | 95% | +|✅| np.ceil (float16) | float16 | 1,000 | 0.0050 | 0.0040 | 1.26× | 79% | +|✅| np.ceil (float16) | float16 | 100,000 | 0.4660 | 0.3448 | 1.35× | 74% | +|✅| np.ceil (float16) | float16 | 10,000,000 | 43.5339 | 33.7644 | 1.29× | 78% | +|▫| np.ceil (float32) | float32 | 1,000 | 0.0005 | 0.0014 | 0.38× | 263% | +|🟠| np.ceil (float32) | float32 | 100,000 | 0.0066 | 0.0305 | 0.22× | 460% | +|✅| np.ceil (float32) | float32 | 10,000,000 | 7.2732 | 4.4002 | 1.65× | 60% | +|🟠| np.ceil (float64) | float64 | 1,000 | 0.0014 | 0.0036 | 0.37× | 270% | +|🔴| np.ceil (float64) | float64 | 100,000 | 0.0120 | 0.0606 | 0.20× | 503% | +|🟡| np.ceil (float64) | float64 | 10,000,000 | 14.6298 | 16.0489 | 0.91× | 110% | +|✅| np.clip(a, -10, 10) (float16) | float16 | 1,000 | 0.0087 | 0.0059 | 1.47× | 68% | +|✅| np.clip(a, -10, 10) (float16) | float16 | 100,000 | 0.9296 | 0.7061 | 1.32× | 76% | +|✅| np.clip(a, -10, 10) (float16) | float16 | 10,000,000 | 93.7915 | 68.7791 | 1.36× | 73% | +|🟠| np.clip(a, -10, 10) (float32) | float32 | 1,000 | 0.0021 | 0.0064 | 0.32× | 308% | +|🟠| np.clip(a, -10, 10) (float32) | float32 | 100,000 | 0.0082 | 0.0345 | 0.24× | 421% | +|✅| np.clip(a, -10, 10) (float32) | float32 | 10,000,000 | 7.5158 | 4.1400 | 1.81× | 55% | +|🟠| np.clip(a, -10, 10) (float64) | float64 | 1,000 | 0.0019 | 0.0047 | 0.40× | 252% | +|🟠| np.clip(a, -10, 10) (float64) | float64 | 100,000 | 0.0166 | 0.0700 | 0.24× | 421% | +|✅| np.clip(a, -10, 10) (float64) | float64 | 10,000,000 | 18.6971 | 13.3810 | 1.40× | 72% | +|🟡| np.cos (float16) | float16 | 1,000 | 0.0051 | 0.0081 | 0.63× | 158% | +|🟡| np.cos (float16) | float16 | 100,000 | 0.7102 | 1.1049 | 0.64× | 156% | +|🟡| np.cos (float16) | float16 | 10,000,000 | 80.1757 | 109.6838 | 0.73× | 137% | +|✅| np.cos (float32) | float32 | 1,000 | 0.0051 | 0.0038 | 1.35× | 74% | +|🟡| np.cos (float32) | float32 | 100,000 | 0.7030 | 0.7111 | 0.99× | 101% | +|✅| np.cos (float32) | float32 | 10,000,000 | 82.6791 | 69.0830 | 1.20× | 84% | +|✅| np.cos (float64) | float64 | 1,000 | 0.0050 | 0.0040 | 1.22× | 82% | +|🟡| np.cos (float64) | float64 | 100,000 | 0.6990 | 0.7313 | 0.96× | 105% | +|✅| np.cos (float64) | float64 | 10,000,000 | 81.9906 | 77.3710 | 1.06× | 94% | +|🟡| np.exp (float16) | float16 | 1,000 | 0.0052 | 0.0066 | 0.79× | 127% | +|🟡| np.exp (float16) | float16 | 100,000 | 0.4392 | 0.6077 | 0.72× | 138% | +|🟡| np.exp (float16) | float16 | 10,000,000 | 44.3829 | 58.6632 | 0.76× | 132% | +|🔴| np.exp (float32) | float32 | 1,000 | 0.0010 | 0.0056 | 0.19× | 539% | +|🟠| np.exp (float32) | float32 | 100,000 | 0.0553 | 0.1841 | 0.30× | 333% | +|🟡| np.exp (float32) | float32 | 10,000,000 | 10.4285 | 17.6747 | 0.59× | 170% | +|🟡| np.exp (float64) | float64 | 1,000 | 0.0030 | 0.0040 | 0.75× | 134% | +|🟡| np.exp (float64) | float64 | 100,000 | 0.2581 | 0.2792 | 0.93× | 108% | +|✅| np.exp (float64) | float64 | 10,000,000 | 33.4427 | 31.7361 | 1.05× | 95% | +|🟠| np.exp2 (float16) | float16 | 1,000 | 0.0047 | 0.0099 | 0.48× | 210% | +|🟠| np.exp2 (float16) | float16 | 100,000 | 0.4598 | 0.9896 | 0.47× | 215% | +|🟠| np.exp2 (float16) | float16 | 10,000,000 | 48.1931 | 97.0656 | 0.50× | 201% | +|🟠| np.exp2 (float32) | float32 | 1,000 | 0.0022 | 0.0100 | 0.22× | 461% | +|🔴| np.exp2 (float32) | float32 | 100,000 | 0.1760 | 0.8987 | 0.20× | 511% | +|🟠| np.exp2 (float32) | float32 | 10,000,000 | 22.9673 | 90.4267 | 0.25× | 394% | +|🟠| np.exp2 (float64) | float64 | 1,000 | 0.0025 | 0.0096 | 0.26× | 389% | +|🟠| np.exp2 (float64) | float64 | 100,000 | 0.2137 | 0.8559 | 0.25× | 400% | +|🟠| np.exp2 (float64) | float64 | 10,000,000 | 37.0195 | 90.0413 | 0.41× | 243% | +|🟡| np.expm1 (float16) | float16 | 1,000 | 0.0058 | 0.0091 | 0.64× | 157% | +|🟡| np.expm1 (float16) | float16 | 100,000 | 0.5474 | 0.8722 | 0.63× | 159% | +|🟡| np.expm1 (float16) | float16 | 10,000,000 | 55.0439 | 85.4215 | 0.64× | 155% | +|🟡| np.expm1 (float32) | float32 | 1,000 | 0.0032 | 0.0046 | 0.70× | 142% | +|✅| np.expm1 (float32) | float32 | 100,000 | 0.2728 | 0.1902 | 1.43× | 70% | +|✅| np.expm1 (float32) | float32 | 10,000,000 | 31.7384 | 18.4160 | 1.72× | 58% | +|🟡| np.expm1 (float64) | float64 | 1,000 | 0.0038 | 0.0040 | 0.95× | 106% | +|✅| np.expm1 (float64) | float64 | 100,000 | 0.3590 | 0.2864 | 1.25× | 80% | +|✅| np.expm1 (float64) | float64 | 10,000,000 | 47.0453 | 33.2374 | 1.42× | 71% | +|✅| np.floor (float16) | float16 | 1,000 | 0.0051 | 0.0039 | 1.30× | 77% | +|✅| np.floor (float16) | float16 | 100,000 | 0.4407 | 0.3400 | 1.30× | 77% | +|✅| np.floor (float16) | float16 | 10,000,000 | 43.3912 | 33.7248 | 1.29× | 78% | +|▫| np.floor (float32) | float32 | 1,000 | 0.0006 | 0.0015 | 0.38× | 267% | +|🔴| np.floor (float32) | float32 | 100,000 | 0.0064 | 0.0319 | 0.20× | 501% | +|✅| np.floor (float32) | float32 | 10,000,000 | 7.3030 | 5.1858 | 1.41× | 71% | +|▫| np.floor (float64) | float64 | 1,000 | 0.0006 | 0.0032 | 0.17× | 575% | +|🟠| np.floor (float64) | float64 | 100,000 | 0.0151 | 0.0632 | 0.24× | 419% | +|🟡| np.floor (float64) | float64 | 10,000,000 | 14.8331 | 19.6220 | 0.76× | 132% | +|🟡| np.log (float16) | float16 | 1,000 | 0.0049 | 0.0069 | 0.71× | 141% | +|🟡| np.log (float16) | float16 | 100,000 | 0.4263 | 0.6316 | 0.68× | 148% | +|🟡| np.log (float16) | float16 | 10,000,000 | 46.0282 | 63.6713 | 0.72× | 138% | +|🟠| np.log (float32) | float32 | 1,000 | 0.0013 | 0.0051 | 0.26× | 384% | +|🟠| np.log (float32) | float32 | 100,000 | 0.0869 | 0.2148 | 0.40× | 247% | +|🟡| np.log (float32) | float32 | 10,000,000 | 13.1800 | 20.9525 | 0.63× | 159% | +|🟡| np.log (float64) | float64 | 1,000 | 0.0028 | 0.0038 | 0.73× | 136% | +|🟡| np.log (float64) | float64 | 100,000 | 0.2424 | 0.2668 | 0.91× | 110% | +|✅| np.log (float64) | float64 | 10,000,000 | 31.9128 | 30.1149 | 1.06× | 94% | +|🟡| np.log10 (float16) | float16 | 1,000 | 0.0052 | 0.0070 | 0.74× | 135% | +|🟡| np.log10 (float16) | float16 | 100,000 | 0.4465 | 0.6426 | 0.69× | 144% | +|🟡| np.log10 (float16) | float16 | 10,000,000 | 47.8418 | 62.9499 | 0.76× | 132% | +|🟡| np.log10 (float32) | float32 | 1,000 | 0.0035 | 0.0050 | 0.70× | 143% | +|🟡| np.log10 (float32) | float32 | 100,000 | 0.1985 | 0.2140 | 0.93× | 108% | +|✅| np.log10 (float32) | float32 | 10,000,000 | 23.5444 | 20.6616 | 1.14× | 88% | +|🟡| np.log10 (float64) | float64 | 1,000 | 0.0029 | 0.0040 | 0.73× | 138% | +|🟡| np.log10 (float64) | float64 | 100,000 | 0.2625 | 0.2698 | 0.97× | 103% | +|✅| np.log10 (float64) | float64 | 10,000,000 | 32.9855 | 30.7610 | 1.07× | 93% | +|🟡| np.log1p (float16) | float16 | 1,000 | 0.0062 | 0.0086 | 0.72× | 139% | +|🟡| np.log1p (float16) | float16 | 100,000 | 0.5885 | 0.7878 | 0.75× | 134% | +|🟡| np.log1p (float16) | float16 | 10,000,000 | 60.5869 | 77.8953 | 0.78× | 129% | +|🟡| np.log1p (float32) | float32 | 1,000 | 0.0039 | 0.0040 | 0.98× | 102% | +|✅| np.log1p (float32) | float32 | 100,000 | 0.3058 | 0.2337 | 1.31× | 76% | +|✅| np.log1p (float32) | float32 | 10,000,000 | 33.3514 | 22.3477 | 1.49× | 67% | +|✅| np.log1p (float64) | float64 | 1,000 | 0.0039 | 0.0033 | 1.19× | 84% | +|✅| np.log1p (float64) | float64 | 100,000 | 0.3412 | 0.2752 | 1.24× | 81% | +|✅| np.log1p (float64) | float64 | 10,000,000 | 48.2083 | 31.5835 | 1.53× | 66% | +|🟡| np.log2 (float16) | float16 | 1,000 | 0.0050 | 0.0070 | 0.72× | 139% | +|🟡| np.log2 (float16) | float16 | 100,000 | 0.4545 | 0.6414 | 0.71× | 141% | +|🟡| np.log2 (float16) | float16 | 10,000,000 | 48.9339 | 62.7084 | 0.78× | 128% | +|🟡| np.log2 (float32) | float32 | 1,000 | 0.0027 | 0.0052 | 0.51× | 195% | +|✅| np.log2 (float32) | float32 | 100,000 | 0.2008 | 0.2006 | 1.00× | 100% | +|✅| np.log2 (float32) | float32 | 10,000,000 | 23.4387 | 19.2540 | 1.22× | 82% | +|🟡| np.log2 (float64) | float64 | 1,000 | 0.0042 | 0.0044 | 0.95× | 106% | +|🟡| np.log2 (float64) | float64 | 100,000 | 0.3739 | 0.3882 | 0.96× | 104% | +|✅| np.log2 (float64) | float64 | 10,000,000 | 50.1019 | 42.1909 | 1.19× | 84% | +|▫| np.negative(a) (float16) | float16 | 1,000 | 0.0007 | 0.0014 | 0.54× | 185% | +|✅| np.negative(a) (float16) | float16 | 100,000 | 0.0278 | 0.0234 | 1.19× | 84% | +|✅| np.negative(a) (float16) | float16 | 10,000,000 | 4.7690 | 2.8837 | 1.65× | 60% | +|▫| np.negative(a) (float32) | float32 | 1,000 | 0.0005 | 0.0015 | 0.35× | 289% | +|🟠| np.negative(a) (float32) | float32 | 100,000 | 0.0066 | 0.0261 | 0.25× | 394% | +|✅| np.negative(a) (float32) | float32 | 10,000,000 | 7.7826 | 4.1048 | 1.90× | 53% | +|▫| np.negative(a) (float64) | float64 | 1,000 | 0.0005 | 0.0036 | 0.15× | 679% | +|🟠| np.negative(a) (float64) | float64 | 100,000 | 0.0121 | 0.0539 | 0.22× | 447% | +|✅| np.negative(a) (float64) | float64 | 10,000,000 | 16.0651 | 13.8831 | 1.16× | 86% | +|▫| np.positive(a) (float16) | float16 | 1,000 | 0.0007 | 0.0010 | 0.69× | 144% | +|✅| np.positive(a) (float16) | float16 | 100,000 | 0.0217 | 0.0130 | 1.67× | 60% | +|✅| np.positive(a) (float16) | float16 | 10,000,000 | 4.2319 | 1.6180 | 2.62× | 38% | +|▫| np.positive(a) (float32) | float32 | 1,000 | 0.0007 | 0.0020 | 0.32× | 310% | +|🟡| np.positive(a) (float32) | float32 | 100,000 | 0.0194 | 0.0250 | 0.78× | 129% | +|✅| np.positive(a) (float32) | float32 | 10,000,000 | 7.7620 | 3.3803 | 2.30× | 44% | +|▫| np.positive(a) (float64) | float64 | 1,000 | 0.0007 | 0.0028 | 0.24× | 409% | +|🟠| np.positive(a) (float64) | float64 | 100,000 | 0.0206 | 0.0520 | 0.40× | 252% | +|✅| np.positive(a) (float64) | float64 | 10,000,000 | 15.1241 | 10.8534 | 1.39× | 72% | +|✅| np.power(a, 0.5) (float16) | float16 | 1,000 | 0.0092 | 0.0057 | 1.62× | 62% | +|✅| np.power(a, 0.5) (float16) | float16 | 100,000 | 0.8324 | 0.3398 | 2.45× | 41% | +|✅| np.power(a, 0.5) (float16) | float16 | 10,000,000 | 84.2019 | 32.9557 | 2.56× | 39% | +|✅| np.power(a, 0.5) (float32) | float32 | 1,000 | 0.0020 | 0.0019 | 1.06× | 94% | +|✅| np.power(a, 0.5) (float32) | float32 | 100,000 | 0.1267 | 0.0285 | 4.45× | 22% | +|✅| np.power(a, 0.5) (float32) | float32 | 10,000,000 | 16.0019 | 4.1327 | 3.87× | 26% | +|🟡| np.power(a, 0.5) (float64) | float64 | 1,000 | 0.0018 | 0.0032 | 0.55× | 181% | +|✅| np.power(a, 0.5) (float64) | float64 | 100,000 | 0.1231 | 0.0638 | 1.93× | 52% | +|✅| np.power(a, 0.5) (float64) | float64 | 10,000,000 | 25.4013 | 13.6356 | 1.86× | 54% | +|✅| np.power(a, 2) (float16) | float16 | 1,000 | 0.0103 | 0.0054 | 1.90× | 53% | +|✅| np.power(a, 2) (float16) | float16 | 100,000 | 1.0690 | 0.4762 | 2.25× | 44% | +|✅| np.power(a, 2) (float16) | float16 | 10,000,000 | 106.8632 | 46.7294 | 2.29× | 44% | +|✅| np.power(a, 2) (float32) | float32 | 1,000 | 0.0024 | 0.0017 | 1.36× | 73% | +|✅| np.power(a, 2) (float32) | float32 | 100,000 | 0.1544 | 0.0283 | 5.46× | 18% | +|✅| np.power(a, 2) (float32) | float32 | 10,000,000 | 18.9400 | 4.1649 | 4.55× | 22% | +|🟡| np.power(a, 2) (float64) | float64 | 1,000 | 0.0022 | 0.0031 | 0.71× | 141% | +|✅| np.power(a, 2) (float64) | float64 | 100,000 | 0.1542 | 0.0590 | 2.62× | 38% | +|✅| np.power(a, 2) (float64) | float64 | 10,000,000 | 28.4405 | 13.5219 | 2.10× | 48% | +|🟡| np.power(a, 3) (float16) | float16 | 1,000 | 0.0126 | 0.0176 | 0.72× | 140% | +|🟡| np.power(a, 3) (float16) | float16 | 100,000 | 1.5343 | 2.4828 | 0.62× | 162% | +|🟡| np.power(a, 3) (float16) | float16 | 10,000,000 | 150.3564 | 250.3069 | 0.60× | 166% | +|🟡| np.power(a, 3) (float32) | float32 | 1,000 | 0.0059 | 0.0082 | 0.73× | 137% | +|🟡| np.power(a, 3) (float32) | float32 | 100,000 | 0.6578 | 0.6780 | 0.97× | 103% | +|✅| np.power(a, 3) (float32) | float32 | 10,000,000 | 72.0628 | 66.4589 | 1.08× | 92% | +|🟡| np.power(a, 3) (float64) | float64 | 1,000 | 0.0098 | 0.0121 | 0.81× | 123% | +|🟡| np.power(a, 3) (float64) | float64 | 100,000 | 1.0724 | 1.0959 | 0.98× | 102% | +|✅| np.power(a, 3) (float64) | float64 | 10,000,000 | 124.6532 | 111.9286 | 1.11× | 90% | +|🟡| np.reciprocal(a) (float16) | float16 | 1,000 | 0.0036 | 0.0045 | 0.79× | 127% | +|🟡| np.reciprocal(a) (float16) | float16 | 100,000 | 0.2193 | 0.4080 | 0.54× | 186% | +|🟡| np.reciprocal(a) (float16) | float16 | 10,000,000 | 23.1324 | 40.0357 | 0.58× | 173% | +|▫| np.reciprocal(a) (float32) | float32 | 1,000 | 0.0006 | 0.0014 | 0.44× | 225% | +|🟡| np.reciprocal(a) (float32) | float32 | 100,000 | 0.0143 | 0.0263 | 0.54× | 184% | +|✅| np.reciprocal(a) (float32) | float32 | 10,000,000 | 7.1620 | 4.0578 | 1.76× | 57% | +|▫| np.reciprocal(a) (float64) | float64 | 1,000 | 0.0008 | 0.0019 | 0.42× | 237% | +|🟡| np.reciprocal(a) (float64) | float64 | 100,000 | 0.0421 | 0.0535 | 0.79× | 127% | +|✅| np.reciprocal(a) (float64) | float64 | 10,000,000 | 14.7266 | 13.7636 | 1.07× | 94% | +|✅| np.round (float16) | float16 | 1,000 | 0.0058 | 0.0046 | 1.24× | 81% | +|✅| np.round (float16) | float16 | 100,000 | 0.4702 | 0.4240 | 1.11× | 90% | +|✅| np.round (float16) | float16 | 10,000,000 | 42.8027 | 40.9331 | 1.05× | 96% | +|🟡| np.round (float32) | float32 | 1,000 | 0.0011 | 0.0015 | 0.73× | 136% | +|🟠| np.round (float32) | float32 | 100,000 | 0.0070 | 0.0281 | 0.25× | 403% | +|✅| np.round (float32) | float32 | 10,000,000 | 7.3586 | 4.6823 | 1.57× | 64% | +|🟠| np.round (float64) | float64 | 1,000 | 0.0012 | 0.0024 | 0.49× | 204% | +|🟠| np.round (float64) | float64 | 100,000 | 0.0128 | 0.0572 | 0.22× | 447% | +|🟡| np.round (float64) | float64 | 10,000,000 | 15.0627 | 15.7393 | 0.96× | 104% | +|🟠| np.sign (float16) | float16 | 1,000 | 0.0014 | 0.0042 | 0.33× | 304% | +|🔴| np.sign (float16) | float16 | 100,000 | 0.0887 | 0.6603 | 0.13× | 744% | +|🔴| np.sign (float16) | float16 | 10,000,000 | 10.5588 | 63.8912 | 0.17× | 605% | +|🟠| np.sign (float32) | float32 | 1,000 | 0.0011 | 0.0038 | 0.29× | 339% | +|🟡| np.sign (float32) | float32 | 100,000 | 0.2971 | 0.4006 | 0.74× | 135% | +|🟡| np.sign (float32) | float32 | 10,000,000 | 36.0421 | 39.7051 | 0.91× | 110% | +|🟠| np.sign (float64) | float64 | 1,000 | 0.0010 | 0.0047 | 0.22× | 448% | +|🟡| np.sign (float64) | float64 | 100,000 | 0.2936 | 0.4000 | 0.73× | 136% | +|🟡| np.sign (float64) | float64 | 10,000,000 | 40.6151 | 45.1700 | 0.90× | 111% | +|🟡| np.sin (float16) | float16 | 1,000 | 0.0057 | 0.0080 | 0.72× | 139% | +|🟡| np.sin (float16) | float16 | 100,000 | 0.7108 | 1.1057 | 0.64× | 156% | +|🟡| np.sin (float16) | float16 | 10,000,000 | 79.6291 | 110.4160 | 0.72× | 139% | +|✅| np.sin (float32) | float32 | 1,000 | 0.0060 | 0.0038 | 1.57× | 64% | +|✅| np.sin (float32) | float32 | 100,000 | 0.7167 | 0.7077 | 1.01× | 99% | +|✅| np.sin (float32) | float32 | 10,000,000 | 80.7643 | 70.4978 | 1.15× | 87% | +|✅| np.sin (float64) | float64 | 1,000 | 0.0049 | 0.0040 | 1.22× | 82% | +|🟡| np.sin (float64) | float64 | 100,000 | 0.7140 | 0.7415 | 0.96× | 104% | +|✅| np.sin (float64) | float64 | 10,000,000 | 82.0560 | 78.2388 | 1.05× | 95% | +|✅| np.sqrt (float16) | float16 | 1,000 | 0.0046 | 0.0039 | 1.18× | 85% | +|✅| np.sqrt (float16) | float16 | 100,000 | 0.4264 | 0.3413 | 1.25× | 80% | +|✅| np.sqrt (float16) | float16 | 10,000,000 | 40.8006 | 33.6967 | 1.21× | 83% | +|▫| np.sqrt (float32) | float32 | 1,000 | 0.0006 | 0.0015 | 0.42× | 238% | +|🟡| np.sqrt (float32) | float32 | 100,000 | 0.0149 | 0.0291 | 0.51× | 195% | +|✅| np.sqrt (float32) | float32 | 10,000,000 | 7.0955 | 4.2557 | 1.67× | 60% | +|🟠| np.sqrt (float64) | float64 | 1,000 | 0.0012 | 0.0025 | 0.47× | 214% | +|🟡| np.sqrt (float64) | float64 | 100,000 | 0.0557 | 0.0656 | 0.85× | 118% | +|✅| np.sqrt (float64) | float64 | 10,000,000 | 15.1785 | 13.7551 | 1.10× | 91% | +|🟡| np.square(a) (float16) | float16 | 1,000 | 0.0034 | 0.0048 | 0.71× | 141% | +|🟠| np.square(a) (float16) | float16 | 100,000 | 0.2122 | 0.4399 | 0.48× | 207% | +|🟡| np.square(a) (float16) | float16 | 10,000,000 | 23.1199 | 42.7721 | 0.54× | 185% | +|▫| np.square(a) (float32) | float32 | 1,000 | 0.0005 | 0.0013 | 0.39× | 258% | +|🟠| np.square(a) (float32) | float32 | 100,000 | 0.0058 | 0.0262 | 0.22× | 448% | +|✅| np.square(a) (float32) | float32 | 10,000,000 | 7.1833 | 4.0587 | 1.77× | 56% | +|▫| np.square(a) (float64) | float64 | 1,000 | 0.0005 | 0.0030 | 0.17× | 596% | +|🟠| np.square(a) (float64) | float64 | 100,000 | 0.0122 | 0.0533 | 0.23× | 436% | +|✅| np.square(a) (float64) | float64 | 10,000,000 | 14.9158 | 13.8405 | 1.08× | 93% | +|🟡| np.tan (float16) | float16 | 1,000 | 0.0045 | 0.0083 | 0.55× | 183% | +|🟡| np.tan (float16) | float16 | 100,000 | 0.8245 | 1.1120 | 0.74× | 135% | +|🟡| np.tan (float16) | float16 | 10,000,000 | 92.5062 | 110.2014 | 0.84× | 119% | +|✅| np.tan (float32) | float32 | 1,000 | 0.0047 | 0.0038 | 1.24× | 81% | +|✅| np.tan (float32) | float32 | 100,000 | 0.8414 | 0.6851 | 1.23× | 81% | +|✅| np.tan (float32) | float32 | 10,000,000 | 92.7529 | 67.5651 | 1.37× | 73% | +|🟡| np.tan (float64) | float64 | 1,000 | 0.0050 | 0.0050 | 0.99× | 101% | +|🟡| np.tan (float64) | float64 | 100,000 | 0.8055 | 0.8447 | 0.95× | 105% | +|✅| np.tan (float64) | float64 | 10,000,000 | 94.5311 | 88.7915 | 1.06× | 94% | +|✅| np.trunc(a) (float16) | float16 | 1,000 | 0.0054 | 0.0038 | 1.40× | 71% | +|✅| np.trunc(a) (float16) | float16 | 100,000 | 0.4827 | 0.3346 | 1.44× | 69% | +|✅| np.trunc(a) (float16) | float16 | 10,000,000 | 42.0396 | 32.4934 | 1.29× | 77% | +|▫| np.trunc(a) (float32) | float32 | 1,000 | 0.0005 | 0.0016 | 0.34× | 290% | +|🟠| np.trunc(a) (float32) | float32 | 100,000 | 0.0058 | 0.0257 | 0.22× | 445% | +|✅| np.trunc(a) (float32) | float32 | 10,000,000 | 7.1817 | 4.0632 | 1.77× | 57% | +|▫| np.trunc(a) (float64) | float64 | 1,000 | 0.0005 | 0.0020 | 0.27× | 373% | +|🟠| np.trunc(a) (float64) | float64 | 100,000 | 0.0115 | 0.0513 | 0.22× | 446% | +|✅| np.trunc(a) (float64) | float64 | 10,000,000 | 14.6674 | 13.6305 | 1.08× | 93% | ### Reduction | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|✅| np.amax (complex128) | complex128 | 1,000 | 0.0030 | 0.0019 | 1.62× | 62% | -|✅| np.amax (complex128) | complex128 | 100,000 | 0.1566 | 0.1153 | 1.36× | 74% | -|✅| np.amax (complex128) | complex128 | 10,000,000 | 16.6373 | 13.6040 | 1.22× | 82% | -|✅| np.amax (float16) | float16 | 1,000 | 0.0039 | 0.0012 | 3.16× | 32% | -|✅| np.amax (float16) | float16 | 100,000 | 0.5006 | 0.3155 | 1.59× | 63% | -|✅| np.amax (float16) | float16 | 10,000,000 | 50.1383 | 33.0696 | 1.52× | 66% | -|▫| np.amax (float32) | float32 | 1,000 | 0.0016 | 0.0007 | 2.32× | 43% | -|🟡| np.amax (float32) | float32 | 100,000 | 0.0059 | 0.0085 | 0.70× | 144% | -|🟡| np.amax (float32) | float32 | 10,000,000 | 1.4178 | 1.6223 | 0.87× | 114% | -|▫| np.amax (float64) | float64 | 1,000 | 0.0016 | 0.0008 | 2.14× | 47% | -|🟡| np.amax (float64) | float64 | 100,000 | 0.0105 | 0.0181 | 0.58× | 173% | -|🟡| np.amax (float64) | float64 | 10,000,000 | 3.2715 | 3.9602 | 0.83× | 121% | -|▫| np.amax (int16) | int16 | 1,000 | 0.0015 | 0.0008 | 2.01× | 50% | -|✅| np.amax (int16) | int16 | 100,000 | 0.0030 | 0.0015 | 1.99× | 50% | -|🟡| np.amax (int16) | int16 | 10,000,000 | 0.2827 | 0.3374 | 0.84× | 119% | -|▫| np.amax (int32) | int32 | 1,000 | 0.0016 | 0.0008 | 2.02× | 50% | -|✅| np.amax (int32) | int32 | 100,000 | 0.0043 | 0.0023 | 1.87× | 54% | -|🟡| np.amax (int32) | int32 | 10,000,000 | 1.0127 | 1.1192 | 0.91× | 110% | -|▫| np.amax (int64) | int64 | 1,000 | 0.0016 | 0.0008 | 2.07× | 48% | -|✅| np.amax (int64) | int64 | 100,000 | 0.0088 | 0.0079 | 1.12× | 89% | -|✅| np.amax (int64) | int64 | 10,000,000 | 3.8239 | 3.5708 | 1.07× | 93% | -|✅| np.amax (int8) | int8 | 1,000 | 0.0016 | 0.0011 | 1.41× | 71% | -|✅| np.amax (int8) | int8 | 100,000 | 0.0023 | 0.0012 | 1.94× | 52% | -|🟡| np.amax (int8) | int8 | 10,000,000 | 0.1343 | 0.1496 | 0.90× | 111% | -|▫| np.amax (uint16) | uint16 | 1,000 | 0.0015 | 0.0007 | 2.08× | 48% | -|✅| np.amax (uint16) | uint16 | 100,000 | 0.0034 | 0.0015 | 2.20× | 45% | -|🟡| np.amax (uint16) | uint16 | 10,000,000 | 0.2964 | 0.3173 | 0.93× | 107% | -|▫| np.amax (uint32) | uint32 | 1,000 | 0.0019 | 0.0007 | 2.57× | 39% | -|✅| np.amax (uint32) | uint32 | 100,000 | 0.0055 | 0.0032 | 1.72× | 58% | -|🟡| np.amax (uint32) | uint32 | 10,000,000 | 1.0003 | 1.0486 | 0.95× | 105% | -|▫| np.amax (uint64) | uint64 | 1,000 | 0.0017 | 0.0007 | 2.54× | 39% | -|✅| np.amax (uint64) | uint64 | 100,000 | 0.0119 | 0.0103 | 1.15× | 87% | -|✅| np.amax (uint64) | uint64 | 10,000,000 | 3.8010 | 3.6843 | 1.03× | 97% | -|✅| np.amax (uint8) | uint8 | 1,000 | 0.0016 | 0.0012 | 1.38× | 72% | -|✅| np.amax (uint8) | uint8 | 100,000 | 0.0024 | 0.0012 | 1.97× | 51% | -|✅| np.amax (uint8) | uint8 | 10,000,000 | 0.1984 | 0.1471 | 1.35× | 74% | -|✅| np.amax axis=0 (complex128) | complex128 | 1,000 | 0.0029 | 0.0029 | 1.02× | 98% | -|✅| np.amax axis=0 (complex128) | complex128 | 100,000 | 0.1417 | 0.1202 | 1.18× | 85% | -|✅| np.amax axis=0 (complex128) | complex128 | 10,000,000 | 15.7452 | 13.1310 | 1.20× | 83% | -|✅| np.amax axis=0 (float16) | float16 | 1,000 | 0.0039 | 0.0021 | 1.92× | 52% | -|✅| np.amax axis=0 (float16) | float16 | 100,000 | 0.5054 | 0.5030 | 1.00× | 100% | -|🟡| np.amax axis=0 (float16) | float16 | 10,000,000 | 49.4449 | 75.7723 | 0.65× | 153% | -|▫| np.amax axis=0 (float32) | float32 | 1,000 | 0.0020 | 0.0008 | 2.47× | 40% | -|🟡| np.amax axis=0 (float32) | float32 | 100,000 | 0.0097 | 0.0112 | 0.87× | 115% | -|🟡| np.amax axis=0 (float32) | float32 | 10,000,000 | 1.7638 | 1.9930 | 0.89× | 113% | -|▫| np.amax axis=0 (float64) | float64 | 1,000 | 0.0020 | 0.0009 | 2.18× | 46% | -|🟡| np.amax axis=0 (float64) | float64 | 100,000 | 0.0165 | 0.0175 | 0.94× | 106% | -|🟡| np.amax axis=0 (float64) | float64 | 10,000,000 | 4.1500 | 4.2356 | 0.98× | 102% | -|▫| np.amax axis=0 (int16) | int16 | 1,000 | 0.0019 | 0.0007 | 2.81× | 36% | -|✅| np.amax axis=0 (int16) | int16 | 100,000 | 0.0062 | 0.0037 | 1.68× | 60% | -|✅| np.amax axis=0 (int16) | int16 | 10,000,000 | 0.4174 | 0.3993 | 1.04× | 96% | -|▫| np.amax axis=0 (int32) | int32 | 1,000 | 0.0019 | 0.0008 | 2.51× | 40% | -|✅| np.amax axis=0 (int32) | int32 | 100,000 | 0.0099 | 0.0055 | 1.81× | 55% | -|✅| np.amax axis=0 (int32) | int32 | 10,000,000 | 1.5302 | 1.4232 | 1.07× | 93% | -|▫| np.amax axis=0 (int64) | int64 | 1,000 | 0.0020 | 0.0008 | 2.46× | 41% | -|✅| np.amax axis=0 (int64) | int64 | 100,000 | 0.0139 | 0.0129 | 1.07× | 93% | -|✅| np.amax axis=0 (int64) | int64 | 10,000,000 | 4.5156 | 3.7700 | 1.20× | 84% | -|✅| np.amax axis=0 (int8) | int8 | 1,000 | 0.0019 | 0.0014 | 1.44× | 70% | -|✅| np.amax axis=0 (int8) | int8 | 100,000 | 0.0075 | 0.0038 | 2.00× | 50% | -|✅| np.amax axis=0 (int8) | int8 | 10,000,000 | 0.1960 | 0.1810 | 1.08× | 92% | -|▫| np.amax axis=0 (uint16) | uint16 | 1,000 | 0.0019 | 0.0007 | 2.79× | 36% | -|✅| np.amax axis=0 (uint16) | uint16 | 100,000 | 0.0062 | 0.0037 | 1.71× | 58% | -|✅| np.amax axis=0 (uint16) | uint16 | 10,000,000 | 0.4066 | 0.4009 | 1.01× | 99% | -|▫| np.amax axis=0 (uint32) | uint32 | 1,000 | 0.0019 | 0.0008 | 2.39× | 42% | -|✅| np.amax axis=0 (uint32) | uint32 | 100,000 | 0.0105 | 0.0055 | 1.90× | 53% | -|✅| np.amax axis=0 (uint32) | uint32 | 10,000,000 | 1.5582 | 1.3678 | 1.14× | 88% | -|▫| np.amax axis=0 (uint64) | uint64 | 1,000 | 0.0020 | 0.0009 | 2.19× | 46% | -|✅| np.amax axis=0 (uint64) | uint64 | 100,000 | 0.0165 | 0.0149 | 1.10× | 90% | -|✅| np.amax axis=0 (uint64) | uint64 | 10,000,000 | 4.8129 | 3.8642 | 1.25× | 80% | -|▫| np.amax axis=0 (uint8) | uint8 | 1,000 | 0.0019 | 0.0007 | 2.90× | 34% | -|✅| np.amax axis=0 (uint8) | uint8 | 100,000 | 0.0079 | 0.0031 | 2.60× | 38% | -|✅| np.amax axis=0 (uint8) | uint8 | 10,000,000 | 0.1980 | 0.1853 | 1.07× | 94% | -|✅| np.amin (complex128) | complex128 | 1,000 | 0.0030 | 0.0018 | 1.65× | 61% | -|✅| np.amin (complex128) | complex128 | 100,000 | 0.1631 | 0.1144 | 1.43× | 70% | -|✅| np.amin (complex128) | complex128 | 10,000,000 | 16.4507 | 13.7923 | 1.19× | 84% | -|✅| np.amin (float16) | float16 | 1,000 | 0.0039 | 0.0011 | 3.40× | 29% | -|✅| np.amin (float16) | float16 | 100,000 | 0.5184 | 0.2957 | 1.75× | 57% | -|✅| np.amin (float16) | float16 | 10,000,000 | 52.2634 | 30.6378 | 1.71× | 59% | -|▫| np.amin (float32) | float32 | 1,000 | 0.0016 | 0.0008 | 1.96× | 51% | -|🟡| np.amin (float32) | float32 | 100,000 | 0.0059 | 0.0085 | 0.70× | 144% | -|🟡| np.amin (float32) | float32 | 10,000,000 | 1.4309 | 1.6079 | 0.89× | 112% | -|▫| np.amin (float64) | float64 | 1,000 | 0.0016 | 0.0008 | 2.13× | 47% | -|🟡| np.amin (float64) | float64 | 100,000 | 0.0102 | 0.0160 | 0.64× | 157% | -|🟡| np.amin (float64) | float64 | 10,000,000 | 3.4121 | 3.9668 | 0.86× | 116% | -|▫| np.amin (int16) | int16 | 1,000 | 0.0016 | 0.0007 | 2.18× | 46% | -|✅| np.amin (int16) | int16 | 100,000 | 0.0034 | 0.0015 | 2.24× | 45% | -|🟡| np.amin (int16) | int16 | 10,000,000 | 0.3086 | 0.3322 | 0.93× | 108% | -|▫| np.amin (int32) | int32 | 1,000 | 0.0016 | 0.0007 | 2.23× | 45% | -|✅| np.amin (int32) | int32 | 100,000 | 0.0043 | 0.0023 | 1.90× | 53% | -|✅| np.amin (int32) | int32 | 10,000,000 | 1.1001 | 1.0492 | 1.05× | 95% | -|▫| np.amin (int64) | int64 | 1,000 | 0.0016 | 0.0007 | 2.32× | 43% | -|✅| np.amin (int64) | int64 | 100,000 | 0.0105 | 0.0078 | 1.34× | 75% | -|🟡| np.amin (int64) | int64 | 10,000,000 | 3.3743 | 3.5906 | 0.94× | 106% | -|▫| np.amin (int8) | int8 | 1,000 | 0.0016 | 0.0007 | 2.21× | 45% | -|✅| np.amin (int8) | int8 | 100,000 | 0.0027 | 0.0012 | 2.21× | 45% | -|🟡| np.amin (int8) | int8 | 10,000,000 | 0.1287 | 0.1462 | 0.88× | 114% | -|▫| np.amin (uint16) | uint16 | 1,000 | 0.0016 | 0.0007 | 2.21× | 45% | -|✅| np.amin (uint16) | uint16 | 100,000 | 0.0039 | 0.0015 | 2.62× | 38% | -|✅| np.amin (uint16) | uint16 | 10,000,000 | 0.3188 | 0.3184 | 1.00× | 100% | -|▫| np.amin (uint32) | uint32 | 1,000 | 0.0016 | 0.0006 | 2.49× | 40% | -|✅| np.amin (uint32) | uint32 | 100,000 | 0.0044 | 0.0032 | 1.35× | 74% | -|✅| np.amin (uint32) | uint32 | 10,000,000 | 1.0969 | 1.0524 | 1.04× | 96% | -|▫| np.amin (uint64) | uint64 | 1,000 | 0.0017 | 0.0008 | 2.01× | 50% | -|✅| np.amin (uint64) | uint64 | 100,000 | 0.0118 | 0.0102 | 1.16× | 86% | -|✅| np.amin (uint64) | uint64 | 10,000,000 | 3.7562 | 3.7107 | 1.01× | 99% | -|▫| np.amin (uint8) | uint8 | 1,000 | 0.0016 | 0.0007 | 2.20× | 45% | -|✅| np.amin (uint8) | uint8 | 100,000 | 0.0028 | 0.0012 | 2.34× | 43% | -|🟡| np.amin (uint8) | uint8 | 10,000,000 | 0.1207 | 0.1490 | 0.81× | 124% | -|🟡| np.amin axis=0 (complex128) | complex128 | 1,000 | 0.0029 | 0.0032 | 0.93× | 107% | -|🟡| np.amin axis=0 (complex128) | complex128 | 100,000 | 0.1390 | 0.1482 | 0.94× | 107% | -|✅| np.amin axis=0 (complex128) | complex128 | 10,000,000 | 15.7175 | 15.3632 | 1.02× | 98% | -|✅| np.amin axis=0 (float16) | float16 | 1,000 | 0.0040 | 0.0021 | 1.94× | 52% | -|🟡| np.amin axis=0 (float16) | float16 | 100,000 | 0.4748 | 0.5203 | 0.91× | 110% | -|🟡| np.amin axis=0 (float16) | float16 | 10,000,000 | 47.0860 | 78.8804 | 0.60× | 168% | -|▫| np.amin axis=0 (float32) | float32 | 1,000 | 0.0020 | 0.0008 | 2.60× | 38% | -|✅| np.amin axis=0 (float32) | float32 | 100,000 | 0.0100 | 0.0098 | 1.01× | 99% | -|🟡| np.amin axis=0 (float32) | float32 | 10,000,000 | 1.6982 | 1.8897 | 0.90× | 111% | -|▫| np.amin axis=0 (float64) | float64 | 1,000 | 0.0020 | 0.0009 | 2.38× | 42% | -|✅| np.amin axis=0 (float64) | float64 | 100,000 | 0.0170 | 0.0168 | 1.01× | 99% | -|🟡| np.amin axis=0 (float64) | float64 | 10,000,000 | 4.0838 | 4.2063 | 0.97× | 103% | -|▫| np.amin axis=0 (int16) | int16 | 1,000 | 0.0019 | 0.0006 | 3.03× | 33% | -|✅| np.amin axis=0 (int16) | int16 | 100,000 | 0.0072 | 0.0036 | 1.98× | 50% | -|✅| np.amin axis=0 (int16) | int16 | 10,000,000 | 0.4065 | 0.3871 | 1.05× | 95% | -|▫| np.amin axis=0 (int32) | int32 | 1,000 | 0.0019 | 0.0007 | 2.88× | 35% | -|✅| np.amin axis=0 (int32) | int32 | 100,000 | 0.0099 | 0.0055 | 1.82× | 55% | -|✅| np.amin axis=0 (int32) | int32 | 10,000,000 | 1.4950 | 1.3257 | 1.13× | 89% | -|▫| np.amin axis=0 (int64) | int64 | 1,000 | 0.0020 | 0.0008 | 2.60× | 38% | -|✅| np.amin axis=0 (int64) | int64 | 100,000 | 0.0151 | 0.0130 | 1.15× | 87% | -|✅| np.amin axis=0 (int64) | int64 | 10,000,000 | 4.2095 | 3.6711 | 1.15× | 87% | -|▫| np.amin axis=0 (int8) | int8 | 1,000 | 0.0021 | 0.0006 | 3.36× | 30% | -|✅| np.amin axis=0 (int8) | int8 | 100,000 | 0.0070 | 0.0042 | 1.67× | 60% | -|✅| np.amin axis=0 (int8) | int8 | 10,000,000 | 0.1946 | 0.1865 | 1.04× | 96% | -|▫| np.amin axis=0 (uint16) | uint16 | 1,000 | 0.0024 | 0.0007 | 3.58× | 28% | -|✅| np.amin axis=0 (uint16) | uint16 | 100,000 | 0.0062 | 0.0037 | 1.69× | 59% | -|✅| np.amin axis=0 (uint16) | uint16 | 10,000,000 | 0.4283 | 0.3837 | 1.12× | 90% | -|▫| np.amin axis=0 (uint32) | uint32 | 1,000 | 0.0019 | 0.0007 | 2.68× | 37% | -|✅| np.amin axis=0 (uint32) | uint32 | 100,000 | 0.0101 | 0.0055 | 1.84× | 54% | -|✅| np.amin axis=0 (uint32) | uint32 | 10,000,000 | 1.4473 | 1.4195 | 1.02× | 98% | -|▫| np.amin axis=0 (uint64) | uint64 | 1,000 | 0.0020 | 0.0008 | 2.51× | 40% | -|✅| np.amin axis=0 (uint64) | uint64 | 100,000 | 0.0162 | 0.0149 | 1.08× | 92% | -|✅| np.amin axis=0 (uint64) | uint64 | 10,000,000 | 6.5201 | 3.9728 | 1.64× | 61% | -|▫| np.amin axis=0 (uint8) | uint8 | 1,000 | 0.0031 | 0.0006 | 4.95× | 20% | -|✅| np.amin axis=0 (uint8) | uint8 | 100,000 | 0.0068 | 0.0050 | 1.36× | 74% | -|✅| np.amin axis=0 (uint8) | uint8 | 10,000,000 | 0.1993 | 0.1898 | 1.05× | 95% | -|🟡| np.argmax (complex128) | complex128 | 1,000 | 0.0019 | 0.0022 | 0.87× | 115% | -|🟡| np.argmax (complex128) | complex128 | 100,000 | 0.1266 | 0.1481 | 0.85× | 117% | -|✅| np.argmax (complex128) | complex128 | 10,000,000 | 13.9015 | 13.8404 | 1.00× | 100% | -|🟡| np.argmax (float16) | float16 | 1,000 | 0.0029 | 0.0031 | 0.94× | 106% | -|✅| np.argmax (float16) | float16 | 100,000 | 0.4335 | 0.2184 | 1.98× | 50% | -|✅| np.argmax (float16) | float16 | 10,000,000 | 43.8671 | 14.1525 | 3.10× | 32% | -|▫| np.argmax (float32) | float32 | 1,000 | 0.0009 | 0.0008 | 1.05× | 95% | -|🔴| np.argmax (float32) | float32 | 100,000 | 0.0087 | 0.0580 | 0.15× | 669% | -|🟠| np.argmax (float32) | float32 | 10,000,000 | 1.9137 | 5.7625 | 0.33× | 301% | -|▫| np.argmax (float64) | float64 | 1,000 | 0.0010 | 0.0012 | 0.78× | 128% | -|🟠| np.argmax (float64) | float64 | 100,000 | 0.0167 | 0.0592 | 0.28× | 355% | -|🟡| np.argmax (float64) | float64 | 10,000,000 | 4.2645 | 6.8295 | 0.62× | 160% | -|▫| np.argmax (int16) | int16 | 1,000 | 0.0008 | 0.0007 | 1.13× | 89% | -|✅| np.argmax (int16) | int16 | 100,000 | 0.0035 | 0.0032 | 1.07× | 93% | -|✅| np.argmax (int16) | int16 | 10,000,000 | 0.4148 | 0.3569 | 1.16× | 86% | -|▫| np.argmax (int32) | int32 | 1,000 | 0.0009 | 0.0007 | 1.17× | 85% | -|✅| np.argmax (int32) | int32 | 100,000 | 0.0060 | 0.0057 | 1.05× | 95% | -|✅| np.argmax (int32) | int32 | 10,000,000 | 1.6383 | 1.1982 | 1.37× | 73% | -|▫| np.argmax (int64) | int64 | 1,000 | 0.0011 | 0.0009 | 1.21× | 83% | -|🟠| np.argmax (int64) | int64 | 100,000 | 0.0156 | 0.0524 | 0.30× | 335% | -|🟡| np.argmax (int64) | int64 | 10,000,000 | 4.3721 | 4.6726 | 0.94× | 107% | -|▫| np.argmax (int8) | int8 | 1,000 | 0.0009 | 0.0007 | 1.23× | 82% | -|🟡| np.argmax (int8) | int8 | 100,000 | 0.0023 | 0.0023 | 1.00× | 100% | -|🟡| np.argmax (int8) | int8 | 10,000,000 | 0.1639 | 0.2730 | 0.60× | 167% | -|▫| np.argmax (uint16) | uint16 | 1,000 | 0.0010 | 0.0007 | 1.41× | 71% | -|✅| np.argmax (uint16) | uint16 | 100,000 | 0.0057 | 0.0033 | 1.74× | 58% | -|✅| np.argmax (uint16) | uint16 | 10,000,000 | 0.5121 | 0.3437 | 1.49× | 67% | -|▫| np.argmax (uint32) | uint32 | 1,000 | 0.0009 | 0.0007 | 1.23× | 81% | -|✅| np.argmax (uint32) | uint32 | 100,000 | 0.0101 | 0.0057 | 1.76× | 57% | -|✅| np.argmax (uint32) | uint32 | 10,000,000 | 1.8813 | 1.2228 | 1.54× | 65% | -|▫| np.argmax (uint64) | uint64 | 1,000 | 0.0010 | 0.0010 | 0.97× | 103% | -|🟠| np.argmax (uint64) | uint64 | 100,000 | 0.0173 | 0.0601 | 0.29× | 347% | -|🟡| np.argmax (uint64) | uint64 | 10,000,000 | 4.6673 | 4.9980 | 0.93× | 107% | -|▫| np.argmax (uint8) | uint8 | 1,000 | 0.0009 | 0.0007 | 1.27× | 79% | -|✅| np.argmax (uint8) | uint8 | 100,000 | 0.0034 | 0.0023 | 1.47× | 68% | -|✅| np.argmax (uint8) | uint8 | 10,000,000 | 0.2183 | 0.1747 | 1.25× | 80% | -|✅| np.argmin (complex128) | complex128 | 1,000 | 0.0021 | 0.0017 | 1.21× | 83% | -|✅| np.argmin (complex128) | complex128 | 100,000 | 0.1271 | 0.1146 | 1.11× | 90% | -|🟡| np.argmin (complex128) | complex128 | 10,000,000 | 14.2050 | 20.0184 | 0.71× | 141% | -|✅| np.argmin (float16) | float16 | 1,000 | 0.0030 | 0.0021 | 1.42× | 70% | -|✅| np.argmin (float16) | float16 | 100,000 | 0.4183 | 0.1428 | 2.93× | 34% | -|✅| np.argmin (float16) | float16 | 10,000,000 | 42.0779 | 21.9963 | 1.91× | 52% | -|▫| np.argmin (float32) | float32 | 1,000 | 0.0009 | 0.0012 | 0.74× | 136% | -|🔴| np.argmin (float32) | float32 | 100,000 | 0.0085 | 0.0566 | 0.15× | 666% | -|🟠| np.argmin (float32) | float32 | 10,000,000 | 1.9936 | 6.5124 | 0.31× | 327% | -|▫| np.argmin (float64) | float64 | 1,000 | 0.0010 | 0.0012 | 0.81× | 124% | -|🟠| np.argmin (float64) | float64 | 100,000 | 0.0172 | 0.0571 | 0.30× | 332% | -|🟠| np.argmin (float64) | float64 | 10,000,000 | 4.2683 | 9.2928 | 0.46× | 218% | -|▫| np.argmin (int16) | int16 | 1,000 | 0.0009 | 0.0007 | 1.22× | 82% | -|✅| np.argmin (int16) | int16 | 100,000 | 0.0041 | 0.0017 | 2.43× | 41% | -|🟠| np.argmin (int16) | int16 | 10,000,000 | 0.3710 | 0.7659 | 0.48× | 206% | -|▫| np.argmin (int32) | int32 | 1,000 | 0.0009 | 0.0007 | 1.18× | 85% | -|✅| np.argmin (int32) | int32 | 100,000 | 0.0064 | 0.0027 | 2.42× | 41% | -|🟠| np.argmin (int32) | int32 | 10,000,000 | 1.5661 | 3.8037 | 0.41× | 243% | -|▫| np.argmin (int64) | int64 | 1,000 | 0.0009 | 0.0009 | 1.05× | 95% | -|🟠| np.argmin (int64) | int64 | 100,000 | 0.0141 | 0.0285 | 0.49× | 202% | -|🟠| np.argmin (int64) | int64 | 10,000,000 | 4.6382 | 10.1031 | 0.46× | 218% | -|▫| np.argmin (int8) | int8 | 1,000 | 0.0009 | 0.0007 | 1.23× | 82% | -|▫| np.argmin (int8) | int8 | 100,000 | 0.0025 | 0.0010 | 2.51× | 40% | -|🟡| np.argmin (int8) | int8 | 10,000,000 | 0.1575 | 0.1631 | 0.97× | 104% | -|▫| np.argmin (uint16) | uint16 | 1,000 | 0.0009 | 0.0007 | 1.26× | 80% | -|✅| np.argmin (uint16) | uint16 | 100,000 | 0.0057 | 0.0017 | 3.38× | 30% | -|🟡| np.argmin (uint16) | uint16 | 10,000,000 | 0.5604 | 0.7560 | 0.74× | 135% | -|▫| np.argmin (uint32) | uint32 | 1,000 | 0.0009 | 0.0007 | 1.17× | 86% | -|✅| np.argmin (uint32) | uint32 | 100,000 | 0.0104 | 0.0036 | 2.85× | 35% | -|🟠| np.argmin (uint32) | uint32 | 10,000,000 | 1.8309 | 4.1115 | 0.45× | 225% | -|▫| np.argmin (uint64) | uint64 | 1,000 | 0.0010 | 0.0009 | 1.06× | 94% | -|🟡| np.argmin (uint64) | uint64 | 100,000 | 0.0172 | 0.0334 | 0.52× | 194% | -|🟠| np.argmin (uint64) | uint64 | 10,000,000 | 4.5163 | 9.1975 | 0.49× | 204% | -|▫| np.argmin (uint8) | uint8 | 1,000 | 0.0009 | 0.0007 | 1.22× | 82% | -|▫| np.argmin (uint8) | uint8 | 100,000 | 0.0031 | 0.0010 | 3.17× | 32% | -|✅| np.argmin (uint8) | uint8 | 10,000,000 | 0.2245 | 0.1651 | 1.36× | 74% | -|🟡| np.cumprod(a) (float16) | float16 | 1,000 | 0.0063 | 0.0099 | 0.64× | 157% | -|🟠| np.cumprod(a) (float16) | float16 | 100,000 | 0.4134 | 0.9650 | 0.43× | 233% | -|🟠| np.cumprod(a) (float16) | float16 | 10,000,000 | 44.2839 | 94.7772 | 0.47× | 214% | -|✅| np.cumprod(a) (float32) | float32 | 1,000 | 0.0038 | 0.0031 | 1.23× | 81% | -|✅| np.cumprod(a) (float32) | float32 | 100,000 | 0.1696 | 0.1138 | 1.49× | 67% | -|✅| np.cumprod(a) (float32) | float32 | 10,000,000 | 20.7899 | 10.2126 | 2.04× | 49% | -|🟡| np.cumprod(a) (float64) | float64 | 1,000 | 0.0039 | 0.0046 | 0.84× | 119% | -|✅| np.cumprod(a) (float64) | float64 | 100,000 | 0.1674 | 0.1473 | 1.14× | 88% | -|✅| np.cumprod(a) (float64) | float64 | 10,000,000 | 25.0159 | 14.1834 | 1.76× | 57% | -|🟡| np.cumsum (complex128) | complex128 | 1,000 | 0.0030 | 0.0032 | 0.95× | 105% | -|✅| np.cumsum (complex128) | complex128 | 100,000 | 0.3630 | 0.2362 | 1.54× | 65% | -|✅| np.cumsum (complex128) | complex128 | 10,000,000 | 52.8389 | 25.0336 | 2.11× | 47% | -|🟡| np.cumsum (float16) | float16 | 1,000 | 0.0072 | 0.0095 | 0.76× | 131% | -|🟡| np.cumsum (float16) | float16 | 100,000 | 0.4668 | 0.9169 | 0.51× | 196% | -|🟡| np.cumsum (float16) | float16 | 10,000,000 | 49.2891 | 91.6228 | 0.54× | 186% | -|✅| np.cumsum (float32) | float32 | 1,000 | 0.0028 | 0.0017 | 1.60× | 62% | -|✅| np.cumsum (float32) | float32 | 100,000 | 0.1619 | 0.0784 | 2.06× | 48% | -|✅| np.cumsum (float32) | float32 | 10,000,000 | 19.8910 | 7.4357 | 2.67× | 37% | -|✅| np.cumsum (float64) | float64 | 1,000 | 0.0029 | 0.0021 | 1.39× | 72% | -|✅| np.cumsum (float64) | float64 | 100,000 | 0.1686 | 0.1272 | 1.33× | 75% | -|✅| np.cumsum (float64) | float64 | 10,000,000 | 24.4835 | 14.3123 | 1.71× | 58% | -|✅| np.cumsum (int16) | int16 | 1,000 | 0.0025 | 0.0019 | 1.30× | 77% | -|✅| np.cumsum (int16) | int16 | 100,000 | 0.3268 | 0.1215 | 2.69× | 37% | -|✅| np.cumsum (int16) | int16 | 10,000,000 | 28.8643 | 11.0684 | 2.61× | 38% | -|✅| np.cumsum (int32) | int32 | 1,000 | 0.0025 | 0.0017 | 1.48× | 67% | -|✅| np.cumsum (int32) | int32 | 100,000 | 0.3144 | 0.1209 | 2.60× | 38% | -|✅| np.cumsum (int32) | int32 | 10,000,000 | 29.3769 | 11.6781 | 2.52× | 40% | -|🟡| np.cumsum (int64) | int64 | 1,000 | 0.0017 | 0.0020 | 0.87× | 115% | -|🟠| np.cumsum (int64) | int64 | 100,000 | 0.0298 | 0.1205 | 0.25× | 404% | -|✅| np.cumsum (int64) | int64 | 10,000,000 | 15.7029 | 15.3993 | 1.02× | 98% | -|✅| np.cumsum (int8) | int8 | 1,000 | 0.0033 | 0.0019 | 1.71× | 58% | -|✅| np.cumsum (int8) | int8 | 100,000 | 0.3408 | 0.1194 | 2.85× | 35% | -|✅| np.cumsum (int8) | int8 | 10,000,000 | 28.6022 | 10.6443 | 2.69× | 37% | -|✅| np.cumsum (uint16) | uint16 | 1,000 | 0.0025 | 0.0019 | 1.28× | 78% | -|✅| np.cumsum (uint16) | uint16 | 100,000 | 0.3213 | 0.1183 | 2.72× | 37% | -|✅| np.cumsum (uint16) | uint16 | 10,000,000 | 29.1560 | 11.1585 | 2.61× | 38% | -|✅| np.cumsum (uint32) | uint32 | 1,000 | 0.0025 | 0.0021 | 1.17× | 86% | -|✅| np.cumsum (uint32) | uint32 | 100,000 | 0.3202 | 0.1199 | 2.67× | 38% | -|✅| np.cumsum (uint32) | uint32 | 10,000,000 | 29.2400 | 12.9004 | 2.27× | 44% | -|🟡| np.cumsum (uint64) | uint64 | 1,000 | 0.0017 | 0.0019 | 0.93× | 108% | -|🟠| np.cumsum (uint64) | uint64 | 100,000 | 0.0335 | 0.1206 | 0.28× | 359% | -|✅| np.cumsum (uint64) | uint64 | 10,000,000 | 16.3669 | 14.9772 | 1.09× | 92% | -|✅| np.cumsum (uint8) | uint8 | 1,000 | 0.0024 | 0.0021 | 1.15× | 87% | -|✅| np.cumsum (uint8) | uint8 | 100,000 | 0.3392 | 0.1173 | 2.89× | 35% | -|✅| np.cumsum (uint8) | uint8 | 10,000,000 | 29.4047 | 10.6918 | 2.75× | 36% | -|✅| np.mean (complex128) | complex128 | 1,000 | 0.0026 | 0.0010 | 2.58× | 39% | -|✅| np.mean (complex128) | complex128 | 100,000 | 0.0303 | 0.0116 | 2.62× | 38% | -|✅| np.mean (complex128) | complex128 | 10,000,000 | 8.5271 | 6.8972 | 1.24× | 81% | +|✅| np.amax (complex128) | complex128 | 1,000 | 0.0030 | 0.0018 | 1.64× | 61% | +|✅| np.amax (complex128) | complex128 | 100,000 | 0.1573 | 0.1142 | 1.38× | 73% | +|✅| np.amax (complex128) | complex128 | 10,000,000 | 16.6240 | 13.6265 | 1.22× | 82% | +|✅| np.amax (float16) | float16 | 1,000 | 0.0037 | 0.0011 | 3.48× | 29% | +|✅| np.amax (float16) | float16 | 100,000 | 0.5066 | 0.3200 | 1.58× | 63% | +|✅| np.amax (float16) | float16 | 10,000,000 | 50.6659 | 33.1861 | 1.53× | 66% | +|▫| np.amax (float32) | float32 | 1,000 | 0.0017 | 0.0006 | 2.73× | 37% | +|✅| np.amax (float32) | float32 | 100,000 | 0.0059 | 0.0038 | 1.54× | 65% | +|✅| np.amax (float32) | float32 | 10,000,000 | 1.5947 | 1.2905 | 1.24× | 81% | +|▫| np.amax (float64) | float64 | 1,000 | 0.0018 | 0.0008 | 2.34× | 43% | +|✅| np.amax (float64) | float64 | 100,000 | 0.0099 | 0.0062 | 1.60× | 62% | +|✅| np.amax (float64) | float64 | 10,000,000 | 3.3475 | 3.2005 | 1.05× | 96% | +|▫| np.amax (int16) | int16 | 1,000 | 0.0015 | 0.0007 | 2.14× | 47% | +|✅| np.amax (int16) | int16 | 100,000 | 0.0031 | 0.0015 | 2.04× | 49% | +|✅| np.amax (int16) | int16 | 10,000,000 | 0.4359 | 0.3359 | 1.30× | 77% | +|▫| np.amax (int32) | int32 | 1,000 | 0.0016 | 0.0007 | 2.28× | 44% | +|✅| np.amax (int32) | int32 | 100,000 | 0.0044 | 0.0023 | 1.94× | 52% | +|✅| np.amax (int32) | int32 | 10,000,000 | 1.2908 | 1.0195 | 1.27× | 79% | +|▫| np.amax (int64) | int64 | 1,000 | 0.0017 | 0.0006 | 2.62× | 38% | +|✅| np.amax (int64) | int64 | 100,000 | 0.0089 | 0.0078 | 1.14× | 88% | +|🟡| np.amax (int64) | int64 | 10,000,000 | 3.4074 | 3.5508 | 0.96× | 104% | +|▫| np.amax (int8) | int8 | 1,000 | 0.0016 | 0.0007 | 2.08× | 48% | +|✅| np.amax (int8) | int8 | 100,000 | 0.0024 | 0.0012 | 2.00× | 50% | +|✅| np.amax (int8) | int8 | 10,000,000 | 0.1508 | 0.1463 | 1.03× | 97% | +|▫| np.amax (uint16) | uint16 | 1,000 | 0.0016 | 0.0008 | 2.12× | 47% | +|✅| np.amax (uint16) | uint16 | 100,000 | 0.0032 | 0.0015 | 2.13× | 47% | +|🟡| np.amax (uint16) | uint16 | 10,000,000 | 0.2938 | 0.3342 | 0.88× | 114% | +|▫| np.amax (uint32) | uint32 | 1,000 | 0.0016 | 0.0007 | 2.21× | 45% | +|✅| np.amax (uint32) | uint32 | 100,000 | 0.0049 | 0.0032 | 1.53× | 65% | +|✅| np.amax (uint32) | uint32 | 10,000,000 | 1.4205 | 1.0043 | 1.41× | 71% | +|▫| np.amax (uint64) | uint64 | 1,000 | 0.0017 | 0.0008 | 2.14× | 47% | +|✅| np.amax (uint64) | uint64 | 100,000 | 0.0129 | 0.0100 | 1.29× | 78% | +|🟡| np.amax (uint64) | uint64 | 10,000,000 | 3.6692 | 3.6868 | 0.99× | 100% | +|▫| np.amax (uint8) | uint8 | 1,000 | 0.0016 | 0.0008 | 2.06× | 49% | +|✅| np.amax (uint8) | uint8 | 100,000 | 0.0024 | 0.0012 | 2.06× | 49% | +|🟡| np.amax (uint8) | uint8 | 10,000,000 | 0.1351 | 0.1468 | 0.92× | 109% | +|✅| np.amax axis=0 (complex128) | complex128 | 1,000 | 0.0030 | 0.0030 | 1.00× | 100% | +|✅| np.amax axis=0 (complex128) | complex128 | 100,000 | 0.1550 | 0.1176 | 1.32× | 76% | +|✅| np.amax axis=0 (complex128) | complex128 | 10,000,000 | 16.0042 | 13.0433 | 1.23× | 82% | +|✅| np.amax axis=0 (float16) | float16 | 1,000 | 0.0042 | 0.0019 | 2.18× | 46% | +|✅| np.amax axis=0 (float16) | float16 | 100,000 | 0.5075 | 0.5048 | 1.00× | 100% | +|🟡| np.amax axis=0 (float16) | float16 | 10,000,000 | 49.7420 | 78.1538 | 0.64× | 157% | +|▫| np.amax axis=0 (float32) | float32 | 1,000 | 0.0021 | 0.0008 | 2.66× | 38% | +|🟡| np.amax axis=0 (float32) | float32 | 100,000 | 0.0091 | 0.0100 | 0.92× | 109% | +|🟡| np.amax axis=0 (float32) | float32 | 10,000,000 | 1.6926 | 1.8598 | 0.91× | 110% | +|▫| np.amax axis=0 (float64) | float64 | 1,000 | 0.0021 | 0.0009 | 2.33× | 43% | +|🟡| np.amax axis=0 (float64) | float64 | 100,000 | 0.0145 | 0.0175 | 0.83× | 120% | +|🟡| np.amax axis=0 (float64) | float64 | 10,000,000 | 3.9104 | 4.2597 | 0.92× | 109% | +|▫| np.amax axis=0 (int16) | int16 | 1,000 | 0.0020 | 0.0007 | 2.94× | 34% | +|✅| np.amax axis=0 (int16) | int16 | 100,000 | 0.0063 | 0.0037 | 1.73× | 58% | +|✅| np.amax axis=0 (int16) | int16 | 10,000,000 | 0.5073 | 0.3767 | 1.35× | 74% | +|▫| np.amax axis=0 (int32) | int32 | 1,000 | 0.0020 | 0.0007 | 2.84× | 35% | +|✅| np.amax axis=0 (int32) | int32 | 100,000 | 0.0082 | 0.0055 | 1.48× | 67% | +|✅| np.amax axis=0 (int32) | int32 | 10,000,000 | 1.7469 | 1.3193 | 1.32× | 76% | +|▫| np.amax axis=0 (int64) | int64 | 1,000 | 0.0021 | 0.0008 | 2.62× | 38% | +|✅| np.amax axis=0 (int64) | int64 | 100,000 | 0.0153 | 0.0118 | 1.30× | 77% | +|✅| np.amax axis=0 (int64) | int64 | 10,000,000 | 4.1410 | 3.6647 | 1.13× | 88% | +|▫| np.amax axis=0 (int8) | int8 | 1,000 | 0.0020 | 0.0007 | 2.79× | 36% | +|✅| np.amax axis=0 (int8) | int8 | 100,000 | 0.0076 | 0.0039 | 1.96× | 51% | +|✅| np.amax axis=0 (int8) | int8 | 10,000,000 | 0.3964 | 0.1724 | 2.30× | 44% | +|▫| np.amax axis=0 (uint16) | uint16 | 1,000 | 0.0020 | 0.0007 | 2.97× | 34% | +|✅| np.amax axis=0 (uint16) | uint16 | 100,000 | 0.0065 | 0.0036 | 1.79× | 56% | +|✅| np.amax axis=0 (uint16) | uint16 | 10,000,000 | 0.4804 | 0.3808 | 1.26× | 79% | +|▫| np.amax axis=0 (uint32) | uint32 | 1,000 | 0.0019 | 0.0007 | 2.81× | 36% | +|✅| np.amax axis=0 (uint32) | uint32 | 100,000 | 0.0082 | 0.0055 | 1.49× | 67% | +|✅| np.amax axis=0 (uint32) | uint32 | 10,000,000 | 1.6177 | 1.2662 | 1.28× | 78% | +|▫| np.amax axis=0 (uint64) | uint64 | 1,000 | 0.0020 | 0.0009 | 2.24× | 45% | +|✅| np.amax axis=0 (uint64) | uint64 | 100,000 | 0.0185 | 0.0138 | 1.34× | 74% | +|✅| np.amax axis=0 (uint64) | uint64 | 10,000,000 | 4.3907 | 3.8229 | 1.15× | 87% | +|▫| np.amax axis=0 (uint8) | uint8 | 1,000 | 0.0019 | 0.0007 | 2.85× | 35% | +|✅| np.amax axis=0 (uint8) | uint8 | 100,000 | 0.0081 | 0.0031 | 2.65× | 38% | +|✅| np.amax axis=0 (uint8) | uint8 | 10,000,000 | 0.2207 | 0.1718 | 1.28× | 78% | +|✅| np.amin (complex128) | complex128 | 1,000 | 0.0031 | 0.0016 | 1.93× | 52% | +|✅| np.amin (complex128) | complex128 | 100,000 | 0.1527 | 0.1141 | 1.34× | 75% | +|✅| np.amin (complex128) | complex128 | 10,000,000 | 16.4378 | 13.5639 | 1.21× | 82% | +|✅| np.amin (float16) | float16 | 1,000 | 0.0041 | 0.0012 | 3.53× | 28% | +|✅| np.amin (float16) | float16 | 100,000 | 0.5120 | 0.3020 | 1.70× | 59% | +|✅| np.amin (float16) | float16 | 10,000,000 | 51.8815 | 31.4161 | 1.65× | 61% | +|▫| np.amin (float32) | float32 | 1,000 | 0.0023 | 0.0008 | 2.97× | 34% | +|✅| np.amin (float32) | float32 | 100,000 | 0.0058 | 0.0035 | 1.68× | 60% | +|✅| np.amin (float32) | float32 | 10,000,000 | 1.4447 | 1.3328 | 1.08× | 92% | +|▫| np.amin (float64) | float64 | 1,000 | 0.0017 | 0.0008 | 2.21× | 45% | +|✅| np.amin (float64) | float64 | 100,000 | 0.0104 | 0.0073 | 1.42× | 70% | +|✅| np.amin (float64) | float64 | 10,000,000 | 3.3498 | 3.2187 | 1.04× | 96% | +|▫| np.amin (int16) | int16 | 1,000 | 0.0016 | 0.0007 | 2.27× | 44% | +|✅| np.amin (int16) | int16 | 100,000 | 0.0035 | 0.0015 | 2.34× | 43% | +|✅| np.amin (int16) | int16 | 10,000,000 | 0.7445 | 0.3330 | 2.24× | 45% | +|▫| np.amin (int32) | int32 | 1,000 | 0.0016 | 0.0007 | 2.25× | 44% | +|✅| np.amin (int32) | int32 | 100,000 | 0.0045 | 0.0023 | 1.98× | 50% | +|✅| np.amin (int32) | int32 | 10,000,000 | 1.2150 | 1.0174 | 1.19× | 84% | +|▫| np.amin (int64) | int64 | 1,000 | 0.0017 | 0.0008 | 2.13× | 47% | +|✅| np.amin (int64) | int64 | 100,000 | 0.0106 | 0.0074 | 1.44× | 70% | +|🟡| np.amin (int64) | int64 | 10,000,000 | 3.3282 | 3.5000 | 0.95× | 105% | +|▫| np.amin (int8) | int8 | 1,000 | 0.0017 | 0.0007 | 2.30× | 44% | +|✅| np.amin (int8) | int8 | 100,000 | 0.0028 | 0.0012 | 2.38× | 42% | +|🟡| np.amin (int8) | int8 | 10,000,000 | 0.1435 | 0.1467 | 0.98× | 102% | +|▫| np.amin (uint16) | uint16 | 1,000 | 0.0016 | 0.0007 | 2.16× | 46% | +|✅| np.amin (uint16) | uint16 | 100,000 | 0.0033 | 0.0015 | 2.21× | 45% | +|🟡| np.amin (uint16) | uint16 | 10,000,000 | 0.3225 | 0.3309 | 0.97× | 103% | +|▫| np.amin (uint32) | uint32 | 1,000 | 0.0016 | 0.0007 | 2.30× | 44% | +|✅| np.amin (uint32) | uint32 | 100,000 | 0.0044 | 0.0032 | 1.36× | 74% | +|✅| np.amin (uint32) | uint32 | 10,000,000 | 1.0859 | 1.0190 | 1.07× | 94% | +|▫| np.amin (uint64) | uint64 | 1,000 | 0.0017 | 0.0008 | 2.12× | 47% | +|✅| np.amin (uint64) | uint64 | 100,000 | 0.0126 | 0.0097 | 1.29× | 77% | +|🟡| np.amin (uint64) | uint64 | 10,000,000 | 3.6037 | 3.7516 | 0.96× | 104% | +|▫| np.amin (uint8) | uint8 | 1,000 | 0.0016 | 0.0007 | 2.26× | 44% | +|✅| np.amin (uint8) | uint8 | 100,000 | 0.0028 | 0.0012 | 2.41× | 42% | +|🟡| np.amin (uint8) | uint8 | 10,000,000 | 0.1350 | 0.1465 | 0.92× | 108% | +|🟡| np.amin axis=0 (complex128) | complex128 | 1,000 | 0.0029 | 0.0030 | 0.99× | 101% | +|🟡| np.amin axis=0 (complex128) | complex128 | 100,000 | 0.1409 | 0.1480 | 0.95× | 105% | +|✅| np.amin axis=0 (complex128) | complex128 | 10,000,000 | 15.7182 | 15.2730 | 1.03× | 97% | +|✅| np.amin axis=0 (float16) | float16 | 1,000 | 0.0040 | 0.0019 | 2.11× | 47% | +|🟡| np.amin axis=0 (float16) | float16 | 100,000 | 0.4771 | 0.5006 | 0.95× | 105% | +|🟡| np.amin axis=0 (float16) | float16 | 10,000,000 | 47.3591 | 77.6081 | 0.61× | 164% | +|▫| np.amin axis=0 (float32) | float32 | 1,000 | 0.0021 | 0.0008 | 2.67× | 37% | +|✅| np.amin axis=0 (float32) | float32 | 100,000 | 0.0116 | 0.0098 | 1.18× | 85% | +|🟡| np.amin axis=0 (float32) | float32 | 10,000,000 | 1.7252 | 1.8255 | 0.94× | 106% | +|▫| np.amin axis=0 (float64) | float64 | 1,000 | 0.0021 | 0.0009 | 2.41× | 42% | +|🟡| np.amin axis=0 (float64) | float64 | 100,000 | 0.0157 | 0.0165 | 0.95× | 105% | +|🟡| np.amin axis=0 (float64) | float64 | 10,000,000 | 4.0124 | 4.1873 | 0.96× | 104% | +|▫| np.amin axis=0 (int16) | int16 | 1,000 | 0.0020 | 0.0007 | 2.90× | 34% | +|✅| np.amin axis=0 (int16) | int16 | 100,000 | 0.0065 | 0.0037 | 1.74× | 58% | +|✅| np.amin axis=0 (int16) | int16 | 10,000,000 | 0.5065 | 0.3754 | 1.35× | 74% | +|▫| np.amin axis=0 (int32) | int32 | 1,000 | 0.0023 | 0.0007 | 3.32× | 30% | +|✅| np.amin axis=0 (int32) | int32 | 100,000 | 0.0088 | 0.0055 | 1.60× | 62% | +|✅| np.amin axis=0 (int32) | int32 | 10,000,000 | 2.0366 | 1.2825 | 1.59× | 63% | +|▫| np.amin axis=0 (int64) | int64 | 1,000 | 0.0027 | 0.0008 | 3.32× | 30% | +|✅| np.amin axis=0 (int64) | int64 | 100,000 | 0.0193 | 0.0118 | 1.63× | 61% | +|✅| np.amin axis=0 (int64) | int64 | 10,000,000 | 4.3888 | 3.6438 | 1.20× | 83% | +|▫| np.amin axis=0 (int8) | int8 | 1,000 | 0.0019 | 0.0006 | 2.99× | 33% | +|✅| np.amin axis=0 (int8) | int8 | 100,000 | 0.0077 | 0.0041 | 1.88× | 53% | +|✅| np.amin axis=0 (int8) | int8 | 10,000,000 | 0.2767 | 0.1726 | 1.60× | 62% | +|▫| np.amin axis=0 (uint16) | uint16 | 1,000 | 0.0020 | 0.0007 | 2.98× | 34% | +|✅| np.amin axis=0 (uint16) | uint16 | 100,000 | 0.0065 | 0.0036 | 1.79× | 56% | +|✅| np.amin axis=0 (uint16) | uint16 | 10,000,000 | 0.4421 | 0.3803 | 1.16× | 86% | +|▫| np.amin axis=0 (uint32) | uint32 | 1,000 | 0.0019 | 0.0007 | 2.77× | 36% | +|✅| np.amin axis=0 (uint32) | uint32 | 100,000 | 0.0084 | 0.0055 | 1.53× | 65% | +|✅| np.amin axis=0 (uint32) | uint32 | 10,000,000 | 1.8904 | 1.2875 | 1.47× | 68% | +|▫| np.amin axis=0 (uint64) | uint64 | 1,000 | 0.0020 | 0.0008 | 2.38× | 42% | +|✅| np.amin axis=0 (uint64) | uint64 | 100,000 | 0.0177 | 0.0137 | 1.29× | 77% | +|✅| np.amin axis=0 (uint64) | uint64 | 10,000,000 | 4.4047 | 3.8707 | 1.14× | 88% | +|▫| np.amin axis=0 (uint8) | uint8 | 1,000 | 0.0019 | 0.0007 | 2.86× | 35% | +|✅| np.amin axis=0 (uint8) | uint8 | 100,000 | 0.0076 | 0.0051 | 1.49× | 67% | +|✅| np.amin axis=0 (uint8) | uint8 | 10,000,000 | 0.2090 | 0.1721 | 1.22× | 82% | +|✅| np.argmax (complex128) | complex128 | 1,000 | 0.0020 | 0.0018 | 1.14× | 88% | +|🟡| np.argmax (complex128) | complex128 | 100,000 | 0.1132 | 0.1141 | 0.99× | 101% | +|✅| np.argmax (complex128) | complex128 | 10,000,000 | 14.1968 | 13.5590 | 1.05× | 96% | +|✅| np.argmax (float16) | float16 | 1,000 | 0.0029 | 0.0021 | 1.41× | 71% | +|✅| np.argmax (float16) | float16 | 100,000 | 0.4331 | 0.1412 | 3.07× | 33% | +|✅| np.argmax (float16) | float16 | 10,000,000 | 43.8538 | 14.1652 | 3.10× | 32% | +|▫| np.argmax (float32) | float32 | 1,000 | 0.0009 | 0.0012 | 0.78× | 129% | +|🔴| np.argmax (float32) | float32 | 100,000 | 0.0087 | 0.0564 | 0.15× | 648% | +|🟠| np.argmax (float32) | float32 | 10,000,000 | 2.3137 | 5.7594 | 0.40× | 249% | +|▫| np.argmax (float64) | float64 | 1,000 | 0.0010 | 0.0012 | 0.82× | 122% | +|🟠| np.argmax (float64) | float64 | 100,000 | 0.0193 | 0.0568 | 0.34× | 294% | +|🟡| np.argmax (float64) | float64 | 10,000,000 | 4.1180 | 6.8003 | 0.61× | 165% | +|▫| np.argmax (int16) | int16 | 1,000 | 0.0009 | 0.0007 | 1.23× | 82% | +|✅| np.argmax (int16) | int16 | 100,000 | 0.0034 | 0.0017 | 2.02× | 50% | +|✅| np.argmax (int16) | int16 | 10,000,000 | 0.6951 | 0.3548 | 1.96× | 51% | +|▫| np.argmax (int32) | int32 | 1,000 | 0.0009 | 0.0007 | 1.23× | 82% | +|✅| np.argmax (int32) | int32 | 100,000 | 0.0056 | 0.0027 | 2.10× | 48% | +|✅| np.argmax (int32) | int32 | 10,000,000 | 1.8979 | 1.2026 | 1.58× | 63% | +|▫| np.argmax (int64) | int64 | 1,000 | 0.0010 | 0.0009 | 1.11× | 90% | +|🟡| np.argmax (int64) | int64 | 100,000 | 0.0146 | 0.0284 | 0.51× | 195% | +|🟡| np.argmax (int64) | int64 | 10,000,000 | 4.1056 | 4.5513 | 0.90× | 111% | +|▫| np.argmax (int8) | int8 | 1,000 | 0.0009 | 0.0007 | 1.25× | 80% | +|✅| np.argmax (int8) | int8 | 100,000 | 0.0023 | 0.0012 | 1.88× | 53% | +|✅| np.argmax (int8) | int8 | 10,000,000 | 0.1717 | 0.1469 | 1.17× | 86% | +|▫| np.argmax (uint16) | uint16 | 1,000 | 0.0009 | 0.0007 | 1.27× | 79% | +|✅| np.argmax (uint16) | uint16 | 100,000 | 0.0049 | 0.0017 | 2.96× | 34% | +|✅| np.argmax (uint16) | uint16 | 10,000,000 | 0.5085 | 0.3549 | 1.43× | 70% | +|▫| np.argmax (uint32) | uint32 | 1,000 | 0.0009 | 0.0007 | 1.30× | 77% | +|✅| np.argmax (uint32) | uint32 | 100,000 | 0.0104 | 0.0036 | 2.91× | 34% | +|✅| np.argmax (uint32) | uint32 | 10,000,000 | 1.8851 | 1.2169 | 1.55× | 65% | +|▫| np.argmax (uint64) | uint64 | 1,000 | 0.0010 | 0.0009 | 1.04× | 96% | +|🟡| np.argmax (uint64) | uint64 | 100,000 | 0.0171 | 0.0332 | 0.52× | 194% | +|🟡| np.argmax (uint64) | uint64 | 10,000,000 | 4.3878 | 4.9463 | 0.89× | 113% | +|▫| np.argmax (uint8) | uint8 | 1,000 | 0.0013 | 0.0007 | 1.95× | 51% | +|✅| np.argmax (uint8) | uint8 | 100,000 | 0.0032 | 0.0012 | 2.60× | 38% | +|✅| np.argmax (uint8) | uint8 | 10,000,000 | 0.2246 | 0.1465 | 1.53× | 65% | +|✅| np.argmin (complex128) | complex128 | 1,000 | 0.0019 | 0.0015 | 1.25× | 80% | +|🟡| np.argmin (complex128) | complex128 | 100,000 | 0.1139 | 0.1141 | 1.00× | 100% | +|✅| np.argmin (complex128) | complex128 | 10,000,000 | 13.8711 | 13.6034 | 1.02× | 98% | +|✅| np.argmin (float16) | float16 | 1,000 | 0.0029 | 0.0020 | 1.42× | 70% | +|✅| np.argmin (float16) | float16 | 100,000 | 0.4248 | 0.1413 | 3.01× | 33% | +|✅| np.argmin (float16) | float16 | 10,000,000 | 42.5861 | 14.1619 | 3.01× | 33% | +|▫| np.argmin (float32) | float32 | 1,000 | 0.0009 | 0.0012 | 0.80× | 125% | +|🔴| np.argmin (float32) | float32 | 100,000 | 0.0087 | 0.0564 | 0.15× | 650% | +|🟠| np.argmin (float32) | float32 | 10,000,000 | 2.7401 | 5.7656 | 0.47× | 210% | +|▫| np.argmin (float64) | float64 | 1,000 | 0.0010 | 0.0012 | 0.83× | 121% | +|🟠| np.argmin (float64) | float64 | 100,000 | 0.0276 | 0.0569 | 0.48× | 206% | +|🟡| np.argmin (float64) | float64 | 10,000,000 | 4.2847 | 6.7686 | 0.63× | 158% | +|▫| np.argmin (int16) | int16 | 1,000 | 0.0011 | 0.0007 | 1.52× | 66% | +|✅| np.argmin (int16) | int16 | 100,000 | 0.0034 | 0.0016 | 2.06× | 48% | +|✅| np.argmin (int16) | int16 | 10,000,000 | 0.7167 | 0.3613 | 1.98× | 50% | +|▫| np.argmin (int32) | int32 | 1,000 | 0.0010 | 0.0007 | 1.38× | 73% | +|✅| np.argmin (int32) | int32 | 100,000 | 0.0055 | 0.0026 | 2.09× | 48% | +|✅| np.argmin (int32) | int32 | 10,000,000 | 1.7646 | 1.1707 | 1.51× | 66% | +|▫| np.argmin (int64) | int64 | 1,000 | 0.0010 | 0.0009 | 1.11× | 90% | +|🟡| np.argmin (int64) | int64 | 100,000 | 0.0142 | 0.0283 | 0.50× | 199% | +|🟡| np.argmin (int64) | int64 | 10,000,000 | 4.0644 | 4.5666 | 0.89× | 112% | +|▫| np.argmin (int8) | int8 | 1,000 | 0.0009 | 0.0007 | 1.28× | 78% | +|✅| np.argmin (int8) | int8 | 100,000 | 0.0023 | 0.0012 | 1.86× | 54% | +|✅| np.argmin (int8) | int8 | 10,000,000 | 0.1626 | 0.1468 | 1.11× | 90% | +|▫| np.argmin (uint16) | uint16 | 1,000 | 0.0009 | 0.0007 | 1.32× | 76% | +|✅| np.argmin (uint16) | uint16 | 100,000 | 0.0050 | 0.0016 | 3.04× | 33% | +|✅| np.argmin (uint16) | uint16 | 10,000,000 | 0.5632 | 0.3583 | 1.57× | 64% | +|▫| np.argmin (uint32) | uint32 | 1,000 | 0.0010 | 0.0007 | 1.36× | 73% | +|✅| np.argmin (uint32) | uint32 | 100,000 | 0.0101 | 0.0035 | 2.86× | 35% | +|✅| np.argmin (uint32) | uint32 | 10,000,000 | 1.8480 | 1.1772 | 1.57× | 64% | +|▫| np.argmin (uint64) | uint64 | 1,000 | 0.0010 | 0.0009 | 1.03× | 97% | +|🟡| np.argmin (uint64) | uint64 | 100,000 | 0.0180 | 0.0331 | 0.54× | 184% | +|🟡| np.argmin (uint64) | uint64 | 10,000,000 | 4.2831 | 4.9246 | 0.87× | 115% | +|▫| np.argmin (uint8) | uint8 | 1,000 | 0.0009 | 0.0007 | 1.28× | 78% | +|✅| np.argmin (uint8) | uint8 | 100,000 | 0.0033 | 0.0012 | 2.70× | 37% | +|✅| np.argmin (uint8) | uint8 | 10,000,000 | 0.2237 | 0.1465 | 1.53× | 66% | +|🟡| np.cumprod(a) (float16) | float16 | 1,000 | 0.0061 | 0.0097 | 0.63× | 159% | +|🟠| np.cumprod(a) (float16) | float16 | 100,000 | 0.4103 | 0.9506 | 0.43× | 232% | +|🟠| np.cumprod(a) (float16) | float16 | 10,000,000 | 43.5457 | 94.2260 | 0.46× | 216% | +|✅| np.cumprod(a) (float32) | float32 | 1,000 | 0.0046 | 0.0028 | 1.65× | 61% | +|✅| np.cumprod(a) (float32) | float32 | 100,000 | 0.1700 | 0.1114 | 1.53× | 66% | +|✅| np.cumprod(a) (float32) | float32 | 10,000,000 | 20.8437 | 10.1707 | 2.05× | 49% | +|✅| np.cumprod(a) (float64) | float64 | 1,000 | 0.0046 | 0.0034 | 1.33× | 75% | +|✅| np.cumprod(a) (float64) | float64 | 100,000 | 0.1694 | 0.1446 | 1.17× | 85% | +|✅| np.cumprod(a) (float64) | float64 | 10,000,000 | 25.4983 | 14.0728 | 1.81× | 55% | +|🟡| np.cumsum (complex128) | complex128 | 1,000 | 0.0030 | 0.0037 | 0.82× | 122% | +|✅| np.cumsum (complex128) | complex128 | 100,000 | 0.3769 | 0.2333 | 1.61× | 62% | +|✅| np.cumsum (complex128) | complex128 | 10,000,000 | 41.3487 | 24.9780 | 1.66× | 60% | +|🟡| np.cumsum (float16) | float16 | 1,000 | 0.0071 | 0.0094 | 0.75× | 134% | +|🟡| np.cumsum (float16) | float16 | 100,000 | 0.4728 | 0.9144 | 0.52× | 193% | +|🟡| np.cumsum (float16) | float16 | 10,000,000 | 49.3071 | 90.7463 | 0.54× | 184% | +|✅| np.cumsum (float32) | float32 | 1,000 | 0.0029 | 0.0018 | 1.65× | 61% | +|✅| np.cumsum (float32) | float32 | 100,000 | 0.1656 | 0.0789 | 2.10× | 48% | +|✅| np.cumsum (float32) | float32 | 10,000,000 | 20.1855 | 7.3471 | 2.75× | 36% | +|✅| np.cumsum (float64) | float64 | 1,000 | 0.0028 | 0.0022 | 1.31× | 76% | +|✅| np.cumsum (float64) | float64 | 100,000 | 0.1698 | 0.1249 | 1.36× | 74% | +|✅| np.cumsum (float64) | float64 | 10,000,000 | 24.6822 | 13.3431 | 1.85× | 54% | +|✅| np.cumsum (int16) | int16 | 1,000 | 0.0025 | 0.0020 | 1.23× | 81% | +|✅| np.cumsum (int16) | int16 | 100,000 | 0.3253 | 0.1209 | 2.69× | 37% | +|✅| np.cumsum (int16) | int16 | 10,000,000 | 48.4266 | 10.6129 | 4.56× | 22% | +|✅| np.cumsum (int32) | int32 | 1,000 | 0.0024 | 0.0019 | 1.27× | 79% | +|✅| np.cumsum (int32) | int32 | 100,000 | 0.3303 | 0.1226 | 2.69× | 37% | +|✅| np.cumsum (int32) | int32 | 10,000,000 | 32.3880 | 11.1306 | 2.91× | 34% | +|🟡| np.cumsum (int64) | int64 | 1,000 | 0.0017 | 0.0019 | 0.92× | 109% | +|🟠| np.cumsum (int64) | int64 | 100,000 | 0.0315 | 0.1231 | 0.26× | 391% | +|✅| np.cumsum (int64) | int64 | 10,000,000 | 16.3798 | 12.7464 | 1.28× | 78% | +|✅| np.cumsum (int8) | int8 | 1,000 | 0.0025 | 0.0017 | 1.49× | 67% | +|✅| np.cumsum (int8) | int8 | 100,000 | 0.3124 | 0.1187 | 2.63× | 38% | +|✅| np.cumsum (int8) | int8 | 10,000,000 | 41.0555 | 10.2543 | 4.00× | 25% | +|✅| np.cumsum (uint16) | uint16 | 1,000 | 0.0026 | 0.0020 | 1.26× | 80% | +|✅| np.cumsum (uint16) | uint16 | 100,000 | 0.3256 | 0.1199 | 2.72× | 37% | +|✅| np.cumsum (uint16) | uint16 | 10,000,000 | 34.5917 | 10.5051 | 3.29× | 30% | +|✅| np.cumsum (uint32) | uint32 | 1,000 | 0.0024 | 0.0021 | 1.17× | 85% | +|✅| np.cumsum (uint32) | uint32 | 100,000 | 0.3374 | 0.1227 | 2.75× | 36% | +|✅| np.cumsum (uint32) | uint32 | 10,000,000 | 29.7223 | 11.0895 | 2.68× | 37% | +|🟡| np.cumsum (uint64) | uint64 | 1,000 | 0.0017 | 0.0020 | 0.82× | 121% | +|🟠| np.cumsum (uint64) | uint64 | 100,000 | 0.0312 | 0.1218 | 0.26× | 391% | +|✅| np.cumsum (uint64) | uint64 | 10,000,000 | 16.2415 | 12.7570 | 1.27× | 78% | +|✅| np.cumsum (uint8) | uint8 | 1,000 | 0.0025 | 0.0019 | 1.27× | 79% | +|✅| np.cumsum (uint8) | uint8 | 100,000 | 0.3198 | 0.1205 | 2.65× | 38% | +|✅| np.cumsum (uint8) | uint8 | 10,000,000 | 28.9566 | 10.2799 | 2.82× | 36% | +|▫| np.mean (complex128) | complex128 | 1,000 | 0.0027 | 0.0009 | 2.85× | 35% | +|✅| np.mean (complex128) | complex128 | 100,000 | 0.0305 | 0.0101 | 3.01× | 33% | +|✅| np.mean (complex128) | complex128 | 10,000,000 | 8.5581 | 6.4565 | 1.33× | 75% | |✅| np.mean (float16) | float16 | 1,000 | 0.0047 | 0.0012 | 3.86× | 26% | -|✅| np.mean (float16) | float16 | 100,000 | 0.1029 | 0.0804 | 1.28× | 78% | -|✅| np.mean (float16) | float16 | 10,000,000 | 10.3777 | 8.0110 | 1.29× | 77% | -|▫| np.mean (float32) | float32 | 1,000 | 0.0037 | 0.0007 | 5.29× | 19% | -|✅| np.mean (float32) | float32 | 100,000 | 0.0170 | 0.0032 | 5.22× | 19% | -|✅| np.mean (float32) | float32 | 10,000,000 | 2.8795 | 1.1770 | 2.45× | 41% | -|▫| np.mean (float64) | float64 | 1,000 | 0.0024 | 0.0008 | 2.98× | 34% | -|✅| np.mean (float64) | float64 | 100,000 | 0.0167 | 0.0062 | 2.70× | 37% | -|✅| np.mean (float64) | float64 | 10,000,000 | 4.8035 | 3.0818 | 1.56× | 64% | -|▫| np.mean (int16) | int16 | 1,000 | 0.0030 | 0.0008 | 3.63× | 28% | -|✅| np.mean (int16) | int16 | 100,000 | 0.0520 | 0.0191 | 2.73× | 37% | -|✅| np.mean (int16) | int16 | 10,000,000 | 5.0434 | 3.6523 | 1.38× | 72% | -|▫| np.mean (int32) | int32 | 1,000 | 0.0031 | 0.0008 | 3.70× | 27% | -|✅| np.mean (int32) | int32 | 100,000 | 0.0384 | 0.0192 | 2.00× | 50% | -|✅| np.mean (int32) | int32 | 10,000,000 | 4.5473 | 2.8394 | 1.60× | 62% | -|▫| np.mean (int64) | int64 | 1,000 | 0.0029 | 0.0007 | 4.32× | 23% | -|✅| np.mean (int64) | int64 | 100,000 | 0.0342 | 0.0064 | 5.37× | 19% | -|✅| np.mean (int64) | int64 | 10,000,000 | 6.1407 | 3.0443 | 2.02× | 50% | -|▫| np.mean (int8) | int8 | 1,000 | 0.0044 | 0.0009 | 5.05× | 20% | -|✅| np.mean (int8) | int8 | 100,000 | 0.0521 | 0.0188 | 2.78× | 36% | -|✅| np.mean (int8) | int8 | 10,000,000 | 5.0129 | 1.8465 | 2.71× | 37% | -|▫| np.mean (uint16) | uint16 | 1,000 | 0.0029 | 0.0008 | 3.55× | 28% | -|✅| np.mean (uint16) | uint16 | 100,000 | 0.0542 | 0.0191 | 2.84× | 35% | -|✅| np.mean (uint16) | uint16 | 10,000,000 | 5.1089 | 2.2867 | 2.23× | 45% | -|▫| np.mean (uint32) | uint32 | 1,000 | 0.0030 | 0.0008 | 3.59× | 28% | -|✅| np.mean (uint32) | uint32 | 100,000 | 0.0399 | 0.0192 | 2.08× | 48% | -|✅| np.mean (uint32) | uint32 | 10,000,000 | 4.6300 | 2.8166 | 1.64× | 61% | -|▫| np.mean (uint64) | uint64 | 1,000 | 0.0030 | 0.0008 | 3.81× | 26% | -|✅| np.mean (uint64) | uint64 | 100,000 | 0.0521 | 0.0064 | 8.16× | 12% | -|✅| np.mean (uint64) | uint64 | 10,000,000 | 7.8297 | 3.0240 | 2.59× | 39% | -|▫| np.mean (uint8) | uint8 | 1,000 | 0.0031 | 0.0009 | 3.54× | 28% | -|✅| np.mean (uint8) | uint8 | 100,000 | 0.0570 | 0.0190 | 3.01× | 33% | -|✅| np.mean (uint8) | uint8 | 10,000,000 | 5.2869 | 1.8522 | 2.85× | 35% | -|✅| np.mean axis=0 (complex128) | complex128 | 1,000 | 0.0031 | 0.0025 | 1.23× | 81% | -|🟡| np.mean axis=0 (complex128) | complex128 | 100,000 | 0.0174 | 0.0243 | 0.71× | 140% | -|✅| np.mean axis=0 (complex128) | complex128 | 10,000,000 | 7.4300 | 7.1318 | 1.04× | 96% | -|✅| np.mean axis=0 (float16) | float16 | 1,000 | 0.0056 | 0.0035 | 1.59× | 63% | -|🟡| np.mean axis=0 (float16) | float16 | 100,000 | 0.0793 | 0.1507 | 0.53× | 190% | -|🟠| np.mean axis=0 (float16) | float16 | 10,000,000 | 7.0247 | 14.1582 | 0.50× | 202% | -|✅| np.mean axis=0 (float32) | float32 | 1,000 | 0.0036 | 0.0021 | 1.71× | 59% | -|🟡| np.mean axis=0 (float32) | float32 | 100,000 | 0.0098 | 0.0101 | 0.97× | 103% | -|🟡| np.mean axis=0 (float32) | float32 | 10,000,000 | 1.3927 | 1.4838 | 0.94× | 106% | -|✅| np.mean axis=0 (float64) | float64 | 1,000 | 0.0029 | 0.0022 | 1.34× | 75% | -|✅| np.mean axis=0 (float64) | float64 | 100,000 | 0.0155 | 0.0150 | 1.03× | 97% | -|✅| np.mean axis=0 (float64) | float64 | 10,000,000 | 3.7262 | 3.5836 | 1.04× | 96% | -|▫| np.mean axis=0 (int16) | int16 | 1,000 | 0.0035 | 0.0008 | 4.58× | 22% | -|✅| np.mean axis=0 (int16) | int16 | 100,000 | 0.0573 | 0.0084 | 6.79× | 15% | -|✅| np.mean axis=0 (int16) | int16 | 10,000,000 | 5.1418 | 0.9907 | 5.19× | 19% | -|▫| np.mean axis=0 (int32) | int32 | 1,000 | 0.0036 | 0.0008 | 4.45× | 22% | -|✅| np.mean axis=0 (int32) | int32 | 100,000 | 0.0399 | 0.0078 | 5.11× | 20% | -|✅| np.mean axis=0 (int32) | int32 | 10,000,000 | 4.4363 | 1.9084 | 2.33× | 43% | -|✅| np.mean axis=0 (int64) | int64 | 1,000 | 0.0034 | 0.0013 | 2.61× | 38% | -|🟠| np.mean axis=0 (int64) | int64 | 100,000 | 0.0298 | 0.0620 | 0.48× | 208% | -|🔴| np.mean axis=0 (int64) | int64 | 10,000,000 | 6.0028 | 36.4496 | 0.17× | 607% | -|▫| np.mean axis=0 (int8) | int8 | 1,000 | 0.0040 | 0.0008 | 5.10× | 20% | -|✅| np.mean axis=0 (int8) | int8 | 100,000 | 0.0481 | 0.0107 | 4.49× | 22% | -|✅| np.mean axis=0 (int8) | int8 | 10,000,000 | 5.1002 | 0.8293 | 6.15× | 16% | -|▫| np.mean axis=0 (uint16) | uint16 | 1,000 | 0.0036 | 0.0008 | 4.56× | 22% | -|✅| np.mean axis=0 (uint16) | uint16 | 100,000 | 0.0487 | 0.0085 | 5.73× | 18% | -|✅| np.mean axis=0 (uint16) | uint16 | 10,000,000 | 5.1212 | 0.9833 | 5.21× | 19% | -|▫| np.mean axis=0 (uint32) | uint32 | 1,000 | 0.0035 | 0.0008 | 4.29× | 23% | -|✅| np.mean axis=0 (uint32) | uint32 | 100,000 | 0.0362 | 0.0094 | 3.85× | 26% | -|✅| np.mean axis=0 (uint32) | uint32 | 10,000,000 | 4.6000 | 2.1895 | 2.10× | 48% | -|✅| np.mean axis=0 (uint64) | uint64 | 1,000 | 0.0036 | 0.0017 | 2.09× | 48% | -|🟡| np.mean axis=0 (uint64) | uint64 | 100,000 | 0.0463 | 0.0898 | 0.52× | 194% | -|🔴| np.mean axis=0 (uint64) | uint64 | 10,000,000 | 7.2856 | 41.5410 | 0.17× | 570% | -|▫| np.mean axis=0 (uint8) | uint8 | 1,000 | 0.0038 | 0.0008 | 4.73× | 21% | -|✅| np.mean axis=0 (uint8) | uint8 | 100,000 | 0.0497 | 0.0082 | 6.03× | 17% | -|✅| np.mean axis=0 (uint8) | uint8 | 10,000,000 | 5.1578 | 0.8219 | 6.28× | 16% | -|✅| np.mean axis=1 (complex128) | complex128 | 1,000 | 0.0031 | 0.0026 | 1.21× | 83% | -|🟡| np.mean axis=1 (complex128) | complex128 | 100,000 | 0.0348 | 0.0349 | 1.00× | 100% | -|✅| np.mean axis=1 (complex128) | complex128 | 10,000,000 | 8.6206 | 8.5984 | 1.00× | 100% | -|✅| np.mean axis=1 (float16) | float16 | 1,000 | 0.0049 | 0.0040 | 1.23× | 82% | -|🟡| np.mean axis=1 (float16) | float16 | 100,000 | 0.0957 | 0.1374 | 0.70× | 144% | -|🟡| np.mean axis=1 (float16) | float16 | 10,000,000 | 8.1108 | 13.0257 | 0.62× | 161% | -|✅| np.mean axis=1 (float32) | float32 | 1,000 | 0.0035 | 0.0021 | 1.69× | 59% | -|✅| np.mean axis=1 (float32) | float32 | 100,000 | 0.0178 | 0.0143 | 1.25× | 80% | -|✅| np.mean axis=1 (float32) | float32 | 10,000,000 | 3.1620 | 1.9854 | 1.59× | 63% | -|✅| np.mean axis=1 (float64) | float64 | 1,000 | 0.0031 | 0.0022 | 1.43× | 70% | -|✅| np.mean axis=1 (float64) | float64 | 100,000 | 0.0210 | 0.0152 | 1.38× | 72% | -|✅| np.mean axis=1 (float64) | float64 | 10,000,000 | 5.5001 | 3.8569 | 1.43× | 70% | -|▫| np.mean axis=1 (int16) | int16 | 1,000 | 0.0035 | 0.0008 | 4.26× | 24% | -|✅| np.mean axis=1 (int16) | int16 | 100,000 | 0.0579 | 0.0080 | 7.25× | 14% | -|✅| np.mean axis=1 (int16) | int16 | 10,000,000 | 5.2057 | 0.8685 | 5.99× | 17% | -|▫| np.mean axis=1 (int32) | int32 | 1,000 | 0.0035 | 0.0008 | 4.47× | 22% | -|✅| np.mean axis=1 (int32) | int32 | 100,000 | 0.0424 | 0.0049 | 8.69× | 12% | -|✅| np.mean axis=1 (int32) | int32 | 10,000,000 | 4.6015 | 1.5143 | 3.04× | 33% | -|✅| np.mean axis=1 (int64) | int64 | 1,000 | 0.0034 | 0.0013 | 2.60× | 38% | -|🟡| np.mean axis=1 (int64) | int64 | 100,000 | 0.0412 | 0.0600 | 0.69× | 146% | -|🟡| np.mean axis=1 (int64) | int64 | 10,000,000 | 6.5756 | 7.1615 | 0.92× | 109% | -|▫| np.mean axis=1 (int8) | int8 | 1,000 | 0.0035 | 0.0009 | 4.11× | 24% | -|✅| np.mean axis=1 (int8) | int8 | 100,000 | 0.0560 | 0.0089 | 6.29× | 16% | -|✅| np.mean axis=1 (int8) | int8 | 10,000,000 | 5.1060 | 0.7330 | 6.97× | 14% | -|▫| np.mean axis=1 (uint16) | uint16 | 1,000 | 0.0035 | 0.0009 | 4.00× | 25% | -|✅| np.mean axis=1 (uint16) | uint16 | 100,000 | 0.0571 | 0.0079 | 7.18× | 14% | -|✅| np.mean axis=1 (uint16) | uint16 | 10,000,000 | 5.2099 | 0.8763 | 5.95× | 17% | -|▫| np.mean axis=1 (uint32) | uint32 | 1,000 | 0.0034 | 0.0008 | 4.22× | 24% | -|✅| np.mean axis=1 (uint32) | uint32 | 100,000 | 0.0462 | 0.0092 | 5.01× | 20% | -|✅| np.mean axis=1 (uint32) | uint32 | 10,000,000 | 4.8969 | 1.9956 | 2.45× | 41% | -|✅| np.mean axis=1 (uint64) | uint64 | 1,000 | 0.0037 | 0.0017 | 2.14× | 47% | -|🟡| np.mean axis=1 (uint64) | uint64 | 100,000 | 0.0536 | 0.0873 | 0.61× | 163% | -|✅| np.mean axis=1 (uint64) | uint64 | 10,000,000 | 11.1462 | 9.3389 | 1.19× | 84% | -|▫| np.mean axis=1 (uint8) | uint8 | 1,000 | 0.0035 | 0.0008 | 4.44× | 22% | -|✅| np.mean axis=1 (uint8) | uint8 | 100,000 | 0.0617 | 0.0079 | 7.80× | 13% | -|✅| np.mean axis=1 (uint8) | uint8 | 10,000,000 | 5.1481 | 0.7363 | 6.99× | 14% | -|✅| np.nanmax(a) (float16) | float16 | 1,000 | 0.0053 | 0.0011 | 4.74× | 21% | -|✅| np.nanmax(a) (float16) | float16 | 100,000 | 0.5135 | 0.3094 | 1.66× | 60% | -|✅| np.nanmax(a) (float16) | float16 | 10,000,000 | 50.3872 | 31.6489 | 1.59× | 63% | -|▫| np.nanmax(a) (float32) | float32 | 1,000 | 0.0029 | 0.0008 | 3.50× | 29% | -|🟠| np.nanmax(a) (float32) | float32 | 100,000 | 0.0071 | 0.0288 | 0.25× | 407% | -|🟠| np.nanmax(a) (float32) | float32 | 10,000,000 | 1.4848 | 3.2658 | 0.46× | 220% | -|✅| np.nanmax(a) (float64) | float64 | 1,000 | 0.0037 | 0.0012 | 3.02× | 33% | -|🟠| np.nanmax(a) (float64) | float64 | 100,000 | 0.0155 | 0.0603 | 0.26× | 390% | -|🟠| np.nanmax(a) (float64) | float64 | 10,000,000 | 3.4599 | 7.0157 | 0.49× | 203% | -|✅| np.nanmean(a) (float16) | float16 | 1,000 | 0.0128 | 0.0017 | 7.46× | 13% | -|✅| np.nanmean(a) (float16) | float16 | 100,000 | 0.3311 | 0.1059 | 3.12× | 32% | -|✅| np.nanmean(a) (float16) | float16 | 10,000,000 | 37.1950 | 10.5785 | 3.52× | 28% | -|✅| np.nanmean(a) (float32) | float32 | 1,000 | 0.0096 | 0.0012 | 7.87× | 13% | -|✅| np.nanmean(a) (float32) | float32 | 100,000 | 0.0749 | 0.0383 | 1.96× | 51% | -|✅| np.nanmean(a) (float32) | float32 | 10,000,000 | 19.4404 | 4.1632 | 4.67× | 21% | -|✅| np.nanmean(a) (float64) | float64 | 1,000 | 0.0097 | 0.0012 | 7.97× | 12% | -|✅| np.nanmean(a) (float64) | float64 | 100,000 | 0.3257 | 0.0387 | 8.42× | 12% | -|✅| np.nanmean(a) (float64) | float64 | 10,000,000 | 31.3597 | 5.6141 | 5.59× | 18% | -|✅| np.nanmedian(a) (float16) | float16 | 1,000 | 0.0183 | 0.0044 | 4.15× | 24% | -|🟡| np.nanmedian(a) (float16) | float16 | 100,000 | 0.9708 | 1.3140 | 0.74× | 135% | -|✅| np.nanmedian(a) (float16) | float16 | 10,000,000 | 116.3105 | 93.3718 | 1.25× | 80% | -|✅| np.nanmedian(a) (float32) | float32 | 1,000 | 0.0137 | 0.0023 | 5.84× | 17% | -|🟡| np.nanmedian(a) (float32) | float32 | 100,000 | 0.4951 | 0.7126 | 0.69× | 144% | -|🟡| np.nanmedian(a) (float32) | float32 | 10,000,000 | 76.8994 | 80.3683 | 0.96× | 104% | -|✅| np.nanmedian(a) (float64) | float64 | 1,000 | 0.0116 | 0.0026 | 4.42× | 23% | -|🟡| np.nanmedian(a) (float64) | float64 | 100,000 | 0.5300 | 0.7595 | 0.70× | 143% | -|🟡| np.nanmedian(a) (float64) | float64 | 10,000,000 | 91.1907 | 92.5204 | 0.99× | 102% | -|✅| np.nanmin(a) (float16) | float16 | 1,000 | 0.0054 | 0.0011 | 4.89× | 20% | -|✅| np.nanmin(a) (float16) | float16 | 100,000 | 0.5116 | 0.3033 | 1.69× | 59% | -|✅| np.nanmin(a) (float16) | float16 | 10,000,000 | 60.5488 | 31.1541 | 1.94× | 52% | -|▫| np.nanmin(a) (float32) | float32 | 1,000 | 0.0029 | 0.0009 | 3.15× | 32% | -|🟠| np.nanmin(a) (float32) | float32 | 100,000 | 0.0070 | 0.0287 | 0.24× | 408% | -|🟠| np.nanmin(a) (float32) | float32 | 10,000,000 | 1.5149 | 3.2889 | 0.46× | 217% | -|✅| np.nanmin(a) (float64) | float64 | 1,000 | 0.0030 | 0.0012 | 2.43× | 41% | -|🔴| np.nanmin(a) (float64) | float64 | 100,000 | 0.0112 | 0.0589 | 0.19× | 525% | -|🟡| np.nanmin(a) (float64) | float64 | 10,000,000 | 3.5942 | 6.8799 | 0.52× | 191% | -|✅| np.nanpercentile(a, 50) (float16) | float16 | 1,000 | 0.0316 | 0.0044 | 7.16× | 14% | -|✅| np.nanpercentile(a, 50) (float16) | float16 | 100,000 | 1.8767 | 1.3135 | 1.43× | 70% | -|✅| np.nanpercentile(a, 50) (float16) | float16 | 10,000,000 | 122.7941 | 94.3214 | 1.30× | 77% | -|✅| np.nanpercentile(a, 50) (float32) | float32 | 1,000 | 0.0269 | 0.0023 | 11.51× | 9% | -|✅| np.nanpercentile(a, 50) (float32) | float32 | 100,000 | 0.7245 | 0.7038 | 1.03× | 97% | -|🟡| np.nanpercentile(a, 50) (float32) | float32 | 10,000,000 | 51.3261 | 80.6873 | 0.64× | 157% | -|✅| np.nanpercentile(a, 50) (float64) | float64 | 1,000 | 0.0303 | 0.0027 | 11.42× | 9% | -|✅| np.nanpercentile(a, 50) (float64) | float64 | 100,000 | 0.7650 | 0.7593 | 1.01× | 99% | -|🟡| np.nanpercentile(a, 50) (float64) | float64 | 10,000,000 | 63.5936 | 93.0204 | 0.68× | 146% | -|✅| np.nanprod(a) (float16) | float16 | 1,000 | 0.0057 | 0.0013 | 4.38× | 23% | -|✅| np.nanprod(a) (float16) | float16 | 100,000 | 0.1646 | 0.0888 | 1.85× | 54% | -|✅| np.nanprod(a) (float16) | float16 | 10,000,000 | 21.9118 | 9.1189 | 2.40× | 42% | -|▫| np.nanprod(a) (float32) | float32 | 1,000 | 0.0050 | 0.0007 | 7.58× | 13% | -|✅| np.nanprod(a) (float32) | float32 | 100,000 | 0.0981 | 0.0120 | 8.18× | 12% | -|✅| np.nanprod(a) (float32) | float32 | 10,000,000 | 17.9167 | 1.8087 | 9.91× | 10% | -|▫| np.nanprod(a) (float64) | float64 | 1,000 | 0.0050 | 0.0009 | 5.45× | 18% | -|✅| np.nanprod(a) (float64) | float64 | 100,000 | 0.1075 | 0.0235 | 4.57× | 22% | -|✅| np.nanprod(a) (float64) | float64 | 10,000,000 | 29.1353 | 4.0320 | 7.23× | 14% | -|✅| np.nanquantile(a, 0.5) (float16) | float16 | 1,000 | 0.0317 | 0.0044 | 7.18× | 14% | -|✅| np.nanquantile(a, 0.5) (float16) | float16 | 100,000 | 1.8705 | 1.3153 | 1.42× | 70% | -|✅| np.nanquantile(a, 0.5) (float16) | float16 | 10,000,000 | 123.5395 | 92.3417 | 1.34× | 75% | -|✅| np.nanquantile(a, 0.5) (float32) | float32 | 1,000 | 0.0277 | 0.0023 | 11.86× | 8% | -|✅| np.nanquantile(a, 0.5) (float32) | float32 | 100,000 | 0.7317 | 0.7122 | 1.03× | 97% | -|🟡| np.nanquantile(a, 0.5) (float32) | float32 | 10,000,000 | 51.3567 | 80.5913 | 0.64× | 157% | -|✅| np.nanquantile(a, 0.5) (float64) | float64 | 1,000 | 0.0256 | 0.0027 | 9.59× | 10% | -|🟡| np.nanquantile(a, 0.5) (float64) | float64 | 100,000 | 0.7457 | 0.7589 | 0.98× | 102% | -|🟡| np.nanquantile(a, 0.5) (float64) | float64 | 10,000,000 | 64.0411 | 92.7650 | 0.69× | 145% | -|✅| np.nanstd(a) (float16) | float16 | 1,000 | 0.0342 | 0.0027 | 12.50× | 8% | -|✅| np.nanstd(a) (float16) | float16 | 100,000 | 1.1469 | 0.2160 | 5.31× | 19% | -|✅| np.nanstd(a) (float16) | float16 | 10,000,000 | 156.8616 | 21.7440 | 7.21× | 14% | -|✅| np.nanstd(a) (float32) | float32 | 1,000 | 0.0195 | 0.0017 | 11.39× | 9% | -|✅| np.nanstd(a) (float32) | float32 | 100,000 | 0.1736 | 0.0865 | 2.01× | 50% | -|✅| np.nanstd(a) (float32) | float32 | 10,000,000 | 32.1553 | 9.2600 | 3.47× | 29% | -|✅| np.nanstd(a) (float64) | float64 | 1,000 | 0.0202 | 0.0015 | 13.33× | 8% | -|✅| np.nanstd(a) (float64) | float64 | 100,000 | 0.4571 | 0.0760 | 6.01× | 17% | -|✅| np.nanstd(a) (float64) | float64 | 10,000,000 | 50.9740 | 11.3108 | 4.51× | 22% | -|✅| np.nansum(a) (float16) | float16 | 1,000 | 0.0063 | 0.0013 | 4.79× | 21% | -|✅| np.nansum(a) (float16) | float16 | 100,000 | 0.2764 | 0.0898 | 3.08× | 32% | -|✅| np.nansum(a) (float16) | float16 | 10,000,000 | 31.6873 | 8.9186 | 3.55× | 28% | -|▫| np.nansum(a) (float32) | float32 | 1,000 | 0.0036 | 0.0007 | 5.42× | 18% | -|✅| np.nansum(a) (float32) | float32 | 100,000 | 0.0337 | 0.0049 | 6.81× | 15% | -|✅| np.nansum(a) (float32) | float32 | 10,000,000 | 13.4916 | 1.4504 | 9.30× | 11% | -|▫| np.nansum(a) (float64) | float64 | 1,000 | 0.0036 | 0.0007 | 5.52× | 18% | -|✅| np.nansum(a) (float64) | float64 | 100,000 | 0.0425 | 0.0098 | 4.36× | 23% | -|✅| np.nansum(a) (float64) | float64 | 10,000,000 | 24.4520 | 3.4763 | 7.03× | 14% | -|✅| np.nanvar(a) (float16) | float16 | 1,000 | 0.0320 | 0.0027 | 11.67× | 9% | -|✅| np.nanvar(a) (float16) | float16 | 100,000 | 1.1778 | 0.2157 | 5.46× | 18% | -|✅| np.nanvar(a) (float16) | float16 | 10,000,000 | 121.9127 | 21.5319 | 5.66× | 18% | -|✅| np.nanvar(a) (float32) | float32 | 1,000 | 0.0189 | 0.0017 | 11.19× | 9% | -|✅| np.nanvar(a) (float32) | float32 | 100,000 | 0.1775 | 0.0878 | 2.02× | 50% | -|✅| np.nanvar(a) (float32) | float32 | 10,000,000 | 32.2343 | 9.2642 | 3.48× | 29% | -|✅| np.nanvar(a) (float64) | float64 | 1,000 | 0.0173 | 0.0015 | 11.42× | 9% | -|✅| np.nanvar(a) (float64) | float64 | 100,000 | 0.4650 | 0.0759 | 6.13× | 16% | -|✅| np.nanvar(a) (float64) | float64 | 10,000,000 | 51.7630 | 11.4041 | 4.54× | 22% | -|▫| np.prod (float64) | float64 | 1,000 | 0.0022 | 0.0008 | 2.84× | 35% | -|✅| np.prod (float64) | float64 | 100,000 | 2.3362 | 0.1699 | 13.75× | 7% | -|✅| np.prod (float64) | float64 | 10,000,000 | 240.6877 | 60.9344 | 3.95× | 25% | -|▫| np.prod (int64) | int64 | 1,000 | 0.0023 | 0.0008 | 3.04× | 33% | -|✅| np.prod (int64) | int64 | 100,000 | 0.0655 | 0.0144 | 4.54× | 22% | -|✅| np.prod (int64) | int64 | 10,000,000 | 6.4666 | 3.8559 | 1.68× | 60% | +|✅| np.mean (float16) | float16 | 100,000 | 0.1088 | 0.0802 | 1.36× | 74% | +|✅| np.mean (float16) | float16 | 10,000,000 | 10.4935 | 8.0190 | 1.31× | 76% | +|▫| np.mean (float32) | float32 | 1,000 | 0.0040 | 0.0007 | 5.42× | 18% | +|✅| np.mean (float32) | float32 | 100,000 | 0.0178 | 0.0032 | 5.55× | 18% | +|✅| np.mean (float32) | float32 | 10,000,000 | 3.6083 | 1.0039 | 3.59× | 28% | +|▫| np.mean (float64) | float64 | 1,000 | 0.0024 | 0.0007 | 3.25× | 31% | +|✅| np.mean (float64) | float64 | 100,000 | 0.0169 | 0.0040 | 4.21× | 24% | +|✅| np.mean (float64) | float64 | 10,000,000 | 4.6544 | 2.7943 | 1.67× | 60% | +|▫| np.mean (int16) | int16 | 1,000 | 0.0031 | 0.0008 | 3.92× | 26% | +|✅| np.mean (int16) | int16 | 100,000 | 0.0532 | 0.0189 | 2.81× | 36% | +|✅| np.mean (int16) | int16 | 10,000,000 | 6.3750 | 1.9609 | 3.25× | 31% | +|▫| np.mean (int32) | int32 | 1,000 | 0.0030 | 0.0008 | 3.71× | 27% | +|✅| np.mean (int32) | int32 | 100,000 | 0.0429 | 0.0191 | 2.24× | 45% | +|✅| np.mean (int32) | int32 | 10,000,000 | 4.9575 | 2.6752 | 1.85× | 54% | +|▫| np.mean (int64) | int64 | 1,000 | 0.0029 | 0.0008 | 3.71× | 27% | +|✅| np.mean (int64) | int64 | 100,000 | 0.0344 | 0.0045 | 7.72× | 13% | +|✅| np.mean (int64) | int64 | 10,000,000 | 6.3414 | 2.7730 | 2.29× | 44% | +|▫| np.mean (int8) | int8 | 1,000 | 0.0030 | 0.0008 | 3.58× | 28% | +|✅| np.mean (int8) | int8 | 100,000 | 0.0567 | 0.0186 | 3.04× | 33% | +|✅| np.mean (int8) | int8 | 10,000,000 | 5.6945 | 1.8461 | 3.08× | 32% | +|▫| np.mean (uint16) | uint16 | 1,000 | 0.0031 | 0.0008 | 3.90× | 26% | +|✅| np.mean (uint16) | uint16 | 100,000 | 0.0526 | 0.0189 | 2.78× | 36% | +|✅| np.mean (uint16) | uint16 | 10,000,000 | 5.1914 | 1.9498 | 2.66× | 38% | +|▫| np.mean (uint32) | uint32 | 1,000 | 0.0030 | 0.0008 | 3.70× | 27% | +|✅| np.mean (uint32) | uint32 | 100,000 | 0.0402 | 0.0191 | 2.11× | 47% | +|✅| np.mean (uint32) | uint32 | 10,000,000 | 4.6035 | 2.6686 | 1.73× | 58% | +|▫| np.mean (uint64) | uint64 | 1,000 | 0.0032 | 0.0008 | 4.16× | 24% | +|✅| np.mean (uint64) | uint64 | 100,000 | 0.0539 | 0.0045 | 12.08× | 8% | +|✅| np.mean (uint64) | uint64 | 10,000,000 | 7.3769 | 2.7725 | 2.66× | 38% | +|▫| np.mean (uint8) | uint8 | 1,000 | 0.0031 | 0.0008 | 3.81× | 26% | +|✅| np.mean (uint8) | uint8 | 100,000 | 0.0541 | 0.0187 | 2.90× | 34% | +|✅| np.mean (uint8) | uint8 | 10,000,000 | 5.0561 | 1.8459 | 2.74× | 36% | +|✅| np.mean axis=0 (complex128) | complex128 | 1,000 | 0.0033 | 0.0025 | 1.30× | 77% | +|✅| np.mean axis=0 (complex128) | complex128 | 100,000 | 0.0286 | 0.0241 | 1.19× | 84% | +|✅| np.mean axis=0 (complex128) | complex128 | 10,000,000 | 7.5298 | 7.0381 | 1.07× | 94% | +|✅| np.mean axis=0 (float16) | float16 | 1,000 | 0.0061 | 0.0035 | 1.75× | 57% | +|🟡| np.mean axis=0 (float16) | float16 | 100,000 | 0.0790 | 0.1492 | 0.53× | 189% | +|🟡| np.mean axis=0 (float16) | float16 | 10,000,000 | 7.3133 | 14.1963 | 0.52× | 194% | +|✅| np.mean axis=0 (float32) | float32 | 1,000 | 0.0039 | 0.0022 | 1.82× | 55% | +|🟡| np.mean axis=0 (float32) | float32 | 100,000 | 0.0086 | 0.0099 | 0.87× | 115% | +|✅| np.mean axis=0 (float32) | float32 | 10,000,000 | 1.4901 | 1.3911 | 1.07× | 93% | +|✅| np.mean axis=0 (float64) | float64 | 1,000 | 0.0030 | 0.0023 | 1.29× | 78% | +|✅| np.mean axis=0 (float64) | float64 | 100,000 | 0.0166 | 0.0148 | 1.12× | 89% | +|🟡| np.mean axis=0 (float64) | float64 | 10,000,000 | 3.3836 | 3.4775 | 0.97× | 103% | +|▫| np.mean axis=0 (int16) | int16 | 1,000 | 0.0038 | 0.0008 | 4.83× | 21% | +|✅| np.mean axis=0 (int16) | int16 | 100,000 | 0.0550 | 0.0084 | 6.53× | 15% | +|✅| np.mean axis=0 (int16) | int16 | 10,000,000 | 5.3822 | 0.9630 | 5.59× | 18% | +|▫| np.mean axis=0 (int32) | int32 | 1,000 | 0.0044 | 0.0008 | 5.78× | 17% | +|✅| np.mean axis=0 (int32) | int32 | 100,000 | 0.0359 | 0.0078 | 4.60× | 22% | +|✅| np.mean axis=0 (int32) | int32 | 10,000,000 | 4.5299 | 1.7225 | 2.63× | 38% | +|✅| np.mean axis=0 (int64) | int64 | 1,000 | 0.0043 | 0.0013 | 3.25× | 31% | +|🟡| np.mean axis=0 (int64) | int64 | 100,000 | 0.0320 | 0.0609 | 0.53× | 190% | +|🔴| np.mean axis=0 (int64) | int64 | 10,000,000 | 5.9854 | 37.4022 | 0.16× | 625% | +|▫| np.mean axis=0 (int8) | int8 | 1,000 | 0.0038 | 0.0008 | 4.56× | 22% | +|✅| np.mean axis=0 (int8) | int8 | 100,000 | 0.0534 | 0.0082 | 6.49× | 15% | +|✅| np.mean axis=0 (int8) | int8 | 10,000,000 | 6.4497 | 0.8079 | 7.98× | 12% | +|▫| np.mean axis=0 (uint16) | uint16 | 1,000 | 0.0037 | 0.0007 | 5.00× | 20% | +|✅| np.mean axis=0 (uint16) | uint16 | 100,000 | 0.0516 | 0.0084 | 6.10× | 16% | +|✅| np.mean axis=0 (uint16) | uint16 | 10,000,000 | 5.8879 | 0.9392 | 6.27× | 16% | +|▫| np.mean axis=0 (uint32) | uint32 | 1,000 | 0.0044 | 0.0008 | 5.64× | 18% | +|✅| np.mean axis=0 (uint32) | uint32 | 100,000 | 0.0471 | 0.0093 | 5.04× | 20% | +|✅| np.mean axis=0 (uint32) | uint32 | 10,000,000 | 4.9815 | 2.0165 | 2.47× | 40% | +|✅| np.mean axis=0 (uint64) | uint64 | 1,000 | 0.0037 | 0.0017 | 2.20× | 46% | +|🟡| np.mean axis=0 (uint64) | uint64 | 100,000 | 0.0479 | 0.0887 | 0.54× | 185% | +|🔴| np.mean axis=0 (uint64) | uint64 | 10,000,000 | 7.1495 | 43.0435 | 0.17× | 602% | +|▫| np.mean axis=0 (uint8) | uint8 | 1,000 | 0.0039 | 0.0007 | 5.20× | 19% | +|✅| np.mean axis=0 (uint8) | uint8 | 100,000 | 0.0545 | 0.0082 | 6.63× | 15% | +|✅| np.mean axis=0 (uint8) | uint8 | 10,000,000 | 5.3637 | 0.7883 | 6.80× | 15% | +|✅| np.mean axis=1 (complex128) | complex128 | 1,000 | 0.0032 | 0.0025 | 1.24× | 81% | +|✅| np.mean axis=1 (complex128) | complex128 | 100,000 | 0.0443 | 0.0348 | 1.27× | 79% | +|✅| np.mean axis=1 (complex128) | complex128 | 10,000,000 | 8.4055 | 8.0660 | 1.04× | 96% | +|✅| np.mean axis=1 (float16) | float16 | 1,000 | 0.0052 | 0.0039 | 1.31× | 76% | +|🟡| np.mean axis=1 (float16) | float16 | 100,000 | 0.0952 | 0.1325 | 0.72× | 139% | +|🟡| np.mean axis=1 (float16) | float16 | 10,000,000 | 7.9626 | 12.5326 | 0.64× | 157% | +|✅| np.mean axis=1 (float32) | float32 | 1,000 | 0.0036 | 0.0022 | 1.67× | 60% | +|✅| np.mean axis=1 (float32) | float32 | 100,000 | 0.0183 | 0.0127 | 1.45× | 69% | +|✅| np.mean axis=1 (float32) | float32 | 10,000,000 | 2.9697 | 1.9066 | 1.56× | 64% | +|✅| np.mean axis=1 (float64) | float64 | 1,000 | 0.0029 | 0.0022 | 1.33× | 75% | +|✅| np.mean axis=1 (float64) | float64 | 100,000 | 0.0198 | 0.0144 | 1.37× | 73% | +|✅| np.mean axis=1 (float64) | float64 | 10,000,000 | 5.1086 | 3.6854 | 1.39× | 72% | +|▫| np.mean axis=1 (int16) | int16 | 1,000 | 0.0037 | 0.0008 | 4.55× | 22% | +|✅| np.mean axis=1 (int16) | int16 | 100,000 | 0.0573 | 0.0080 | 7.19× | 14% | +|✅| np.mean axis=1 (int16) | int16 | 10,000,000 | 6.0592 | 0.8322 | 7.28× | 14% | +|▫| np.mean axis=1 (int32) | int32 | 1,000 | 0.0037 | 0.0007 | 5.00× | 20% | +|✅| np.mean axis=1 (int32) | int32 | 100,000 | 0.0428 | 0.0049 | 8.79× | 11% | +|✅| np.mean axis=1 (int32) | int32 | 10,000,000 | 4.8185 | 1.4394 | 3.35× | 30% | +|✅| np.mean axis=1 (int64) | int64 | 1,000 | 0.0034 | 0.0013 | 2.55× | 39% | +|🟡| np.mean axis=1 (int64) | int64 | 100,000 | 0.0394 | 0.0596 | 0.66× | 151% | +|🟡| np.mean axis=1 (int64) | int64 | 10,000,000 | 6.3625 | 6.9185 | 0.92× | 109% | +|▫| np.mean axis=1 (int8) | int8 | 1,000 | 0.0036 | 0.0008 | 4.50× | 22% | +|✅| np.mean axis=1 (int8) | int8 | 100,000 | 0.0571 | 0.0079 | 7.21× | 14% | +|✅| np.mean axis=1 (int8) | int8 | 10,000,000 | 5.5023 | 0.7110 | 7.74× | 13% | +|▫| np.mean axis=1 (uint16) | uint16 | 1,000 | 0.0038 | 0.0008 | 4.68× | 21% | +|✅| np.mean axis=1 (uint16) | uint16 | 100,000 | 0.0629 | 0.0080 | 7.90× | 13% | +|✅| np.mean axis=1 (uint16) | uint16 | 10,000,000 | 5.4615 | 0.8406 | 6.50× | 15% | +|▫| np.mean axis=1 (uint32) | uint32 | 1,000 | 0.0034 | 0.0008 | 4.12× | 24% | +|✅| np.mean axis=1 (uint32) | uint32 | 100,000 | 0.0467 | 0.0091 | 5.12× | 20% | +|✅| np.mean axis=1 (uint32) | uint32 | 10,000,000 | 4.9555 | 1.8041 | 2.75× | 36% | +|✅| np.mean axis=1 (uint64) | uint64 | 1,000 | 0.0037 | 0.0016 | 2.23× | 45% | +|🟡| np.mean axis=1 (uint64) | uint64 | 100,000 | 0.0569 | 0.0860 | 0.66× | 151% | +|🟡| np.mean axis=1 (uint64) | uint64 | 10,000,000 | 7.3817 | 9.0110 | 0.82× | 122% | +|▫| np.mean axis=1 (uint8) | uint8 | 1,000 | 0.0035 | 0.0008 | 4.50× | 22% | +|✅| np.mean axis=1 (uint8) | uint8 | 100,000 | 0.0587 | 0.0079 | 7.42× | 14% | +|✅| np.mean axis=1 (uint8) | uint8 | 10,000,000 | 5.2924 | 0.7077 | 7.48× | 13% | +|✅| np.nanmax(a) (float16) | float16 | 1,000 | 0.0054 | 0.0011 | 4.87× | 21% | +|✅| np.nanmax(a) (float16) | float16 | 100,000 | 0.5409 | 0.3172 | 1.71× | 59% | +|✅| np.nanmax(a) (float16) | float16 | 10,000,000 | 51.2440 | 32.5673 | 1.57× | 64% | +|▫| np.nanmax(a) (float32) | float32 | 1,000 | 0.0030 | 0.0009 | 3.29× | 30% | +|🟠| np.nanmax(a) (float32) | float32 | 100,000 | 0.0081 | 0.0287 | 0.28× | 356% | +|🟠| np.nanmax(a) (float32) | float32 | 10,000,000 | 1.5124 | 3.2798 | 0.46× | 217% | +|✅| np.nanmax(a) (float64) | float64 | 1,000 | 0.0030 | 0.0012 | 2.52× | 40% | +|🔴| np.nanmax(a) (float64) | float64 | 100,000 | 0.0115 | 0.0590 | 0.20× | 512% | +|🟡| np.nanmax(a) (float64) | float64 | 10,000,000 | 3.5081 | 6.8740 | 0.51× | 196% | +|✅| np.nanmean(a) (float16) | float16 | 1,000 | 0.0127 | 0.0017 | 7.37× | 14% | +|✅| np.nanmean(a) (float16) | float16 | 100,000 | 0.3723 | 0.1054 | 3.53× | 28% | +|✅| np.nanmean(a) (float16) | float16 | 10,000,000 | 37.8624 | 10.5382 | 3.59× | 28% | +|▫| np.nanmean(a) (float32) | float32 | 1,000 | 0.0102 | 0.0008 | 12.61× | 8% | +|✅| np.nanmean(a) (float32) | float32 | 100,000 | 0.0751 | 0.0383 | 1.96× | 51% | +|✅| np.nanmean(a) (float32) | float32 | 10,000,000 | 19.3182 | 4.1701 | 4.63× | 22% | +|✅| np.nanmean(a) (float64) | float64 | 1,000 | 0.0088 | 0.0012 | 7.49× | 13% | +|✅| np.nanmean(a) (float64) | float64 | 100,000 | 0.3101 | 0.0378 | 8.21× | 12% | +|✅| np.nanmean(a) (float64) | float64 | 10,000,000 | 29.7719 | 5.5602 | 5.35× | 19% | +|✅| np.nanmedian(a) (float16) | float16 | 1,000 | 0.0171 | 0.0043 | 3.93× | 26% | +|🟡| np.nanmedian(a) (float16) | float16 | 100,000 | 0.9889 | 1.3085 | 0.76× | 132% | +|✅| np.nanmedian(a) (float16) | float16 | 10,000,000 | 114.7082 | 92.8901 | 1.24× | 81% | +|✅| np.nanmedian(a) (float32) | float32 | 1,000 | 0.0132 | 0.0022 | 5.94× | 17% | +|🟡| np.nanmedian(a) (float32) | float32 | 100,000 | 0.4888 | 0.6965 | 0.70× | 142% | +|🟡| np.nanmedian(a) (float32) | float32 | 10,000,000 | 77.3793 | 80.5553 | 0.96× | 104% | +|✅| np.nanmedian(a) (float64) | float64 | 1,000 | 0.0127 | 0.0023 | 5.54× | 18% | +|🟡| np.nanmedian(a) (float64) | float64 | 100,000 | 0.5210 | 0.7022 | 0.74× | 135% | +|✅| np.nanmedian(a) (float64) | float64 | 10,000,000 | 92.4864 | 89.4458 | 1.03× | 97% | +|✅| np.nanmin(a) (float16) | float16 | 1,000 | 0.0053 | 0.0011 | 4.77× | 21% | +|✅| np.nanmin(a) (float16) | float16 | 100,000 | 0.5322 | 0.3019 | 1.76× | 57% | +|✅| np.nanmin(a) (float16) | float16 | 10,000,000 | 51.4324 | 31.1142 | 1.65× | 60% | +|▫| np.nanmin(a) (float32) | float32 | 1,000 | 0.0030 | 0.0009 | 3.24× | 31% | +|🟠| np.nanmin(a) (float32) | float32 | 100,000 | 0.0072 | 0.0287 | 0.25× | 400% | +|🟠| np.nanmin(a) (float32) | float32 | 10,000,000 | 1.5001 | 3.3155 | 0.45× | 221% | +|▫| np.nanmin(a) (float64) | float64 | 1,000 | 0.0042 | 0.0009 | 4.51× | 22% | +|🔴| np.nanmin(a) (float64) | float64 | 100,000 | 0.0114 | 0.0589 | 0.19× | 519% | +|🟡| np.nanmin(a) (float64) | float64 | 10,000,000 | 3.6863 | 6.8141 | 0.54× | 185% | +|✅| np.nanpercentile(a, 50) (float16) | float16 | 1,000 | 0.0365 | 0.0043 | 8.45× | 12% | +|✅| np.nanpercentile(a, 50) (float16) | float16 | 100,000 | 1.8857 | 1.3044 | 1.45× | 69% | +|✅| np.nanpercentile(a, 50) (float16) | float16 | 10,000,000 | 120.9498 | 92.9097 | 1.30× | 77% | +|✅| np.nanpercentile(a, 50) (float32) | float32 | 1,000 | 0.0267 | 0.0022 | 11.99× | 8% | +|✅| np.nanpercentile(a, 50) (float32) | float32 | 100,000 | 0.7602 | 0.6960 | 1.09× | 92% | +|🟡| np.nanpercentile(a, 50) (float32) | float32 | 10,000,000 | 51.8789 | 80.6571 | 0.64× | 156% | +|✅| np.nanpercentile(a, 50) (float64) | float64 | 1,000 | 0.0265 | 0.0023 | 11.56× | 9% | +|✅| np.nanpercentile(a, 50) (float64) | float64 | 100,000 | 0.7628 | 0.7034 | 1.08× | 92% | +|🟡| np.nanpercentile(a, 50) (float64) | float64 | 10,000,000 | 63.5626 | 89.5666 | 0.71× | 141% | +|✅| np.nanprod(a) (float16) | float16 | 1,000 | 0.0061 | 0.0013 | 4.73× | 21% | +|✅| np.nanprod(a) (float16) | float16 | 100,000 | 0.1701 | 0.0886 | 1.92× | 52% | +|✅| np.nanprod(a) (float16) | float16 | 10,000,000 | 20.5179 | 8.9271 | 2.30× | 44% | +|▫| np.nanprod(a) (float32) | float32 | 1,000 | 0.0052 | 0.0008 | 6.31× | 16% | +|✅| np.nanprod(a) (float32) | float32 | 100,000 | 0.1604 | 0.0119 | 13.53× | 7% | +|✅| np.nanprod(a) (float32) | float32 | 10,000,000 | 18.0353 | 1.7540 | 10.28× | 10% | +|▫| np.nanprod(a) (float64) | float64 | 1,000 | 0.0055 | 0.0008 | 6.96× | 14% | +|✅| np.nanprod(a) (float64) | float64 | 100,000 | 0.1079 | 0.0235 | 4.60× | 22% | +|✅| np.nanprod(a) (float64) | float64 | 10,000,000 | 28.6688 | 3.9531 | 7.25× | 14% | +|✅| np.nanquantile(a, 0.5) (float16) | float16 | 1,000 | 0.0336 | 0.0043 | 7.73× | 13% | +|✅| np.nanquantile(a, 0.5) (float16) | float16 | 100,000 | 1.8626 | 1.3071 | 1.43× | 70% | +|✅| np.nanquantile(a, 0.5) (float16) | float16 | 10,000,000 | 121.1817 | 92.8534 | 1.30× | 77% | +|✅| np.nanquantile(a, 0.5) (float32) | float32 | 1,000 | 0.0280 | 0.0022 | 12.60× | 8% | +|✅| np.nanquantile(a, 0.5) (float32) | float32 | 100,000 | 0.7231 | 0.6973 | 1.04× | 96% | +|🟡| np.nanquantile(a, 0.5) (float32) | float32 | 10,000,000 | 51.1057 | 80.6773 | 0.63× | 158% | +|✅| np.nanquantile(a, 0.5) (float64) | float64 | 1,000 | 0.0257 | 0.0023 | 11.24× | 9% | +|✅| np.nanquantile(a, 0.5) (float64) | float64 | 100,000 | 0.7484 | 0.7037 | 1.06× | 94% | +|🟡| np.nanquantile(a, 0.5) (float64) | float64 | 10,000,000 | 64.0584 | 89.4386 | 0.72× | 140% | +|✅| np.nanstd(a) (float16) | float16 | 1,000 | 0.0341 | 0.0028 | 12.26× | 8% | +|✅| np.nanstd(a) (float16) | float16 | 100,000 | 1.2279 | 0.2159 | 5.69× | 18% | +|✅| np.nanstd(a) (float16) | float16 | 10,000,000 | 119.9407 | 21.6231 | 5.55× | 18% | +|✅| np.nanstd(a) (float32) | float32 | 1,000 | 0.0202 | 0.0017 | 11.92× | 8% | +|✅| np.nanstd(a) (float32) | float32 | 100,000 | 0.1683 | 0.0864 | 1.95× | 51% | +|✅| np.nanstd(a) (float32) | float32 | 10,000,000 | 31.9536 | 9.2060 | 3.47× | 29% | +|✅| np.nanstd(a) (float64) | float64 | 1,000 | 0.0190 | 0.0015 | 12.52× | 8% | +|✅| np.nanstd(a) (float64) | float64 | 100,000 | 0.4497 | 0.0761 | 5.91× | 17% | +|✅| np.nanstd(a) (float64) | float64 | 10,000,000 | 49.6062 | 11.3264 | 4.38× | 23% | +|✅| np.nansum(a) (float16) | float16 | 1,000 | 0.0063 | 0.0013 | 4.84× | 21% | +|✅| np.nansum(a) (float16) | float16 | 100,000 | 0.2885 | 0.0901 | 3.20× | 31% | +|✅| np.nansum(a) (float16) | float16 | 10,000,000 | 31.6077 | 8.9086 | 3.55× | 28% | +|▫| np.nansum(a) (float32) | float32 | 1,000 | 0.0037 | 0.0008 | 4.83× | 21% | +|✅| np.nansum(a) (float32) | float32 | 100,000 | 0.0321 | 0.0049 | 6.51× | 15% | +|✅| np.nansum(a) (float32) | float32 | 10,000,000 | 13.3987 | 1.4131 | 9.48× | 10% | +|▫| np.nansum(a) (float64) | float64 | 1,000 | 0.0037 | 0.0008 | 4.61× | 22% | +|✅| np.nansum(a) (float64) | float64 | 100,000 | 0.0471 | 0.0097 | 4.88× | 20% | +|✅| np.nansum(a) (float64) | float64 | 10,000,000 | 23.8754 | 3.4851 | 6.85× | 15% | +|✅| np.nanvar(a) (float16) | float16 | 1,000 | 0.0324 | 0.0028 | 11.72× | 8% | +|✅| np.nanvar(a) (float16) | float16 | 100,000 | 1.2270 | 0.2160 | 5.68× | 18% | +|✅| np.nanvar(a) (float16) | float16 | 10,000,000 | 120.0573 | 21.5187 | 5.58× | 18% | +|✅| np.nanvar(a) (float32) | float32 | 1,000 | 0.0195 | 0.0017 | 11.49× | 9% | +|✅| np.nanvar(a) (float32) | float32 | 100,000 | 0.1645 | 0.0864 | 1.90× | 52% | +|✅| np.nanvar(a) (float32) | float32 | 10,000,000 | 32.0612 | 9.2119 | 3.48× | 29% | +|✅| np.nanvar(a) (float64) | float64 | 1,000 | 0.0198 | 0.0015 | 13.06× | 8% | +|✅| np.nanvar(a) (float64) | float64 | 100,000 | 0.4356 | 0.0762 | 5.72× | 18% | +|✅| np.nanvar(a) (float64) | float64 | 10,000,000 | 51.1759 | 11.2532 | 4.55× | 22% | +|▫| np.prod (float64) | float64 | 1,000 | 0.0022 | 0.0008 | 2.89× | 35% | +|✅| np.prod (float64) | float64 | 100,000 | 2.3310 | 0.1700 | 13.71× | 7% | +|✅| np.prod (float64) | float64 | 10,000,000 | 239.6474 | 60.4092 | 3.97× | 25% | +|▫| np.prod (int64) | int64 | 1,000 | 0.0021 | 0.0007 | 2.87× | 35% | +|✅| np.prod (int64) | int64 | 100,000 | 0.0572 | 0.0143 | 4.00× | 25% | +|✅| np.prod (int64) | int64 | 10,000,000 | 6.3161 | 3.8044 | 1.66× | 60% | |▫| np.prod axis=0 (float64) | float64 | 1,000 | 0.0019 | 0.0007 | 2.75× | 36% | -|✅| np.prod axis=0 (float64) | float64 | 100,000 | 0.0140 | 0.0099 | 1.41× | 71% | -|✅| np.prod axis=0 (float64) | float64 | 10,000,000 | 22.2109 | 19.6546 | 1.13× | 88% | -|▫| np.prod axis=0 (int64) | int64 | 1,000 | 0.0019 | 0.0008 | 2.35× | 43% | -|✅| np.prod axis=0 (int64) | int64 | 100,000 | 0.0285 | 0.0149 | 1.91× | 52% | -|✅| np.prod axis=0 (int64) | int64 | 10,000,000 | 5.8931 | 4.2660 | 1.38× | 72% | -|▫| np.prod axis=1 (float64) | float64 | 1,000 | 0.0019 | 0.0008 | 2.41× | 42% | -|✅| np.prod axis=1 (float64) | float64 | 100,000 | 0.0576 | 0.0071 | 8.14× | 12% | -|▫| np.prod axis=1 (float64) | float64 | 10,000,000 | 70.8781 | 2.9565 | 23.97× | 4% | -|▫| np.prod axis=1 (int64) | int64 | 1,000 | 0.0019 | 0.0010 | 1.98× | 50% | -|✅| np.prod axis=1 (int64) | int64 | 100,000 | 0.0457 | 0.0171 | 2.67× | 37% | -|✅| np.prod axis=1 (int64) | int64 | 10,000,000 | 6.2300 | 3.8578 | 1.61× | 62% | -|✅| np.std (float16) | float16 | 1,000 | 0.0198 | 0.0021 | 9.46× | 11% | -|✅| np.std (float16) | float16 | 100,000 | 0.9087 | 0.1873 | 4.85× | 21% | -|✅| np.std (float16) | float16 | 10,000,000 | 98.8955 | 18.9743 | 5.21× | 19% | -|▫| np.std (float32) | float32 | 1,000 | 0.0107 | 0.0008 | 12.79× | 8% | -|✅| np.std (float32) | float32 | 100,000 | 0.0453 | 0.0100 | 4.54× | 22% | -|✅| np.std (float32) | float32 | 10,000,000 | 16.0146 | 3.3593 | 4.77× | 21% | -|▫| np.std (float64) | float64 | 1,000 | 0.0066 | 0.0010 | 6.74× | 15% | -|✅| np.std (float64) | float64 | 100,000 | 0.0575 | 0.0202 | 2.84× | 35% | -|✅| np.std (float64) | float64 | 10,000,000 | 30.5191 | 7.2652 | 4.20× | 24% | -|✅| np.std axis=0 (float16) | float16 | 1,000 | 0.0196 | 0.0047 | 4.14× | 24% | -|✅| np.std axis=0 (float16) | float16 | 100,000 | 1.0975 | 0.3754 | 2.92× | 34% | -|✅| np.std axis=0 (float16) | float16 | 10,000,000 | 107.7057 | 88.6112 | 1.22× | 82% | -|✅| np.std axis=0 (float32) | float32 | 1,000 | 0.0085 | 0.0017 | 4.90× | 20% | -|✅| np.std axis=0 (float32) | float32 | 100,000 | 0.0373 | 0.0237 | 1.57× | 64% | -|✅| np.std axis=0 (float32) | float32 | 10,000,000 | 14.4059 | 5.5122 | 2.61× | 38% | -|▫| np.std axis=0 (float64) | float64 | 1,000 | 0.0074 | 0.0010 | 7.55× | 13% | -|✅| np.std axis=0 (float64) | float64 | 100,000 | 0.0685 | 0.0221 | 3.10× | 32% | -|✅| np.std axis=0 (float64) | float64 | 10,000,000 | 39.8234 | 8.1253 | 4.90× | 20% | -|▫| np.sum (complex128) | complex128 | 1,000 | 0.0018 | 0.0010 | 1.84× | 54% | -|✅| np.sum (complex128) | complex128 | 100,000 | 0.0306 | 0.0104 | 2.93× | 34% | -|✅| np.sum (complex128) | complex128 | 10,000,000 | 8.5843 | 7.2562 | 1.18× | 84% | -|✅| np.sum (float16) | float16 | 1,000 | 0.0036 | 0.0012 | 3.02× | 33% | -|✅| np.sum (float16) | float16 | 100,000 | 0.2020 | 0.0835 | 2.42× | 41% | -|✅| np.sum (float16) | float16 | 10,000,000 | 19.9349 | 8.2364 | 2.42× | 41% | -|▫| np.sum (float32) | float32 | 1,000 | 0.0016 | 0.0008 | 2.04× | 49% | -|✅| np.sum (float32) | float32 | 100,000 | 0.0152 | 0.0032 | 4.72× | 21% | -|✅| np.sum (float32) | float32 | 10,000,000 | 2.8487 | 1.3964 | 2.04× | 49% | -|▫| np.sum (float64) | float64 | 1,000 | 0.0017 | 0.0008 | 2.12× | 47% | -|🔴| np.sum (float64) | float64 | 100,000 | 0.0159 | 0.2139 | 0.074× | 1345% | -|✅| np.sum (float64) | float64 | 10,000,000 | 4.8472 | 3.6648 | 1.32× | 76% | -|▫| np.sum (int16) | int16 | 1,000 | 0.0020 | 0.0009 | 2.21× | 45% | -|✅| np.sum (int16) | int16 | 100,000 | 0.0328 | 0.0193 | 1.70× | 59% | -|✅| np.sum (int16) | int16 | 10,000,000 | 3.2851 | 2.1615 | 1.52× | 66% | -|▫| np.sum (int32) | int32 | 1,000 | 0.0022 | 0.0009 | 2.61× | 38% | -|✅| np.sum (int32) | int32 | 100,000 | 0.0377 | 0.0193 | 1.96× | 51% | -|✅| np.sum (int32) | int32 | 10,000,000 | 3.9883 | 2.8904 | 1.38× | 72% | -|▫| np.sum (int64) | int64 | 1,000 | 0.0017 | 0.0007 | 2.48× | 40% | -|✅| np.sum (int64) | int64 | 100,000 | 0.0181 | 0.0065 | 2.79× | 36% | -|✅| np.sum (int64) | int64 | 10,000,000 | 4.3268 | 3.2874 | 1.32× | 76% | -|▫| np.sum (int8) | int8 | 1,000 | 0.0021 | 0.0008 | 2.51× | 40% | -|✅| np.sum (int8) | int8 | 100,000 | 0.0333 | 0.0188 | 1.77× | 56% | -|✅| np.sum (int8) | int8 | 10,000,000 | 3.1769 | 1.8950 | 1.68× | 60% | -|▫| np.sum (uint16) | uint16 | 1,000 | 0.0020 | 0.0010 | 2.09× | 48% | -|✅| np.sum (uint16) | uint16 | 100,000 | 0.0334 | 0.0193 | 1.73× | 58% | -|✅| np.sum (uint16) | uint16 | 10,000,000 | 3.3411 | 2.0295 | 1.65× | 61% | -|▫| np.sum (uint32) | uint32 | 1,000 | 0.0021 | 0.0008 | 2.47× | 40% | -|✅| np.sum (uint32) | uint32 | 100,000 | 0.0328 | 0.0194 | 1.69× | 59% | -|✅| np.sum (uint32) | uint32 | 10,000,000 | 4.0355 | 2.9673 | 1.36× | 74% | -|▫| np.sum (uint64) | uint64 | 1,000 | 0.0017 | 0.0008 | 2.29× | 44% | -|✅| np.sum (uint64) | uint64 | 100,000 | 0.0200 | 0.0064 | 3.12× | 32% | -|✅| np.sum (uint64) | uint64 | 10,000,000 | 4.8125 | 3.4080 | 1.41× | 71% | -|▫| np.sum (uint8) | uint8 | 1,000 | 0.0023 | 0.0009 | 2.56× | 39% | -|✅| np.sum (uint8) | uint8 | 100,000 | 0.0349 | 0.0189 | 1.85× | 54% | -|✅| np.sum (uint8) | uint8 | 10,000,000 | 3.1844 | 1.9225 | 1.66× | 60% | -|✅| np.sum axis=0 (complex128) | complex128 | 1,000 | 0.0020 | 0.0017 | 1.19× | 84% | -|🟡| np.sum axis=0 (complex128) | complex128 | 100,000 | 0.0174 | 0.0207 | 0.84× | 119% | -|🟡| np.sum axis=0 (complex128) | complex128 | 10,000,000 | 7.4801 | 7.6616 | 0.98× | 102% | -|✅| np.sum axis=0 (float16) | float16 | 1,000 | 0.0049 | 0.0040 | 1.22× | 82% | -|✅| np.sum axis=0 (float16) | float16 | 100,000 | 0.2901 | 0.1489 | 1.95× | 51% | -|✅| np.sum axis=0 (float16) | float16 | 10,000,000 | 29.7526 | 14.4612 | 2.06× | 49% | -|🟡| np.sum axis=0 (float32) | float32 | 1,000 | 0.0019 | 0.0019 | 0.98× | 102% | -|✅| np.sum axis=0 (float32) | float32 | 100,000 | 0.0081 | 0.0079 | 1.02× | 98% | -|🟡| np.sum axis=0 (float32) | float32 | 10,000,000 | 1.3909 | 1.4413 | 0.96× | 104% | -|🟡| np.sum axis=0 (float64) | float64 | 1,000 | 0.0019 | 0.0020 | 0.97× | 103% | -|🟡| np.sum axis=0 (float64) | float64 | 100,000 | 0.0122 | 0.0129 | 0.95× | 106% | -|✅| np.sum axis=0 (float64) | float64 | 10,000,000 | 3.6033 | 3.5571 | 1.01× | 99% | -|✅| np.sum axis=0 (int16) | int16 | 1,000 | 0.0024 | 0.0010 | 2.33× | 43% | -|✅| np.sum axis=0 (int16) | int16 | 100,000 | 0.0484 | 0.0047 | 10.34× | 10% | -|✅| np.sum axis=0 (int16) | int16 | 10,000,000 | 4.6103 | 0.7373 | 6.25× | 16% | -|▫| np.sum axis=0 (int32) | int32 | 1,000 | 0.0025 | 0.0007 | 3.51× | 28% | -|✅| np.sum axis=0 (int32) | int32 | 100,000 | 0.0479 | 0.0086 | 5.59× | 18% | -|✅| np.sum axis=0 (int32) | int32 | 10,000,000 | 5.3488 | 1.9895 | 2.69× | 37% | -|▫| np.sum axis=0 (int64) | int64 | 1,000 | 0.0020 | 0.0007 | 2.72× | 37% | -|✅| np.sum axis=0 (int64) | int64 | 100,000 | 0.0294 | 0.0104 | 2.84× | 35% | -|✅| np.sum axis=0 (int64) | int64 | 10,000,000 | 5.9812 | 3.4043 | 1.76× | 57% | -|▫| np.sum axis=0 (int8) | int8 | 1,000 | 0.0024 | 0.0009 | 2.72× | 37% | -|✅| np.sum axis=0 (int8) | int8 | 100,000 | 0.0467 | 0.0043 | 10.78× | 9% | -|✅| np.sum axis=0 (int8) | int8 | 10,000,000 | 4.4559 | 0.3992 | 11.16× | 9% | -|▫| np.sum axis=0 (uint16) | uint16 | 1,000 | 0.0024 | 0.0010 | 2.48× | 40% | -|✅| np.sum axis=0 (uint16) | uint16 | 100,000 | 0.0471 | 0.0047 | 10.03× | 10% | -|✅| np.sum axis=0 (uint16) | uint16 | 10,000,000 | 4.6567 | 0.7216 | 6.45× | 16% | -|▫| np.sum axis=0 (uint32) | uint32 | 1,000 | 0.0024 | 0.0007 | 3.63× | 28% | -|✅| np.sum axis=0 (uint32) | uint32 | 100,000 | 0.0475 | 0.0086 | 5.52× | 18% | -|✅| np.sum axis=0 (uint32) | uint32 | 10,000,000 | 5.3266 | 1.9748 | 2.70× | 37% | -|▫| np.sum axis=0 (uint64) | uint64 | 1,000 | 0.0020 | 0.0007 | 2.88× | 35% | -|✅| np.sum axis=0 (uint64) | uint64 | 100,000 | 0.0277 | 0.0104 | 2.67× | 37% | -|✅| np.sum axis=0 (uint64) | uint64 | 10,000,000 | 5.3910 | 3.4393 | 1.57× | 64% | -|▫| np.sum axis=0 (uint8) | uint8 | 1,000 | 0.0026 | 0.0010 | 2.63× | 38% | -|✅| np.sum axis=0 (uint8) | uint8 | 100,000 | 0.0474 | 0.0050 | 9.40× | 11% | -|✅| np.sum axis=0 (uint8) | uint8 | 10,000,000 | 4.4480 | 0.5649 | 7.87× | 13% | -|✅| np.sum axis=1 (complex128) | complex128 | 1,000 | 0.0021 | 0.0018 | 1.11× | 90% | -|✅| np.sum axis=1 (complex128) | complex128 | 100,000 | 0.0324 | 0.0313 | 1.03× | 97% | -|🟡| np.sum axis=1 (complex128) | complex128 | 10,000,000 | 8.5166 | 8.9144 | 0.95× | 105% | -|✅| np.sum axis=1 (float16) | float16 | 1,000 | 0.0038 | 0.0038 | 1.02× | 98% | -|✅| np.sum axis=1 (float16) | float16 | 100,000 | 0.2099 | 0.1355 | 1.55× | 64% | -|✅| np.sum axis=1 (float16) | float16 | 10,000,000 | 19.3738 | 13.2482 | 1.46× | 68% | -|🟡| np.sum axis=1 (float32) | float32 | 1,000 | 0.0018 | 0.0020 | 0.94× | 106% | -|✅| np.sum axis=1 (float32) | float32 | 100,000 | 0.0168 | 0.0128 | 1.32× | 76% | -|✅| np.sum axis=1 (float32) | float32 | 10,000,000 | 3.1793 | 2.0144 | 1.58× | 63% | -|🟡| np.sum axis=1 (float64) | float64 | 1,000 | 0.0019 | 0.0019 | 0.96× | 104% | -|✅| np.sum axis=1 (float64) | float64 | 100,000 | 0.0175 | 0.0132 | 1.33× | 75% | -|✅| np.sum axis=1 (float64) | float64 | 10,000,000 | 5.1383 | 3.9398 | 1.30× | 77% | -|▫| np.sum axis=1 (int16) | int16 | 1,000 | 0.0023 | 0.0007 | 3.34× | 30% | -|✅| np.sum axis=1 (int16) | int16 | 100,000 | 0.0372 | 0.0038 | 9.75× | 10% | -|✅| np.sum axis=1 (int16) | int16 | 10,000,000 | 3.3329 | 0.6803 | 4.90× | 20% | -|▫| np.sum axis=1 (int32) | int32 | 1,000 | 0.0025 | 0.0007 | 3.60× | 28% | -|✅| np.sum axis=1 (int32) | int32 | 100,000 | 0.0364 | 0.0053 | 6.86× | 15% | -|✅| np.sum axis=1 (int32) | int32 | 10,000,000 | 4.0800 | 1.7611 | 2.32× | 43% | +|✅| np.prod axis=0 (float64) | float64 | 100,000 | 0.0136 | 0.0089 | 1.54× | 65% | +|🟡| np.prod axis=0 (float64) | float64 | 10,000,000 | 19.8072 | 19.8419 | 1.00× | 100% | +|▫| np.prod axis=0 (int64) | int64 | 1,000 | 0.0020 | 0.0009 | 2.24× | 45% | +|✅| np.prod axis=0 (int64) | int64 | 100,000 | 0.0272 | 0.0152 | 1.79× | 56% | +|✅| np.prod axis=0 (int64) | int64 | 10,000,000 | 5.0107 | 4.1147 | 1.22× | 82% | +|▫| np.prod axis=1 (float64) | float64 | 1,000 | 0.0019 | 0.0008 | 2.47× | 40% | +|✅| np.prod axis=1 (float64) | float64 | 100,000 | 0.0601 | 0.0050 | 12.13× | 8% | +|▫| np.prod axis=1 (float64) | float64 | 10,000,000 | 69.9249 | 3.0274 | 23.10× | 4% | +|▫| np.prod axis=1 (int64) | int64 | 1,000 | 0.0019 | 0.0010 | 1.96× | 51% | +|✅| np.prod axis=1 (int64) | int64 | 100,000 | 0.0458 | 0.0171 | 2.68× | 37% | +|✅| np.prod axis=1 (int64) | int64 | 10,000,000 | 6.2759 | 3.9188 | 1.60× | 62% | +|✅| np.std (float16) | float16 | 1,000 | 0.0203 | 0.0021 | 9.63× | 10% | +|✅| np.std (float16) | float16 | 100,000 | 0.9085 | 0.1830 | 4.96× | 20% | +|✅| np.std (float16) | float16 | 10,000,000 | 90.8149 | 18.0508 | 5.03× | 20% | +|▫| np.std (float32) | float32 | 1,000 | 0.0086 | 0.0007 | 12.74× | 8% | +|✅| np.std (float32) | float32 | 100,000 | 0.0450 | 0.0096 | 4.67× | 21% | +|✅| np.std (float32) | float32 | 10,000,000 | 17.8322 | 2.6281 | 6.79× | 15% | +|▫| np.std (float64) | float64 | 1,000 | 0.0068 | 0.0004 | 15.61× | 6% | +|✅| np.std (float64) | float64 | 100,000 | 0.0570 | 0.0190 | 3.00× | 33% | +|✅| np.std (float64) | float64 | 10,000,000 | 30.3403 | 6.6087 | 4.59× | 22% | +|✅| np.std axis=0 (float16) | float16 | 1,000 | 0.0214 | 0.0045 | 4.78× | 21% | +|✅| np.std axis=0 (float16) | float16 | 100,000 | 1.1043 | 0.3567 | 3.10× | 32% | +|✅| np.std axis=0 (float16) | float16 | 10,000,000 | 110.0006 | 83.8826 | 1.31× | 76% | +|✅| np.std axis=0 (float32) | float32 | 1,000 | 0.0087 | 0.0016 | 5.57× | 18% | +|✅| np.std axis=0 (float32) | float32 | 100,000 | 0.0377 | 0.0231 | 1.63× | 61% | +|✅| np.std axis=0 (float32) | float32 | 10,000,000 | 14.2545 | 4.6258 | 3.08× | 32% | +|▫| np.std axis=0 (float64) | float64 | 1,000 | 0.0076 | 0.0009 | 8.36× | 12% | +|✅| np.std axis=0 (float64) | float64 | 100,000 | 0.0637 | 0.0263 | 2.42× | 41% | +|✅| np.std axis=0 (float64) | float64 | 10,000,000 | 30.0409 | 7.5189 | 4.00× | 25% | +|▫| np.sum (complex128) | complex128 | 1,000 | 0.0018 | 0.0009 | 2.02× | 50% | +|✅| np.sum (complex128) | complex128 | 100,000 | 0.0294 | 0.0101 | 2.91× | 34% | +|✅| np.sum (complex128) | complex128 | 10,000,000 | 8.9925 | 6.4940 | 1.39× | 72% | +|✅| np.sum (float16) | float16 | 1,000 | 0.0040 | 0.0012 | 3.33× | 30% | +|✅| np.sum (float16) | float16 | 100,000 | 0.1981 | 0.0802 | 2.47× | 40% | +|✅| np.sum (float16) | float16 | 10,000,000 | 19.9449 | 8.0365 | 2.48× | 40% | +|▫| np.sum (float32) | float32 | 1,000 | 0.0017 | 0.0008 | 2.26× | 44% | +|✅| np.sum (float32) | float32 | 100,000 | 0.0171 | 0.0032 | 5.40× | 18% | +|✅| np.sum (float32) | float32 | 10,000,000 | 3.0579 | 1.0257 | 2.98× | 34% | +|▫| np.sum (float64) | float64 | 1,000 | 0.0017 | 0.0008 | 2.24× | 45% | +|🔴| np.sum (float64) | float64 | 100,000 | 0.0166 | 0.2090 | 0.079× | 1260% | +|✅| np.sum (float64) | float64 | 10,000,000 | 4.8117 | 2.8908 | 1.66× | 60% | +|▫| np.sum (int16) | int16 | 1,000 | 0.0020 | 0.0008 | 2.55× | 39% | +|✅| np.sum (int16) | int16 | 100,000 | 0.0339 | 0.0189 | 1.79× | 56% | +|✅| np.sum (int16) | int16 | 10,000,000 | 3.5547 | 1.9468 | 1.83× | 55% | +|▫| np.sum (int32) | int32 | 1,000 | 0.0023 | 0.0008 | 2.85× | 35% | +|✅| np.sum (int32) | int32 | 100,000 | 0.0345 | 0.0190 | 1.82× | 55% | +|✅| np.sum (int32) | int32 | 10,000,000 | 4.7348 | 2.6909 | 1.76× | 57% | +|▫| np.sum (int64) | int64 | 1,000 | 0.0018 | 0.0008 | 2.27× | 44% | +|✅| np.sum (int64) | int64 | 100,000 | 0.0153 | 0.0045 | 3.42× | 29% | +|✅| np.sum (int64) | int64 | 10,000,000 | 4.1490 | 2.8310 | 1.47× | 68% | +|▫| np.sum (int8) | int8 | 1,000 | 0.0022 | 0.0008 | 2.77× | 36% | +|✅| np.sum (int8) | int8 | 100,000 | 0.0342 | 0.0186 | 1.84× | 54% | +|✅| np.sum (int8) | int8 | 10,000,000 | 3.2333 | 1.8404 | 1.76× | 57% | +|▫| np.sum (uint16) | uint16 | 1,000 | 0.0022 | 0.0008 | 2.84× | 35% | +|✅| np.sum (uint16) | uint16 | 100,000 | 0.0334 | 0.0189 | 1.77× | 57% | +|✅| np.sum (uint16) | uint16 | 10,000,000 | 3.7579 | 1.9368 | 1.94× | 52% | +|▫| np.sum (uint32) | uint32 | 1,000 | 0.0023 | 0.0008 | 2.83× | 35% | +|✅| np.sum (uint32) | uint32 | 100,000 | 0.0332 | 0.0190 | 1.75× | 57% | +|✅| np.sum (uint32) | uint32 | 10,000,000 | 4.0560 | 2.6922 | 1.51× | 66% | +|▫| np.sum (uint64) | uint64 | 1,000 | 0.0018 | 0.0008 | 2.28× | 44% | +|✅| np.sum (uint64) | uint64 | 100,000 | 0.0200 | 0.0044 | 4.52× | 22% | +|✅| np.sum (uint64) | uint64 | 10,000,000 | 4.2549 | 2.9226 | 1.46× | 69% | +|▫| np.sum (uint8) | uint8 | 1,000 | 0.0024 | 0.0008 | 3.07× | 32% | +|✅| np.sum (uint8) | uint8 | 100,000 | 0.0348 | 0.0186 | 1.87× | 54% | +|✅| np.sum (uint8) | uint8 | 10,000,000 | 3.2589 | 1.8460 | 1.76× | 57% | +|✅| np.sum axis=0 (complex128) | complex128 | 1,000 | 0.0020 | 0.0017 | 1.16× | 86% | +|✅| np.sum axis=0 (complex128) | complex128 | 100,000 | 0.0309 | 0.0204 | 1.51× | 66% | +|✅| np.sum axis=0 (complex128) | complex128 | 10,000,000 | 7.4501 | 7.3478 | 1.01× | 99% | +|✅| np.sum axis=0 (float16) | float16 | 1,000 | 0.0053 | 0.0040 | 1.32× | 76% | +|✅| np.sum axis=0 (float16) | float16 | 100,000 | 0.2950 | 0.1473 | 2.00× | 50% | +|✅| np.sum axis=0 (float16) | float16 | 10,000,000 | 29.0440 | 14.1979 | 2.05× | 49% | +|✅| np.sum axis=0 (float32) | float32 | 1,000 | 0.0022 | 0.0019 | 1.18× | 85% | +|✅| np.sum axis=0 (float32) | float32 | 100,000 | 0.0083 | 0.0080 | 1.04× | 96% | +|✅| np.sum axis=0 (float32) | float32 | 10,000,000 | 1.8895 | 1.3370 | 1.41× | 71% | +|✅| np.sum axis=0 (float64) | float64 | 1,000 | 0.0020 | 0.0019 | 1.03× | 97% | +|✅| np.sum axis=0 (float64) | float64 | 100,000 | 0.0142 | 0.0115 | 1.23× | 81% | +|✅| np.sum axis=0 (float64) | float64 | 10,000,000 | 3.4762 | 3.4200 | 1.02× | 98% | +|▫| np.sum axis=0 (int16) | int16 | 1,000 | 0.0025 | 0.0009 | 2.70× | 37% | +|✅| np.sum axis=0 (int16) | int16 | 100,000 | 0.0474 | 0.0047 | 10.08× | 10% | +|✅| np.sum axis=0 (int16) | int16 | 10,000,000 | 5.0614 | 0.5369 | 9.43× | 11% | +|▫| np.sum axis=0 (int32) | int32 | 1,000 | 0.0028 | 0.0007 | 3.80× | 26% | +|✅| np.sum axis=0 (int32) | int32 | 100,000 | 0.0485 | 0.0086 | 5.62× | 18% | +|✅| np.sum axis=0 (int32) | int32 | 10,000,000 | 5.6694 | 1.8929 | 3.00× | 33% | +|▫| np.sum axis=0 (int64) | int64 | 1,000 | 0.0020 | 0.0007 | 2.74× | 36% | +|✅| np.sum axis=0 (int64) | int64 | 100,000 | 0.0278 | 0.0104 | 2.67× | 37% | +|✅| np.sum axis=0 (int64) | int64 | 10,000,000 | 5.6011 | 3.2402 | 1.73× | 58% | +|▫| np.sum axis=0 (int8) | int8 | 1,000 | 0.0024 | 0.0009 | 2.52× | 40% | +|✅| np.sum axis=0 (int8) | int8 | 100,000 | 0.0474 | 0.0044 | 10.75× | 9% | +|✅| np.sum axis=0 (int8) | int8 | 10,000,000 | 4.6440 | 0.5247 | 8.85× | 11% | +|▫| np.sum axis=0 (uint16) | uint16 | 1,000 | 0.0025 | 0.0009 | 2.71× | 37% | +|✅| np.sum axis=0 (uint16) | uint16 | 100,000 | 0.0479 | 0.0053 | 9.02× | 11% | +|✅| np.sum axis=0 (uint16) | uint16 | 10,000,000 | 4.7415 | 0.6972 | 6.80× | 15% | +|▫| np.sum axis=0 (uint32) | uint32 | 1,000 | 0.0028 | 0.0007 | 3.86× | 26% | +|✅| np.sum axis=0 (uint32) | uint32 | 100,000 | 0.0506 | 0.0086 | 5.87× | 17% | +|✅| np.sum axis=0 (uint32) | uint32 | 10,000,000 | 5.5471 | 1.7971 | 3.09× | 32% | +|▫| np.sum axis=0 (uint64) | uint64 | 1,000 | 0.0020 | 0.0007 | 2.73× | 37% | +|✅| np.sum axis=0 (uint64) | uint64 | 100,000 | 0.0274 | 0.0104 | 2.64× | 38% | +|✅| np.sum axis=0 (uint64) | uint64 | 10,000,000 | 5.1406 | 3.2350 | 1.59× | 63% | +|▫| np.sum axis=0 (uint8) | uint8 | 1,000 | 0.0026 | 0.0009 | 2.85× | 35% | +|✅| np.sum axis=0 (uint8) | uint8 | 100,000 | 0.0513 | 0.0044 | 11.69× | 9% | +|✅| np.sum axis=0 (uint8) | uint8 | 10,000,000 | 4.5577 | 0.5260 | 8.66× | 12% | +|✅| np.sum axis=1 (complex128) | complex128 | 1,000 | 0.0021 | 0.0018 | 1.17× | 86% | +|✅| np.sum axis=1 (complex128) | complex128 | 100,000 | 0.0340 | 0.0306 | 1.11× | 90% | +|✅| np.sum axis=1 (complex128) | complex128 | 10,000,000 | 9.2453 | 8.6134 | 1.07× | 93% | +|✅| np.sum axis=1 (float16) | float16 | 1,000 | 0.0039 | 0.0038 | 1.04× | 96% | +|✅| np.sum axis=1 (float16) | float16 | 100,000 | 0.1987 | 0.1303 | 1.52× | 66% | +|✅| np.sum axis=1 (float16) | float16 | 10,000,000 | 19.7912 | 12.4799 | 1.59× | 63% | +|✅| np.sum axis=1 (float32) | float32 | 1,000 | 0.0030 | 0.0019 | 1.53× | 65% | +|✅| np.sum axis=1 (float32) | float32 | 100,000 | 0.0162 | 0.0104 | 1.55× | 64% | +|✅| np.sum axis=1 (float32) | float32 | 10,000,000 | 3.3850 | 1.8916 | 1.79× | 56% | +|✅| np.sum axis=1 (float64) | float64 | 1,000 | 0.0019 | 0.0016 | 1.21× | 83% | +|✅| np.sum axis=1 (float64) | float64 | 100,000 | 0.0200 | 0.0127 | 1.58× | 63% | +|✅| np.sum axis=1 (float64) | float64 | 10,000,000 | 5.1036 | 3.6624 | 1.39× | 72% | +|▫| np.sum axis=1 (int16) | int16 | 1,000 | 0.0023 | 0.0007 | 3.51× | 28% | +|✅| np.sum axis=1 (int16) | int16 | 100,000 | 0.0376 | 0.0038 | 9.79× | 10% | +|✅| np.sum axis=1 (int16) | int16 | 10,000,000 | 3.6422 | 0.4546 | 8.01× | 12% | +|▫| np.sum axis=1 (int32) | int32 | 1,000 | 0.0025 | 0.0007 | 3.66× | 27% | +|✅| np.sum axis=1 (int32) | int32 | 100,000 | 0.0396 | 0.0053 | 7.49× | 13% | +|✅| np.sum axis=1 (int32) | int32 | 10,000,000 | 4.2337 | 1.6246 | 2.61× | 38% | |▫| np.sum axis=1 (int64) | int64 | 1,000 | 0.0019 | 0.0007 | 2.60× | 38% | -|✅| np.sum axis=1 (int64) | int64 | 100,000 | 0.0182 | 0.0075 | 2.41× | 42% | -|✅| np.sum axis=1 (int64) | int64 | 10,000,000 | 4.5921 | 3.0301 | 1.52× | 66% | -|▫| np.sum axis=1 (int8) | int8 | 1,000 | 0.0028 | 0.0008 | 3.60× | 28% | -|✅| np.sum axis=1 (int8) | int8 | 100,000 | 0.0367 | 0.0038 | 9.66× | 10% | -|✅| np.sum axis=1 (int8) | int8 | 10,000,000 | 3.1418 | 0.3053 | 10.29× | 10% | -|▫| np.sum axis=1 (uint16) | uint16 | 1,000 | 0.0023 | 0.0007 | 3.49× | 29% | -|✅| np.sum axis=1 (uint16) | uint16 | 100,000 | 0.0378 | 0.0037 | 10.17× | 10% | -|✅| np.sum axis=1 (uint16) | uint16 | 10,000,000 | 3.3093 | 0.4828 | 6.85× | 15% | -|▫| np.sum axis=1 (uint32) | uint32 | 1,000 | 0.0023 | 0.0007 | 3.52× | 28% | -|✅| np.sum axis=1 (uint32) | uint32 | 100,000 | 0.0363 | 0.0053 | 6.84× | 15% | -|✅| np.sum axis=1 (uint32) | uint32 | 10,000,000 | 4.0964 | 1.7182 | 2.38× | 42% | -|▫| np.sum axis=1 (uint64) | uint64 | 1,000 | 0.0019 | 0.0008 | 2.51× | 40% | -|✅| np.sum axis=1 (uint64) | uint64 | 100,000 | 0.0182 | 0.0076 | 2.40× | 42% | -|✅| np.sum axis=1 (uint64) | uint64 | 10,000,000 | 5.0124 | 2.9323 | 1.71× | 58% | -|▫| np.sum axis=1 (uint8) | uint8 | 1,000 | 0.0026 | 0.0007 | 3.91× | 26% | -|✅| np.sum axis=1 (uint8) | uint8 | 100,000 | 0.0377 | 0.0037 | 10.22× | 10% | -|✅| np.sum axis=1 (uint8) | uint8 | 10,000,000 | 3.1519 | 0.3047 | 10.34× | 10% | -|✅| np.var (float16) | float16 | 1,000 | 0.0196 | 0.0022 | 9.03× | 11% | -|✅| np.var (float16) | float16 | 100,000 | 0.8945 | 0.1898 | 4.71× | 21% | -|✅| np.var (float16) | float16 | 10,000,000 | 90.2053 | 18.7450 | 4.81× | 21% | -|▫| np.var (float32) | float32 | 1,000 | 0.0077 | 0.0010 | 7.83× | 13% | -|✅| np.var (float32) | float32 | 100,000 | 0.0460 | 0.0122 | 3.77× | 26% | -|✅| np.var (float32) | float32 | 10,000,000 | 16.3933 | 3.3367 | 4.91× | 20% | -|▫| np.var (float64) | float64 | 1,000 | 0.0085 | 0.0010 | 8.62× | 12% | -|✅| np.var (float64) | float64 | 100,000 | 0.0651 | 0.0197 | 3.30× | 30% | -|✅| np.var (float64) | float64 | 10,000,000 | 30.9039 | 10.2952 | 3.00× | 33% | -|✅| np.var axis=0 (float16) | float16 | 1,000 | 0.0191 | 0.0047 | 4.09× | 24% | -|✅| np.var axis=0 (float16) | float16 | 100,000 | 1.0938 | 0.3838 | 2.85× | 35% | -|✅| np.var axis=0 (float16) | float16 | 10,000,000 | 107.4462 | 86.2534 | 1.25× | 80% | -|✅| np.var axis=0 (float32) | float32 | 1,000 | 0.0082 | 0.0019 | 4.18× | 24% | -|✅| np.var axis=0 (float32) | float32 | 100,000 | 0.0367 | 0.0235 | 1.56× | 64% | -|✅| np.var axis=0 (float32) | float32 | 10,000,000 | 13.9379 | 5.6308 | 2.48× | 40% | -|▫| np.var axis=0 (float64) | float64 | 1,000 | 0.0071 | 0.0010 | 7.21× | 14% | -|✅| np.var axis=0 (float64) | float64 | 100,000 | 0.0665 | 0.0227 | 2.93× | 34% | -|✅| np.var axis=0 (float64) | float64 | 10,000,000 | 44.1983 | 8.6817 | 5.09× | 20% | +|✅| np.sum axis=1 (int64) | int64 | 100,000 | 0.0175 | 0.0075 | 2.33× | 43% | +|✅| np.sum axis=1 (int64) | int64 | 10,000,000 | 4.7220 | 2.8722 | 1.64× | 61% | +|▫| np.sum axis=1 (int8) | int8 | 1,000 | 0.0023 | 0.0007 | 3.54× | 28% | +|✅| np.sum axis=1 (int8) | int8 | 100,000 | 0.0379 | 0.0039 | 9.63× | 10% | +|✅| np.sum axis=1 (int8) | int8 | 10,000,000 | 3.5111 | 0.2640 | 13.30× | 8% | +|▫| np.sum axis=1 (uint16) | uint16 | 1,000 | 0.0027 | 0.0008 | 3.44× | 29% | +|✅| np.sum axis=1 (uint16) | uint16 | 100,000 | 0.0375 | 0.0037 | 10.00× | 10% | +|✅| np.sum axis=1 (uint16) | uint16 | 10,000,000 | 3.8775 | 0.4583 | 8.46× | 12% | +|▫| np.sum axis=1 (uint32) | uint32 | 1,000 | 0.0026 | 0.0007 | 3.63× | 28% | +|✅| np.sum axis=1 (uint32) | uint32 | 100,000 | 0.0413 | 0.0053 | 7.79× | 13% | +|✅| np.sum axis=1 (uint32) | uint32 | 10,000,000 | 4.2817 | 1.6385 | 2.61× | 38% | +|▫| np.sum axis=1 (uint64) | uint64 | 1,000 | 0.0019 | 0.0007 | 2.69× | 37% | +|✅| np.sum axis=1 (uint64) | uint64 | 100,000 | 0.0173 | 0.0075 | 2.30× | 44% | +|✅| np.sum axis=1 (uint64) | uint64 | 10,000,000 | 4.6457 | 2.8809 | 1.61× | 62% | +|▫| np.sum axis=1 (uint8) | uint8 | 1,000 | 0.0025 | 0.0007 | 3.81× | 26% | +|✅| np.sum axis=1 (uint8) | uint8 | 100,000 | 0.0402 | 0.0038 | 10.47× | 10% | +|✅| np.sum axis=1 (uint8) | uint8 | 10,000,000 | 3.2063 | 0.2647 | 12.11× | 8% | +|✅| np.var (float16) | float16 | 1,000 | 0.0200 | 0.0021 | 9.51× | 10% | +|✅| np.var (float16) | float16 | 100,000 | 0.9060 | 0.1822 | 4.97× | 20% | +|✅| np.var (float16) | float16 | 10,000,000 | 91.4660 | 18.1038 | 5.05× | 20% | +|▫| np.var (float32) | float32 | 1,000 | 0.0083 | 0.0007 | 11.92× | 8% | +|✅| np.var (float32) | float32 | 100,000 | 0.0451 | 0.0096 | 4.69× | 21% | +|✅| np.var (float32) | float32 | 10,000,000 | 17.1259 | 2.6208 | 6.54× | 15% | +|▫| np.var (float64) | float64 | 1,000 | 0.0067 | 0.0008 | 8.40× | 12% | +|✅| np.var (float64) | float64 | 100,000 | 0.0573 | 0.0191 | 3.00× | 33% | +|✅| np.var (float64) | float64 | 10,000,000 | 30.5902 | 6.6728 | 4.58× | 22% | +|✅| np.var axis=0 (float16) | float16 | 1,000 | 0.0200 | 0.0044 | 4.51× | 22% | +|✅| np.var axis=0 (float16) | float16 | 100,000 | 1.1366 | 0.3566 | 3.19× | 31% | +|✅| np.var axis=0 (float16) | float16 | 10,000,000 | 109.8825 | 83.9268 | 1.31× | 76% | +|✅| np.var axis=0 (float32) | float32 | 1,000 | 0.0086 | 0.0017 | 5.19× | 19% | +|✅| np.var axis=0 (float32) | float32 | 100,000 | 0.0371 | 0.0226 | 1.64× | 61% | +|✅| np.var axis=0 (float32) | float32 | 10,000,000 | 14.0847 | 4.7475 | 2.97× | 34% | +|▫| np.var axis=0 (float64) | float64 | 1,000 | 0.0075 | 0.0009 | 7.97× | 12% | +|✅| np.var axis=0 (float64) | float64 | 100,000 | 0.0680 | 0.0252 | 2.70× | 37% | +|✅| np.var axis=0 (float64) | float64 | 10,000,000 | 28.5127 | 7.5344 | 3.78× | 26% | ### Broadcasting | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|⚪| matrix + col_vector (N,M)+(N,1) | float64 | 1,000 | 0.0012 | - | - | - | -|⚪| matrix + col_vector (N,M)+(N,1) | float64 | 100,000 | 0.0282 | - | - | - | -|✅| matrix + col_vector (N,M)+(N,1) | float64 | 10,000,000 | 16.1367 | 14.9780 | 1.08× | 93% | +|⚪| matrix + col_vector (N,M)+(N,1) | float64 | 1,000 | 0.0013 | - | - | - | +|⚪| matrix + col_vector (N,M)+(N,1) | float64 | 100,000 | 0.1968 | - | - | - | +|✅| matrix + col_vector (N,M)+(N,1) | float64 | 10,000,000 | 16.0261 | 13.9778 | 1.15× | 87% | |⚪| matrix + row_vector (N,M)+(M,) | float64 | 1,000 | 0.0012 | - | - | - | -|⚪| matrix + row_vector (N,M)+(M,) | float64 | 100,000 | 0.0268 | - | - | - | -|✅| matrix + row_vector (N,M)+(M,) | float64 | 10,000,000 | 16.5907 | 14.7614 | 1.12× | 89% | +|⚪| matrix + row_vector (N,M)+(M,) | float64 | 100,000 | 0.1942 | - | - | - | +|✅| matrix + row_vector (N,M)+(M,) | float64 | 10,000,000 | 16.3420 | 13.3161 | 1.23× | 82% | |⚪| matrix + scalar | float64 | 1,000 | 0.0007 | - | - | - | -|⚪| matrix + scalar | float64 | 100,000 | 0.0127 | - | - | - | -|✅| matrix + scalar | float64 | 10,000,000 | 16.0746 | 14.6726 | 1.10× | 91% | -|⚪| np.broadcast_to(row, (N,M)) | float64 | 1,000 | 0.0019 | - | - | - | -|⚪| np.broadcast_to(row, (N,M)) | float64 | 100,000 | 0.0018 | - | - | - | -|▫| np.broadcast_to(row, (N,M)) | float64 | 10,000,000 | 0.0018 | 0.0007 | 2.68× | 37% | +|⚪| matrix + scalar | float64 | 100,000 | 0.0142 | - | - | - | +|✅| matrix + scalar | float64 | 10,000,000 | 15.6849 | 13.5809 | 1.16× | 87% | +|⚪| np.broadcast_to(row, (N,M)) | float64 | 1,000 | 0.0018 | - | - | - | +|⚪| np.broadcast_to(row, (N,M)) | float64 | 100,000 | 0.0021 | - | - | - | +|▫| np.broadcast_to(row, (N,M)) | float64 | 10,000,000 | 0.0018 | 0.0006 | 3.22× | 31% | ### Creation | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|▫| np.copy (float32) | float32 | 1,000 | 0.0006 | 0.0107 | 0.056× | 1785% | -|🟡| np.copy (float32) | float32 | 100,000 | 0.0060 | 0.0072 | 0.84× | 119% | -|✅| np.copy (float32) | float32 | 10,000,000 | 6.3236 | 2.8506 | 2.22× | 45% | -|▫| np.copy (float64) | float64 | 1,000 | 0.0006 | 0.0146 | 0.041× | 2444% | -|🟡| np.copy (float64) | float64 | 100,000 | 0.0115 | 0.0133 | 0.86× | 116% | -|🟡| np.copy (float64) | float64 | 10,000,000 | 13.1481 | 14.2102 | 0.93× | 108% | -|▫| np.copy (int32) | int32 | 1,000 | 0.0006 | 0.0101 | 0.057× | 1765% | -|🟡| np.copy (int32) | int32 | 100,000 | 0.0068 | 0.0070 | 0.97× | 103% | -|✅| np.copy (int32) | int32 | 10,000,000 | 6.4479 | 2.3947 | 2.69× | 37% | -|▫| np.copy (int64) | int64 | 1,000 | 0.0006 | 0.0155 | 0.040× | 2479% | -|🟡| np.copy (int64) | int64 | 100,000 | 0.0116 | 0.0133 | 0.87× | 114% | -|🟡| np.copy (int64) | int64 | 10,000,000 | 13.0440 | 13.5955 | 0.96× | 104% | -|▫| np.empty (float32) | float32 | 1,000 | 0.0003 | 0.0079 | 0.041× | 2410% | -|▫| np.empty (float32) | float32 | 100,000 | 0.0003 | 0.0123 | 0.026× | 3788% | -|🟡| np.empty (float32) | float32 | 10,000,000 | 0.0136 | 0.0154 | 0.89× | 113% | -|▫| np.empty (float64) | float64 | 1,000 | 0.0003 | 0.0141 | 0.021× | 4785% | -|▫| np.empty (float64) | float64 | 100,000 | 0.0003 | 0.0118 | 0.024× | 4137% | -|🟡| np.empty (float64) | float64 | 10,000,000 | 0.0102 | 0.0143 | 0.72× | 139% | -|▫| np.empty (int32) | int32 | 1,000 | 0.0003 | 0.0104 | 0.031× | 3239% | -|▫| np.empty (int32) | int32 | 100,000 | 0.0003 | 0.0141 | 0.021× | 4659% | -|✅| np.empty (int32) | int32 | 10,000,000 | 0.0167 | 0.0132 | 1.26× | 79% | -|▫| np.empty (int64) | int64 | 1,000 | 0.0003 | 0.0121 | 0.026× | 3888% | -|▫| np.empty (int64) | int64 | 100,000 | 0.0003 | 0.0139 | 0.023× | 4389% | -|🟡| np.empty (int64) | int64 | 10,000,000 | 0.0108 | 0.0174 | 0.62× | 162% | -|▫| np.full (float32) | float32 | 1,000 | 0.0009 | 0.0106 | 0.083× | 1199% | -|🟠| np.full (float32) | float32 | 100,000 | 0.0057 | 0.0129 | 0.44× | 228% | -|✅| np.full (float32) | float32 | 10,000,000 | 7.2846 | 2.6587 | 2.74× | 36% | -|▫| np.full (float64) | float64 | 1,000 | 0.0009 | 0.0106 | 0.082× | 1218% | -|🟡| np.full (float64) | float64 | 100,000 | 0.0100 | 0.0144 | 0.69× | 145% | -|✅| np.full (float64) | float64 | 10,000,000 | 14.8069 | 13.2111 | 1.12× | 89% | -|▫| np.full (int32) | int32 | 1,000 | 0.0009 | 0.0088 | 0.11× | 925% | -|🟠| np.full (int32) | int32 | 100,000 | 0.0054 | 0.0127 | 0.43× | 235% | -|✅| np.full (int32) | int32 | 10,000,000 | 7.2671 | 2.6612 | 2.73× | 37% | -|▫| np.full (int64) | int64 | 1,000 | 0.0009 | 0.0124 | 0.069× | 1448% | -|🟡| np.full (int64) | int64 | 100,000 | 0.0099 | 0.0152 | 0.65× | 154% | -|✅| np.full (int64) | int64 | 10,000,000 | 14.7618 | 13.5495 | 1.09× | 92% | -|▫| np.ones (float32) | float32 | 1,000 | 0.0009 | 0.0097 | 0.093× | 1080% | -|🟠| np.ones (float32) | float32 | 100,000 | 0.0053 | 0.0125 | 0.42× | 236% | -|✅| np.ones (float32) | float32 | 10,000,000 | 7.4168 | 2.5871 | 2.87× | 35% | -|▫| np.ones (float64) | float64 | 1,000 | 0.0009 | 0.0140 | 0.062× | 1605% | -|🟡| np.ones (float64) | float64 | 100,000 | 0.0098 | 0.0153 | 0.64× | 156% | -|✅| np.ones (float64) | float64 | 10,000,000 | 14.7708 | 13.0423 | 1.13× | 88% | -|▫| np.ones (int32) | int32 | 1,000 | 0.0009 | 0.0088 | 0.10× | 1004% | -|🟡| np.ones (int32) | int32 | 100,000 | 0.0076 | 0.0124 | 0.61× | 164% | -|✅| np.ones (int32) | int32 | 10,000,000 | 7.3873 | 2.6333 | 2.81× | 36% | -|▫| np.ones (int64) | int64 | 1,000 | 0.0008 | 0.0139 | 0.061× | 1638% | -|🟡| np.ones (int64) | int64 | 100,000 | 0.0098 | 0.0149 | 0.66× | 152% | -|✅| np.ones (int64) | int64 | 10,000,000 | 14.7014 | 13.3927 | 1.10× | 91% | -|▫| np.zeros (float32) | float32 | 1,000 | 0.0005 | 0.0105 | 0.051× | 1958% | -|✅| np.zeros (float32) | float32 | 100,000 | 0.0048 | 0.0021 | 2.31× | 43% | -|✅| np.zeros (float32) | float32 | 10,000,000 | 0.0110 | 0.0068 | 1.62× | 62% | -|▫| np.zeros (float64) | float64 | 1,000 | 0.0004 | 0.0130 | 0.028× | 3535% | -|✅| np.zeros (float64) | float64 | 100,000 | 0.0093 | 0.0026 | 3.57× | 28% | -|✅| np.zeros (float64) | float64 | 10,000,000 | 0.0120 | 0.0068 | 1.76× | 57% | -|▫| np.zeros (int32) | int32 | 1,000 | 0.0004 | 0.0114 | 0.035× | 2881% | -|✅| np.zeros (int32) | int32 | 100,000 | 0.0048 | 0.0020 | 2.44× | 41% | -|✅| np.zeros (int32) | int32 | 10,000,000 | 0.0139 | 0.0058 | 2.39× | 42% | -|▫| np.zeros (int64) | int64 | 1,000 | 0.0004 | 0.0084 | 0.046× | 2187% | -|✅| np.zeros (int64) | int64 | 100,000 | 0.0096 | 0.0026 | 3.63× | 28% | -|✅| np.zeros (int64) | int64 | 10,000,000 | 0.0112 | 0.0091 | 1.23× | 81% | -|🔴| np.zeros_like (float32) | float32 | 1,000 | 0.0011 | 0.0088 | 0.12× | 826% | -|✅| np.zeros_like (float32) | float32 | 100,000 | 0.0056 | 0.0022 | 2.59× | 39% | -|▫| np.zeros_like (float32) | float32 | 10,000,000 | 7.3581 | 0.0069 | 1061.51× | 0% | -|🔴| np.zeros_like (float64) | float64 | 1,000 | 0.0010 | 0.0148 | 0.068× | 1471% | -|✅| np.zeros_like (float64) | float64 | 100,000 | 0.0101 | 0.0027 | 3.69× | 27% | -|▫| np.zeros_like (float64) | float64 | 10,000,000 | 14.7410 | 0.0069 | 2150.69× | 0% | -|🔴| np.zeros_like (int32) | int32 | 1,000 | 0.0011 | 0.0111 | 0.095× | 1052% | -|✅| np.zeros_like (int32) | int32 | 100,000 | 0.0054 | 0.0021 | 2.58× | 39% | -|▫| np.zeros_like (int32) | int32 | 10,000,000 | 7.4489 | 0.0061 | 1230.61× | 0% | -|🔴| np.zeros_like (int64) | int64 | 1,000 | 0.0011 | 0.0118 | 0.095× | 1056% | -|✅| np.zeros_like (int64) | int64 | 100,000 | 0.0100 | 0.0028 | 3.55× | 28% | -|▫| np.zeros_like (int64) | int64 | 10,000,000 | 14.7656 | 0.0093 | 1585.88× | 0% | +|▫| np.copy (float32) | float32 | 1,000 | 0.0006 | 0.0117 | 0.050× | 1983% | +|🟡| np.copy (float32) | float32 | 100,000 | 0.0061 | 0.0071 | 0.86× | 117% | +|✅| np.copy (float32) | float32 | 10,000,000 | 6.4782 | 1.8708 | 3.46× | 29% | +|▫| np.copy (float64) | float64 | 1,000 | 0.0006 | 0.0198 | 0.031× | 3272% | +|🟡| np.copy (float64) | float64 | 100,000 | 0.0113 | 0.0135 | 0.83× | 120% | +|✅| np.copy (float64) | float64 | 10,000,000 | 13.0404 | 12.7447 | 1.02× | 98% | +|▫| np.copy (int32) | int32 | 1,000 | 0.0006 | 0.0116 | 0.051× | 1958% | +|🟡| np.copy (int32) | int32 | 100,000 | 0.0060 | 0.0069 | 0.88× | 114% | +|✅| np.copy (int32) | int32 | 10,000,000 | 6.3440 | 1.8390 | 3.45× | 29% | +|▫| np.copy (int64) | int64 | 1,000 | 0.0006 | 0.0133 | 0.046× | 2177% | +|🟡| np.copy (int64) | int64 | 100,000 | 0.0115 | 0.0140 | 0.82× | 122% | +|✅| np.copy (int64) | int64 | 10,000,000 | 13.2030 | 12.7437 | 1.04× | 96% | +|▫| np.empty (float32) | float32 | 1,000 | 0.0003 | 0.0085 | 0.039× | 2532% | +|▫| np.empty (float32) | float32 | 100,000 | 0.0003 | 0.0141 | 0.024× | 4090% | +|🟡| np.empty (float32) | float32 | 10,000,000 | 0.0087 | 0.0143 | 0.61× | 165% | +|▫| np.empty (float64) | float64 | 1,000 | 0.0005 | 0.0139 | 0.038× | 2598% | +|▫| np.empty (float64) | float64 | 100,000 | 0.0003 | 0.0140 | 0.021× | 4722% | +|🟡| np.empty (float64) | float64 | 10,000,000 | 0.0080 | 0.0138 | 0.58× | 172% | +|▫| np.empty (int32) | int32 | 1,000 | 0.0003 | 0.0089 | 0.038× | 2635% | +|▫| np.empty (int32) | int32 | 100,000 | 0.0003 | 0.0133 | 0.026× | 3857% | +|🟡| np.empty (int32) | int32 | 10,000,000 | 0.0108 | 0.0140 | 0.77× | 130% | +|▫| np.empty (int64) | int64 | 1,000 | 0.0003 | 0.0150 | 0.021× | 4695% | +|▫| np.empty (int64) | int64 | 100,000 | 0.0003 | 0.0152 | 0.022× | 4622% | +|🟡| np.empty (int64) | int64 | 10,000,000 | 0.0119 | 0.0141 | 0.85× | 118% | +|▫| np.full (float32) | float32 | 1,000 | 0.0010 | 0.0117 | 0.084× | 1196% | +|🟠| np.full (float32) | float32 | 100,000 | 0.0053 | 0.0126 | 0.42× | 239% | +|✅| np.full (float32) | float32 | 10,000,000 | 7.3482 | 1.8309 | 4.01× | 25% | +|▫| np.full (float64) | float64 | 1,000 | 0.0010 | 0.0142 | 0.068× | 1466% | +|🟡| np.full (float64) | float64 | 100,000 | 0.0098 | 0.0148 | 0.66× | 151% | +|✅| np.full (float64) | float64 | 10,000,000 | 14.6927 | 12.3279 | 1.19× | 84% | +|▫| np.full (int32) | int32 | 1,000 | 0.0009 | 0.0104 | 0.088× | 1138% | +|🟠| np.full (int32) | int32 | 100,000 | 0.0054 | 0.0123 | 0.44× | 228% | +|✅| np.full (int32) | int32 | 10,000,000 | 7.3322 | 1.9121 | 3.83× | 26% | +|▫| np.full (int64) | int64 | 1,000 | 0.0009 | 0.0135 | 0.064× | 1571% | +|🟡| np.full (int64) | int64 | 100,000 | 0.0098 | 0.0150 | 0.65× | 153% | +|✅| np.full (int64) | int64 | 10,000,000 | 14.7304 | 12.3185 | 1.20× | 84% | +|▫| np.ones (float32) | float32 | 1,000 | 0.0010 | 0.0094 | 0.10× | 953% | +|🟠| np.ones (float32) | float32 | 100,000 | 0.0054 | 0.0124 | 0.43× | 232% | +|✅| np.ones (float32) | float32 | 10,000,000 | 7.3143 | 1.8034 | 4.06× | 25% | +|▫| np.ones (float64) | float64 | 1,000 | 0.0009 | 0.0119 | 0.073× | 1370% | +|🟡| np.ones (float64) | float64 | 100,000 | 0.0098 | 0.0150 | 0.66× | 152% | +|✅| np.ones (float64) | float64 | 10,000,000 | 14.5911 | 12.4361 | 1.17× | 85% | +|▫| np.ones (int32) | int32 | 1,000 | 0.0009 | 0.0111 | 0.080× | 1257% | +|🟠| np.ones (int32) | int32 | 100,000 | 0.0053 | 0.0124 | 0.43× | 234% | +|✅| np.ones (int32) | int32 | 10,000,000 | 7.1344 | 2.0772 | 3.44× | 29% | +|▫| np.ones (int64) | int64 | 1,000 | 0.0009 | 0.0145 | 0.059× | 1685% | +|🟡| np.ones (int64) | int64 | 100,000 | 0.0099 | 0.0148 | 0.67× | 149% | +|✅| np.ones (int64) | int64 | 10,000,000 | 14.7038 | 12.3182 | 1.19× | 84% | +|▫| np.zeros (float32) | float32 | 1,000 | 0.0004 | 0.0107 | 0.039× | 2555% | +|✅| np.zeros (float32) | float32 | 100,000 | 0.0048 | 0.0020 | 2.37× | 42% | +|✅| np.zeros (float32) | float32 | 10,000,000 | 0.0090 | 0.0020 | 4.51× | 22% | +|▫| np.zeros (float64) | float64 | 1,000 | 0.0004 | 0.0137 | 0.026× | 3875% | +|✅| np.zeros (float64) | float64 | 100,000 | 0.0093 | 0.0026 | 3.62× | 28% | +|✅| np.zeros (float64) | float64 | 10,000,000 | 0.0083 | 0.0022 | 3.70× | 27% | +|▫| np.zeros (int32) | int32 | 1,000 | 0.0004 | 0.0107 | 0.036× | 2816% | +|✅| np.zeros (int32) | int32 | 100,000 | 0.0049 | 0.0020 | 2.45× | 41% | +|✅| np.zeros (int32) | int32 | 10,000,000 | 0.0111 | 0.0065 | 1.71× | 58% | +|▫| np.zeros (int64) | int64 | 1,000 | 0.0004 | 0.0140 | 0.027× | 3638% | +|✅| np.zeros (int64) | int64 | 100,000 | 0.0094 | 0.0026 | 3.70× | 27% | +|✅| np.zeros (int64) | int64 | 10,000,000 | 0.0114 | 0.0057 | 1.99× | 50% | +|🔴| np.zeros_like (float32) | float32 | 1,000 | 0.0011 | 0.0091 | 0.12× | 861% | +|✅| np.zeros_like (float32) | float32 | 100,000 | 0.0055 | 0.0021 | 2.63× | 38% | +|▫| np.zeros_like (float32) | float32 | 10,000,000 | 7.1806 | 0.0021 | 3422.48× | 0% | +|🔴| np.zeros_like (float64) | float64 | 1,000 | 0.0012 | 0.0135 | 0.092× | 1086% | +|✅| np.zeros_like (float64) | float64 | 100,000 | 0.0099 | 0.0027 | 3.70× | 27% | +|▫| np.zeros_like (float64) | float64 | 10,000,000 | 14.8132 | 0.0023 | 6363.47× | 0% | +|🔴| np.zeros_like (int32) | int32 | 1,000 | 0.0011 | 0.0089 | 0.12× | 842% | +|✅| np.zeros_like (int32) | int32 | 100,000 | 0.0057 | 0.0020 | 2.78× | 36% | +|▫| np.zeros_like (int32) | int32 | 10,000,000 | 7.2566 | 0.0066 | 1091.90× | 0% | +|🔴| np.zeros_like (int64) | int64 | 1,000 | 0.0010 | 0.0132 | 0.079× | 1261% | +|✅| np.zeros_like (int64) | int64 | 100,000 | 0.0100 | 0.0026 | 3.79× | 26% | +|▫| np.zeros_like (int64) | int64 | 10,000,000 | 14.7069 | 0.0056 | 2623.48× | 0% | ### Manipulation @@ -1505,17 +1505,17 @@ _Ranked over 1398 credible comparisons (both sides ≥1µs, within 20×); 384 ne |⚪| a.T (2D) | float64 | 100,000 | 0.0001 | - | - | - | |⚪| a.T (2D) | float64 | 10,000,000 | 0.0001 | - | - | - | |⚪| a.flatten | float64 | 1,000 | 0.0005 | - | - | - | -|🔴| a.flatten | float64 | 100,000 | 0.0152 | 0.0997 | 0.15× | 657% | -|✅| a.flatten | float64 | 10,000,000 | 13.1072 | 12.5501 | 1.04× | 96% | +|🔴| a.flatten | float64 | 100,000 | 0.0113 | 0.0883 | 0.13× | 782% | +|✅| a.flatten | float64 | 10,000,000 | 13.0852 | 11.4613 | 1.14× | 88% | |⚪| np.concatenate | float64 | 1,000 | 0.0010 | - | - | - | -|⚪| np.concatenate | float64 | 100,000 | 0.3267 | - | - | - | -|⚪| np.concatenate | float64 | 10,000,000 | 34.0246 | - | - | - | -|⚪| np.ravel | float64 | 1,000 | 0.0003 | - | - | - | -|▫| np.ravel | float64 | 100,000 | 0.0003 | 0.0006 | 0.55× | 181% | -|▫| np.ravel | float64 | 10,000,000 | 0.0004 | 0.0006 | 0.59× | 168% | -|⚪| np.stack | float64 | 1,000 | 0.0021 | - | - | - | -|⚪| np.stack | float64 | 100,000 | 0.3333 | - | - | - | -|⚪| np.stack | float64 | 10,000,000 | 33.5715 | - | - | - | +|⚪| np.concatenate | float64 | 100,000 | 0.3154 | - | - | - | +|⚪| np.concatenate | float64 | 10,000,000 | 32.8142 | - | - | - | +|⚪| np.ravel | float64 | 1,000 | 0.0004 | - | - | - | +|▫| np.ravel | float64 | 100,000 | 0.0003 | 0.0005 | 0.69× | 145% | +|▫| np.ravel | float64 | 10,000,000 | 0.0003 | 0.0005 | 0.62× | 162% | +|⚪| np.stack | float64 | 1,000 | 0.0022 | - | - | - | +|⚪| np.stack | float64 | 100,000 | 0.3130 | - | - | - | +|⚪| np.stack | float64 | 10,000,000 | 33.9601 | - | - | - | |⚪| np.transpose (2D) | float64 | 1,000 | 0.0004 | - | - | - | |⚪| np.transpose (2D) | float64 | 100,000 | 0.0004 | - | - | - | |⚪| np.transpose (2D) | float64 | 10,000,000 | 0.0004 | - | - | - | @@ -1523,481 +1523,480 @@ _Ranked over 1398 credible comparisons (both sides ≥1µs, within 20×); 384 ne |⚪| reshape 1D->2D | float64 | 100,000 | 0.0002 | - | - | - | |⚪| reshape 1D->2D | float64 | 10,000,000 | 0.0002 | - | - | - | |⚪| reshape 2D->1D | float64 | 1,000 | 0.0002 | - | - | - | -|▫| reshape 2D->1D | float64 | 100,000 | 0.0002 | 0.0006 | 0.29× | 349% | -|▫| reshape 2D->1D | float64 | 10,000,000 | 0.0002 | 0.0007 | 0.25× | 405% | +|▫| reshape 2D->1D | float64 | 100,000 | 0.0002 | 0.0005 | 0.33× | 304% | +|▫| reshape 2D->1D | float64 | 10,000,000 | 0.0002 | 0.0006 | 0.32× | 317% | ### Slicing | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| |⚪| a[100:1000] (contiguous) | float64 | 1,000 | 0.0002 | - | - | - | -|⚪| a[100:1000] (contiguous) | float64 | 100,000 | 0.0002 | - | - | - | -|⚪| a[100:1000] (contiguous) | float64 | 10,000,000 | 0.0002 | - | - | - | +|⚪| a[100:1000] (contiguous) | float64 | 100,000 | 0.0001 | - | - | - | +|⚪| a[100:1000] (contiguous) | float64 | 10,000,000 | 0.0001 | - | - | - | |⚪| a[::-1] (reversed) | float64 | 1,000 | 0.0001 | - | - | - | -|▫| a[::-1] (reversed) | float64 | 100,000 | 0.0002 | 0.0015 | 0.11× | 937% | -|▫| a[::-1] (reversed) | float64 | 10,000,000 | 0.0001 | 0.0013 | 0.092× | 1091% | -|⚪| a[::2] (strided) | float64 | 1,000 | 0.0001 | - | - | - | +|▫| a[::-1] (reversed) | float64 | 100,000 | 0.0002 | 0.0012 | 0.13× | 781% | +|▫| a[::-1] (reversed) | float64 | 10,000,000 | 0.0002 | 0.0012 | 0.13× | 789% | +|⚪| a[::2] (strided) | float64 | 1,000 | 0.0002 | - | - | - | |⚪| a[::2] (strided) | float64 | 100,000 | 0.0001 | - | - | - | -|⚪| a[::2] (strided) | float64 | 10,000,000 | 0.0002 | - | - | - | -|⚪| np.sum(contiguous_slice) | float64 | 900 | 0.0016 | - | - | - | -|⚪| np.sum(contiguous_slice) | float64 | 900 | 0.0016 | - | - | - | +|⚪| a[::2] (strided) | float64 | 10,000,000 | 0.0001 | - | - | - | +|⚪| np.sum(contiguous_slice) | float64 | 900 | 0.0017 | - | - | - | |⚪| np.sum(contiguous_slice) | float64 | 900 | 0.0017 | - | - | - | +|⚪| np.sum(contiguous_slice) | float64 | 900 | 0.0016 | - | - | - | |⚪| np.sum(strided_slice) | float64 | 500 | 0.0016 | - | - | - | -|⚪| np.sum(strided_slice) | float64 | 50,000 | 0.0097 | - | - | - | -|⚪| np.sum(strided_slice) | float64 | 5,000,000 | 4.3559 | - | - | - | +|⚪| np.sum(strided_slice) | float64 | 50,000 | 0.0096 | - | - | - | +|⚪| np.sum(strided_slice) | float64 | 5,000,000 | 4.6738 | - | - | - | ### Comparison | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|▫| a != b (float32) | float32 | 1,000 | 0.0005 | 0.0008 | 0.66× | 151% | -|🟠| a != b (float32) | float32 | 100,000 | 0.0055 | 0.0210 | 0.26× | 385% | -|✅| a != b (float32) | float32 | 10,000,000 | 4.0035 | 3.8343 | 1.04× | 96% | -|▫| a != b (float64) | float64 | 1,000 | 0.0004 | 0.0008 | 0.53× | 188% | -|🟠| a != b (float64) | float64 | 100,000 | 0.0099 | 0.0261 | 0.38× | 264% | -|🟡| a != b (float64) | float64 | 10,000,000 | 6.8612 | 6.8679 | 1.00× | 100% | -|▫| a != b (int32) | int32 | 1,000 | 0.0004 | 0.0008 | 0.54× | 184% | -|🟠| a != b (int32) | int32 | 100,000 | 0.0070 | 0.0195 | 0.36× | 280% | -|✅| a != b (int32) | int32 | 10,000,000 | 4.4415 | 3.8892 | 1.14× | 88% | -|▫| a != b (int64) | int64 | 1,000 | 0.0005 | 0.0009 | 0.56× | 177% | -|🟠| a != b (int64) | int64 | 100,000 | 0.0126 | 0.0280 | 0.45× | 222% | -|✅| a != b (int64) | int64 | 10,000,000 | 7.4973 | 6.9054 | 1.09× | 92% | -|▫| a < b (float32) | float32 | 1,000 | 0.0005 | 0.0008 | 0.62× | 160% | -|🟠| a < b (float32) | float32 | 100,000 | 0.0054 | 0.0215 | 0.25× | 396% | -|🟡| a < b (float32) | float32 | 10,000,000 | 3.9556 | 3.9752 | 0.99× | 100% | -|▫| a < b (float64) | float64 | 1,000 | 0.0004 | 0.0008 | 0.55× | 183% | -|🟠| a < b (float64) | float64 | 100,000 | 0.0102 | 0.0287 | 0.36× | 280% | -|🟡| a < b (float64) | float64 | 10,000,000 | 6.7457 | 6.7981 | 0.99× | 101% | -|▫| a < b (int32) | int32 | 1,000 | 0.0004 | 0.0008 | 0.52× | 191% | -|🟠| a < b (int32) | int32 | 100,000 | 0.0070 | 0.0175 | 0.40× | 249% | -|✅| a < b (int32) | int32 | 10,000,000 | 4.2024 | 3.8654 | 1.09× | 92% | -|▫| a < b (int64) | int64 | 1,000 | 0.0005 | 0.0010 | 0.55× | 182% | -|🟡| a < b (int64) | int64 | 100,000 | 0.0178 | 0.0271 | 0.66× | 152% | -|✅| a < b (int64) | int64 | 10,000,000 | 7.2004 | 6.6980 | 1.07× | 93% | -|▫| a <= b (float32) | float32 | 1,000 | 0.0004 | 0.0009 | 0.43× | 231% | -|🟠| a <= b (float32) | float32 | 100,000 | 0.0058 | 0.0206 | 0.28× | 355% | -|✅| a <= b (float32) | float32 | 10,000,000 | 4.0141 | 3.9178 | 1.02× | 98% | -|▫| a <= b (float64) | float64 | 1,000 | 0.0005 | 0.0008 | 0.56× | 180% | -|🟠| a <= b (float64) | float64 | 100,000 | 0.0102 | 0.0279 | 0.37× | 274% | -|🟡| a <= b (float64) | float64 | 10,000,000 | 6.9314 | 7.5527 | 0.92× | 109% | -|▫| a <= b (int32) | int32 | 1,000 | 0.0004 | 0.0008 | 0.53× | 190% | -|🟠| a <= b (int32) | int32 | 100,000 | 0.0070 | 0.0183 | 0.39× | 260% | -|✅| a <= b (int32) | int32 | 10,000,000 | 4.3206 | 3.9855 | 1.08× | 92% | -|▫| a <= b (int64) | int64 | 1,000 | 0.0033 | 0.0008 | 4.37× | 23% | -|🟡| a <= b (int64) | int64 | 100,000 | 0.0179 | 0.0277 | 0.65× | 155% | -|✅| a <= b (int64) | int64 | 10,000,000 | 7.3167 | 6.8152 | 1.07× | 93% | -|▫| a == b (float32) | float32 | 1,000 | 0.0005 | 0.0008 | 0.60× | 167% | -|🟠| a == b (float32) | float32 | 100,000 | 0.0057 | 0.0222 | 0.26× | 391% | -|✅| a == b (float32) | float32 | 10,000,000 | 4.2784 | 3.8917 | 1.10× | 91% | -|▫| a == b (float64) | float64 | 1,000 | 0.0004 | 0.0008 | 0.53× | 190% | -|🟠| a == b (float64) | float64 | 100,000 | 0.0102 | 0.0256 | 0.40× | 250% | -|✅| a == b (float64) | float64 | 10,000,000 | 6.7360 | 6.6513 | 1.01× | 99% | -|▫| a == b (int32) | int32 | 1,000 | 0.0005 | 0.0008 | 0.65× | 153% | -|🟠| a == b (int32) | int32 | 100,000 | 0.0070 | 0.0179 | 0.39× | 257% | -|✅| a == b (int32) | int32 | 10,000,000 | 4.3115 | 3.8389 | 1.12× | 89% | -|▫| a == b (int64) | int64 | 1,000 | 0.0004 | 0.0008 | 0.53× | 189% | -|🟠| a == b (int64) | int64 | 100,000 | 0.0124 | 0.0277 | 0.45× | 223% | -|✅| a == b (int64) | int64 | 10,000,000 | 7.1056 | 6.8700 | 1.03× | 97% | -|▫| a > b (float32) | float32 | 1,000 | 0.0004 | 0.0008 | 0.54× | 184% | -|🟠| a > b (float32) | float32 | 100,000 | 0.0057 | 0.0211 | 0.27× | 369% | -|✅| a > b (float32) | float32 | 10,000,000 | 4.0557 | 4.0290 | 1.01× | 99% | -|▫| a > b (float64) | float64 | 1,000 | 0.0004 | 0.0008 | 0.53× | 189% | -|🟠| a > b (float64) | float64 | 100,000 | 0.0100 | 0.0257 | 0.39× | 256% | -|🟡| a > b (float64) | float64 | 10,000,000 | 6.7723 | 6.8430 | 0.99× | 101% | -|▫| a > b (int32) | int32 | 1,000 | 0.0004 | 0.0008 | 0.52× | 192% | -|🟠| a > b (int32) | int32 | 100,000 | 0.0070 | 0.0199 | 0.35× | 284% | -|✅| a > b (int32) | int32 | 10,000,000 | 4.3282 | 3.9012 | 1.11× | 90% | -|▫| a > b (int64) | int64 | 1,000 | 0.0005 | 0.0008 | 0.66× | 151% | -|🟡| a > b (int64) | int64 | 100,000 | 0.0189 | 0.0269 | 0.70× | 142% | -|✅| a > b (int64) | int64 | 10,000,000 | 7.3191 | 6.5042 | 1.12× | 89% | -|▫| a >= b (float32) | float32 | 1,000 | 0.0004 | 0.0008 | 0.50× | 200% | -|🟠| a >= b (float32) | float32 | 100,000 | 0.0059 | 0.0205 | 0.29× | 349% | -|🟡| a >= b (float32) | float32 | 10,000,000 | 3.9175 | 3.9675 | 0.99× | 101% | -|▫| a >= b (float64) | float64 | 1,000 | 0.0004 | 0.0008 | 0.53× | 190% | -|🟠| a >= b (float64) | float64 | 100,000 | 0.0103 | 0.0262 | 0.39× | 256% | -|🟡| a >= b (float64) | float64 | 10,000,000 | 6.7369 | 7.2300 | 0.93× | 107% | -|▫| a >= b (int32) | int32 | 1,000 | 0.0005 | 0.0008 | 0.56× | 179% | -|🟠| a >= b (int32) | int32 | 100,000 | 0.0069 | 0.0191 | 0.36× | 275% | -|✅| a >= b (int32) | int32 | 10,000,000 | 4.4423 | 3.9289 | 1.13× | 88% | -|▫| a >= b (int64) | int64 | 1,000 | 0.0005 | 0.0008 | 0.68× | 148% | -|🟡| a >= b (int64) | int64 | 100,000 | 0.0178 | 0.0284 | 0.63× | 160% | -|✅| a >= b (int64) | int64 | 10,000,000 | 7.5761 | 6.8095 | 1.11× | 90% | +|▫| a != b (float32) | float32 | 1,000 | 0.0004 | 0.0007 | 0.60× | 166% | +|🟠| a != b (float32) | float32 | 100,000 | 0.0059 | 0.0188 | 0.31× | 321% | +|✅| a != b (float32) | float32 | 10,000,000 | 3.9247 | 3.3366 | 1.18× | 85% | +|▫| a != b (float64) | float64 | 1,000 | 0.0005 | 0.0008 | 0.62× | 162% | +|🟠| a != b (float64) | float64 | 100,000 | 0.0110 | 0.0263 | 0.42× | 239% | +|✅| a != b (float64) | float64 | 10,000,000 | 6.7684 | 5.8470 | 1.16× | 86% | +|▫| a != b (int32) | int32 | 1,000 | 0.0004 | 0.0008 | 0.54× | 186% | +|🟠| a != b (int32) | int32 | 100,000 | 0.0069 | 0.0201 | 0.34× | 291% | +|✅| a != b (int32) | int32 | 10,000,000 | 4.2916 | 3.4612 | 1.24× | 81% | +|▫| a != b (int64) | int64 | 1,000 | 0.0005 | 0.0008 | 0.60× | 166% | +|🟡| a != b (int64) | int64 | 100,000 | 0.0140 | 0.0260 | 0.54× | 186% | +|✅| a != b (int64) | int64 | 10,000,000 | 7.2974 | 5.8513 | 1.25× | 80% | +|▫| a < b (float32) | float32 | 1,000 | 0.0004 | 0.0007 | 0.56× | 179% | +|🟠| a < b (float32) | float32 | 100,000 | 0.0056 | 0.0189 | 0.29× | 339% | +|✅| a < b (float32) | float32 | 10,000,000 | 3.8362 | 3.2974 | 1.16× | 86% | +|▫| a < b (float64) | float64 | 1,000 | 0.0005 | 0.0008 | 0.62× | 162% | +|🟠| a < b (float64) | float64 | 100,000 | 0.0106 | 0.0279 | 0.38× | 264% | +|✅| a < b (float64) | float64 | 10,000,000 | 6.5961 | 5.7503 | 1.15× | 87% | +|▫| a < b (int32) | int32 | 1,000 | 0.0004 | 0.0007 | 0.62× | 162% | +|🟠| a < b (int32) | int32 | 100,000 | 0.0071 | 0.0187 | 0.38× | 265% | +|✅| a < b (int32) | int32 | 10,000,000 | 4.3674 | 3.2941 | 1.33× | 75% | +|▫| a < b (int64) | int64 | 1,000 | 0.0005 | 0.0007 | 0.73× | 137% | +|🟡| a < b (int64) | int64 | 100,000 | 0.0182 | 0.0250 | 0.73× | 137% | +|✅| a < b (int64) | int64 | 10,000,000 | 7.0769 | 5.6821 | 1.25× | 80% | +|▫| a <= b (float32) | float32 | 1,000 | 0.0006 | 0.0007 | 0.80× | 126% | +|🟠| a <= b (float32) | float32 | 100,000 | 0.0063 | 0.0181 | 0.35× | 289% | +|✅| a <= b (float32) | float32 | 10,000,000 | 3.8528 | 3.2691 | 1.18× | 85% | +|▫| a <= b (float64) | float64 | 1,000 | 0.0004 | 0.0007 | 0.60× | 167% | +|🟠| a <= b (float64) | float64 | 100,000 | 0.0104 | 0.0283 | 0.37× | 272% | +|✅| a <= b (float64) | float64 | 10,000,000 | 6.4781 | 5.7355 | 1.13× | 88% | +|▫| a <= b (int32) | int32 | 1,000 | 0.0004 | 0.0007 | 0.58× | 173% | +|🟠| a <= b (int32) | int32 | 100,000 | 0.0070 | 0.0192 | 0.37× | 273% | +|✅| a <= b (int32) | int32 | 10,000,000 | 4.2354 | 3.3183 | 1.28× | 78% | +|▫| a <= b (int64) | int64 | 1,000 | 0.0005 | 0.0007 | 0.72× | 138% | +|🟡| a <= b (int64) | int64 | 100,000 | 0.0186 | 0.0268 | 0.69× | 144% | +|✅| a <= b (int64) | int64 | 10,000,000 | 8.2513 | 5.9870 | 1.38× | 73% | +|▫| a == b (float32) | float32 | 1,000 | 0.0005 | 0.0007 | 0.61× | 163% | +|🟠| a == b (float32) | float32 | 100,000 | 0.0059 | 0.0199 | 0.29× | 339% | +|✅| a == b (float32) | float32 | 10,000,000 | 3.9574 | 3.2149 | 1.23× | 81% | +|▫| a == b (float64) | float64 | 1,000 | 0.0004 | 0.0007 | 0.58× | 172% | +|🟠| a == b (float64) | float64 | 100,000 | 0.0114 | 0.0279 | 0.41× | 244% | +|✅| a == b (float64) | float64 | 10,000,000 | 7.1028 | 5.6882 | 1.25× | 80% | +|▫| a == b (int32) | int32 | 1,000 | 0.0004 | 0.0007 | 0.60× | 167% | +|🟠| a == b (int32) | int32 | 100,000 | 0.0072 | 0.0183 | 0.40× | 253% | +|✅| a == b (int32) | int32 | 10,000,000 | 4.1736 | 3.3406 | 1.25× | 80% | +|▫| a == b (int64) | int64 | 1,000 | 0.0004 | 0.0008 | 0.58× | 172% | +|🟡| a == b (int64) | int64 | 100,000 | 0.0131 | 0.0248 | 0.53× | 189% | +|✅| a == b (int64) | int64 | 10,000,000 | 7.1546 | 5.7300 | 1.25× | 80% | +|▫| a > b (float32) | float32 | 1,000 | 0.0004 | 0.0007 | 0.56× | 178% | +|🟠| a > b (float32) | float32 | 100,000 | 0.0056 | 0.0185 | 0.31× | 327% | +|✅| a > b (float32) | float32 | 10,000,000 | 3.8956 | 3.3476 | 1.16× | 86% | +|▫| a > b (float64) | float64 | 1,000 | 0.0004 | 0.0008 | 0.58× | 171% | +|🟠| a > b (float64) | float64 | 100,000 | 0.0105 | 0.0262 | 0.40× | 248% | +|✅| a > b (float64) | float64 | 10,000,000 | 6.6876 | 5.7415 | 1.17× | 86% | +|▫| a > b (int32) | int32 | 1,000 | 0.0004 | 0.0008 | 0.54× | 186% | +|🟠| a > b (int32) | int32 | 100,000 | 0.0070 | 0.0195 | 0.36× | 280% | +|✅| a > b (int32) | int32 | 10,000,000 | 4.1882 | 3.3117 | 1.26× | 79% | +|▫| a > b (int64) | int64 | 1,000 | 0.0005 | 0.0007 | 0.74× | 134% | +|🟡| a > b (int64) | int64 | 100,000 | 0.0231 | 0.0256 | 0.90× | 111% | +|✅| a > b (int64) | int64 | 10,000,000 | 7.6396 | 5.7329 | 1.33× | 75% | +|▫| a >= b (float32) | float32 | 1,000 | 0.0004 | 0.0007 | 0.61× | 165% | +|🟠| a >= b (float32) | float32 | 100,000 | 0.0063 | 0.0186 | 0.34× | 295% | +|✅| a >= b (float32) | float32 | 10,000,000 | 3.8402 | 3.2664 | 1.18× | 85% | +|▫| a >= b (float64) | float64 | 1,000 | 0.0005 | 0.0007 | 0.60× | 166% | +|🟠| a >= b (float64) | float64 | 100,000 | 0.0118 | 0.0241 | 0.49× | 203% | +|✅| a >= b (float64) | float64 | 10,000,000 | 7.1505 | 5.6739 | 1.26× | 79% | +|▫| a >= b (int32) | int32 | 1,000 | 0.0004 | 0.0008 | 0.56× | 177% | +|🟠| a >= b (int32) | int32 | 100,000 | 0.0069 | 0.0191 | 0.36× | 277% | +|✅| a >= b (int32) | int32 | 10,000,000 | 4.5128 | 3.3235 | 1.36× | 74% | +|▫| a >= b (int64) | int64 | 1,000 | 0.0008 | 0.0008 | 1.00× | 100% | +|🟡| a >= b (int64) | int64 | 100,000 | 0.0188 | 0.0266 | 0.71× | 142% | +|✅| a >= b (int64) | int64 | 10,000,000 | 7.8543 | 5.9554 | 1.32× | 76% | ### Bitwise | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|▫| a & b (bool) | bool | 1,000 | 0.0004 | 0.0016 | 0.24× | 418% | -|🔴| a & b (bool) | bool | 100,000 | 0.0029 | 0.0240 | 0.12× | 815% | -|🟡| a & b (bool) | bool | 10,000,000 | 1.9362 | 2.3708 | 0.82× | 122% | -|▫| a & b (int16) | int16 | 1,000 | 0.0008 | 0.0021 | 0.36× | 275% | -|✅| a & b (int16) | int16 | 100,000 | 0.0369 | 0.0131 | 2.81× | 36% | -|✅| a & b (int16) | int16 | 10,000,000 | 5.1340 | 3.0763 | 1.67× | 60% | -|▫| a & b (int32) | int32 | 1,000 | 0.0008 | 0.0069 | 0.11× | 876% | -|🟡| a & b (int32) | int32 | 100,000 | 0.0291 | 0.0311 | 0.94× | 107% | -|✅| a & b (int32) | int32 | 10,000,000 | 9.2134 | 6.2718 | 1.47× | 68% | -|▫| a & b (int64) | int64 | 1,000 | 0.0008 | 0.0120 | 0.065× | 1544% | -|🟡| a & b (int64) | int64 | 100,000 | 0.0328 | 0.0569 | 0.57× | 174% | -|🟡| a & b (int64) | int64 | 10,000,000 | 18.5987 | 19.4528 | 0.96× | 105% | -|▫| a & b (int8) | int8 | 1,000 | 0.0007 | 0.0013 | 0.58× | 172% | -|✅| a & b (int8) | int8 | 100,000 | 0.0288 | 0.0085 | 3.39× | 30% | -|✅| a & b (int8) | int8 | 10,000,000 | 3.8601 | 1.5868 | 2.43× | 41% | -|▫| a & b (uint16) | uint16 | 1,000 | 0.0008 | 0.0022 | 0.35× | 283% | -|✅| a & b (uint16) | uint16 | 100,000 | 0.0291 | 0.0138 | 2.11× | 48% | -|✅| a & b (uint16) | uint16 | 10,000,000 | 7.4077 | 2.9658 | 2.50× | 40% | -|▫| a & b (uint32) | uint32 | 1,000 | 0.0008 | 0.0023 | 0.37× | 270% | -|✅| a & b (uint32) | uint32 | 100,000 | 0.0337 | 0.0278 | 1.21× | 82% | -|✅| a & b (uint32) | uint32 | 10,000,000 | 9.0297 | 5.8148 | 1.55× | 64% | -|▫| a & b (uint64) | uint64 | 1,000 | 0.0008 | 0.0066 | 0.12× | 856% | -|🟡| a & b (uint64) | uint64 | 100,000 | 0.0353 | 0.0608 | 0.58× | 172% | -|🟡| a & b (uint64) | uint64 | 10,000,000 | 17.0949 | 18.1468 | 0.94× | 106% | -|▫| a & b (uint8) | uint8 | 1,000 | 0.0006 | 0.0013 | 0.50× | 201% | -|✅| a & b (uint8) | uint8 | 100,000 | 0.0285 | 0.0080 | 3.58× | 28% | -|✅| a & b (uint8) | uint8 | 10,000,000 | 3.8145 | 1.5470 | 2.47× | 41% | -|▫| a ^ b (bool) | bool | 1,000 | 0.0004 | 0.0015 | 0.25× | 401% | -|🔴| a ^ b (bool) | bool | 100,000 | 0.0030 | 0.0228 | 0.13× | 752% | -|🟡| a ^ b (bool) | bool | 10,000,000 | 1.9149 | 2.4520 | 0.78× | 128% | -|▫| a ^ b (int16) | int16 | 1,000 | 0.0008 | 0.0022 | 0.35× | 281% | -|✅| a ^ b (int16) | int16 | 100,000 | 0.0285 | 0.0144 | 1.98× | 50% | -|✅| a ^ b (int16) | int16 | 10,000,000 | 5.2473 | 2.9006 | 1.81× | 55% | -|🟠| a ^ b (int32) | int32 | 1,000 | 0.0019 | 0.0073 | 0.26× | 381% | -|🟡| a ^ b (int32) | int32 | 100,000 | 0.0289 | 0.0304 | 0.95× | 105% | -|✅| a ^ b (int32) | int32 | 10,000,000 | 8.9648 | 6.1135 | 1.47× | 68% | -|▫| a ^ b (int64) | int64 | 1,000 | 0.0008 | 0.0123 | 0.064× | 1554% | -|🟡| a ^ b (int64) | int64 | 100,000 | 0.0311 | 0.0617 | 0.50× | 198% | -|✅| a ^ b (int64) | int64 | 10,000,000 | 20.4191 | 19.7104 | 1.04× | 96% | -|▫| a ^ b (int8) | int8 | 1,000 | 0.0006 | 0.0014 | 0.47× | 215% | -|✅| a ^ b (int8) | int8 | 100,000 | 0.0285 | 0.0083 | 3.43× | 29% | -|✅| a ^ b (int8) | int8 | 10,000,000 | 3.8174 | 1.6025 | 2.38× | 42% | -|▫| a ^ b (uint16) | uint16 | 1,000 | 0.0008 | 0.0023 | 0.33× | 302% | -|✅| a ^ b (uint16) | uint16 | 100,000 | 0.0287 | 0.0146 | 1.97× | 51% | -|✅| a ^ b (uint16) | uint16 | 10,000,000 | 5.8436 | 2.9363 | 1.99× | 50% | -|▫| a ^ b (uint32) | uint32 | 1,000 | 0.0008 | 0.0035 | 0.22× | 446% | -|✅| a ^ b (uint32) | uint32 | 100,000 | 0.0286 | 0.0274 | 1.04× | 96% | -|✅| a ^ b (uint32) | uint32 | 10,000,000 | 8.8120 | 5.8908 | 1.50× | 67% | -|▫| a ^ b (uint64) | uint64 | 1,000 | 0.0009 | 0.0128 | 0.069× | 1457% | -|🟡| a ^ b (uint64) | uint64 | 100,000 | 0.0300 | 0.0587 | 0.51× | 196% | -|🟡| a ^ b (uint64) | uint64 | 10,000,000 | 17.2203 | 17.4055 | 0.99× | 101% | -|▫| a ^ b (uint8) | uint8 | 1,000 | 0.0007 | 0.0013 | 0.50× | 200% | -|✅| a ^ b (uint8) | uint8 | 100,000 | 0.0286 | 0.0097 | 2.94× | 34% | -|✅| a ^ b (uint8) | uint8 | 10,000,000 | 4.0447 | 1.4824 | 2.73× | 37% | -|▫| a | b (bool) | bool | 1,000 | 0.0004 | 0.0014 | 0.27× | 377% | -|🔴| a | b (bool) | bool | 100,000 | 0.0029 | 0.0244 | 0.12× | 852% | -|🟡| a | b (bool) | bool | 10,000,000 | 2.0040 | 2.5681 | 0.78× | 128% | -|▫| a | b (int16) | int16 | 1,000 | 0.0009 | 0.0022 | 0.40× | 251% | -|✅| a | b (int16) | int16 | 100,000 | 0.0286 | 0.0138 | 2.08× | 48% | -|✅| a | b (int16) | int16 | 10,000,000 | 5.2591 | 2.9088 | 1.81× | 55% | -|▫| a | b (int32) | int32 | 1,000 | 0.0008 | 0.0052 | 0.15× | 654% | -|🟡| a | b (int32) | int32 | 100,000 | 0.0287 | 0.0301 | 0.95× | 105% | -|✅| a | b (int32) | int32 | 10,000,000 | 10.1933 | 6.1209 | 1.67× | 60% | -|▫| a | b (int64) | int64 | 1,000 | 0.0008 | 0.0116 | 0.069× | 1454% | -|🟡| a | b (int64) | int64 | 100,000 | 0.0325 | 0.0643 | 0.51× | 198% | -|🟡| a | b (int64) | int64 | 10,000,000 | 17.4904 | 19.2407 | 0.91× | 110% | -|▫| a | b (int8) | int8 | 1,000 | 0.0007 | 0.0013 | 0.53× | 188% | -|✅| a | b (int8) | int8 | 100,000 | 0.0298 | 0.0093 | 3.20× | 31% | -|✅| a | b (int8) | int8 | 10,000,000 | 4.0815 | 1.7639 | 2.31× | 43% | -|▫| a | b (uint16) | uint16 | 1,000 | 0.0008 | 0.0026 | 0.30× | 332% | -|✅| a | b (uint16) | uint16 | 100,000 | 0.0300 | 0.0151 | 1.98× | 50% | -|✅| a | b (uint16) | uint16 | 10,000,000 | 5.8621 | 2.8445 | 2.06× | 48% | -|▫| a | b (uint32) | uint32 | 1,000 | 0.0009 | 0.0040 | 0.22× | 461% | -|🟡| a | b (uint32) | uint32 | 100,000 | 0.0287 | 0.0292 | 0.98× | 102% | -|✅| a | b (uint32) | uint32 | 10,000,000 | 8.7585 | 5.7854 | 1.51× | 66% | -|▫| a | b (uint64) | uint64 | 1,000 | 0.0008 | 0.0087 | 0.091× | 1094% | -|🟡| a | b (uint64) | uint64 | 100,000 | 0.0383 | 0.0625 | 0.61× | 163% | -|✅| a | b (uint64) | uint64 | 10,000,000 | 17.6590 | 17.4118 | 1.01× | 99% | -|▫| a | b (uint8) | uint8 | 1,000 | 0.0007 | 0.0012 | 0.57× | 176% | -|✅| a | b (uint8) | uint8 | 100,000 | 0.0292 | 0.0107 | 2.73× | 37% | -|✅| a | b (uint8) | uint8 | 10,000,000 | 4.0708 | 1.5442 | 2.64× | 38% | -|▫| np.invert(a) (bool) | bool | 1,000 | 0.0004 | 0.0015 | 0.24× | 412% | -|🔴| np.invert(a) (bool) | bool | 100,000 | 0.0022 | 0.0237 | 0.094× | 1068% | -|🟡| np.invert(a) (bool) | bool | 10,000,000 | 1.7472 | 2.4556 | 0.71× | 140% | -|▫| np.invert(a) (int16) | int16 | 1,000 | 0.0009 | 0.0021 | 0.43× | 230% | -|✅| np.invert(a) (int16) | int16 | 100,000 | 0.0257 | 0.0133 | 1.94× | 52% | -|✅| np.invert(a) (int16) | int16 | 10,000,000 | 4.6982 | 2.3993 | 1.96× | 51% | -|▫| np.invert(a) (int32) | int32 | 1,000 | 0.0008 | 0.0024 | 0.32× | 309% | -|🟡| np.invert(a) (int32) | int32 | 100,000 | 0.0261 | 0.0327 | 0.80× | 125% | -|✅| np.invert(a) (int32) | int32 | 10,000,000 | 8.1259 | 5.3159 | 1.53× | 65% | -|▫| np.invert(a) (int64) | int64 | 1,000 | 0.0007 | 0.0071 | 0.10× | 969% | -|🟠| np.invert(a) (int64) | int64 | 100,000 | 0.0269 | 0.0565 | 0.48× | 210% | -|🟡| np.invert(a) (int64) | int64 | 10,000,000 | 15.5655 | 20.8043 | 0.75× | 134% | -|▫| np.invert(a) (int8) | int8 | 1,000 | 0.0006 | 0.0010 | 0.60× | 165% | -|✅| np.invert(a) (int8) | int8 | 100,000 | 0.0257 | 0.0086 | 2.98× | 34% | -|✅| np.invert(a) (int8) | int8 | 10,000,000 | 3.5401 | 1.3515 | 2.62× | 38% | -|▫| np.invert(a) (uint16) | uint16 | 1,000 | 0.0007 | 0.0026 | 0.28× | 354% | -|✅| np.invert(a) (uint16) | uint16 | 100,000 | 0.0258 | 0.0152 | 1.70× | 59% | -|✅| np.invert(a) (uint16) | uint16 | 10,000,000 | 4.7607 | 2.5025 | 1.90× | 53% | -|▫| np.invert(a) (uint32) | uint32 | 1,000 | 0.0007 | 0.0052 | 0.14× | 699% | -|✅| np.invert(a) (uint32) | uint32 | 100,000 | 0.0347 | 0.0255 | 1.36× | 73% | -|✅| np.invert(a) (uint32) | uint32 | 10,000,000 | 7.9103 | 4.3619 | 1.81× | 55% | -|▫| np.invert(a) (uint64) | uint64 | 1,000 | 0.0007 | 0.0084 | 0.086× | 1162% | -|🟠| np.invert(a) (uint64) | uint64 | 100,000 | 0.0261 | 0.0561 | 0.47× | 215% | -|🟡| np.invert(a) (uint64) | uint64 | 10,000,000 | 15.3698 | 16.1587 | 0.95× | 105% | -|▫| np.invert(a) (uint8) | uint8 | 1,000 | 0.0006 | 0.0012 | 0.52× | 193% | -|✅| np.invert(a) (uint8) | uint8 | 100,000 | 0.0268 | 0.0072 | 3.73× | 27% | -|✅| np.invert(a) (uint8) | uint8 | 10,000,000 | 3.5368 | 1.2344 | 2.87× | 35% | -|⚪| np.left_shift(a, 2) (bool) | bool | 1,000 | 0.0015 | - | - | - | -|⚪| np.left_shift(a, 2) (bool) | bool | 100,000 | 0.0449 | - | - | - | -|⚪| np.left_shift(a, 2) (bool) | bool | 10,000,000 | 15.2171 | - | - | - | -|🔴| np.left_shift(a, 2) (int16) | int16 | 1,000 | 0.0010 | 0.0053 | 0.20× | 510% | -|🔴| np.left_shift(a, 2) (int16) | int16 | 100,000 | 0.0282 | 0.3813 | 0.074× | 1350% | -|🔴| np.left_shift(a, 2) (int16) | int16 | 10,000,000 | 4.9430 | 37.1258 | 0.13× | 751% | -|▫| np.left_shift(a, 2) (int32) | int32 | 1,000 | 0.0010 | 0.0089 | 0.11× | 897% | -|🔴| np.left_shift(a, 2) (int32) | int32 | 100,000 | 0.0192 | 0.3904 | 0.049× | 2035% | -|🟠| np.left_shift(a, 2) (int32) | int32 | 10,000,000 | 7.7457 | 37.0641 | 0.21× | 478% | -|▫| np.left_shift(a, 2) (int64) | int64 | 1,000 | 0.0010 | 0.0133 | 0.075× | 1342% | -|🔴| np.left_shift(a, 2) (int64) | int64 | 100,000 | 0.0202 | 0.3924 | 0.051× | 1945% | -|🟠| np.left_shift(a, 2) (int64) | int64 | 10,000,000 | 15.1483 | 48.2592 | 0.31× | 319% | -|▫| np.left_shift(a, 2) (int8) | int8 | 1,000 | 0.0009 | 0.0054 | 0.17× | 584% | -|🔴| np.left_shift(a, 2) (int8) | int8 | 100,000 | 0.0305 | 0.3768 | 0.081× | 1236% | -|🔴| np.left_shift(a, 2) (int8) | int8 | 10,000,000 | 3.7550 | 37.7355 | 0.10× | 1005% | -|🔴| np.left_shift(a, 2) (uint16) | uint16 | 1,000 | 0.0011 | 0.0053 | 0.20× | 503% | -|🔴| np.left_shift(a, 2) (uint16) | uint16 | 100,000 | 0.0293 | 0.3879 | 0.076× | 1322% | -|🔴| np.left_shift(a, 2) (uint16) | uint16 | 10,000,000 | 5.3824 | 37.4437 | 0.14× | 696% | -|🔴| np.left_shift(a, 2) (uint32) | uint32 | 1,000 | 0.0014 | 0.0072 | 0.20× | 503% | -|🔴| np.left_shift(a, 2) (uint32) | uint32 | 100,000 | 0.0201 | 0.3908 | 0.051× | 1944% | -|🟠| np.left_shift(a, 2) (uint32) | uint32 | 10,000,000 | 7.7136 | 37.4067 | 0.21× | 485% | -|▫| np.left_shift(a, 2) (uint64) | uint64 | 1,000 | 0.0010 | 0.0122 | 0.079× | 1264% | -|🔴| np.left_shift(a, 2) (uint64) | uint64 | 100,000 | 0.0197 | 0.3923 | 0.050× | 1992% | -|🟠| np.left_shift(a, 2) (uint64) | uint64 | 10,000,000 | 15.1562 | 43.8575 | 0.35× | 289% | -|▫| np.left_shift(a, 2) (uint8) | uint8 | 1,000 | 0.0009 | 0.0053 | 0.17× | 579% | -|🔴| np.left_shift(a, 2) (uint8) | uint8 | 100,000 | 0.0282 | 0.3753 | 0.075× | 1330% | -|🔴| np.left_shift(a, 2) (uint8) | uint8 | 10,000,000 | 3.7382 | 37.0483 | 0.10× | 991% | -|⚪| np.right_shift(a, 2) (bool) | bool | 1,000 | 0.0016 | - | - | - | -|⚪| np.right_shift(a, 2) (bool) | bool | 100,000 | 0.0562 | - | - | - | -|⚪| np.right_shift(a, 2) (bool) | bool | 10,000,000 | 15.4174 | - | - | - | -|🟠| np.right_shift(a, 2) (int16) | int16 | 1,000 | 0.0012 | 0.0054 | 0.22× | 460% | -|🔴| np.right_shift(a, 2) (int16) | int16 | 100,000 | 0.0374 | 0.3824 | 0.098× | 1021% | -|🔴| np.right_shift(a, 2) (int16) | int16 | 10,000,000 | 5.7902 | 37.1819 | 0.16× | 642% | -|🔴| np.right_shift(a, 2) (int32) | int32 | 1,000 | 0.0013 | 0.0090 | 0.15× | 676% | -|🔴| np.right_shift(a, 2) (int32) | int32 | 100,000 | 0.0288 | 0.3903 | 0.074× | 1353% | -|🟠| np.right_shift(a, 2) (int32) | int32 | 10,000,000 | 7.9852 | 37.4325 | 0.21× | 469% | -|🔴| np.right_shift(a, 2) (int64) | int64 | 1,000 | 0.0010 | 0.0127 | 0.080× | 1249% | -|🔴| np.right_shift(a, 2) (int64) | int64 | 100,000 | 0.0289 | 0.3926 | 0.074× | 1357% | -|🟠| np.right_shift(a, 2) (int64) | int64 | 10,000,000 | 15.2694 | 46.0316 | 0.33× | 302% | -|🟠| np.right_shift(a, 2) (int8) | int8 | 1,000 | 0.0011 | 0.0054 | 0.20× | 487% | -|🔴| np.right_shift(a, 2) (int8) | int8 | 100,000 | 0.0383 | 0.3764 | 0.10× | 982% | -|🔴| np.right_shift(a, 2) (int8) | int8 | 10,000,000 | 4.6221 | 37.4222 | 0.12× | 810% | -|🔴| np.right_shift(a, 2) (uint16) | uint16 | 1,000 | 0.0011 | 0.0054 | 0.20× | 512% | -|🔴| np.right_shift(a, 2) (uint16) | uint16 | 100,000 | 0.0285 | 0.3868 | 0.074× | 1355% | -|🔴| np.right_shift(a, 2) (uint16) | uint16 | 10,000,000 | 5.2827 | 37.5630 | 0.14× | 711% | -|▫| np.right_shift(a, 2) (uint32) | uint32 | 1,000 | 0.0010 | 0.0135 | 0.073× | 1366% | -|🔴| np.right_shift(a, 2) (uint32) | uint32 | 100,000 | 0.0202 | 0.3900 | 0.052× | 1930% | -|🟠| np.right_shift(a, 2) (uint32) | uint32 | 10,000,000 | 7.8598 | 37.2613 | 0.21× | 474% | -|▫| np.right_shift(a, 2) (uint64) | uint64 | 1,000 | 0.0010 | 0.0136 | 0.072× | 1384% | -|🔴| np.right_shift(a, 2) (uint64) | uint64 | 100,000 | 0.0206 | 0.3933 | 0.052× | 1908% | -|🟠| np.right_shift(a, 2) (uint64) | uint64 | 10,000,000 | 15.2120 | 44.4632 | 0.34× | 292% | -|▫| np.right_shift(a, 2) (uint8) | uint8 | 1,000 | 0.0009 | 0.0054 | 0.17× | 583% | -|🔴| np.right_shift(a, 2) (uint8) | uint8 | 100,000 | 0.0286 | 0.3756 | 0.076× | 1315% | -|🔴| np.right_shift(a, 2) (uint8) | uint8 | 10,000,000 | 3.7457 | 37.0296 | 0.10× | 989% | +|▫| a & b (bool) | bool | 1,000 | 0.0004 | 0.0016 | 0.24× | 409% | +|🔴| a & b (bool) | bool | 100,000 | 0.0028 | 0.0215 | 0.13× | 763% | +|🟡| a & b (bool) | bool | 10,000,000 | 1.8589 | 3.0929 | 0.60× | 166% | +|▫| a & b (int16) | int16 | 1,000 | 0.0008 | 0.0027 | 0.29× | 349% | +|✅| a & b (int16) | int16 | 100,000 | 0.0290 | 0.0169 | 1.72× | 58% | +|✅| a & b (int16) | int16 | 10,000,000 | 5.2942 | 2.9661 | 1.78× | 56% | +|▫| a & b (int32) | int32 | 1,000 | 0.0008 | 0.0060 | 0.13× | 779% | +|🟡| a & b (int32) | int32 | 100,000 | 0.0286 | 0.0342 | 0.84× | 120% | +|✅| a & b (int32) | int32 | 10,000,000 | 9.1881 | 5.3408 | 1.72× | 58% | +|▫| a & b (int64) | int64 | 1,000 | 0.0008 | 0.0069 | 0.11× | 891% | +|🟡| a & b (int64) | int64 | 100,000 | 0.0338 | 0.0634 | 0.53× | 188% | +|✅| a & b (int64) | int64 | 10,000,000 | 17.1008 | 16.6525 | 1.03× | 97% | +|▫| a & b (int8) | int8 | 1,000 | 0.0007 | 0.0013 | 0.49× | 204% | +|✅| a & b (int8) | int8 | 100,000 | 0.0286 | 0.0100 | 2.86× | 35% | +|✅| a & b (int8) | int8 | 10,000,000 | 3.8854 | 1.5338 | 2.53× | 40% | +|▫| a & b (uint16) | uint16 | 1,000 | 0.0008 | 0.0034 | 0.23× | 440% | +|✅| a & b (uint16) | uint16 | 100,000 | 0.0301 | 0.0180 | 1.68× | 60% | +|✅| a & b (uint16) | uint16 | 10,000,000 | 5.2834 | 3.0212 | 1.75× | 57% | +|▫| a & b (uint32) | uint32 | 1,000 | 0.0008 | 0.0039 | 0.20× | 492% | +|🟡| a & b (uint32) | uint32 | 100,000 | 0.0339 | 0.0400 | 0.85× | 118% | +|✅| a & b (uint32) | uint32 | 10,000,000 | 8.5821 | 6.0168 | 1.43× | 70% | +|▫| a & b (uint64) | uint64 | 1,000 | 0.0008 | 0.0024 | 0.32× | 310% | +|🟠| a & b (uint64) | uint64 | 100,000 | 0.0356 | 0.0739 | 0.48× | 207% | +|✅| a & b (uint64) | uint64 | 10,000,000 | 17.0644 | 16.1487 | 1.06× | 95% | +|▫| a & b (uint8) | uint8 | 1,000 | 0.0006 | 0.0010 | 0.62× | 162% | +|✅| a & b (uint8) | uint8 | 100,000 | 0.0296 | 0.0103 | 2.88× | 35% | +|✅| a & b (uint8) | uint8 | 10,000,000 | 3.8191 | 1.5272 | 2.50× | 40% | +|▫| a ^ b (bool) | bool | 1,000 | 0.0004 | 0.0017 | 0.22× | 447% | +|🔴| a ^ b (bool) | bool | 100,000 | 0.0026 | 0.0214 | 0.12× | 822% | +|🟡| a ^ b (bool) | bool | 10,000,000 | 1.8620 | 3.0807 | 0.60× | 166% | +|▫| a ^ b (int16) | int16 | 1,000 | 0.0008 | 0.0026 | 0.30× | 328% | +|✅| a ^ b (int16) | int16 | 100,000 | 0.0285 | 0.0149 | 1.92× | 52% | +|✅| a ^ b (int16) | int16 | 10,000,000 | 5.2041 | 2.9252 | 1.78× | 56% | +|▫| a ^ b (int32) | int32 | 1,000 | 0.0008 | 0.0047 | 0.16× | 615% | +|🟡| a ^ b (int32) | int32 | 100,000 | 0.0288 | 0.0321 | 0.90× | 111% | +|✅| a ^ b (int32) | int32 | 10,000,000 | 8.9067 | 5.3599 | 1.66× | 60% | +|▫| a ^ b (int64) | int64 | 1,000 | 0.0008 | 0.0040 | 0.20× | 511% | +|🟡| a ^ b (int64) | int64 | 100,000 | 0.0337 | 0.0666 | 0.51× | 198% | +|✅| a ^ b (int64) | int64 | 10,000,000 | 17.2489 | 15.6910 | 1.10× | 91% | +|▫| a ^ b (int8) | int8 | 1,000 | 0.0007 | 0.0012 | 0.52× | 191% | +|✅| a ^ b (int8) | int8 | 100,000 | 0.0295 | 0.0102 | 2.90× | 34% | +|✅| a ^ b (int8) | int8 | 10,000,000 | 3.8764 | 1.5503 | 2.50× | 40% | +|▫| a ^ b (uint16) | uint16 | 1,000 | 0.0008 | 0.0028 | 0.28× | 361% | +|✅| a ^ b (uint16) | uint16 | 100,000 | 0.0287 | 0.0163 | 1.77× | 57% | +|✅| a ^ b (uint16) | uint16 | 10,000,000 | 5.2129 | 2.9215 | 1.78× | 56% | +|▫| a ^ b (uint32) | uint32 | 1,000 | 0.0008 | 0.0048 | 0.16× | 610% | +|🟡| a ^ b (uint32) | uint32 | 100,000 | 0.0285 | 0.0315 | 0.91× | 110% | +|✅| a ^ b (uint32) | uint32 | 10,000,000 | 8.4267 | 5.7479 | 1.47× | 68% | +|▫| a ^ b (uint64) | uint64 | 1,000 | 0.0009 | 0.0111 | 0.078× | 1287% | +|🟡| a ^ b (uint64) | uint64 | 100,000 | 0.0341 | 0.0681 | 0.50× | 200% | +|✅| a ^ b (uint64) | uint64 | 10,000,000 | 16.9004 | 16.6136 | 1.02× | 98% | +|▫| a ^ b (uint8) | uint8 | 1,000 | 0.0006 | 0.0012 | 0.53× | 187% | +|✅| a ^ b (uint8) | uint8 | 100,000 | 0.0288 | 0.0119 | 2.42× | 41% | +|✅| a ^ b (uint8) | uint8 | 10,000,000 | 3.8171 | 1.4591 | 2.62× | 38% | +|▫| a | b (bool) | bool | 1,000 | 0.0004 | 0.0015 | 0.31× | 326% | +|🔴| a | b (bool) | bool | 100,000 | 0.0025 | 0.0232 | 0.11× | 912% | +|🟡| a | b (bool) | bool | 10,000,000 | 1.8887 | 2.8568 | 0.66× | 151% | +|▫| a | b (int16) | int16 | 1,000 | 0.0008 | 0.0023 | 0.35× | 288% | +|✅| a | b (int16) | int16 | 100,000 | 0.0285 | 0.0178 | 1.60× | 62% | +|✅| a | b (int16) | int16 | 10,000,000 | 5.2268 | 2.9832 | 1.75× | 57% | +|▫| a | b (int32) | int32 | 1,000 | 0.0008 | 0.0074 | 0.10× | 950% | +|🟡| a | b (int32) | int32 | 100,000 | 0.0286 | 0.0380 | 0.75× | 133% | +|✅| a | b (int32) | int32 | 10,000,000 | 9.0217 | 5.4574 | 1.65× | 60% | +|▫| a | b (int64) | int64 | 1,000 | 0.0008 | 0.0079 | 0.10× | 1003% | +|🟠| a | b (int64) | int64 | 100,000 | 0.0339 | 0.0707 | 0.48× | 209% | +|✅| a | b (int64) | int64 | 10,000,000 | 17.1245 | 15.6955 | 1.09× | 92% | +|▫| a | b (int8) | int8 | 1,000 | 0.0007 | 0.0014 | 0.48× | 210% | +|✅| a | b (int8) | int8 | 100,000 | 0.0300 | 0.0116 | 2.58× | 39% | +|✅| a | b (int8) | int8 | 10,000,000 | 4.1649 | 1.5132 | 2.75× | 36% | +|▫| a | b (uint16) | uint16 | 1,000 | 0.0008 | 0.0019 | 0.43× | 234% | +|✅| a | b (uint16) | uint16 | 100,000 | 0.0291 | 0.0168 | 1.73× | 58% | +|✅| a | b (uint16) | uint16 | 10,000,000 | 5.1942 | 2.8924 | 1.80× | 56% | +|▫| a | b (uint32) | uint32 | 1,000 | 0.0008 | 0.0035 | 0.22× | 446% | +|🟡| a | b (uint32) | uint32 | 100,000 | 0.0285 | 0.0334 | 0.85× | 117% | +|✅| a | b (uint32) | uint32 | 10,000,000 | 8.5824 | 5.4787 | 1.57× | 64% | +|▫| a | b (uint64) | uint64 | 1,000 | 0.0008 | 0.0022 | 0.35× | 282% | +|🟡| a | b (uint64) | uint64 | 100,000 | 0.0353 | 0.0677 | 0.52× | 192% | +|✅| a | b (uint64) | uint64 | 10,000,000 | 16.8283 | 16.1102 | 1.04× | 96% | +|▫| a | b (uint8) | uint8 | 1,000 | 0.0007 | 0.0012 | 0.56× | 177% | +|✅| a | b (uint8) | uint8 | 100,000 | 0.0296 | 0.0105 | 2.83× | 35% | +|✅| a | b (uint8) | uint8 | 10,000,000 | 4.1042 | 1.4855 | 2.76× | 36% | +|▫| np.invert(a) (bool) | bool | 1,000 | 0.0004 | 0.0015 | 0.24× | 409% | +|🔴| np.invert(a) (bool) | bool | 100,000 | 0.0021 | 0.0230 | 0.092× | 1083% | +|🟡| np.invert(a) (bool) | bool | 10,000,000 | 1.6689 | 2.5576 | 0.65× | 153% | +|▫| np.invert(a) (int16) | int16 | 1,000 | 0.0007 | 0.0018 | 0.40× | 247% | +|✅| np.invert(a) (int16) | int16 | 100,000 | 0.0257 | 0.0140 | 1.83× | 55% | +|✅| np.invert(a) (int16) | int16 | 10,000,000 | 4.7670 | 2.5132 | 1.90× | 53% | +|▫| np.invert(a) (int32) | int32 | 1,000 | 0.0008 | 0.0058 | 0.13× | 758% | +|🟡| np.invert(a) (int32) | int32 | 100,000 | 0.0264 | 0.0305 | 0.86× | 116% | +|✅| np.invert(a) (int32) | int32 | 10,000,000 | 8.1792 | 4.7721 | 1.71× | 58% | +|▫| np.invert(a) (int64) | int64 | 1,000 | 0.0007 | 0.0059 | 0.13× | 796% | +|🟠| np.invert(a) (int64) | int64 | 100,000 | 0.0263 | 0.0661 | 0.40× | 251% | +|✅| np.invert(a) (int64) | int64 | 10,000,000 | 15.1634 | 14.2955 | 1.06× | 94% | +|▫| np.invert(a) (int8) | int8 | 1,000 | 0.0006 | 0.0014 | 0.46× | 218% | +|✅| np.invert(a) (int8) | int8 | 100,000 | 0.0293 | 0.0108 | 2.72× | 37% | +|✅| np.invert(a) (int8) | int8 | 10,000,000 | 3.5390 | 1.3133 | 2.69× | 37% | +|▫| np.invert(a) (uint16) | uint16 | 1,000 | 0.0007 | 0.0022 | 0.33× | 302% | +|✅| np.invert(a) (uint16) | uint16 | 100,000 | 0.0258 | 0.0206 | 1.25× | 80% | +|✅| np.invert(a) (uint16) | uint16 | 10,000,000 | 4.7281 | 2.4541 | 1.93× | 52% | +|▫| np.invert(a) (uint32) | uint32 | 1,000 | 0.0008 | 0.0036 | 0.22× | 459% | +|✅| np.invert(a) (uint32) | uint32 | 100,000 | 0.0332 | 0.0290 | 1.15× | 87% | +|✅| np.invert(a) (uint32) | uint32 | 10,000,000 | 7.8329 | 4.8460 | 1.62× | 62% | +|▫| np.invert(a) (uint64) | uint64 | 1,000 | 0.0007 | 0.0035 | 0.21× | 484% | +|🟠| np.invert(a) (uint64) | uint64 | 100,000 | 0.0263 | 0.0652 | 0.40× | 248% | +|🟡| np.invert(a) (uint64) | uint64 | 10,000,000 | 15.2896 | 15.3374 | 1.00× | 100% | +|▫| np.invert(a) (uint8) | uint8 | 1,000 | 0.0006 | 0.0014 | 0.46× | 216% | +|✅| np.invert(a) (uint8) | uint8 | 100,000 | 0.0268 | 0.0110 | 2.45× | 41% | +|✅| np.invert(a) (uint8) | uint8 | 10,000,000 | 3.5162 | 1.3311 | 2.64× | 38% | +|🔴| np.left_shift(a, 2) (bool) | bool | 1,000 | 0.0015 | 0.0125 | 0.12× | 845% | +|🟡| np.left_shift(a, 2) (bool) | bool | 100,000 | 0.0430 | 0.0779 | 0.55× | 181% | +|✅| np.left_shift(a, 2) (bool) | bool | 10,000,000 | 15.2024 | 10.3241 | 1.47× | 68% | +|🟠| np.left_shift(a, 2) (int16) | int16 | 1,000 | 0.0010 | 0.0031 | 0.33× | 302% | +|✅| np.left_shift(a, 2) (int16) | int16 | 100,000 | 0.0285 | 0.0150 | 1.90× | 53% | +|✅| np.left_shift(a, 2) (int16) | int16 | 10,000,000 | 4.9855 | 2.6983 | 1.85× | 54% | +|▫| np.left_shift(a, 2) (int32) | int32 | 1,000 | 0.0010 | 0.0057 | 0.18× | 569% | +|🟡| np.left_shift(a, 2) (int32) | int32 | 100,000 | 0.0190 | 0.0373 | 0.51× | 196% | +|✅| np.left_shift(a, 2) (int32) | int32 | 10,000,000 | 7.6421 | 4.5654 | 1.67× | 60% | +|▫| np.left_shift(a, 2) (int64) | int64 | 1,000 | 0.0009 | 0.0038 | 0.24× | 420% | +|🟠| np.left_shift(a, 2) (int64) | int64 | 100,000 | 0.0190 | 0.0672 | 0.28× | 353% | +|✅| np.left_shift(a, 2) (int64) | int64 | 10,000,000 | 15.2053 | 14.9107 | 1.02× | 98% | +|▫| np.left_shift(a, 2) (int8) | int8 | 1,000 | 0.0009 | 0.0019 | 0.49× | 204% | +|✅| np.left_shift(a, 2) (int8) | int8 | 100,000 | 0.0305 | 0.0085 | 3.58× | 28% | +|✅| np.left_shift(a, 2) (int8) | int8 | 10,000,000 | 3.7477 | 1.2738 | 2.94× | 34% | +|🟠| np.left_shift(a, 2) (uint16) | uint16 | 1,000 | 0.0011 | 0.0023 | 0.45× | 222% | +|✅| np.left_shift(a, 2) (uint16) | uint16 | 100,000 | 0.0290 | 0.0163 | 1.78× | 56% | +|✅| np.left_shift(a, 2) (uint16) | uint16 | 10,000,000 | 5.3571 | 2.5057 | 2.14× | 47% | +|▫| np.left_shift(a, 2) (uint32) | uint32 | 1,000 | 0.0010 | 0.0045 | 0.22× | 456% | +|🟡| np.left_shift(a, 2) (uint32) | uint32 | 100,000 | 0.0192 | 0.0272 | 0.70× | 142% | +|✅| np.left_shift(a, 2) (uint32) | uint32 | 10,000,000 | 7.9134 | 5.0757 | 1.56× | 64% | +|▫| np.left_shift(a, 2) (uint64) | uint64 | 1,000 | 0.0010 | 0.0096 | 0.10× | 1000% | +|🟠| np.left_shift(a, 2) (uint64) | uint64 | 100,000 | 0.0194 | 0.0697 | 0.28× | 359% | +|🟡| np.left_shift(a, 2) (uint64) | uint64 | 10,000,000 | 15.2926 | 15.7026 | 0.97× | 103% | +|▫| np.left_shift(a, 2) (uint8) | uint8 | 1,000 | 0.0009 | 0.0023 | 0.40× | 250% | +|✅| np.left_shift(a, 2) (uint8) | uint8 | 100,000 | 0.0292 | 0.0131 | 2.22× | 45% | +|✅| np.left_shift(a, 2) (uint8) | uint8 | 10,000,000 | 3.6979 | 1.2844 | 2.88× | 35% | +|🔴| np.right_shift(a, 2) (bool) | bool | 1,000 | 0.0016 | 0.0135 | 0.12× | 844% | +|🟡| np.right_shift(a, 2) (bool) | bool | 100,000 | 0.0529 | 0.0750 | 0.71× | 142% | +|✅| np.right_shift(a, 2) (bool) | bool | 10,000,000 | 15.2744 | 9.3866 | 1.63× | 62% | +|🟠| np.right_shift(a, 2) (int16) | int16 | 1,000 | 0.0011 | 0.0026 | 0.44× | 226% | +|✅| np.right_shift(a, 2) (int16) | int16 | 100,000 | 0.0377 | 0.0158 | 2.38× | 42% | +|✅| np.right_shift(a, 2) (int16) | int16 | 10,000,000 | 5.7282 | 2.6585 | 2.15× | 46% | +|🟠| np.right_shift(a, 2) (int32) | int32 | 1,000 | 0.0011 | 0.0038 | 0.28× | 354% | +|🟡| np.right_shift(a, 2) (int32) | int32 | 100,000 | 0.0288 | 0.0378 | 0.76× | 131% | +|✅| np.right_shift(a, 2) (int32) | int32 | 10,000,000 | 7.8870 | 4.3972 | 1.79× | 56% | +|🟠| np.right_shift(a, 2) (int64) | int64 | 1,000 | 0.0013 | 0.0039 | 0.32× | 309% | +|🟠| np.right_shift(a, 2) (int64) | int64 | 100,000 | 0.0287 | 0.0702 | 0.41× | 244% | +|✅| np.right_shift(a, 2) (int64) | int64 | 10,000,000 | 15.0917 | 14.2202 | 1.06× | 94% | +|🟡| np.right_shift(a, 2) (int8) | int8 | 1,000 | 0.0010 | 0.0019 | 0.54× | 185% | +|✅| np.right_shift(a, 2) (int8) | int8 | 100,000 | 0.0436 | 0.0108 | 4.02× | 25% | +|✅| np.right_shift(a, 2) (int8) | int8 | 10,000,000 | 4.7856 | 1.2716 | 3.76× | 27% | +|🟠| np.right_shift(a, 2) (uint16) | uint16 | 1,000 | 0.0011 | 0.0029 | 0.36× | 275% | +|✅| np.right_shift(a, 2) (uint16) | uint16 | 100,000 | 0.0283 | 0.0170 | 1.67× | 60% | +|✅| np.right_shift(a, 2) (uint16) | uint16 | 10,000,000 | 5.1786 | 2.5878 | 2.00× | 50% | +|▫| np.right_shift(a, 2) (uint32) | uint32 | 1,000 | 0.0010 | 0.0036 | 0.27× | 368% | +|🟡| np.right_shift(a, 2) (uint32) | uint32 | 100,000 | 0.0205 | 0.0284 | 0.72× | 138% | +|✅| np.right_shift(a, 2) (uint32) | uint32 | 10,000,000 | 7.7130 | 4.7119 | 1.64× | 61% | +|▫| np.right_shift(a, 2) (uint64) | uint64 | 1,000 | 0.0010 | 0.0108 | 0.091× | 1100% | +|🟠| np.right_shift(a, 2) (uint64) | uint64 | 100,000 | 0.0212 | 0.0750 | 0.28× | 354% | +|🟡| np.right_shift(a, 2) (uint64) | uint64 | 10,000,000 | 14.9083 | 16.6474 | 0.90× | 112% | +|▫| np.right_shift(a, 2) (uint8) | uint8 | 1,000 | 0.0009 | 0.0021 | 0.44× | 227% | +|✅| np.right_shift(a, 2) (uint8) | uint8 | 100,000 | 0.0296 | 0.0095 | 3.12× | 32% | +|✅| np.right_shift(a, 2) (uint8) | uint8 | 10,000,000 | 3.7353 | 1.2943 | 2.89× | 35% | ### Logic | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| |⚪| np.all(a) (bool) | bool | 1,000 | 0.0015 | - | - | - | -|⚪| np.all(a) (bool) | bool | 100,000 | 0.0015 | - | - | - | +|⚪| np.all(a) (bool) | bool | 100,000 | 0.0014 | - | - | - | |⚪| np.all(a) (bool) | bool | 10,000,000 | 0.0014 | - | - | - | -|⚪| np.allclose(a, b) (float16) | float16 | 1,000 | 0.0332 | - | - | - | -|⚪| np.allclose(a, b) (float16) | float16 | 100,000 | 1.7625 | - | - | - | -|⚪| np.allclose(a, b) (float16) | float16 | 10,000,000 | 192.9437 | - | - | - | -|⚪| np.allclose(a, b) (float32) | float32 | 1,000 | 0.0130 | - | - | - | -|⚪| np.allclose(a, b) (float32) | float32 | 100,000 | 0.3409 | - | - | - | -|⚪| np.allclose(a, b) (float32) | float32 | 10,000,000 | 54.6204 | - | - | - | -|⚪| np.allclose(a, b) (float64) | float64 | 1,000 | 0.0132 | - | - | - | -|⚪| np.allclose(a, b) (float64) | float64 | 100,000 | 0.6240 | - | - | - | -|⚪| np.allclose(a, b) (float64) | float64 | 10,000,000 | 107.6998 | - | - | - | -|⚪| np.any(a) (bool) | bool | 1,000 | 0.0016 | - | - | - | -|⚪| np.any(a) (bool) | bool | 100,000 | 0.0014 | - | - | - | +|⚪| np.allclose(a, b) (float16) | float16 | 1,000 | 0.0305 | - | - | - | +|⚪| np.allclose(a, b) (float16) | float16 | 100,000 | 1.7794 | - | - | - | +|⚪| np.allclose(a, b) (float16) | float16 | 10,000,000 | 192.9692 | - | - | - | +|⚪| np.allclose(a, b) (float32) | float32 | 1,000 | 0.0132 | - | - | - | +|⚪| np.allclose(a, b) (float32) | float32 | 100,000 | 0.3660 | - | - | - | +|⚪| np.allclose(a, b) (float32) | float32 | 10,000,000 | 55.8239 | - | - | - | +|⚪| np.allclose(a, b) (float64) | float64 | 1,000 | 0.0165 | - | - | - | +|⚪| np.allclose(a, b) (float64) | float64 | 100,000 | 0.6299 | - | - | - | +|⚪| np.allclose(a, b) (float64) | float64 | 10,000,000 | 107.5215 | - | - | - | +|⚪| np.any(a) (bool) | bool | 1,000 | 0.0015 | - | - | - | +|⚪| np.any(a) (bool) | bool | 100,000 | 0.0017 | - | - | - | |⚪| np.any(a) (bool) | bool | 10,000,000 | 0.0014 | - | - | - | -|✅| np.array_equal(a, b) (float16) | float16 | 1,000 | 0.0025 | 0.0011 | 2.26× | 44% | -|✅| np.array_equal(a, b) (float16) | float16 | 100,000 | 0.0956 | 0.0710 | 1.35× | 74% | -|✅| np.array_equal(a, b) (float16) | float16 | 10,000,000 | 9.4225 | 6.3415 | 1.49× | 67% | -|▫| np.array_equal(a, b) (float32) | float32 | 1,000 | 0.0015 | 0.0007 | 2.14× | 47% | -|🟡| np.array_equal(a, b) (float32) | float32 | 100,000 | 0.0087 | 0.0166 | 0.52× | 191% | -|🟡| np.array_equal(a, b) (float32) | float32 | 10,000,000 | 3.8713 | 3.8855 | 1.00× | 100% | -|▫| np.array_equal(a, b) (float64) | float64 | 1,000 | 0.0016 | 0.0008 | 2.01× | 50% | -|🟠| np.array_equal(a, b) (float64) | float64 | 100,000 | 0.0121 | 0.0297 | 0.41× | 245% | -|✅| np.array_equal(a, b) (float64) | float64 | 10,000,000 | 7.0010 | 6.4820 | 1.08× | 93% | -|⚪| np.isclose(a, b) (float16) | float16 | 1,000 | 0.0296 | - | - | - | -|⚪| np.isclose(a, b) (float16) | float16 | 100,000 | 1.7611 | - | - | - | -|⚪| np.isclose(a, b) (float16) | float16 | 10,000,000 | 199.5655 | - | - | - | -|⚪| np.isclose(a, b) (float32) | float32 | 1,000 | 0.0116 | - | - | - | -|⚪| np.isclose(a, b) (float32) | float32 | 100,000 | 0.3355 | - | - | - | -|⚪| np.isclose(a, b) (float32) | float32 | 10,000,000 | 55.8382 | - | - | - | +|✅| np.array_equal(a, b) (float16) | float16 | 1,000 | 0.0025 | 0.0013 | 1.90× | 53% | +|✅| np.array_equal(a, b) (float16) | float16 | 100,000 | 0.0873 | 0.0697 | 1.25× | 80% | +|✅| np.array_equal(a, b) (float16) | float16 | 10,000,000 | 9.5077 | 6.1828 | 1.54× | 65% | +|▫| np.array_equal(a, b) (float32) | float32 | 1,000 | 0.0016 | 0.0008 | 1.95× | 51% | +|🟠| np.array_equal(a, b) (float32) | float32 | 100,000 | 0.0067 | 0.0186 | 0.36× | 276% | +|✅| np.array_equal(a, b) (float32) | float32 | 10,000,000 | 3.9686 | 3.4029 | 1.17× | 86% | +|▫| np.array_equal(a, b) (float64) | float64 | 1,000 | 0.0018 | 0.0007 | 2.36× | 42% | +|🟠| np.array_equal(a, b) (float64) | float64 | 100,000 | 0.0121 | 0.0248 | 0.49× | 204% | +|✅| np.array_equal(a, b) (float64) | float64 | 10,000,000 | 6.8297 | 5.8386 | 1.17× | 86% | +|⚪| np.isclose(a, b) (float16) | float16 | 1,000 | 0.0300 | - | - | - | +|⚪| np.isclose(a, b) (float16) | float16 | 100,000 | 1.7764 | - | - | - | +|⚪| np.isclose(a, b) (float16) | float16 | 10,000,000 | 195.6207 | - | - | - | +|⚪| np.isclose(a, b) (float32) | float32 | 1,000 | 0.0113 | - | - | - | +|⚪| np.isclose(a, b) (float32) | float32 | 100,000 | 0.3666 | - | - | - | +|⚪| np.isclose(a, b) (float32) | float32 | 10,000,000 | 55.1472 | - | - | - | |⚪| np.isclose(a, b) (float64) | float64 | 1,000 | 0.0116 | - | - | - | -|⚪| np.isclose(a, b) (float64) | float64 | 100,000 | 0.6394 | - | - | - | -|⚪| np.isclose(a, b) (float64) | float64 | 10,000,000 | 102.1851 | - | - | - | -|▫| np.isfinite(a) (float16) | float16 | 1,000 | 0.0009 | 0.0010 | 0.86× | 116% | -|🟡| np.isfinite(a) (float16) | float16 | 100,000 | 0.0482 | 0.0528 | 0.91× | 110% | -|✅| np.isfinite(a) (float16) | float16 | 10,000,000 | 5.7788 | 3.3303 | 1.74× | 58% | -|▫| np.isfinite(a) (float32) | float32 | 1,000 | 0.0004 | 0.0011 | 0.36× | 281% | -|🔴| np.isfinite(a) (float32) | float32 | 100,000 | 0.0053 | 0.0362 | 0.14× | 688% | -|🟡| np.isfinite(a) (float32) | float32 | 10,000,000 | 3.2475 | 4.1421 | 0.78× | 128% | -|▫| np.isfinite(a) (float64) | float64 | 1,000 | 0.0004 | 0.0011 | 0.39× | 256% | -|🟠| np.isfinite(a) (float64) | float64 | 100,000 | 0.0098 | 0.0395 | 0.25× | 405% | -|🟡| np.isfinite(a) (float64) | float64 | 10,000,000 | 5.1256 | 6.2088 | 0.83× | 121% | -|▫| np.isinf(a) (float16) | float16 | 1,000 | 0.0009 | 0.0011 | 0.85× | 118% | -|🟡| np.isinf(a) (float16) | float16 | 100,000 | 0.0498 | 0.0543 | 0.92× | 109% | -|✅| np.isinf(a) (float16) | float16 | 10,000,000 | 5.9810 | 3.2027 | 1.87× | 54% | -|▫| np.isinf(a) (float32) | float32 | 1,000 | 0.0005 | 0.0012 | 0.46× | 219% | -|🔴| np.isinf(a) (float32) | float32 | 100,000 | 0.0054 | 0.0421 | 0.13× | 780% | -|🟡| np.isinf(a) (float32) | float32 | 10,000,000 | 3.4097 | 4.4158 | 0.77× | 130% | -|▫| np.isinf(a) (float64) | float64 | 1,000 | 0.0005 | 0.0012 | 0.39× | 259% | -|🟠| np.isinf(a) (float64) | float64 | 100,000 | 0.0101 | 0.0483 | 0.21× | 480% | -|🟡| np.isinf(a) (float64) | float64 | 10,000,000 | 4.9636 | 6.7550 | 0.73× | 136% | -|▫| np.isnan(a) (float16) | float16 | 1,000 | 0.0012 | 0.0010 | 1.18× | 85% | -|✅| np.isnan(a) (float16) | float16 | 100,000 | 0.0678 | 0.0500 | 1.36× | 74% | -|✅| np.isnan(a) (float16) | float16 | 10,000,000 | 7.7367 | 3.2338 | 2.39× | 42% | -|▫| np.isnan(a) (float32) | float32 | 1,000 | 0.0005 | 0.0010 | 0.51× | 197% | -|🔴| np.isnan(a) (float32) | float32 | 100,000 | 0.0041 | 0.0486 | 0.085× | 1177% | -|🟡| np.isnan(a) (float32) | float32 | 10,000,000 | 3.1759 | 4.6187 | 0.69× | 145% | -|▫| np.isnan(a) (float64) | float64 | 1,000 | 0.0004 | 0.0013 | 0.33× | 304% | -|🔴| np.isnan(a) (float64) | float64 | 100,000 | 0.0086 | 0.0490 | 0.18× | 566% | -|🟡| np.isnan(a) (float64) | float64 | 10,000,000 | 4.9428 | 6.3496 | 0.78× | 128% | -|🟡| np.maximum(a, b) (float16) | float16 | 1,000 | 0.0031 | 0.0040 | 0.79× | 127% | -|✅| np.maximum(a, b) (float16) | float16 | 100,000 | 0.7598 | 0.6773 | 1.12× | 89% | -|✅| np.maximum(a, b) (float16) | float16 | 10,000,000 | 80.6049 | 65.8783 | 1.22× | 82% | -|▫| np.maximum(a, b) (float32) | float32 | 1,000 | 0.0006 | 0.0027 | 0.21× | 478% | -|🟠| np.maximum(a, b) (float32) | float32 | 100,000 | 0.0085 | 0.0375 | 0.23× | 439% | -|✅| np.maximum(a, b) (float32) | float32 | 10,000,000 | 8.4970 | 5.1052 | 1.66× | 60% | -|▫| np.maximum(a, b) (float64) | float64 | 1,000 | 0.0006 | 0.0033 | 0.18× | 557% | -|🟠| np.maximum(a, b) (float64) | float64 | 100,000 | 0.0291 | 0.0937 | 0.31× | 322% | -|🟡| np.maximum(a, b) (float64) | float64 | 10,000,000 | 16.6866 | 21.8878 | 0.76× | 131% | -|🟡| np.minimum(a, b) (float16) | float16 | 1,000 | 0.0034 | 0.0039 | 0.87× | 116% | -|✅| np.minimum(a, b) (float16) | float16 | 100,000 | 0.7651 | 0.6905 | 1.11× | 90% | -|✅| np.minimum(a, b) (float16) | float16 | 10,000,000 | 81.5912 | 66.2103 | 1.23× | 81% | -|▫| np.minimum(a, b) (float32) | float32 | 1,000 | 0.0006 | 0.0026 | 0.21× | 468% | -|🟠| np.minimum(a, b) (float32) | float32 | 100,000 | 0.0084 | 0.0380 | 0.22× | 450% | -|✅| np.minimum(a, b) (float32) | float32 | 10,000,000 | 8.4896 | 4.9888 | 1.70× | 59% | -|▫| np.minimum(a, b) (float64) | float64 | 1,000 | 0.0006 | 0.0027 | 0.22× | 454% | -|🟠| np.minimum(a, b) (float64) | float64 | 100,000 | 0.0294 | 0.0879 | 0.33× | 300% | -|🟡| np.minimum(a, b) (float64) | float64 | 10,000,000 | 16.7333 | 22.1221 | 0.76× | 132% | +|⚪| np.isclose(a, b) (float64) | float64 | 100,000 | 0.6275 | - | - | - | +|⚪| np.isclose(a, b) (float64) | float64 | 10,000,000 | 104.7217 | - | - | - | +|▫| np.isfinite(a) (float16) | float16 | 1,000 | 0.0008 | 0.0015 | 0.57× | 175% | +|✅| np.isfinite(a) (float16) | float16 | 100,000 | 0.0496 | 0.0388 | 1.28× | 78% | +|✅| np.isfinite(a) (float16) | float16 | 10,000,000 | 5.8734 | 3.1098 | 1.89× | 53% | +|▫| np.isfinite(a) (float32) | float32 | 1,000 | 0.0004 | 0.0012 | 0.33× | 302% | +|🔴| np.isfinite(a) (float32) | float32 | 100,000 | 0.0059 | 0.0364 | 0.16× | 617% | +|🟡| np.isfinite(a) (float32) | float32 | 10,000,000 | 3.3272 | 3.8128 | 0.87× | 115% | +|▫| np.isfinite(a) (float64) | float64 | 1,000 | 0.0005 | 0.0014 | 0.32× | 314% | +|🟠| np.isfinite(a) (float64) | float64 | 100,000 | 0.0098 | 0.0365 | 0.27× | 374% | +|🟡| np.isfinite(a) (float64) | float64 | 10,000,000 | 5.3098 | 5.6920 | 0.93× | 107% | +|▫| np.isinf(a) (float16) | float16 | 1,000 | 0.0009 | 0.0012 | 0.76× | 132% | +|✅| np.isinf(a) (float16) | float16 | 100,000 | 0.0506 | 0.0386 | 1.31× | 76% | +|✅| np.isinf(a) (float16) | float16 | 10,000,000 | 6.0644 | 3.1035 | 1.95× | 51% | +|▫| np.isinf(a) (float32) | float32 | 1,000 | 0.0006 | 0.0013 | 0.41× | 241% | +|🔴| np.isinf(a) (float32) | float32 | 100,000 | 0.0058 | 0.0416 | 0.14× | 721% | +|🟡| np.isinf(a) (float32) | float32 | 10,000,000 | 3.0154 | 3.9983 | 0.75× | 133% | +|▫| np.isinf(a) (float64) | float64 | 1,000 | 0.0005 | 0.0015 | 0.32× | 312% | +|🟠| np.isinf(a) (float64) | float64 | 100,000 | 0.0101 | 0.0433 | 0.23× | 429% | +|🟡| np.isinf(a) (float64) | float64 | 10,000,000 | 5.2606 | 5.9503 | 0.88× | 113% | +|✅| np.isnan(a) (float16) | float16 | 1,000 | 0.0014 | 0.0012 | 1.12× | 89% | +|✅| np.isnan(a) (float16) | float16 | 100,000 | 0.0692 | 0.0397 | 1.74× | 57% | +|✅| np.isnan(a) (float16) | float16 | 10,000,000 | 7.7981 | 3.1130 | 2.50× | 40% | +|▫| np.isnan(a) (float32) | float32 | 1,000 | 0.0005 | 0.0016 | 0.34× | 298% | +|🔴| np.isnan(a) (float32) | float32 | 100,000 | 0.0044 | 0.0482 | 0.092× | 1091% | +|🟡| np.isnan(a) (float32) | float32 | 10,000,000 | 2.9244 | 4.7104 | 0.62× | 161% | +|▫| np.isnan(a) (float64) | float64 | 1,000 | 0.0004 | 0.0013 | 0.33× | 300% | +|🔴| np.isnan(a) (float64) | float64 | 100,000 | 0.0085 | 0.0483 | 0.18× | 569% | +|🟡| np.isnan(a) (float64) | float64 | 10,000,000 | 4.9310 | 6.0302 | 0.82× | 122% | +|✅| np.maximum(a, b) (float16) | float16 | 1,000 | 0.0031 | 0.0030 | 1.02× | 98% | +|✅| np.maximum(a, b) (float16) | float16 | 100,000 | 0.7598 | 0.6663 | 1.14× | 88% | +|✅| np.maximum(a, b) (float16) | float16 | 10,000,000 | 80.9858 | 66.1452 | 1.22× | 82% | +|▫| np.maximum(a, b) (float32) | float32 | 1,000 | 0.0006 | 0.0013 | 0.45× | 223% | +|🟠| np.maximum(a, b) (float32) | float32 | 100,000 | 0.0084 | 0.0343 | 0.25× | 407% | +|✅| np.maximum(a, b) (float32) | float32 | 10,000,000 | 8.3101 | 4.3746 | 1.90× | 53% | +|▫| np.maximum(a, b) (float64) | float64 | 1,000 | 0.0006 | 0.0018 | 0.31× | 323% | +|🟠| np.maximum(a, b) (float64) | float64 | 100,000 | 0.0293 | 0.0803 | 0.36× | 274% | +|🟡| np.maximum(a, b) (float64) | float64 | 10,000,000 | 16.5750 | 17.2376 | 0.96× | 104% | +|✅| np.minimum(a, b) (float16) | float16 | 1,000 | 0.0032 | 0.0031 | 1.03× | 97% | +|✅| np.minimum(a, b) (float16) | float16 | 100,000 | 0.7620 | 0.6658 | 1.14× | 87% | +|✅| np.minimum(a, b) (float16) | float16 | 10,000,000 | 81.4032 | 65.4062 | 1.25× | 80% | +|▫| np.minimum(a, b) (float32) | float32 | 1,000 | 0.0005 | 0.0013 | 0.42× | 240% | +|🟠| np.minimum(a, b) (float32) | float32 | 100,000 | 0.0084 | 0.0345 | 0.24× | 411% | +|✅| np.minimum(a, b) (float32) | float32 | 10,000,000 | 8.4413 | 4.4356 | 1.90× | 52% | +|▫| np.minimum(a, b) (float64) | float64 | 1,000 | 0.0006 | 0.0013 | 0.44× | 228% | +|🟠| np.minimum(a, b) (float64) | float64 | 100,000 | 0.0290 | 0.0866 | 0.34× | 298% | +|🟡| np.minimum(a, b) (float64) | float64 | 10,000,000 | 16.8958 | 20.5756 | 0.82× | 122% | ### Statistics | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|✅| np.average(a) (float16) | float16 | 1,000 | 0.0055 | 0.0013 | 4.13× | 24% | -|✅| np.average(a) (float16) | float16 | 100,000 | 0.1051 | 0.0820 | 1.28× | 78% | -|✅| np.average(a) (float16) | float16 | 10,000,000 | 17.2176 | 8.1660 | 2.11× | 47% | -|▫| np.average(a) (float32) | float32 | 1,000 | 0.0050 | 0.0007 | 6.67× | 15% | -|✅| np.average(a) (float32) | float32 | 100,000 | 0.0174 | 0.0022 | 7.92× | 13% | -|✅| np.average(a) (float32) | float32 | 10,000,000 | 10.2401 | 1.2354 | 8.29× | 12% | -|▫| np.average(a) (float64) | float64 | 1,000 | 0.0035 | 0.0009 | 3.81× | 26% | -|✅| np.average(a) (float64) | float64 | 100,000 | 0.0171 | 0.0064 | 2.69× | 37% | -|✅| np.average(a) (float64) | float64 | 10,000,000 | 15.3117 | 3.1971 | 4.79× | 21% | -|▫| np.count_nonzero(a) (float16) | float16 | 1,000 | 0.0018 | 0.0005 | 3.72× | 27% | -|✅| np.count_nonzero(a) (float16) | float16 | 100,000 | 0.1526 | 0.0451 | 3.39× | 30% | -|✅| np.count_nonzero(a) (float16) | float16 | 10,000,000 | 25.1066 | 4.3725 | 5.74× | 17% | -|▫| np.count_nonzero(a) (float32) | float32 | 1,000 | 0.0008 | 0.0001 | 9.41× | 11% | -|✅| np.count_nonzero(a) (float32) | float32 | 100,000 | 0.0376 | 0.0048 | 7.77× | 13% | -|✅| np.count_nonzero(a) (float32) | float32 | 10,000,000 | 8.0489 | 2.0107 | 4.00× | 25% | -|▫| np.count_nonzero(a) (float64) | float64 | 1,000 | 0.0010 | 0.0001 | 7.20× | 14% | -|✅| np.count_nonzero(a) (float64) | float64 | 100,000 | 0.0405 | 0.0102 | 3.98× | 25% | -|✅| np.count_nonzero(a) (float64) | float64 | 10,000,000 | 10.1563 | 4.2387 | 2.40× | 42% | -|✅| np.median(a) (float16) | float16 | 1,000 | 0.0136 | 0.0042 | 3.26× | 31% | -|🟡| np.median(a) (float16) | float16 | 100,000 | 0.9048 | 1.2881 | 0.70× | 142% | -|✅| np.median(a) (float16) | float16 | 10,000,000 | 134.8925 | 92.0448 | 1.47× | 68% | -|✅| np.median(a) (float32) | float32 | 1,000 | 0.0109 | 0.0023 | 4.77× | 21% | -|🟡| np.median(a) (float32) | float32 | 100,000 | 0.4875 | 0.7182 | 0.68× | 147% | -|✅| np.median(a) (float32) | float32 | 10,000,000 | 96.5370 | 82.6295 | 1.17× | 86% | -|✅| np.median(a) (float64) | float64 | 1,000 | 0.0110 | 0.0024 | 4.53× | 22% | -|🟡| np.median(a) (float64) | float64 | 100,000 | 0.4789 | 0.7222 | 0.66× | 151% | -|✅| np.median(a) (float64) | float64 | 10,000,000 | 112.1137 | 92.2792 | 1.22× | 82% | -|✅| np.percentile(a, 50) (float16) | float16 | 1,000 | 0.0323 | 0.0042 | 7.69× | 13% | -|✅| np.percentile(a, 50) (float16) | float16 | 100,000 | 1.8092 | 1.2858 | 1.41× | 71% | -|✅| np.percentile(a, 50) (float16) | float16 | 10,000,000 | 156.5275 | 91.5503 | 1.71× | 58% | -|✅| np.percentile(a, 50) (float32) | float32 | 1,000 | 0.0241 | 0.0023 | 10.51× | 10% | -|✅| np.percentile(a, 50) (float32) | float32 | 100,000 | 0.7427 | 0.7156 | 1.04× | 96% | -|🟡| np.percentile(a, 50) (float32) | float32 | 10,000,000 | 63.9717 | 81.0735 | 0.79× | 127% | -|✅| np.percentile(a, 50) (float64) | float64 | 1,000 | 0.0300 | 0.0025 | 12.18× | 8% | -|✅| np.percentile(a, 50) (float64) | float64 | 100,000 | 0.7387 | 0.7299 | 1.01× | 99% | -|🟡| np.percentile(a, 50) (float64) | float64 | 10,000,000 | 80.6078 | 93.3931 | 0.86× | 116% | -|✅| np.ptp(a) (float16) | float16 | 1,000 | 0.0073 | 0.0032 | 2.28× | 44% | -|✅| np.ptp(a) (float16) | float16 | 100,000 | 1.0217 | 0.6418 | 1.59× | 63% | -|✅| np.ptp(a) (float16) | float16 | 10,000,000 | 130.8630 | 65.0541 | 2.01× | 50% | -|✅| np.ptp(a) (float32) | float32 | 1,000 | 0.0034 | 0.0024 | 1.45× | 69% | -|🟡| np.ptp(a) (float32) | float32 | 100,000 | 0.0119 | 0.0176 | 0.68× | 147% | -|✅| np.ptp(a) (float32) | float32 | 10,000,000 | 8.0532 | 3.5153 | 2.29× | 44% | -|✅| np.ptp(a) (float64) | float64 | 1,000 | 0.0041 | 0.0027 | 1.50× | 67% | -|🟡| np.ptp(a) (float64) | float64 | 100,000 | 0.0194 | 0.0330 | 0.59× | 170% | -|✅| np.ptp(a) (float64) | float64 | 10,000,000 | 17.0859 | 8.6509 | 1.98× | 51% | -|✅| np.quantile(a, 0.5) (float16) | float16 | 1,000 | 0.0287 | 0.0042 | 6.76× | 15% | -|✅| np.quantile(a, 0.5) (float16) | float16 | 100,000 | 1.7839 | 1.3025 | 1.37× | 73% | -|✅| np.quantile(a, 0.5) (float16) | float16 | 10,000,000 | 156.4810 | 91.1789 | 1.72× | 58% | -|✅| np.quantile(a, 0.5) (float32) | float32 | 1,000 | 0.0234 | 0.0023 | 10.31× | 10% | -|✅| np.quantile(a, 0.5) (float32) | float32 | 100,000 | 0.7177 | 0.7119 | 1.01× | 99% | -|🟡| np.quantile(a, 0.5) (float32) | float32 | 10,000,000 | 63.5667 | 80.9988 | 0.79× | 127% | -|✅| np.quantile(a, 0.5) (float64) | float64 | 1,000 | 0.0233 | 0.0025 | 9.44× | 11% | -|🟡| np.quantile(a, 0.5) (float64) | float64 | 100,000 | 0.7353 | 0.7477 | 0.98× | 102% | -|🟡| np.quantile(a, 0.5) (float64) | float64 | 10,000,000 | 81.6003 | 91.1410 | 0.90× | 112% | +|✅| np.average(a) (float16) | float16 | 1,000 | 0.0054 | 0.0012 | 4.48× | 22% | +|✅| np.average(a) (float16) | float16 | 100,000 | 0.1147 | 0.0798 | 1.44× | 70% | +|✅| np.average(a) (float16) | float16 | 10,000,000 | 10.5817 | 8.0311 | 1.32× | 76% | +|▫| np.average(a) (float32) | float32 | 1,000 | 0.0042 | 0.0007 | 5.80× | 17% | +|✅| np.average(a) (float32) | float32 | 100,000 | 0.0177 | 0.0032 | 5.57× | 18% | +|✅| np.average(a) (float32) | float32 | 10,000,000 | 2.8186 | 0.9734 | 2.90× | 34% | +|▫| np.average(a) (float64) | float64 | 1,000 | 0.0029 | 0.0007 | 3.89× | 26% | +|✅| np.average(a) (float64) | float64 | 100,000 | 0.0171 | 0.0040 | 4.26× | 23% | +|✅| np.average(a) (float64) | float64 | 10,000,000 | 4.6529 | 2.8474 | 1.63× | 61% | +|▫| np.count_nonzero(a) (float16) | float16 | 1,000 | 0.0018 | 0.0008 | 2.29× | 44% | +|✅| np.count_nonzero(a) (float16) | float16 | 100,000 | 0.1447 | 0.0436 | 3.32× | 30% | +|✅| np.count_nonzero(a) (float16) | float16 | 10,000,000 | 15.7587 | 4.3062 | 3.66× | 27% | +|▫| np.count_nonzero(a) (float32) | float32 | 1,000 | 0.0006 | 0.0003 | 1.87× | 53% | +|✅| np.count_nonzero(a) (float32) | float32 | 100,000 | 0.0438 | 0.0050 | 8.69× | 12% | +|✅| np.count_nonzero(a) (float32) | float32 | 10,000,000 | 4.2117 | 1.6445 | 2.56× | 39% | +|▫| np.count_nonzero(a) (float64) | float64 | 1,000 | 0.0007 | 0.0004 | 1.82× | 55% | +|✅| np.count_nonzero(a) (float64) | float64 | 100,000 | 0.0376 | 0.0090 | 4.19× | 24% | +|✅| np.count_nonzero(a) (float64) | float64 | 10,000,000 | 5.6444 | 3.9133 | 1.44× | 69% | +|✅| np.median(a) (float16) | float16 | 1,000 | 0.0144 | 0.0041 | 3.52× | 28% | +|🟡| np.median(a) (float16) | float16 | 100,000 | 0.8796 | 1.2503 | 0.70× | 142% | +|✅| np.median(a) (float16) | float16 | 10,000,000 | 105.7899 | 88.7294 | 1.19× | 84% | +|✅| np.median(a) (float32) | float32 | 1,000 | 0.0110 | 0.0023 | 4.80× | 21% | +|🟡| np.median(a) (float32) | float32 | 100,000 | 0.4686 | 0.6988 | 0.67× | 149% | +|🟡| np.median(a) (float32) | float32 | 10,000,000 | 73.7480 | 79.7326 | 0.93× | 108% | +|✅| np.median(a) (float64) | float64 | 1,000 | 0.0098 | 0.0023 | 4.23× | 24% | +|🟡| np.median(a) (float64) | float64 | 100,000 | 0.4653 | 0.7089 | 0.66× | 152% | +|🟡| np.median(a) (float64) | float64 | 10,000,000 | 86.4309 | 88.8375 | 0.97× | 103% | +|✅| np.percentile(a, 50) (float16) | float16 | 1,000 | 0.0288 | 0.0041 | 6.97× | 14% | +|✅| np.percentile(a, 50) (float16) | float16 | 100,000 | 1.7825 | 1.2489 | 1.43× | 70% | +|✅| np.percentile(a, 50) (float16) | float16 | 10,000,000 | 113.4806 | 88.7652 | 1.28× | 78% | +|✅| np.percentile(a, 50) (float32) | float32 | 1,000 | 0.0243 | 0.0023 | 10.61× | 9% | +|✅| np.percentile(a, 50) (float32) | float32 | 100,000 | 0.7026 | 0.7007 | 1.00× | 100% | +|🟡| np.percentile(a, 50) (float32) | float32 | 10,000,000 | 47.5374 | 79.7748 | 0.60× | 168% | +|✅| np.percentile(a, 50) (float64) | float64 | 1,000 | 0.0258 | 0.0023 | 11.00× | 9% | +|🟡| np.percentile(a, 50) (float64) | float64 | 100,000 | 0.7045 | 0.7086 | 0.99× | 101% | +|🟡| np.percentile(a, 50) (float64) | float64 | 10,000,000 | 58.5689 | 88.9709 | 0.66× | 152% | +|✅| np.ptp(a) (float16) | float16 | 1,000 | 0.0077 | 0.0037 | 2.10× | 48% | +|✅| np.ptp(a) (float16) | float16 | 100,000 | 1.0004 | 0.6489 | 1.54× | 65% | +|✅| np.ptp(a) (float16) | float16 | 10,000,000 | 103.1114 | 67.0357 | 1.54× | 65% | +|✅| np.ptp(a) (float32) | float32 | 1,000 | 0.0035 | 0.0022 | 1.59× | 63% | +|✅| np.ptp(a) (float32) | float32 | 100,000 | 0.0114 | 0.0080 | 1.44× | 70% | +|✅| np.ptp(a) (float32) | float32 | 10,000,000 | 2.6949 | 2.4569 | 1.10× | 91% | +|✅| np.ptp(a) (float64) | float64 | 1,000 | 0.0037 | 0.0020 | 1.88× | 53% | +|✅| np.ptp(a) (float64) | float64 | 100,000 | 0.0200 | 0.0134 | 1.49× | 67% | +|✅| np.ptp(a) (float64) | float64 | 10,000,000 | 6.9463 | 6.2250 | 1.12× | 90% | +|✅| np.quantile(a, 0.5) (float16) | float16 | 1,000 | 0.0283 | 0.0041 | 6.89× | 14% | +|✅| np.quantile(a, 0.5) (float16) | float16 | 100,000 | 1.7668 | 1.2481 | 1.42× | 71% | +|✅| np.quantile(a, 0.5) (float16) | float16 | 10,000,000 | 112.7790 | 88.6407 | 1.27× | 79% | +|✅| np.quantile(a, 0.5) (float32) | float32 | 1,000 | 0.0237 | 0.0023 | 10.32× | 10% | +|🟡| np.quantile(a, 0.5) (float32) | float32 | 100,000 | 0.6938 | 0.7016 | 0.99× | 101% | +|🟡| np.quantile(a, 0.5) (float32) | float32 | 10,000,000 | 47.6198 | 79.7894 | 0.60× | 168% | +|✅| np.quantile(a, 0.5) (float64) | float64 | 1,000 | 0.0239 | 0.0023 | 10.21× | 10% | +|🟡| np.quantile(a, 0.5) (float64) | float64 | 100,000 | 0.7004 | 0.7083 | 0.99× | 101% | +|🟡| np.quantile(a, 0.5) (float64) | float64 | 10,000,000 | 58.1853 | 89.0162 | 0.65× | 153% | ### Sorting | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|🟡| np.argsort(a) (float32) | float32 | 1,000 | 0.0121 | 0.0163 | 0.74× | 135% | -|✅| np.argsort(a) (float32) | float32 | 100,000 | 1.5773 | 1.3462 | 1.17× | 85% | -|✅| np.argsort(a) (float32) | float32 | 10,000,000 | 657.2035 | 164.2182 | 4.00× | 25% | -|🟠| np.argsort(a) (float64) | float64 | 1,000 | 0.0101 | 0.0229 | 0.44× | 228% | -|🟡| np.argsort(a) (float64) | float64 | 100,000 | 1.4552 | 2.6803 | 0.54× | 184% | -|✅| np.argsort(a) (float64) | float64 | 10,000,000 | 612.9057 | 305.7646 | 2.00× | 50% | -|🟡| np.argsort(a) (int32) | int32 | 1,000 | 0.0122 | 0.0150 | 0.81× | 123% | -|🟠| np.argsort(a) (int32) | int32 | 100,000 | 0.4357 | 1.1883 | 0.37× | 273% | -|✅| np.argsort(a) (int32) | int32 | 10,000,000 | 306.9013 | 130.5309 | 2.35× | 42% | -|🟡| np.argsort(a) (int64) | int64 | 1,000 | 0.0133 | 0.0204 | 0.65× | 154% | -|🔴| np.argsort(a) (int64) | int64 | 100,000 | 0.5113 | 2.9221 | 0.17× | 572% | -|✅| np.argsort(a) (int64) | int64 | 10,000,000 | 390.5333 | 240.1491 | 1.63× | 62% | -|✅| np.nonzero(a) (float32) | float32 | 1,000 | 0.0025 | 0.0021 | 1.20× | 84% | -|🟡| np.nonzero(a) (float32) | float32 | 100,000 | 0.1872 | 0.2217 | 0.84× | 118% | -|✅| np.nonzero(a) (float32) | float32 | 10,000,000 | 25.5561 | 15.7379 | 1.62× | 62% | -|✅| np.nonzero(a) (float64) | float64 | 1,000 | 0.0028 | 0.0019 | 1.50× | 66% | -|🟡| np.nonzero(a) (float64) | float64 | 100,000 | 0.1789 | 0.2447 | 0.73× | 137% | -|✅| np.nonzero(a) (float64) | float64 | 10,000,000 | 32.2888 | 18.0774 | 1.79× | 56% | -|🟡| np.nonzero(a) (int32) | int32 | 1,000 | 0.0017 | 0.0030 | 0.56× | 178% | -|🟠| np.nonzero(a) (int32) | int32 | 100,000 | 0.1019 | 0.2350 | 0.43× | 231% | -|✅| np.nonzero(a) (int32) | int32 | 10,000,000 | 29.9222 | 27.4282 | 1.09× | 92% | -|🟡| np.nonzero(a) (int64) | int64 | 1,000 | 0.0018 | 0.0031 | 0.57× | 176% | -|🟠| np.nonzero(a) (int64) | int64 | 100,000 | 0.1143 | 0.2499 | 0.46× | 219% | -|✅| np.nonzero(a) (int64) | int64 | 10,000,000 | 29.8745 | 19.8804 | 1.50× | 66% | -|✅| np.searchsorted(a, v) (float32) | float32 | 1,000 | 0.0074 | 0.0056 | 1.31× | 76% | -|✅| np.searchsorted(a, v) (float32) | float32 | 100,000 | 2.0207 | 1.7645 | 1.15× | 87% | -|✅| np.searchsorted(a, v) (float32) | float32 | 10,000,000 | 239.6437 | 195.4530 | 1.23× | 82% | -|✅| np.searchsorted(a, v) (float64) | float64 | 1,000 | 0.0079 | 0.0055 | 1.45× | 69% | -|✅| np.searchsorted(a, v) (float64) | float64 | 100,000 | 2.0950 | 1.7708 | 1.18× | 84% | -|✅| np.searchsorted(a, v) (float64) | float64 | 10,000,000 | 249.7088 | 192.5328 | 1.30× | 77% | -|✅| np.searchsorted(a, v) (int32) | int32 | 1,000 | 0.0204 | 0.0077 | 2.65× | 38% | -|✅| np.searchsorted(a, v) (int32) | int32 | 100,000 | 2.8565 | 2.2922 | 1.25× | 80% | -|✅| np.searchsorted(a, v) (int32) | int32 | 10,000,000 | 578.2002 | 253.6528 | 2.28× | 44% | -|✅| np.searchsorted(a, v) (int64) | int64 | 1,000 | 0.0202 | 0.0073 | 2.77× | 36% | -|✅| np.searchsorted(a, v) (int64) | int64 | 100,000 | 2.8969 | 2.2836 | 1.27× | 79% | -|✅| np.searchsorted(a, v) (int64) | int64 | 10,000,000 | 559.5927 | 247.0748 | 2.27× | 44% | +|🟡| np.argsort(a) (float32) | float32 | 1,000 | 0.0120 | 0.0223 | 0.54× | 186% | +|✅| np.argsort(a) (float32) | float32 | 100,000 | 1.5922 | 1.0142 | 1.57× | 64% | +|✅| np.argsort(a) (float32) | float32 | 10,000,000 | 1348.2730 | 132.4664 | 10.18× | 10% | +|🟡| np.argsort(a) (float64) | float64 | 1,000 | 0.0104 | 0.0177 | 0.59× | 171% | +|🟡| np.argsort(a) (float64) | float64 | 100,000 | 1.4280 | 2.5193 | 0.57× | 176% | +|✅| np.argsort(a) (float64) | float64 | 10,000,000 | 1768.7035 | 251.4880 | 7.03× | 14% | +|🟡| np.argsort(a) (int32) | int32 | 1,000 | 0.0113 | 0.0221 | 0.51× | 196% | +|🟠| np.argsort(a) (int32) | int32 | 100,000 | 0.4031 | 0.8504 | 0.47× | 211% | +|✅| np.argsort(a) (int32) | int32 | 10,000,000 | 174.0979 | 107.5057 | 1.62× | 62% | +|🟡| np.argsort(a) (int64) | int64 | 1,000 | 0.0131 | 0.0167 | 0.78× | 128% | +|🟠| np.argsort(a) (int64) | int64 | 100,000 | 0.4917 | 2.2549 | 0.22× | 459% | +|✅| np.argsort(a) (int64) | int64 | 10,000,000 | 310.5208 | 188.5734 | 1.65× | 61% | +|✅| np.nonzero(a) (float32) | float32 | 1,000 | 0.0027 | 0.0021 | 1.25× | 80% | +|✅| np.nonzero(a) (float32) | float32 | 100,000 | 0.1988 | 0.1946 | 1.02× | 98% | +|✅| np.nonzero(a) (float32) | float32 | 10,000,000 | 39.8712 | 14.1945 | 2.81× | 36% | +|✅| np.nonzero(a) (float64) | float64 | 1,000 | 0.0029 | 0.0020 | 1.44× | 70% | +|🟡| np.nonzero(a) (float64) | float64 | 100,000 | 0.1923 | 0.2002 | 0.96× | 104% | +|✅| np.nonzero(a) (float64) | float64 | 10,000,000 | 42.6329 | 17.6206 | 2.42× | 41% | +|🟡| np.nonzero(a) (int32) | int32 | 1,000 | 0.0017 | 0.0028 | 0.61× | 164% | +|🟡| np.nonzero(a) (int32) | int32 | 100,000 | 0.1051 | 0.1932 | 0.54× | 184% | +|✅| np.nonzero(a) (int32) | int32 | 10,000,000 | 20.1149 | 14.3503 | 1.40× | 71% | +|🟡| np.nonzero(a) (int64) | int64 | 1,000 | 0.0018 | 0.0026 | 0.69× | 144% | +|🟡| np.nonzero(a) (int64) | int64 | 100,000 | 0.1073 | 0.2034 | 0.53× | 190% | +|✅| np.nonzero(a) (int64) | int64 | 10,000,000 | 23.7474 | 18.2560 | 1.30× | 77% | +|✅| np.searchsorted(a, v) (float32) | float32 | 1,000 | 0.0083 | 0.0053 | 1.56× | 64% | +|✅| np.searchsorted(a, v) (float32) | float32 | 100,000 | 2.0222 | 1.7299 | 1.17× | 86% | +|✅| np.searchsorted(a, v) (float32) | float32 | 10,000,000 | 312.1823 | 191.5647 | 1.63× | 61% | +|✅| np.searchsorted(a, v) (float64) | float64 | 1,000 | 0.0083 | 0.0053 | 1.56× | 64% | +|✅| np.searchsorted(a, v) (float64) | float64 | 100,000 | 2.0961 | 1.7424 | 1.20× | 83% | +|✅| np.searchsorted(a, v) (float64) | float64 | 10,000,000 | 310.2307 | 192.5449 | 1.61× | 62% | +|✅| np.searchsorted(a, v) (int32) | int32 | 1,000 | 0.0190 | 0.0075 | 2.54× | 39% | +|✅| np.searchsorted(a, v) (int32) | int32 | 100,000 | 2.8983 | 2.2559 | 1.28× | 78% | +|✅| np.searchsorted(a, v) (int32) | int32 | 10,000,000 | 390.1343 | 243.3194 | 1.60× | 62% | +|✅| np.searchsorted(a, v) (int64) | int64 | 1,000 | 0.0186 | 0.0071 | 2.61× | 38% | +|✅| np.searchsorted(a, v) (int64) | int64 | 100,000 | 2.9016 | 2.2381 | 1.30× | 77% | +|✅| np.searchsorted(a, v) (int64) | int64 | 10,000,000 | 568.2151 | 243.9343 | 2.33× | 43% | ### LinearAlgebra | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|▫| np.dot(a, b) (float64) | float64 | 1,000 | 0.0007 | 0.0006 | 1.13× | 89% | -|✅| np.dot(a, b) (float64) | float64 | 100,000 | 0.0994 | 0.0070 | 14.17× | 7% | -|🟠| np.dot(a, b) (float64) | float64 | 10,000,000 | 0.9192 | 3.1020 | 0.30× | 338% | -|🟠| np.matmul(A, B) (float64) | float64 | 1,000 | 0.0027 | 0.0062 | 0.43× | 234% | -|🔴| np.matmul(A, B) (float64) | float64 | 100,000 | 0.5265 | 3.0608 | 0.17× | 581% | -|🔴| np.matmul(A, B) (float64) | float64 | 10,000,000 | 0.7113 | 4.2590 | 0.17× | 599% | -|🟠| np.outer(a, b) (float64) | float64 | 1,000 | 0.0022 | 0.0057 | 0.38× | 262% | -|🟠| np.outer(a, b) (float64) | float64 | 100,000 | 0.0365 | 0.0742 | 0.49× | 204% | -|✅| np.outer(a, b) (float64) | float64 | 10,000,000 | 13.5041 | 11.9438 | 1.13× | 88% | +|▫| np.dot(a, b) (float64) | float64 | 1,000 | 0.0007 | 0.0007 | 1.06× | 94% | +|✅| np.dot(a, b) (float64) | float64 | 100,000 | 0.1123 | 0.0093 | 12.05× | 8% | +|🟠| np.dot(a, b) (float64) | float64 | 10,000,000 | 0.8690 | 3.0303 | 0.29× | 349% | +|🟠| np.matmul(A, B) (float64) | float64 | 1,000 | 0.0027 | 0.0060 | 0.45× | 223% | +|🟠| np.matmul(A, B) (float64) | float64 | 100,000 | 0.5771 | 2.7815 | 0.21× | 482% | +|🔴| np.matmul(A, B) (float64) | float64 | 10,000,000 | 0.6896 | 4.4742 | 0.15× | 649% | +|🟠| np.outer(a, b) (float64) | float64 | 1,000 | 0.0022 | 0.0061 | 0.35× | 284% | +|🟡| np.outer(a, b) (float64) | float64 | 100,000 | 0.0380 | 0.0708 | 0.54× | 186% | +|✅| np.outer(a, b) (float64) | float64 | 10,000,000 | 13.4954 | 11.7542 | 1.15× | 87% | ### Selection | | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | |:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| -|🟡| np.where(cond) (float64) | float64 | 1,000 | 0.0010 | 0.0014 | 0.74× | 134% | -|🟠| np.where(cond) (float64) | float64 | 100,000 | 0.0309 | 0.1016 | 0.30× | 329% | -|✅| np.where(cond) (float64) | float64 | 10,000,000 | 7.9232 | 7.3884 | 1.07× | 93% | -|🟡| np.where(cond, a, b) (float64) | float64 | 1,000 | 0.0017 | 0.0020 | 0.84× | 119% | -|🟡| np.where(cond, a, b) (float64) | float64 | 100,000 | 0.0542 | 0.0678 | 0.80× | 125% | -|✅| np.where(cond, a, b) (float64) | float64 | 10,000,000 | 17.9453 | 15.2212 | 1.18× | 85% | - +|▫| np.where(cond) (float64) | float64 | 1,000 | 0.0009 | 0.0014 | 0.66× | 152% | +|🟠| np.where(cond) (float64) | float64 | 100,000 | 0.0294 | 0.0981 | 0.30× | 333% | +|✅| np.where(cond) (float64) | float64 | 10,000,000 | 7.4500 | 6.9913 | 1.07× | 94% | +|🟡| np.where(cond, a, b) (float64) | float64 | 1,000 | 0.0017 | 0.0020 | 0.85× | 117% | +|🟡| np.where(cond, a, b) (float64) | float64 | 100,000 | 0.0399 | 0.0688 | 0.58× | 173% | +|✅| np.where(cond, a, b) (float64) | float64 | 10,000,000 | 17.1294 | 14.8189 | 1.16× | 86% | --- @@ -2006,7 +2005,7 @@ _Ranked over 1398 credible comparisons (both sides ≥1µs, within 20×); 384 ne _Complementary harness: measures the iterator machinery itself (construction, traversal, reductions, selection, dtypes, pathologies, dividends) across cache tiers — not part of the op/dtype/N matrix above. speedup = NumPy / NumSharp; NA = section ignored due to a known intermittent NumSharp AccessViolation._ ``` -NumSharp NDIter — canonical benchmark · 2026-06-23 · speedup = NumPy ÷ NumSharp (>1.0× = NumSharp faster) +NumSharp NDIter — canonical benchmark · 2026-06-29 · speedup = NumPy ÷ NumSharp (>1.0× = NumSharp faster) 198 measured pairs (35 NA) · best-of-rounds, Release · matched kernels/ids %NumPy🕐 = NumSharp ÷ NumPy × 100 = share of NumPy's time NumSharp uses (8% = takes only 8% as long; <100% = faster) @@ -2014,55 +2013,55 @@ AV POLICY — a NumSharp section that crashes all retries (known intermittent AccessViolation, an unmanaged-storage lifetime bug) is reported NA / IGNORED and excluded from every geomean below. THIS RUN: NA across selection. -HEADLINE — operation matrix: 1.18× geomean · 85%🕐 of NumPy's time · 72 win / 58 lose over 130 cells +HEADLINE — operation matrix: 1.20× geomean · 83%🕐 of NumPy's time · 77 win / 53 lose over 130 cells OPERATIONS — BY SIZE TIER (geomean over all families) slower ◄───────── 1.0 (parity) ─────────► faster -scalar ██████████▉ ........ 1.10× 91%🕐 ( 12 win / 14 lose) -1K ███████████▊ ....... 1.19× 84%🕐 ( 15 win / 11 lose) -100K ██████████▊ ........ 1.08× 93%🕐 ( 12 win / 14 lose) -1M █████████████ ...... 1.31× 77%🕐 ( 17 win / 9 lose) -10M ████████████▎ ...... 1.23× 81%🕐 ( 16 win / 10 lose) -ALL ███████████▊ ....... 1.18× 85%🕐 ( 72 win / 58 lose) +scalar ████████████▌ ...... 1.26× 79%🕐 ( 17 win / 9 lose) +1K ███████████▌ ....... 1.16× 86%🕐 ( 15 win / 11 lose) +100K ██████████▋ ........ 1.07× 94%🕐 ( 12 win / 14 lose) +1M ████████████▉ ...... 1.30× 77%🕐 ( 17 win / 9 lose) +10M ████████████▎ ...... 1.23× 82%🕐 ( 16 win / 10 lose) +ALL ███████████▉ ....... 1.20× 83%🕐 ( 77 win / 53 lose) OPERATIONS — BY CATEGORY (geomean over its families, all sizes) slower ◄───────── 1.0 (parity) ─────────► faster -elementwise███████████▊ ....... 1.18× 85%🕐 ( 28 win / 12 lose) -reductions █████████████████▍ 1.75× 57%🕐 ( 28 win / 12 lose) +elementwise████████████▊ ...... 1.28× 78%🕐 ( 31 win / 9 lose) +reductions █████████████████▍ 1.74× 57%🕐 ( 29 win / 11 lose) selection (no data) -copy/cast ███████▎ ........... 0.73× 137%🕐 ( 8 win / 17 lose) ◄ SLOWER -index-math ███████▌ ........... 0.75× 133%🕐 ( 3 win / 7 lose) ◄ SLOWER -dtypes ████████████▏ ...... 1.22× 82%🕐 ( 5 win / 10 lose) +copy/cast ███████▎ ........... 0.73× 138%🕐 ( 9 win / 16 lose) ◄ SLOWER +index-math ███████▋ ........... 0.77× 130%🕐 ( 4 win / 6 lose) ◄ SLOWER +dtypes ███████████▌ ....... 1.16× 86%🕐 ( 4 win / 11 lose) CATEGORY × TIER geomean category scalar 1K 100K 1M 10M -elementwise 1.05× 1.54× 1.18× 1.09× 1.11× -reductions 2.67× 1.99× 1.51× 1.44× 1.42× +elementwise 1.65× 1.54× 1.14× 1.10× 1.08× +reductions 2.68× 2.00× 1.51× 1.43× 1.38× selection - - - - - -copy/cast 0.61× 0.59× 0.40× 1.39× 1.06× -index-math 0.32× 0.51× 0.97× 1.22× 1.22× -dtypes 0.71× 0.85× 1.97× 1.54× 1.47× +copy/cast 0.59× 0.53× 0.41× 1.40× 1.12× +index-math 0.34× 0.51× 0.99× 1.23× 1.26× +dtypes 0.70× 0.81× 1.87× 1.39× 1.44× PER-FAMILY × TIER (NumPy ÷ NumSharp; >1.0 = NumSharp faster) family scalar 1K 100K 1M 10M geomean -- elementwise - add 1.01× 1.48× 1.03× 0.88× 1.01× 1.06× - sqrt 0.85× 1.15× 1.00× 1.01× 1.02× 1.00× - copy 0.88× 2.59× 1.78× 1.33× 1.72× 1.56× - strided 0.89× 1.12× 1.00× 1.02× 0.99× 1.00× - bcast 0.89× 1.13× 1.02× 0.98× 1.03× 1.01× - reversed 0.85× 1.28× 0.90× 0.99× 1.00× 0.99× - castbuf 1.98× 2.29× 1.65× 1.35× 1.16× 1.64× - mixbuf 1.49× 1.94× 1.40× 1.24× 1.09× 1.40× + add 1.61× 1.43× 0.96× 1.00× 1.02× 1.18× + sqrt 1.67× 1.16× 1.01× 1.00× 1.01× 1.15× + copy 1.67× 2.35× 1.62× 1.39× 1.64× 1.71× + strided 1.65× 1.28× 0.93× 1.01× 0.99× 1.15× + bcast 1.68× 1.32× 0.89× 0.95× 0.95× 1.12× + reversed 1.65× 1.26× 0.93× 0.99× 0.95× 1.13× + castbuf 1.87× 2.20× 1.63× 1.36× 1.12× 1.59× + mixbuf 1.42× 1.72× 1.41× 1.20× 1.07× 1.35× -- reductions - sum 1.84× 1.78× 2.79× 2.21× 1.76× 2.04× - sum ax0 1.90× 0.86× 0.96× 1.00× 0.94× 1.08× - sum ax1 1.85× 0.86× 1.51× 1.83× 1.57× 1.47× - sum dt= 1.97× 1.47× 0.49× 0.47× 0.55× 0.82× - amin 1.70× 1.61× 0.71× 0.70× 0.82× 1.02× - cumsum 1.47× 1.09× 1.06× 1.80× 1.68× 1.39× - any(F) 8.89× 8.41× 2.12× 0.98× 1.00× 2.74× - any(hit) 9.01× 8.50× 8.50× 7.87× 8.22× 8.41× + sum 1.92× 1.85× 2.58× 1.76× 1.60× 1.92× + sum ax0 1.71× 0.86× 1.10× 0.96× 0.96× 1.09× + sum ax1 1.81× 0.92× 1.52× 1.79× 1.58× 1.48× + sum dt= 1.89× 1.35× 0.48× 0.46× 0.54× 0.79× + amin 1.69× 1.62× 0.71× 0.71× 0.76× 1.01× + cumsum 1.35× 1.13× 1.07× 1.87× 1.65× 1.38× + any(F) 10.04× 8.39× 2.00× 1.23× 1.00× 2.90× + any(hit) 10.25× 8.49× 8.50× 7.88× 7.98× 8.58× -- selection where NA NA NA NA NA a[mask] NA NA NA NA NA @@ -2072,55 +2071,55 @@ family scalar 1K 100K 1M 10M geomean a[idx] NA NA NA NA NA a[idx]= NA NA NA NA NA -- copy/cast - flatten 0.43× 0.44× 0.17× 2.17× 0.90× 0.57× - astype 0.30× 0.53× 0.59× 1.97× 1.90× 0.81× - ravel.T 0.45× 0.73× 0.48× 2.11× 1.01× 0.80× - in-place 1.77× 0.81× 0.81× 1.06× 1.02× 1.05× - less->b 0.81× 0.52× 0.26× 0.54× 0.76× 0.54× + flatten 0.41× 0.33× 0.17× 2.21× 1.13× 0.56× + astype 0.31× 0.53× 0.54× 1.94× 1.89× 0.80× + ravel.T 0.50× 0.58× 0.53× 2.22× 1.09× 0.82× + in-place 1.45× 0.77× 0.96× 1.03× 1.04× 1.03× + less->b 0.81× 0.52× 0.25× 0.55× 0.75× 0.53× -- index-math - unravel 0.33× 0.50× 0.95× 1.01× 0.97× 0.68× - ravel_mi 0.32× 0.52× 0.99× 1.49× 1.53× 0.82× + unravel 0.36× 0.50× 0.96× 1.00× 1.04× 0.71× + ravel_mi 0.32× 0.53× 1.01× 1.53× 1.54× 0.83× -- dtypes - complex 0.74× 0.63× 1.01× 0.76× 0.89× 0.80× - float16 0.72× 0.65× 0.62× 0.62× 0.62× 0.65× - int8 0.67× 1.47× 12.09× 7.70× 5.78× 3.51× + complex 0.71× 0.58× 0.97× 0.77× 0.93× 0.78× + float16 0.72× 0.64× 0.58× 0.56× 0.57× 0.61× + int8 0.66× 1.43× 11.53× 6.16× 5.66× 3.28× CONSTRUCTION — iterator build+dispose vs np.nditer (size-invariant, 1K) slower ◄───────── 1.0 (parity) ─────────► faster -1op ██████████████████▋ 1.86× 54%🕐 ( 1 win / 0 lose) -3op_exl ███████████████████▶ 4.43× 23%🕐 ( 1 win / 0 lose) -ufunc ███████████████████▶ 4.98× 20%🕐 ( 1 win / 0 lose) -bufcast ███████████████████▶ 3.49× 29%🕐 ( 1 win / 0 lose) -multiindex ███████████████████▶ 2.56× 39%🕐 ( 1 win / 0 lose) -8op ███████████████████▶ 5.26× 19%🕐 ( 1 win / 0 lose) -4d ███████████████████▶ 2.94× 34%🕐 ( 1 win / 0 lose) -8d ███████████████████▶ 2.65× 38%🕐 ( 1 win / 0 lose) -strided2d ███████████████████▶ 3.35× 30%🕐 ( 1 win / 0 lose) -geomean ███████████████████▶ 3.33× 30%🕐 ( 9 win / 0 lose) +1op █████████▋ ......... 0.97× 103%🕐 ( 0 win / 1 lose) ◄ SLOWER +3op_exl ███████████████████▶ 2.44× 41%🕐 ( 1 win / 0 lose) +ufunc ███████████████████▶ 2.91× 34%🕐 ( 1 win / 0 lose) +bufcast ███████████████████▶ 2.56× 39%🕐 ( 1 win / 0 lose) +multiindex █████████████▉ ..... 1.40× 72%🕐 ( 1 win / 0 lose) +8op ███████████████████▶ 3.67× 27%🕐 ( 1 win / 0 lose) +4d █████████████████▊ 1.78× 56%🕐 ( 1 win / 0 lose) +8d ██████████████████▌ 1.85× 54%🕐 ( 1 win / 0 lose) +strided2d ███████████████████▌ 1.95× 51%🕐 ( 1 win / 0 lose) +geomean ███████████████████▶ 2.03× 49%🕐 ( 8 win / 1 lose) CHUNK-WIDTH dispatch — strided rows, 2M total, inner width w (NumPy = np.positive) slower ◄───────── 1.0 (parity) ─────────► faster -w=4 ███████ ............ 0.71× 141%🕐 ( 0 win / 1 lose) ◄ SLOWER -w=16 ██████████▏ ........ 1.02× 98%🕐 ( 1 win / 0 lose) ◄ PARITY -w=64 ███████████▍ ....... 1.15× 87%🕐 ( 1 win / 0 lose) -w=256 █████████████▍ ..... 1.34× 75%🕐 ( 1 win / 0 lose) -w=1024 ███████████████ .... 1.51× 66%🕐 ( 1 win / 0 lose) +w=4 ██████▉ ............ 0.69× 145%🕐 ( 0 win / 1 lose) ◄ SLOWER +w=16 █████████▊ ......... 0.98× 102%🕐 ( 0 win / 1 lose) ◄ PARITY +w=64 ██████████▏ ........ 1.02× 98%🕐 ( 1 win / 0 lose) ◄ PARITY +w=256 ██████████████▌ .... 1.45× 69%🕐 ( 1 win / 0 lose) +w=1024 █████████████ ...... 1.31× 76%🕐 ( 1 win / 0 lose) PATHOLOGY canaries — known taxes/losses to track (NumPy ÷ NumSharp) - bcast_reduce 538.56× (538.6× faster, faster) - allocate 1.10× (1.1× faster, faster) - overlap_copy 1.78× (1.8× faster, faster) - forder_out 1.28× (1.3× faster, faster) - zerodim 1.26× (1.3× faster, faster) + bcast_reduce 522.42× (522.4× faster, faster) + allocate 1.06× (1.1× faster, faster) + overlap_copy 1.77× (1.8× faster, faster) + forder_out 1.17× (1.2× faster, faster) + zerodim 1.55× (1.6× faster, faster) DIVIDENDS — NumSharp-only machinery (NumPy baseline = closest it can do) scalar 1K 100K 1M 10M note -fuse7 12.65× 3.80× 1.39× 1.62× 2.01× vs chained 6× add -reuse 5.63× 5.30× 0.97× 1.04× 1.06× vs rebuild each call -par8 - 0.66× 2.70× 3.09× 4.25× vs single-thread +fuse7 12.55× 3.86× 1.44× 1.67× 2.09× vs chained 6× add +reuse 6.06× 6.12× 1.07× 1.00× 1.00× vs rebuild each call +par8 - 0.67× 2.78× 3.95× 5.40× vs single-thread -biggest NumSharp wins: i8@100K 12.09× · anyeh@1 9.01× · anyff@1 8.89× · anyeh@100K 8.50× · anyeh@1K 8.50× -most behind: flatten@100K 0.17× · lessbool@100K 0.26× · astype@1 0.30× · ravelmi@1 0.32× · unravel@1 0.33× +biggest NumSharp wins: i8@100K 11.53× · anyeh@1 10.25× · anyff@1 10.04× · anyeh@100K 8.50× · anyeh@1K 8.49× +most behind: flatten@100K 0.17× · lessbool@100K 0.25× · astype@1 0.31× · ravelmi@1 0.32× · flatten@1K 0.33× ``` @@ -2137,42 +2136,42 @@ Layouts (8, harmonized with the cast subsystem): `C`, `F` (Fortran), `T` (transp | size | C | F | T | strided | negrow | negcol | sliced | bcast | |---|---|---|---|---|---|---|---|---| -| 100K | 1.01 ✅ | 1.05 ✅ | 1.04 ✅ | 0.49 🟠 | 0.61 🟡 | 0.56 🟡 | 0.68 🟡 | 0.59 🟡 | -| 1M | 0.92 🟡 | 0.94 🟡 | 0.94 🟡 | 0.56 🟡 | 0.76 🟡 | 0.58 🟡 | 0.70 🟡 | 0.50 🟡 | +| 100K | 0.99 🟡 | 1.02 ✅ | 1.02 ✅ | 0.55 🟡 | 0.70 🟡 | 0.70 🟡 | 0.72 🟡 | 0.74 🟡 | +| 1M | 0.95 🟡 | 0.94 🟡 | 0.95 🟡 | 0.65 🟡 | 0.87 🟡 | 0.73 🟡 | 0.75 🟡 | 0.68 🟡 | **Geomean by dt** | size | f64 | f32 | c128 | dec | f16 | i32 | i64 | |---|---|---|---|---|---|---|---| -| 100K | 0.78 🟡 | 0.85 🟡 | 1.01 ✅ | 0.08 🔴 | 1.04 ✅ | 0.89 🟡 | 1.15 ✅ | -| 1M | 0.88 🟡 | 1.05 ✅ | 1.02 ✅ | 0.07 🔴 | 1.00 ✅ | 0.80 🟡 | 1.02 ✅ | +| 100K | 0.87 🟡 | 1.00 🟡 | 0.98 🟡 | 0.08 🔴 | 1.03 ✅ | 1.33 ✅ | 1.14 ✅ | +| 1M | 1.12 ✅ | 1.15 ✅ | 1.04 ✅ | 0.07 🔴 | 1.02 ✅ | 1.19 ✅ | 1.02 ✅ | **Geomean by op** | size | sum | min | max | prod | |---|---|---|---|---| -| 100K | 0.72 🟡 | 0.61 🟡 | 0.61 🟡 | 1.06 ✅ | -| 1M | 0.74 🟡 | 0.59 🟡 | 0.58 🟡 | 1.14 ✅ | +| 100K | 0.80 🟡 | 0.66 🟡 | 0.66 🟡 | 1.19 ✅ | +| 1M | 0.84 🟡 | 0.65 🟡 | 0.64 🟡 | 1.28 ✅ | **Worst 15 cells (NumSharp slowest vs NumPy)** | key | NumSharp ms | NumPy ms | ratio | |---|---|---|---| -| 1M\|dec\|bcast\|sum\|ax0 | 5.3741 | 0.0657 | 0.01 🔴 | -| 100K\|dec\|bcast\|sum\|ax0 | 0.5412 | 0.0101 | 0.02 🔴 | -| 1M\|dec\|sliced\|sum\|ax0 | 5.4381 | 0.1108 | 0.02 🔴 | -| 100K\|dec\|C\|sum\|ax0 | 0.5494 | 0.0114 | 0.02 🔴 | -| 1M\|dec\|negrow\|sum\|ax0 | 5.4461 | 0.1134 | 0.02 🔴 | -| 100K\|dec\|negrow\|sum\|ax0 | 0.5371 | 0.0114 | 0.02 🔴 | -| 100K\|dec\|T\|sum\|ax1 | 0.5356 | 0.0114 | 0.02 🔴 | -| 1M\|dec\|F\|sum\|ax1 | 5.3609 | 0.1202 | 0.02 🔴 | -| 1M\|dec\|T\|sum\|ax1 | 5.5377 | 0.1270 | 0.02 🔴 | -| 100K\|dec\|sliced\|sum\|ax0 | 0.5332 | 0.0123 | 0.02 🔴 | -| 100K\|dec\|F\|sum\|ax1 | 0.5381 | 0.0126 | 0.02 🔴 | -| 1M\|dec\|C\|sum\|ax0 | 5.4777 | 0.1298 | 0.02 🔴 | -| 1M\|i32\|bcast\|sum\|ax1 | 4.1105 | 0.1203 | 0.03 🔴 | -| 100K\|i32\|bcast\|sum\|ax1 | 0.4050 | 0.0168 | 0.04 🔴 | -| 100K\|dec\|C\|max\|ax0 | 0.2871 | 0.0137 | 0.05 🔴 | +| 1M\|dec\|bcast\|sum\|ax0 | 5.5269 | 0.0655 | 0.01 🔴 | +| 100K\|dec\|bcast\|sum\|ax0 | 0.5555 | 0.0087 | 0.02 🔴 | +| 100K\|dec\|F\|sum\|ax1 | 0.5585 | 0.0094 | 0.02 🔴 | +| 100K\|dec\|C\|sum\|ax0 | 0.5615 | 0.0111 | 0.02 🔴 | +| 100K\|dec\|T\|sum\|ax1 | 0.5582 | 0.0111 | 0.02 🔴 | +| 100K\|dec\|negrow\|sum\|ax0 | 0.5592 | 0.0114 | 0.02 🔴 | +| 100K\|dec\|sliced\|sum\|ax0 | 0.5517 | 0.0115 | 0.02 🔴 | +| 1M\|dec\|sliced\|sum\|ax0 | 5.5802 | 0.1240 | 0.02 🔴 | +| 1M\|dec\|negrow\|sum\|ax0 | 5.5707 | 0.1242 | 0.02 🔴 | +| 1M\|dec\|F\|sum\|ax1 | 5.6157 | 0.1254 | 0.02 🔴 | +| 1M\|dec\|T\|sum\|ax1 | 5.5849 | 0.1278 | 0.02 🔴 | +| 1M\|dec\|C\|sum\|ax0 | 5.5736 | 0.1524 | 0.03 🔴 | +| 100K\|dec\|C\|min\|ax0 | 0.3021 | 0.0141 | 0.05 🔴 | +| 1M\|dec\|negcol\|sum\|ax0 | 5.6678 | 0.2866 | 0.05 🔴 | +| 100K\|dec\|negcol\|sum\|ax0 | 0.5594 | 0.0302 | 0.05 🔴 | ### Copy / identity-ufunc (np.positive) @@ -2180,35 +2179,35 @@ Layouts (8, harmonized with the cast subsystem): `C`, `F` (Fortran), `T` (transp | size | C | F | T | strided | sliced | negrow | negcol | bcast | |---|---|---|---|---|---|---|---|---| -| 100K | 1.16 ✅ | 1.47 ✅ | 1.25 ✅ | 0.86 🟡 | 1.56 ✅ | 1.63 ✅ | 2.22 ✅ | 1.57 ✅ | -| 1M | 2.87 ✅ | 2.95 ✅ | 2.89 ✅ | 1.96 ✅ | 2.67 ✅ | 2.62 ✅ | 3.24 ✅ | 2.67 ✅ | +| 100K | 1.22 ✅ | 1.51 ✅ | 1.28 ✅ | 0.83 🟡 | 1.54 ✅ | 1.48 ✅ | 2.20 ✅ | 1.62 ✅ | +| 1M | 2.82 ✅ | 2.89 ✅ | 2.88 ✅ | 2.08 ✅ | 2.58 ✅ | 2.64 ✅ | 3.16 ✅ | 2.66 ✅ | **Geomean by dt** | size | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| 100K | 0.93 🟡 | 1.41 ✅ | 1.75 ✅ | 1.80 ✅ | 0.99 🟡 | 1.09 ✅ | 1.66 ✅ | 1.03 ✅ | 2.44 ✅ | 2.15 ✅ | 1.06 ✅ | 0.84 🟡 | 2.60 ✅ | -| 1M | 4.51 ✅ | 4.77 ✅ | 2.15 ✅ | 2.11 ✅ | 2.11 ✅ | 2.22 ✅ | 2.61 ✅ | 2.64 ✅ | 2.15 ✅ | 2.14 ✅ | 2.14 ✅ | 2.57 ✅ | 5.31 ✅ | +| 100K | 0.92 🟡 | 1.03 ✅ | 1.51 ✅ | 1.99 ✅ | 1.02 ✅ | 1.09 ✅ | 1.65 ✅ | 1.08 ✅ | 2.56 ✅ | 2.47 ✅ | 1.15 ✅ | 0.85 🟡 | 2.56 ✅ | +| 1M | 4.60 ✅ | 4.87 ✅ | 2.13 ✅ | 2.14 ✅ | 2.11 ✅ | 2.12 ✅ | 2.56 ✅ | 2.46 ✅ | 2.20 ✅ | 2.10 ✅ | 2.14 ✅ | 2.59 ✅ | 5.47 ✅ | **Worst 15 cells (NumSharp slowest vs NumPy)** | key | NumSharp ms | NumPy ms | ratio | |---|---|---|---| -| 100K\|i64\|strided\|pos | 0.0257 | 0.0110 | 0.43 🟠 | -| 100K\|f64\|strided\|pos | 0.0232 | 0.0105 | 0.45 🟠 | -| 100K\|u64\|strided\|pos | 0.0240 | 0.0109 | 0.45 🟠 | -| 100K\|i64\|C\|pos | 0.0441 | 0.0208 | 0.47 🟠 | -| 100K\|f32\|strided\|pos | 0.0202 | 0.0103 | 0.51 🟡 | -| 100K\|i32\|strided\|pos | 0.0200 | 0.0109 | 0.54 🟡 | -| 100K\|i64\|T\|pos | 0.0376 | 0.0208 | 0.55 🟡 | -| 100K\|u8\|negrow\|pos | 0.0419 | 0.0232 | 0.56 🟡 | -| 100K\|u8\|sliced\|pos | 0.0410 | 0.0230 | 0.56 🟡 | -| 100K\|u8\|bcast\|pos | 0.0409 | 0.0231 | 0.56 🟡 | -| 100K\|f64\|C\|pos | 0.0418 | 0.0242 | 0.58 🟡 | -| 100K\|f64\|F\|pos | 0.0388 | 0.0227 | 0.58 🟡 | -| 100K\|u32\|strided\|pos | 0.0180 | 0.0112 | 0.62 🟡 | -| 100K\|f64\|T\|pos | 0.0364 | 0.0230 | 0.63 🟡 | -| 100K\|u64\|T\|pos | 0.0358 | 0.0290 | 0.81 🟡 | +| 100K\|f64\|strided\|pos | 0.0243 | 0.0100 | 0.41 🟠 | +| 100K\|u64\|strided\|pos | 0.0241 | 0.0110 | 0.46 🟠 | +| 100K\|i64\|strided\|pos | 0.0237 | 0.0109 | 0.46 🟠 | +| 100K\|f32\|strided\|pos | 0.0216 | 0.0100 | 0.46 🟠 | +| 100K\|i32\|strided\|pos | 0.0213 | 0.0110 | 0.52 🟡 | +| 100K\|i64\|C\|pos | 0.0400 | 0.0210 | 0.53 🟡 | +| 100K\|i64\|T\|pos | 0.0387 | 0.0208 | 0.54 🟡 | +| 100K\|i8\|sliced\|pos | 0.0423 | 0.0228 | 0.54 🟡 | +| 100K\|u8\|bcast\|pos | 0.0416 | 0.0229 | 0.55 🟡 | +| 100K\|u8\|sliced\|pos | 0.0410 | 0.0227 | 0.55 🟡 | +| 100K\|u8\|negrow\|pos | 0.0410 | 0.0231 | 0.56 🟡 | +| 100K\|u32\|strided\|pos | 0.0191 | 0.0109 | 0.57 🟡 | +| 100K\|f64\|T\|pos | 0.0389 | 0.0227 | 0.58 🟡 | +| 100K\|f64\|F\|pos | 0.0362 | 0.0227 | 0.63 🟡 | +| 100K\|f64\|C\|pos | 0.0361 | 0.0228 | 0.63 🟡 | ### Elementwise (add/mul/neg/abs/sqrt/less/copy) @@ -2216,42 +2215,42 @@ Layouts (8, harmonized with the cast subsystem): `C`, `F` (Fortran), `T` (transp | size | C | F | T | strided | sliced | negrow | negcol | bcast | |---|---|---|---|---|---|---|---|---| -| 100K | 0.54 🟡 | 0.75 🟡 | 0.68 🟡 | 0.53 🟡 | 0.84 🟡 | 0.81 🟡 | 1.16 ✅ | 0.84 🟡 | -| 1M | 1.57 ✅ | 1.54 ✅ | 1.53 ✅ | 1.15 ✅ | 1.66 ✅ | 1.65 ✅ | 1.80 ✅ | 1.67 ✅ | +| 100K | 0.73 🟡 | 0.70 🟡 | 0.68 🟡 | 0.53 🟡 | 0.84 🟡 | 0.85 🟡 | 1.18 ✅ | 0.85 🟡 | +| 1M | 1.54 ✅ | 1.48 ✅ | 1.48 ✅ | 1.12 ✅ | 1.64 ✅ | 1.65 ✅ | 1.80 ✅ | 1.63 ✅ | **Geomean by dt** | size | f64 | f32 | c128 | f16 | i32 | i64 | |---|---|---|---|---|---|---| -| 100K | 0.58 🟡 | 0.53 🟡 | 1.18 ✅ | 0.75 🟡 | 0.79 🟡 | 0.81 🟡 | -| 1M | 1.91 ✅ | 1.59 ✅ | 1.74 ✅ | 0.96 🟡 | 1.55 ✅ | 1.82 ✅ | +| 100K | 0.67 🟡 | 0.57 🟡 | 1.15 ✅ | 0.75 🟡 | 0.82 🟡 | 0.81 🟡 | +| 1M | 1.86 ✅ | 1.53 ✅ | 1.73 ✅ | 0.94 🟡 | 1.54 ✅ | 1.79 ✅ | **Geomean by op** | size | add | mul | neg | abs | sqrt | less | copy | |---|---|---|---|---|---|---|---| -| 100K | 0.97 🟡 | 0.93 🟡 | 0.71 🟡 | 0.70 🟡 | 0.94 🟡 | 0.61 🟡 | 0.50 🟡 | -| 1M | 1.81 ✅ | 1.80 ✅ | 2.12 ✅ | 1.66 ✅ | 1.55 ✅ | 0.69 🟡 | 1.82 ✅ | +| 100K | 1.04 ✅ | 0.99 🟡 | 0.76 🟡 | 0.74 🟡 | 0.94 🟡 | 0.61 🟡 | 0.51 🟡 | +| 1M | 1.81 ✅ | 1.78 ✅ | 2.11 ✅ | 1.62 ✅ | 1.50 ✅ | 0.68 🟡 | 1.75 ✅ | **Worst 15 cells (NumSharp slowest vs NumPy)** | key | NumSharp ms | NumPy ms | ratio | |---|---|---|---| -| 100K\|f64\|strided\|abs | 0.0481 | 0.0075 | 0.16 🔴 | -| 100K\|f64\|C\|copy | 0.0700 | 0.0112 | 0.16 🔴 | -| 100K\|f64\|strided\|neg | 0.0502 | 0.0082 | 0.16 🔴 | -| 100K\|f64\|C\|abs | 0.0653 | 0.0113 | 0.17 🔴 | -| 100K\|f32\|C\|abs | 0.0310 | 0.0056 | 0.18 🔴 | -| 100K\|f16\|C\|copy | 0.0169 | 0.0031 | 0.18 🔴 | -| 100K\|f64\|C\|mul | 0.0690 | 0.0129 | 0.19 🔴 | -| 100K\|f64\|C\|neg | 0.0649 | 0.0129 | 0.20 🔴 | -| 100K\|f64\|C\|add | 0.0638 | 0.0130 | 0.20 🟠 | -| 100K\|f16\|negrow\|copy | 0.0178 | 0.0038 | 0.21 🟠 | -| 100K\|f32\|C\|mul | 0.0316 | 0.0069 | 0.22 🟠 | -| 100K\|i32\|bcast\|copy | 0.0224 | 0.0050 | 0.22 🟠 | -| 100K\|f32\|C\|add | 0.0311 | 0.0069 | 0.22 🟠 | -| 100K\|f32\|T\|neg | 0.0270 | 0.0060 | 0.22 🟠 | -| 100K\|f32\|T\|add | 0.0302 | 0.0068 | 0.23 🟠 | +| 100K\|f64\|strided\|abs | 0.0466 | 0.0077 | 0.17 🔴 | +| 100K\|f64\|strided\|neg | 0.0473 | 0.0088 | 0.19 🔴 | +| 100K\|i32\|bcast\|copy | 0.0233 | 0.0050 | 0.21 🟠 | +| 100K\|f16\|bcast\|copy | 0.0134 | 0.0030 | 0.22 🟠 | +| 100K\|f16\|negrow\|copy | 0.0168 | 0.0038 | 0.23 🟠 | +| 100K\|f32\|C\|copy | 0.0244 | 0.0059 | 0.24 🟠 | +| 100K\|i64\|strided\|neg | 0.0296 | 0.0076 | 0.26 🟠 | +| 100K\|f64\|strided\|copy | 0.0413 | 0.0108 | 0.26 🟠 | +| 100K\|f64\|negrow\|copy | 0.0595 | 0.0160 | 0.27 🟠 | +| 100K\|f16\|sliced\|copy | 0.0134 | 0.0037 | 0.27 🟠 | +| 100K\|f64\|sliced\|copy | 0.0592 | 0.0162 | 0.27 🟠 | +| 100K\|i32\|negrow\|copy | 0.0234 | 0.0064 | 0.28 🟠 | +| 100K\|f32\|C\|add | 0.0240 | 0.0066 | 0.28 🟠 | +| 100K\|f64\|strided\|mul | 0.0509 | 0.0142 | 0.28 🟠 | +| 100K\|f64\|strided\|add | 0.0516 | 0.0147 | 0.28 🟠 | --- @@ -2262,33 +2261,33 @@ The layout classes the per-operand layout grid (benchmark/layout) can't express. | case | f64 | f32 | f16 | i32 | i64 | c128 | geomean | |---|---|---|---|---|---|---|---| -| 1-D contiguous (a+a) | 2.55 ✅ | 2.18 ✅ | 0.62 🟡 | 2.04 ✅ | 2.54 ✅ | 2.38 ✅ | 1.87 ✅ | -| 1-D strided a[::2] | 1.83 ✅ | 1.36 ✅ | 0.52 🟡 | 1.37 ✅ | 1.77 ✅ | 1.86 ✅ | 1.34 ✅ | -| 1-D reversed a[::-1] | 2.26 ✅ | 2.09 ✅ | 0.56 🟡 | 2.00 ✅ | 2.14 ✅ | 2.23 ✅ | 1.71 ✅ | -| array + scalar | 2.63 ✅ | 2.11 ✅ | 0.63 🟡 | 1.80 ✅ | 2.17 ✅ | 2.58 ✅ | 1.81 ✅ | -| scalar + array | 2.35 ✅ | 2.10 ✅ | 0.64 🟡 | 1.98 ✅ | 2.48 ✅ | 2.59 ✅ | 1.85 ✅ | -| mixed C + F | 2.12 ✅ | 2.01 ✅ | 0.62 🟡 | 1.98 ✅ | 2.02 ✅ | 2.26 ✅ | 1.70 ✅ | -| mixed C + T | 2.59 ✅ | 2.13 ✅ | 0.62 🟡 | 2.04 ✅ | 2.20 ✅ | 2.51 ✅ | 1.84 ✅ | -| binary broadcast +row(1,C) | 2.72 ✅ | 2.28 ✅ | 0.63 🟡 | 2.00 ✅ | 2.42 ✅ | 2.42 ✅ | 1.89 ✅ | -| binary broadcast +col(R,1) | 2.68 ✅ | 2.14 ✅ | 0.56 🟡 | 1.99 ✅ | 2.45 ✅ | 2.82 ✅ | 1.88 ✅ | -| col-broadcast unary (inner stride-0) | 2.55 ✅ | 1.76 ✅ | 0.86 🟡 | 1.69 ✅ | 2.66 ✅ | 6.09 ✅ | 2.18 ✅ | +| 1-D contiguous (a+a) | 2.41 ✅ | 2.16 ✅ | 0.63 🟡 | 2.13 ✅ | 2.45 ✅ | 2.35 ✅ | 1.85 ✅ | +| 1-D strided a[::2] | 1.84 ✅ | 1.46 ✅ | 0.53 🟡 | 1.51 ✅ | 1.81 ✅ | 1.93 ✅ | 1.40 ✅ | +| 1-D reversed a[::-1] | 2.49 ✅ | 1.95 ✅ | 0.56 🟡 | 2.14 ✅ | 2.29 ✅ | 2.22 ✅ | 1.76 ✅ | +| array + scalar | 2.72 ✅ | 1.93 ✅ | 0.63 🟡 | 1.91 ✅ | 2.26 ✅ | 2.64 ✅ | 1.83 ✅ | +| scalar + array | 2.40 ✅ | 1.98 ✅ | 0.64 🟡 | 2.01 ✅ | 2.19 ✅ | 2.69 ✅ | 1.82 ✅ | +| mixed C + F | 2.29 ✅ | 2.02 ✅ | 0.62 🟡 | 2.02 ✅ | 2.09 ✅ | 1.87 ✅ | 1.68 ✅ | +| mixed C + T | 2.53 ✅ | 2.04 ✅ | 0.62 🟡 | 2.01 ✅ | 2.33 ✅ | 2.30 ✅ | 1.80 ✅ | +| binary broadcast +row(1,C) | 2.71 ✅ | 2.09 ✅ | 0.63 🟡 | 1.95 ✅ | 2.52 ✅ | 2.58 ✅ | 1.89 ✅ | +| binary broadcast +col(R,1) | 2.61 ✅ | 2.02 ✅ | 0.56 🟡 | 2.07 ✅ | 2.54 ✅ | 2.96 ✅ | 1.89 ✅ | +| col-broadcast unary (inner stride-0) | 2.54 ✅ | 1.56 ✅ | 0.88 🟡 | 1.65 ✅ | 2.64 ✅ | 6.22 ✅ | 2.13 ✅ | **Worst 12 cells** | key | NumSharp ms | NumPy ms | ratio | |---|---|---|---| -| 1d_strided|f16 | 2.6414 | 1.3865 | 0.52 🟡 | -| 1d_rev|f16 | 5.3054 | 2.9810 | 0.56 🟡 | -| bcast_col|f16 | 5.2996 | 2.9861 | 0.56 🟡 | -| mix_C_T|f16 | 5.3156 | 3.2865 | 0.62 🟡 | -| 1d_C|f16 | 4.7503 | 2.9588 | 0.62 🟡 | -| mix_C_F|f16 | 5.3110 | 3.3155 | 0.62 🟡 | -| bcast_row|f16 | 4.7682 | 2.9983 | 0.63 🟡 | -| scalar_rhs|f16 | 4.7137 | 2.9711 | 0.63 🟡 | -| scalar_lhs|f16 | 4.6575 | 2.9643 | 0.64 🟡 | -| colbcast_unary|f16 | 0.4898 | 0.4232 | 0.86 🟡 | -| 1d_strided|f32 | 0.2652 | 0.3608 | 1.36 ✅ | -| 1d_strided|i32 | 0.2798 | 0.3832 | 1.37 ✅ | +| 1d_strided|f16 | 2.6261 | 1.3926 | 0.53 🟡 | +| 1d_rev|f16 | 5.2838 | 2.9653 | 0.56 🟡 | +| bcast_col|f16 | 5.2887 | 2.9837 | 0.56 🟡 | +| mix_C_T|f16 | 5.3031 | 3.2873 | 0.62 🟡 | +| mix_C_F|f16 | 5.3153 | 3.3015 | 0.62 🟡 | +| 1d_C|f16 | 4.7456 | 2.9682 | 0.63 🟡 | +| bcast_row|f16 | 4.7695 | 2.9999 | 0.63 🟡 | +| scalar_rhs|f16 | 4.7058 | 2.9602 | 0.63 🟡 | +| scalar_lhs|f16 | 4.6383 | 2.9742 | 0.64 🟡 | +| colbcast_unary|f16 | 0.4753 | 0.4173 | 0.88 🟡 | +| 1d_strided|f32 | 0.2366 | 0.3444 | 1.46 ✅ | +| 1d_strided|i32 | 0.2433 | 0.3676 | 1.51 ✅ | _60 comparable cells._ @@ -2302,326 +2301,315 @@ Full `astype(dst, copy:true)` sweep over every src→dst dtype pair × 8 memory ## Summary -- **129 / 1568** comparable cells lag (<1.0); **1439** win (≥1.0). -- **🔴 <0.2** — 5 cells. Top: 3× * → bool; 2× same-type diagonal (copy) -- **🟠 0.2–0.5** — 10 cells. Top: 8× same-type diagonal (copy); 2× * → bool -- **🟡 0.5–1.0** — 114 cells. Top: 29× float/cplx → narrow-int (bool/u8/i8/i16/u16/char); 20× int → sub-word (narrow); 8× f32 → u64 +- **118 / 1568** comparable cells lag (<1.0); **1450** win (≥1.0). +- **🔴 <0.2** — 0 cells. +- **🟠 0.2–0.5** — 1 cells. Top: 1× * → bool +- **🟡 0.5–1.0** — 117 cells. Top: 23× int → sub-word (narrow); 21× float/cplx → narrow-int (bool/u8/i8/i16/u16/char); 8× f16 → f64 -**float/complex → narrow-int geomean by src** (the historical cliff): `f32`→narrow **1.95**, `f64`→narrow **1.39**, `f16`→narrow **3.77**, `c128`→narrow **1.01**. +**float/complex → narrow-int geomean by src** (the historical cliff): `f32`→narrow **1.96**, `f64`→narrow **1.38**, `f16`→narrow **3.71**, `c128`→narrow **1.08**. ## Geomean by layout (all src×dst, excl. Decimal) | C | F | T | sliced | negrow | negcol | strided | bcast | |---|---|---|---|---|---|---|---| -| 1.82 ✅ | 1.97 ✅ | 1.88 ✅ | 1.87 ✅ | 1.89 ✅ | 1.81 ✅ | 1.47 ✅ | 2.21 ✅ | +| 1.86 ✅ | 1.88 ✅ | 1.93 ✅ | 1.95 ✅ | 1.97 ✅ | 1.82 ✅ | 1.47 ✅ | 2.25 ✅ | ## Geomean by src dtype (all layouts×dst) | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| 2.06 ✅ | 1.99 ✅ | 2.02 ✅ | 2.26 ✅ | 2.21 ✅ | 1.96 ✅ | 1.94 ✅ | 1.64 ✅ | 1.58 ✅ | 1.98 ✅ | 2.41 ✅ | 1.75 ✅ | 1.45 ✅ | nan ? | 1.16 ✅ | +| 2.35 ✅ | 2.18 ✅ | 2.16 ✅ | 2.19 ✅ | 2.14 ✅ | 1.92 ✅ | 1.88 ✅ | 1.62 ✅ | 1.55 ✅ | 1.98 ✅ | 2.36 ✅ | 1.75 ✅ | 1.43 ✅ | nan ? | 1.23 ✅ | ## Geomean by dst dtype (all layouts×src) | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| 1.95 ✅ | 1.76 ✅ | 1.80 ✅ | 1.63 ✅ | 1.61 ✅ | 1.82 ✅ | 1.66 ✅ | 1.93 ✅ | 1.76 ✅ | 1.63 ✅ | 2.48 ✅ | 1.63 ✅ | 2.04 ✅ | nan ? | 2.55 ✅ | +| 2.17 ✅ | 1.94 ✅ | 1.93 ✅ | 1.61 ✅ | 1.60 ✅ | 1.80 ✅ | 1.64 ✅ | 1.89 ✅ | 1.73 ✅ | 1.60 ✅ | 2.48 ✅ | 1.60 ✅ | 2.03 ✅ | nan ? | 2.59 ✅ | ## Layout: C (rows=src, cols=dst) | src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| **bool** | 0.16🔴 | 1.50✅ | 1.74✅ | 2.01✅ | 1.99✅ | 1.49✅ | 1.50✅ | 2.33✅ | 2.41✅ | 1.91✅ | 3.43✅ | 1.73✅ | 2.74✅ | — | 2.95✅ | -| **u8** | 2.13✅ | 0.20🟠 | 2.40✅ | 1.71✅ | 1.79✅ | 1.98✅ | 2.10✅ | 2.43✅ | 2.57✅ | 1.78✅ | 4.04✅ | 1.41✅ | 2.60✅ | — | 3.03✅ | -| **i8** | 3.06✅ | 2.41✅ | 0.26🟠 | 1.91✅ | 1.73✅ | 2.29✅ | 1.98✅ | 2.30✅ | 2.45✅ | 1.72✅ | 3.93✅ | 1.77✅ | 2.53✅ | — | 3.15✅ | -| **i16** | 3.33✅ | 4.04✅ | 3.46✅ | 1.50✅ | 1.97✅ | 1.95✅ | 2.16✅ | 2.31✅ | 2.43✅ | 1.67✅ | 3.85✅ | 2.09✅ | 2.47✅ | — | 2.95✅ | -| **u16** | 3.02✅ | 2.65✅ | 2.69✅ | 2.15✅ | 1.36✅ | 1.90✅ | 2.08✅ | 2.38✅ | 2.43✅ | 1.06✅ | 3.75✅ | 2.10✅ | 2.45✅ | — | 2.85✅ | -| **i32** | 1.85✅ | 1.27✅ | 1.20✅ | 1.50✅ | 1.54✅ | 1.73✅ | 2.25✅ | 2.17✅ | 2.47✅ | 1.52✅ | 3.64✅ | 1.74✅ | 2.36✅ | — | 2.48✅ | -| **u32** | 1.76✅ | 1.07✅ | 1.12✅ | 1.50✅ | 1.63✅ | 2.35✅ | 1.69✅ | 2.57✅ | 2.36✅ | 1.44✅ | 3.80✅ | 1.52✅ | 2.35✅ | — | 2.73✅ | -| **i64** | 1.10✅ | 1.10✅ | 0.94🟡 | 1.24✅ | 1.31✅ | 1.77✅ | 1.79✅ | 2.09✅ | 2.77✅ | 1.39✅ | 1.74✅ | 1.63✅ | 2.52✅ | — | 2.42✅ | -| **u64** | 1.29✅ | 0.90🟡 | 0.99🟡 | 1.30✅ | 1.25✅ | 1.80✅ | 1.91✅ | 2.51✅ | 2.25✅ | 1.15✅ | 1.58✅ | 1.19✅ | 1.67✅ | — | 2.42✅ | -| **char** | 2.05✅ | 1.96✅ | 2.13✅ | 1.59✅ | 0.98🟡 | 1.70✅ | 1.66✅ | 2.32✅ | 2.29✅ | 1.31✅ | 3.67✅ | 1.46✅ | 2.24✅ | — | 2.74✅ | -| **f16** | 4.79✅ | 4.88✅ | 5.20✅ | 4.18✅ | 3.90✅ | 3.79✅ | 1.99✅ | 3.33✅ | 0.97🟡 | 4.24✅ | 1.27✅ | 1.16✅ | 0.93🟡 | — | 1.61✅ | -| **f32** | 3.23✅ | 2.00✅ | 2.00✅ | 1.54✅ | 1.71✅ | 1.92✅ | 1.34✅ | 0.87🟡 | 0.86🟡 | 1.67✅ | 3.61✅ | 1.76✅ | 2.38✅ | — | 2.35✅ | -| **f64** | 1.90✅ | 0.84🟡 | 0.90🟡 | 1.33✅ | 1.42✅ | 1.64✅ | 1.60✅ | 0.90🟡 | 0.92🟡 | 1.35✅ | 1.05✅ | 1.73✅ | 2.13✅ | — | 2.43✅ | +| **bool** | 3.04✅ | 2.60✅ | 2.76✅ | 2.10✅ | 1.98✅ | 1.53✅ | 1.46✅ | 2.04✅ | 2.25✅ | 1.73✅ | 3.29✅ | 1.71✅ | 2.49✅ | — | 2.94✅ | +| **u8** | 3.55✅ | 1.55✅ | 3.56✅ | 1.69✅ | 1.83✅ | 2.01✅ | 1.89✅ | 2.21✅ | 2.47✅ | 1.62✅ | 3.90✅ | 1.38✅ | 2.41✅ | — | 3.07✅ | +| **i8** | 2.61✅ | 3.17✅ | 0.91🟡 | 1.82✅ | 1.76✅ | 1.95✅ | 1.95✅ | 2.35✅ | 2.66✅ | 1.76✅ | 3.94✅ | 1.79✅ | 2.64✅ | — | 2.98✅ | +| **i16** | 2.20✅ | 2.29✅ | 2.59✅ | 1.39✅ | 2.11✅ | 2.09✅ | 1.95✅ | 2.42✅ | 2.52✅ | 1.57✅ | 3.71✅ | 1.92✅ | 2.61✅ | — | 2.79✅ | +| **u16** | 2.22✅ | 2.80✅ | 2.81✅ | 2.12✅ | 1.24✅ | 2.09✅ | 2.14✅ | 2.63✅ | 2.22✅ | 1.08✅ | 3.71✅ | 1.93✅ | 2.46✅ | — | 2.97✅ | +| **i32** | 1.50✅ | 1.15✅ | 1.16✅ | 1.46✅ | 1.51✅ | 1.70✅ | 2.12✅ | 2.26✅ | 2.27✅ | 1.43✅ | 3.64✅ | 1.64✅ | 2.62✅ | — | 2.86✅ | +| **u32** | 1.77✅ | 1.18✅ | 1.17✅ | 1.35✅ | 1.44✅ | 2.17✅ | 1.65✅ | 2.19✅ | 2.29✅ | 1.48✅ | 3.66✅ | 1.47✅ | 2.34✅ | — | 2.69✅ | +| **i64** | 1.08✅ | 0.98🟡 | 0.96🟡 | 1.23✅ | 1.27✅ | 1.83✅ | 1.78✅ | 1.87✅ | 2.76✅ | 1.38✅ | 1.81✅ | 1.81✅ | 2.22✅ | — | 2.52✅ | +| **u64** | 1.14✅ | 0.92🟡 | 0.95🟡 | 1.22✅ | 1.18✅ | 1.73✅ | 1.73✅ | 2.56✅ | 2.09✅ | 1.30✅ | 1.65✅ | 1.14✅ | 1.79✅ | — | 2.45✅ | +| **char** | 2.07✅ | 2.02✅ | 2.14✅ | 1.49✅ | 0.97🟡 | 1.55✅ | 1.53✅ | 2.26✅ | 2.20✅ | 1.21✅ | 3.67✅ | 1.42✅ | 2.11✅ | — | 2.62✅ | +| **f16** | 4.51✅ | 5.42✅ | 5.35✅ | 3.82✅ | 3.94✅ | 3.80✅ | 1.96✅ | 2.94✅ | 0.99🟡 | 4.16✅ | 1.25✅ | 1.10✅ | 0.92🟡 | — | 1.64✅ | +| **f32** | 2.82✅ | 1.92✅ | 2.05✅ | 1.64✅ | 1.55✅ | 1.84✅ | 1.38✅ | 0.83🟡 | 0.87🟡 | 1.48✅ | 3.59✅ | 1.76✅ | 2.21✅ | — | 2.35✅ | +| **f64** | 1.81✅ | 0.81🟡 | 0.81🟡 | 1.32✅ | 1.29✅ | 1.68✅ | 1.51✅ | 0.86🟡 | 0.89🟡 | 1.38✅ | 1.04✅ | 1.72✅ | 2.05✅ | — | 2.60✅ | | **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | -| **c128** | 0.96🟡 | 0.98🟡 | 1.00✅ | 1.04✅ | 1.02✅ | 1.58✅ | 1.20✅ | 0.92🟡 | 0.80🟡 | 1.04✅ | 1.41✅ | 1.26✅ | 1.40✅ | — | 3.06✅ | +| **c128** | 1.11✅ | 0.93🟡 | 0.94🟡 | 1.19✅ | 1.13✅ | 1.69✅ | 1.35✅ | 0.92🟡 | 0.84🟡 | 1.10✅ | 1.50✅ | 1.39✅ | 1.75✅ | — | 3.18✅ | ## Layout: F (rows=src, cols=dst) | src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| **bool** | 3.00✅ | 4.04✅ | 4.05✅ | 1.97✅ | 2.06✅ | 1.47✅ | 1.46✅ | 2.39✅ | 2.24✅ | 1.97✅ | 4.07✅ | 1.76✅ | 2.59✅ | — | 3.12✅ | -| **u8** | 3.49✅ | 3.15✅ | 4.05✅ | 1.81✅ | 1.98✅ | 2.04✅ | 2.08✅ | 2.24✅ | 2.37✅ | 1.90✅ | 3.86✅ | 1.47✅ | 2.42✅ | — | 3.03✅ | -| **i8** | 3.86✅ | 4.23✅ | 3.36✅ | 2.07✅ | 1.74✅ | 2.06✅ | 2.10✅ | 2.50✅ | 2.43✅ | 1.74✅ | 3.73✅ | 1.84✅ | 2.48✅ | — | 3.08✅ | -| **i16** | 2.96✅ | 2.60✅ | 3.13✅ | 1.43✅ | 2.05✅ | 2.03✅ | 2.03✅ | 2.34✅ | 2.30✅ | 1.99✅ | 3.84✅ | 2.23✅ | 2.53✅ | — | 2.61✅ | -| **u16** | 2.98✅ | 3.38✅ | 3.06✅ | 2.27✅ | 1.52✅ | 1.97✅ | 2.26✅ | 2.50✅ | 2.44✅ | 1.27✅ | 3.50✅ | 1.67✅ | 2.47✅ | — | 2.76✅ | -| **i32** | 1.74✅ | 1.14✅ | 1.06✅ | 1.44✅ | 1.66✅ | 1.71✅ | 2.29✅ | 2.35✅ | 2.42✅ | 1.80✅ | 3.74✅ | 1.75✅ | 2.73✅ | — | 2.77✅ | -| **u32** | 1.84✅ | 1.10✅ | 1.18✅ | 1.52✅ | 1.53✅ | 2.06✅ | 1.78✅ | 2.52✅ | 2.32✅ | 1.60✅ | 3.66✅ | 1.49✅ | 2.27✅ | — | 2.72✅ | -| **i64** | 1.21✅ | 1.11✅ | 0.95🟡 | 1.17✅ | 1.26✅ | 1.71✅ | 1.88✅ | 2.03✅ | 2.58✅ | 1.37✅ | 1.84✅ | 1.73✅ | 2.28✅ | — | 2.68✅ | -| **u64** | 1.13✅ | 0.92🟡 | 0.92🟡 | 1.24✅ | 1.21✅ | 1.72✅ | 1.78✅ | 2.81✅ | 2.11✅ | 1.34✅ | 2.45✅ | 1.18✅ | 1.86✅ | — | 2.37✅ | -| **char** | 2.14✅ | 1.99✅ | 1.91✅ | 1.93✅ | 1.34✅ | 1.61✅ | 1.61✅ | 2.42✅ | 2.37✅ | 1.34✅ | 3.61✅ | 1.50✅ | 2.19✅ | — | 2.72✅ | -| **f16** | 4.92✅ | 5.47✅ | 6.19✅ | 4.06✅ | 4.35✅ | 3.67✅ | 2.04✅ | 3.25✅ | 1.13✅ | 4.29✅ | 1.34✅ | 2.29✅ | 0.97🟡 | — | 1.69✅ | -| **f32** | 3.21✅ | 2.20✅ | 2.17✅ | 1.69✅ | 1.72✅ | 1.87✅ | 1.38✅ | 0.85🟡 | 0.88🟡 | 1.64✅ | 3.68✅ | 1.71✅ | 2.10✅ | — | 2.28✅ | -| **f64** | 1.87✅ | 0.99🟡 | 1.54✅ | 1.30✅ | 1.40✅ | 1.77✅ | 1.50✅ | 0.88🟡 | 0.88🟡 | 1.46✅ | 1.61✅ | 1.68✅ | 1.97✅ | — | 2.44✅ | +| **bool** | 0.24🟠 | 2.72✅ | 2.71✅ | 1.94✅ | 1.91✅ | 1.43✅ | 1.42✅ | 2.24✅ | 2.40✅ | 1.89✅ | 4.03✅ | 1.74✅ | 2.55✅ | — | 2.98✅ | +| **u8** | 4.01✅ | 1.11✅ | 3.48✅ | 1.70✅ | 1.71✅ | 2.07✅ | 2.06✅ | 2.11✅ | 2.33✅ | 1.62✅ | 3.89✅ | 1.39✅ | 2.49✅ | — | 3.06✅ | +| **i8** | 3.53✅ | 3.24✅ | 1.05✅ | 1.93✅ | 1.80✅ | 2.14✅ | 2.13✅ | 2.59✅ | 2.51✅ | 1.71✅ | 3.88✅ | 1.77✅ | 2.40✅ | — | 2.95✅ | +| **i16** | 1.87✅ | 2.06✅ | 2.52✅ | 1.31✅ | 1.98✅ | 1.84✅ | 2.01✅ | 2.59✅ | 2.39✅ | 2.19✅ | 3.71✅ | 2.05✅ | 2.39✅ | — | 3.01✅ | +| **u16** | 2.26✅ | 2.74✅ | 2.63✅ | 1.93✅ | 1.27✅ | 1.88✅ | 2.02✅ | 2.68✅ | 2.26✅ | 1.35✅ | 3.65✅ | 1.96✅ | 2.17✅ | — | 2.85✅ | +| **i32** | 1.69✅ | 1.11✅ | 1.18✅ | 1.48✅ | 1.49✅ | 1.76✅ | 2.17✅ | 2.46✅ | 2.48✅ | 1.50✅ | 3.90✅ | 1.72✅ | 2.33✅ | — | 2.85✅ | +| **u32** | 1.85✅ | 1.13✅ | 1.06✅ | 1.54✅ | 1.43✅ | 2.15✅ | 1.64✅ | 2.31✅ | 2.26✅ | 1.50✅ | 3.87✅ | 1.55✅ | 2.16✅ | — | 2.46✅ | +| **i64** | 1.11✅ | 0.94🟡 | 0.92🟡 | 1.17✅ | 1.26✅ | 1.88✅ | 1.75✅ | 2.16✅ | 2.70✅ | 1.28✅ | 1.87✅ | 1.55✅ | 2.17✅ | — | 2.24✅ | +| **u64** | 1.15✅ | 0.92🟡 | 0.94🟡 | 1.21✅ | 1.13✅ | 1.73✅ | 1.73✅ | 2.46✅ | 2.05✅ | 1.39✅ | 2.48✅ | 1.14✅ | 1.76✅ | — | 2.34✅ | +| **char** | 2.08✅ | 2.63✅ | 2.03✅ | 1.90✅ | 1.20✅ | 1.67✅ | 1.69✅ | 2.52✅ | 2.46✅ | 1.20✅ | 3.60✅ | 1.41✅ | 2.10✅ | — | 2.89✅ | +| **f16** | 4.49✅ | 6.02✅ | 6.33✅ | 3.79✅ | 4.31✅ | 3.73✅ | 2.06✅ | 2.85✅ | 1.11✅ | 4.33✅ | 1.24✅ | 2.44✅ | 0.95🟡 | — | 1.62✅ | +| **f32** | 3.44✅ | 2.12✅ | 2.26✅ | 1.62✅ | 1.64✅ | 1.92✅ | 1.35✅ | 0.82🟡 | 0.84🟡 | 1.69✅ | 3.63✅ | 1.67✅ | 2.21✅ | — | 2.26✅ | +| **f64** | 1.90✅ | 1.27✅ | 1.33✅ | 1.40✅ | 1.43✅ | 1.95✅ | 1.47✅ | 0.87🟡 | 0.90🟡 | 1.41✅ | 1.61✅ | 1.73✅ | 2.05✅ | — | 2.69✅ | | **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | -| **c128** | 0.93🟡 | 0.85🟡 | 0.86🟡 | 1.03✅ | 1.14✅ | 1.47✅ | 1.35✅ | 0.87🟡 | 0.82🟡 | 1.08✅ | 1.47✅ | 1.45✅ | 1.46✅ | — | 2.98✅ | +| **c128** | 0.99🟡 | 0.91🟡 | 0.88🟡 | 1.15✅ | 1.07✅ | 1.57✅ | 1.33✅ | 0.96🟡 | 0.81🟡 | 1.09✅ | 1.49✅ | 1.15✅ | 1.75✅ | — | 3.26✅ | ## Layout: T (rows=src, cols=dst) | src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| **bool** | 0.18🔴 | 2.40✅ | 3.93✅ | 2.10✅ | 2.10✅ | 1.52✅ | 1.47✅ | 2.49✅ | 2.41✅ | 1.98✅ | 4.20✅ | 1.74✅ | 2.57✅ | — | 3.21✅ | -| **u8** | 2.17✅ | 0.16🔴 | 2.00✅ | 1.94✅ | 1.88✅ | 1.96✅ | 2.15✅ | 2.26✅ | 2.66✅ | 1.72✅ | 3.89✅ | 1.49✅ | 2.54✅ | — | 2.75✅ | -| **i8** | 2.29✅ | 3.03✅ | 0.20🟠 | 2.03✅ | 1.63✅ | 2.00✅ | 2.20✅ | 2.43✅ | 2.29✅ | 1.76✅ | 3.61✅ | 1.68✅ | 1.79✅ | — | 2.76✅ | -| **i16** | 2.45✅ | 3.08✅ | 2.89✅ | 1.35✅ | 2.02✅ | 2.03✅ | 1.90✅ | 2.45✅ | 2.34✅ | 2.04✅ | 3.88✅ | 2.02✅ | 2.36✅ | — | 2.80✅ | -| **u16** | 2.32✅ | 2.26✅ | 3.09✅ | 2.09✅ | 1.78✅ | 1.83✅ | 2.02✅ | 2.46✅ | 2.52✅ | 1.49✅ | 3.92✅ | 2.03✅ | 2.54✅ | — | 2.87✅ | -| **i32** | 1.90✅ | 1.23✅ | 1.27✅ | 1.58✅ | 1.56✅ | 1.81✅ | 2.16✅ | 2.42✅ | 2.41✅ | 1.55✅ | 3.89✅ | 1.84✅ | 2.24✅ | — | 2.82✅ | -| **u32** | 2.00✅ | 1.12✅ | 1.04✅ | 1.57✅ | 1.56✅ | 2.43✅ | 1.73✅ | 2.32✅ | 2.17✅ | 1.55✅ | 3.87✅ | 1.58✅ | 2.38✅ | — | 2.75✅ | -| **i64** | 1.18✅ | 1.15✅ | 1.00🟡 | 1.18✅ | 1.28✅ | 1.88✅ | 1.79✅ | 2.08✅ | 2.65✅ | 1.42✅ | 1.85✅ | 1.67✅ | 2.31✅ | — | 2.64✅ | -| **u64** | 1.18✅ | 0.93🟡 | 0.95🟡 | 1.14✅ | 1.26✅ | 1.73✅ | 1.71✅ | 2.68✅ | 2.11✅ | 1.40✅ | 2.51✅ | 1.20✅ | 1.74✅ | — | 2.35✅ | -| **char** | 2.45✅ | 2.21✅ | 2.31✅ | 1.87✅ | 1.26✅ | 1.72✅ | 1.59✅ | 2.24✅ | 2.45✅ | 1.29✅ | 3.58✅ | 1.57✅ | 2.49✅ | — | 2.28✅ | -| **f16** | 5.96✅ | 6.20✅ | 6.19✅ | 4.19✅ | 4.48✅ | 4.05✅ | 2.09✅ | 3.27✅ | 1.15✅ | 4.22✅ | 1.22✅ | 2.84✅ | 0.99🟡 | — | 1.66✅ | -| **f32** | 3.16✅ | 2.19✅ | 2.28✅ | 1.73✅ | 1.71✅ | 1.87✅ | 1.40✅ | 0.84🟡 | 0.87🟡 | 1.66✅ | 3.80✅ | 1.82✅ | 2.48✅ | — | 2.30✅ | -| **f64** | 2.29✅ | 1.22✅ | 1.27✅ | 1.47✅ | 1.52✅ | 1.69✅ | 1.52✅ | 0.87🟡 | 0.90🟡 | 1.43✅ | 1.64✅ | 1.80✅ | 2.15✅ | — | 2.46✅ | +| **bool** | 3.40✅ | 3.39✅ | 3.74✅ | 1.71✅ | 1.80✅ | 1.47✅ | 1.41✅ | 2.31✅ | 2.32✅ | 1.91✅ | 4.20✅ | 1.78✅ | 2.68✅ | — | 2.99✅ | +| **u8** | 3.94✅ | 1.21✅ | 2.55✅ | 1.68✅ | 1.68✅ | 2.05✅ | 1.92✅ | 2.21✅ | 2.56✅ | 1.67✅ | 3.98✅ | 1.41✅ | 2.41✅ | — | 2.98✅ | +| **i8** | 2.58✅ | 3.43✅ | 0.85🟡 | 1.84✅ | 1.75✅ | 2.01✅ | 2.12✅ | 2.50✅ | 2.42✅ | 1.76✅ | 3.79✅ | 1.80✅ | 2.46✅ | — | 3.04✅ | +| **i16** | 2.29✅ | 2.12✅ | 3.21✅ | 1.25✅ | 2.11✅ | 2.01✅ | 2.04✅ | 2.43✅ | 2.25✅ | 2.00✅ | 3.86✅ | 2.02✅ | 2.46✅ | — | 3.02✅ | +| **u16** | 2.23✅ | 2.51✅ | 2.45✅ | 1.91✅ | 1.26✅ | 2.03✅ | 1.99✅ | 2.44✅ | 2.39✅ | 1.44✅ | 3.67✅ | 1.90✅ | 2.29✅ | — | 2.50✅ | +| **i32** | 1.77✅ | 1.13✅ | 1.19✅ | 1.52✅ | 1.52✅ | 1.69✅ | 2.31✅ | 2.07✅ | 2.11✅ | 1.66✅ | 3.96✅ | 1.65✅ | 2.57✅ | — | 2.95✅ | +| **u32** | 1.89✅ | 1.16✅ | 1.16✅ | 1.49✅ | 1.47✅ | 2.25✅ | 1.72✅ | 2.18✅ | 2.15✅ | 1.47✅ | 3.71✅ | 1.49✅ | 2.27✅ | — | 2.82✅ | +| **i64** | 1.28✅ | 1.04✅ | 0.93🟡 | 1.25✅ | 1.27✅ | 1.79✅ | 1.78✅ | 2.09✅ | 2.64✅ | 1.33✅ | 1.86✅ | 1.74✅ | 2.23✅ | — | 2.69✅ | +| **u64** | 1.20✅ | 0.94🟡 | 0.95🟡 | 1.21✅ | 1.16✅ | 1.79✅ | 1.62✅ | 2.52✅ | 2.00✅ | 1.36✅ | 2.47✅ | 1.12✅ | 1.90✅ | — | 2.54✅ | +| **char** | 2.14✅ | 2.15✅ | 2.63✅ | 1.84✅ | 1.22✅ | 1.61✅ | 1.62✅ | 2.24✅ | 2.47✅ | 1.31✅ | 3.50✅ | 1.44✅ | 2.32✅ | — | 2.56✅ | +| **f16** | 5.17✅ | 6.28✅ | 6.15✅ | 4.27✅ | 4.26✅ | 3.56✅ | 2.03✅ | 3.02✅ | 1.14✅ | 4.33✅ | 1.18✅ | 2.84✅ | 1.00🟡 | — | 1.65✅ | +| **f32** | 3.33✅ | 2.16✅ | 2.30✅ | 1.77✅ | 1.63✅ | 2.04✅ | 1.38✅ | 0.83🟡 | 0.86🟡 | 1.63✅ | 3.75✅ | 1.70✅ | 2.56✅ | — | 2.20✅ | +| **f64** | 2.00✅ | 1.35✅ | 1.35✅ | 1.37✅ | 1.41✅ | 1.81✅ | 1.48✅ | 0.87🟡 | 0.89🟡 | 1.31✅ | 1.63✅ | 1.74✅ | 1.93✅ | — | 2.61✅ | | **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | -| **c128** | 1.00✅ | 0.92🟡 | 0.89🟡 | 1.14✅ | 1.14✅ | 1.46✅ | 1.19✅ | 0.89🟡 | 0.86🟡 | 1.16✅ | 1.50✅ | 1.29✅ | 1.45✅ | — | 3.29✅ | +| **c128** | 1.00✅ | 1.11✅ | 1.21✅ | 1.27✅ | 1.18✅ | 1.66✅ | 1.28✅ | 0.90🟡 | 0.85🟡 | 1.11✅ | 1.53✅ | 1.41✅ | 1.79✅ | — | 3.42✅ | ## Layout: sliced (rows=src, cols=dst) | src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| **bool** | 0.20🔴 | 2.59✅ | 3.31✅ | 2.05✅ | 2.00✅ | 1.50✅ | 1.58✅ | 2.39✅ | 2.28✅ | 1.95✅ | 3.98✅ | 1.87✅ | 2.42✅ | — | 3.28✅ | -| **u8** | 2.61✅ | 0.22🟠 | 2.86✅ | 1.96✅ | 1.85✅ | 2.02✅ | 1.98✅ | 2.25✅ | 2.37✅ | 1.95✅ | 3.73✅ | 1.50✅ | 2.53✅ | — | 3.04✅ | -| **i8** | 2.69✅ | 3.27✅ | 0.29🟠 | 1.88✅ | 1.77✅ | 2.12✅ | 1.94✅ | 2.37✅ | 2.49✅ | 1.67✅ | 3.83✅ | 1.60✅ | 2.44✅ | — | 2.79✅ | -| **i16** | 2.61✅ | 2.75✅ | 2.62✅ | 1.47✅ | 1.56✅ | 2.09✅ | 1.91✅ | 2.22✅ | 2.38✅ | 1.66✅ | 3.56✅ | 2.05✅ | 2.08✅ | — | 2.36✅ | -| **u16** | 2.51✅ | 3.01✅ | 2.99✅ | 1.60✅ | 1.40✅ | 2.05✅ | 2.10✅ | 2.55✅ | 2.25✅ | 1.54✅ | 3.70✅ | 2.13✅ | 2.72✅ | — | 2.72✅ | -| **i32** | 1.72✅ | 2.09✅ | 2.42✅ | 1.58✅ | 1.63✅ | 1.85✅ | 1.87✅ | 2.35✅ | 2.39✅ | 1.50✅ | 3.68✅ | 1.62✅ | 2.37✅ | — | 2.87✅ | -| **u32** | 1.91✅ | 2.15✅ | 2.19✅ | 1.55✅ | 1.47✅ | 1.88✅ | 1.99✅ | 2.01✅ | 2.13✅ | 1.62✅ | 3.67✅ | 1.55✅ | 2.22✅ | — | 2.63✅ | -| **i64** | 1.13✅ | 1.27✅ | 1.26✅ | 1.36✅ | 1.38✅ | 1.80✅ | 1.88✅ | 2.64✅ | 2.52✅ | 1.29✅ | 1.81✅ | 1.63✅ | 2.23✅ | — | 2.59✅ | -| **u64** | 1.21✅ | 1.25✅ | 1.27✅ | 1.30✅ | 1.25✅ | 1.76✅ | 1.83✅ | 2.20✅ | 2.68✅ | 1.31✅ | 2.32✅ | 1.00🟡 | 1.63✅ | — | 2.43✅ | -| **char** | 1.45✅ | 2.45✅ | 2.83✅ | 1.61✅ | 1.48✅ | 1.64✅ | 1.68✅ | 2.03✅ | 2.39✅ | 1.40✅ | 3.38✅ | 1.49✅ | 2.17✅ | — | 2.92✅ | -| **f16** | 5.03✅ | 5.33✅ | 5.42✅ | 3.92✅ | 3.92✅ | 3.57✅ | 2.01✅ | 3.10✅ | 1.11✅ | 4.00✅ | 1.37✅ | 2.81✅ | 1.01✅ | — | 1.61✅ | -| **f32** | 3.18✅ | 2.29✅ | 2.34✅ | 1.71✅ | 1.61✅ | 1.87✅ | 1.37✅ | 0.84🟡 | 0.90🟡 | 1.54✅ | 3.54✅ | 1.82✅ | 2.32✅ | — | 2.36✅ | -| **f64** | 1.82✅ | 1.17✅ | 1.18✅ | 1.36✅ | 1.33✅ | 0.80🟡 | 1.34✅ | 0.86🟡 | 0.89🟡 | 1.50✅ | 1.58✅ | 1.85✅ | 2.30✅ | — | 2.51✅ | +| **bool** | 3.14✅ | 5.84✅ | 5.99✅ | 1.96✅ | 2.00✅ | 1.49✅ | 1.56✅ | 2.38✅ | 2.06✅ | 1.99✅ | 3.95✅ | 1.74✅ | 2.66✅ | — | 3.02✅ | +| **u8** | 2.74✅ | 1.25✅ | 3.91✅ | 1.79✅ | 1.88✅ | 2.24✅ | 2.14✅ | 2.44✅ | 2.55✅ | 1.72✅ | 3.71✅ | 1.40✅ | 2.83✅ | — | 3.17✅ | +| **i8** | 2.96✅ | 3.03✅ | 0.92🟡 | 1.75✅ | 1.74✅ | 1.99✅ | 1.92✅ | 2.30✅ | 2.14✅ | 1.65✅ | 3.86✅ | 1.69✅ | 2.48✅ | — | 3.07✅ | +| **i16** | 2.25✅ | 2.82✅ | 2.56✅ | 1.45✅ | 1.71✅ | 1.93✅ | 1.90✅ | 2.47✅ | 2.50✅ | 1.62✅ | 3.54✅ | 2.06✅ | 2.58✅ | — | 2.85✅ | +| **u16** | 2.98✅ | 2.66✅ | 2.90✅ | 1.46✅ | 1.33✅ | 2.06✅ | 1.96✅ | 2.50✅ | 2.20✅ | 1.36✅ | 3.62✅ | 1.88✅ | 2.45✅ | — | 3.01✅ | +| **i32** | 1.94✅ | 2.20✅ | 2.10✅ | 1.43✅ | 1.54✅ | 1.90✅ | 1.81✅ | 2.14✅ | 2.23✅ | 1.55✅ | 3.70✅ | 1.58✅ | 2.39✅ | — | 2.86✅ | +| **u32** | 1.99✅ | 2.01✅ | 2.13✅ | 1.44✅ | 1.53✅ | 2.02✅ | 1.85✅ | 2.10✅ | 2.22✅ | 1.55✅ | 3.72✅ | 1.47✅ | 2.12✅ | — | 2.74✅ | +| **i64** | 1.20✅ | 1.18✅ | 1.28✅ | 1.42✅ | 1.33✅ | 1.78✅ | 1.78✅ | 2.22✅ | 2.35✅ | 1.42✅ | 1.82✅ | 1.52✅ | 2.36✅ | — | 2.68✅ | +| **u64** | 1.29✅ | 1.24✅ | 1.22✅ | 1.33✅ | 1.30✅ | 1.74✅ | 1.79✅ | 2.14✅ | 2.22✅ | 1.36✅ | 2.39✅ | 0.97🟡 | 1.58✅ | — | 2.45✅ | +| **char** | 2.16✅ | 2.26✅ | 2.43✅ | 1.50✅ | 1.31✅ | 1.61✅ | 1.60✅ | 2.27✅ | 2.32✅ | 1.31✅ | 3.47✅ | 1.42✅ | 2.23✅ | — | 2.80✅ | +| **f16** | 4.70✅ | 4.95✅ | 4.99✅ | 4.11✅ | 4.05✅ | 4.02✅ | 1.97✅ | 3.08✅ | 1.10✅ | 4.21✅ | 1.33✅ | 2.70✅ | 0.96🟡 | — | 1.49✅ | +| **f32** | 3.63✅ | 2.20✅ | 2.35✅ | 1.64✅ | 1.67✅ | 1.90✅ | 1.34✅ | 0.86🟡 | 0.88🟡 | 1.59✅ | 3.62✅ | 1.97✅ | 2.04✅ | — | 2.35✅ | +| **f64** | 2.01✅ | 1.26✅ | 1.31✅ | 1.42✅ | 1.33✅ | 0.70🟡 | 1.49✅ | 0.84🟡 | 0.84🟡 | 1.34✅ | 1.58✅ | 1.71✅ | 2.49✅ | — | 2.58✅ | | **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | -| **c128** | 0.97🟡 | 0.93🟡 | 0.92🟡 | 1.15✅ | 0.99🟡 | 1.47✅ | 1.08✅ | 0.86🟡 | 0.77🟡 | 1.07✅ | 1.44✅ | 1.26✅ | 1.55✅ | — | 2.15✅ | +| **c128** | 0.97🟡 | 1.07✅ | 0.96🟡 | 1.13✅ | 1.14✅ | 1.72✅ | 1.30✅ | 0.93🟡 | 0.80🟡 | 0.91🟡 | 1.47✅ | 1.40✅ | 1.75✅ | — | 2.20✅ | ## Layout: negrow (rows=src, cols=dst) | src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| **bool** | 0.24🟠 | 3.08✅ | 3.39✅ | 1.93✅ | 2.01✅ | 1.50✅ | 1.55✅ | 2.17✅ | 2.33✅ | 1.98✅ | 3.85✅ | 1.67✅ | 2.62✅ | — | 2.97✅ | -| **u8** | 3.02✅ | 0.23🟠 | 2.94✅ | 1.80✅ | 1.74✅ | 2.00✅ | 2.04✅ | 2.24✅ | 2.33✅ | 1.76✅ | 3.81✅ | 1.49✅ | 2.53✅ | — | 3.11✅ | -| **i8** | 2.84✅ | 2.66✅ | 0.24🟠 | 1.88✅ | 1.70✅ | 2.05✅ | 2.07✅ | 2.53✅ | 2.57✅ | 1.88✅ | 3.79✅ | 1.65✅ | 2.41✅ | — | 3.07✅ | -| **i16** | 3.40✅ | 2.43✅ | 2.21✅ | 1.46✅ | 1.76✅ | 1.92✅ | 2.07✅ | 2.21✅ | 2.23✅ | 1.73✅ | 3.49✅ | 2.14✅ | 2.19✅ | — | 2.63✅ | -| **u16** | 2.52✅ | 2.65✅ | 3.08✅ | 1.60✅ | 1.44✅ | 2.04✅ | 2.07✅ | 2.60✅ | 2.42✅ | 1.44✅ | 3.65✅ | 2.03✅ | 2.49✅ | — | 2.83✅ | -| **i32** | 2.08✅ | 2.13✅ | 2.38✅ | 1.59✅ | 1.58✅ | 1.99✅ | 1.97✅ | 2.34✅ | 2.27✅ | 1.58✅ | 3.60✅ | 1.69✅ | 2.39✅ | — | 2.67✅ | -| **u32** | 1.71✅ | 2.19✅ | 2.32✅ | 1.59✅ | 1.59✅ | 1.98✅ | 1.93✅ | 2.51✅ | 2.09✅ | 1.53✅ | 3.72✅ | 1.54✅ | 2.17✅ | — | 2.77✅ | -| **i64** | 1.18✅ | 1.26✅ | 1.25✅ | 1.36✅ | 1.38✅ | 1.82✅ | 1.84✅ | 2.39✅ | 2.23✅ | 1.44✅ | 1.79✅ | 1.55✅ | 2.23✅ | — | 2.36✅ | -| **u64** | 1.20✅ | 1.17✅ | 1.21✅ | 1.43✅ | 1.37✅ | 1.87✅ | 1.84✅ | 2.20✅ | 2.51✅ | 1.43✅ | 2.33✅ | 0.99🟡 | 1.48✅ | — | 2.42✅ | -| **char** | 2.22✅ | 2.50✅ | 2.15✅ | 1.59✅ | 1.33✅ | 1.71✅ | 1.57✅ | 2.35✅ | 2.30✅ | 1.44✅ | 3.40✅ | 1.49✅ | 2.29✅ | — | 2.68✅ | -| **f16** | 5.36✅ | 4.77✅ | 4.63✅ | 3.79✅ | 4.08✅ | 3.86✅ | 2.03✅ | 3.19✅ | 1.07✅ | 3.86✅ | 1.38✅ | 2.80✅ | 0.98🟡 | — | 1.62✅ | -| **f32** | 3.48✅ | 2.27✅ | 2.31✅ | 1.60✅ | 1.54✅ | 2.01✅ | 1.37✅ | 0.87🟡 | 0.88🟡 | 1.73✅ | 3.57✅ | 1.84✅ | 2.23✅ | — | 2.24✅ | -| **f64** | 1.97✅ | 1.25✅ | 1.28✅ | 1.48✅ | 1.55✅ | 1.77✅ | 1.50✅ | 0.87🟡 | 0.91🟡 | 1.64✅ | 1.60✅ | 1.87✅ | 2.36✅ | — | 2.46✅ | +| **bool** | 3.38✅ | 4.26✅ | 4.97✅ | 1.92✅ | 2.03✅ | 1.41✅ | 1.57✅ | 2.32✅ | 2.40✅ | 1.98✅ | 3.91✅ | 1.79✅ | 2.59✅ | — | 3.34✅ | +| **u8** | 3.67✅ | 1.14✅ | 3.99✅ | 1.71✅ | 1.63✅ | 2.15✅ | 2.24✅ | 2.37✅ | 2.35✅ | 1.87✅ | 3.81✅ | 1.44✅ | 2.29✅ | — | 3.05✅ | +| **i8** | 3.35✅ | 3.21✅ | 0.97🟡 | 2.04✅ | 1.87✅ | 2.02✅ | 2.04✅ | 2.32✅ | 2.48✅ | 1.72✅ | 3.67✅ | 1.73✅ | 2.58✅ | — | 3.25✅ | +| **i16** | 3.19✅ | 2.80✅ | 2.70✅ | 1.39✅ | 1.68✅ | 1.99✅ | 2.03✅ | 2.29✅ | 2.48✅ | 1.78✅ | 3.72✅ | 2.09✅ | 2.42✅ | — | 3.13✅ | +| **u16** | 2.41✅ | 2.59✅ | 2.35✅ | 1.53✅ | 1.32✅ | 1.77✅ | 1.84✅ | 2.45✅ | 2.56✅ | 1.53✅ | 3.57✅ | 2.04✅ | 2.46✅ | — | 2.82✅ | +| **i32** | 2.01✅ | 2.01✅ | 2.04✅ | 1.52✅ | 1.53✅ | 1.97✅ | 1.89✅ | 2.21✅ | 2.32✅ | 1.57✅ | 3.58✅ | 1.54✅ | 2.04✅ | — | 2.86✅ | +| **u32** | 1.96✅ | 2.10✅ | 2.14✅ | 1.47✅ | 1.49✅ | 1.80✅ | 1.91✅ | 2.27✅ | 2.52✅ | 1.59✅ | 3.59✅ | 1.50✅ | 2.18✅ | — | 2.54✅ | +| **i64** | 1.17✅ | 1.21✅ | 1.33✅ | 1.39✅ | 1.41✅ | 1.84✅ | 1.78✅ | 2.65✅ | 2.22✅ | 1.31✅ | 1.84✅ | 1.46✅ | 2.33✅ | — | 2.59✅ | +| **u64** | 1.13✅ | 1.26✅ | 1.29✅ | 1.33✅ | 1.37✅ | 1.83✅ | 1.96✅ | 2.17✅ | 2.39✅ | 1.31✅ | 2.35✅ | 1.00🟡 | 1.59✅ | — | 2.32✅ | +| **char** | 2.36✅ | 2.38✅ | 2.28✅ | 1.71✅ | 1.37✅ | 1.57✅ | 1.58✅ | 2.34✅ | 2.32✅ | 1.33✅ | 3.52✅ | 1.49✅ | 2.37✅ | — | 2.79✅ | +| **f16** | 5.08✅ | 4.80✅ | 4.78✅ | 3.80✅ | 3.83✅ | 3.85✅ | 2.03✅ | 3.13✅ | 1.09✅ | 3.83✅ | 1.34✅ | 2.87✅ | 0.97🟡 | — | 1.60✅ | +| **f32** | 3.30✅ | 2.23✅ | 2.25✅ | 1.60✅ | 1.67✅ | 1.91✅ | 1.36✅ | 0.84🟡 | 0.85🟡 | 1.66✅ | 3.58✅ | 1.95✅ | 2.09✅ | — | 2.25✅ | +| **f64** | 1.87✅ | 1.23✅ | 1.33✅ | 1.44✅ | 1.48✅ | 1.87✅ | 1.44✅ | 0.80🟡 | 0.88🟡 | 1.46✅ | 1.58✅ | 1.69✅ | 2.36✅ | — | 2.54✅ | | **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | -| **c128** | 0.83🟡 | 0.77🟡 | 0.93🟡 | 1.15✅ | 1.13✅ | 1.53✅ | 1.14✅ | 0.90🟡 | 0.81🟡 | 1.08✅ | 1.42✅ | 1.27✅ | 1.38✅ | — | 2.33✅ | +| **c128** | 1.10✅ | 0.94🟡 | 1.01✅ | 1.15✅ | 1.11✅ | 1.47✅ | 1.37✅ | 0.87🟡 | 0.92🟡 | 1.26✅ | 1.41✅ | 1.37✅ | 1.61✅ | — | 2.29✅ | ## Layout: negcol (rows=src, cols=dst) | src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| **bool** | 2.32✅ | 5.20✅ | 4.90✅ | 2.08✅ | 2.28✅ | 1.53✅ | 1.53✅ | 2.33✅ | 2.49✅ | 2.29✅ | 2.86✅ | 1.92✅ | 2.42✅ | — | 2.91✅ | -| **u8** | 1.20✅ | 2.65✅ | 3.15✅ | 1.96✅ | 1.85✅ | 1.90✅ | 1.84✅ | 2.21✅ | 2.35✅ | 1.86✅ | 2.55✅ | 1.48✅ | 2.46✅ | — | 2.98✅ | -| **i8** | 1.20✅ | 2.97✅ | 3.04✅ | 1.84✅ | 1.94✅ | 1.85✅ | 1.83✅ | 2.32✅ | 2.68✅ | 1.93✅ | 2.64✅ | 1.63✅ | 2.13✅ | — | 2.96✅ | -| **i16** | 4.16✅ | 3.05✅ | 2.48✅ | 1.72✅ | 1.79✅ | 1.83✅ | 1.77✅ | 2.44✅ | 2.57✅ | 1.89✅ | 2.00✅ | 1.67✅ | 2.46✅ | — | 2.72✅ | -| **u16** | 3.53✅ | 3.04✅ | 2.95✅ | 1.70✅ | 1.68✅ | 1.74✅ | 1.69✅ | 2.39✅ | 2.23✅ | 1.70✅ | 2.04✅ | 1.46✅ | 2.56✅ | — | 2.76✅ | -| **i32** | 1.96✅ | 1.93✅ | 1.58✅ | 1.69✅ | 1.76✅ | 1.70✅ | 1.64✅ | 2.66✅ | 2.48✅ | 1.71✅ | 2.01✅ | 1.71✅ | 2.29✅ | — | 2.73✅ | -| **u32** | 2.01✅ | 1.57✅ | 1.63✅ | 1.66✅ | 1.69✅ | 1.65✅ | 1.73✅ | 2.28✅ | 2.30✅ | 1.76✅ | 2.07✅ | 1.48✅ | 2.35✅ | — | 2.81✅ | -| **i64** | 1.23✅ | 0.95🟡 | 0.95🟡 | 1.33✅ | 1.39✅ | 1.85✅ | 1.77✅ | 2.40✅ | 2.29✅ | 1.31✅ | 1.30✅ | 1.59✅ | 2.56✅ | — | 2.54✅ | -| **u64** | 1.25✅ | 1.05✅ | 0.92🟡 | 1.33✅ | 1.36✅ | 1.60✅ | 1.75✅ | 2.56✅ | 2.42✅ | 1.37✅ | 1.64✅ | 1.07✅ | 1.61✅ | — | 2.35✅ | -| **char** | 4.04✅ | 2.53✅ | 2.58✅ | 1.62✅ | 1.65✅ | 1.70✅ | 1.69✅ | 2.12✅ | 2.26✅ | 1.66✅ | 1.94✅ | 1.56✅ | 2.41✅ | — | 2.70✅ | -| **f16** | 5.66✅ | 1.74✅ | 1.75✅ | 1.80✅ | 1.81✅ | 2.18✅ | 1.31✅ | 2.04✅ | 0.90🟡 | 1.79✅ | 1.77✅ | 1.57✅ | 0.99🟡 | — | 1.58✅ | -| **f32** | 2.29✅ | 1.70✅ | 1.66✅ | 1.77✅ | 1.82✅ | 1.93✅ | 1.10✅ | 0.88🟡 | 0.82🟡 | 1.78✅ | 1.96✅ | 1.41✅ | 1.19✅ | — | 2.21✅ | -| **f64** | 1.49✅ | 1.08✅ | 1.10✅ | 1.32✅ | 1.45✅ | 1.92✅ | 1.13✅ | 0.85🟡 | 0.82🟡 | 1.36✅ | 1.15✅ | 1.45✅ | 2.37✅ | — | 2.39✅ | +| **bool** | 4.56✅ | 7.06✅ | 6.59✅ | 2.26✅ | 2.21✅ | 1.54✅ | 1.48✅ | 2.25✅ | 2.21✅ | 2.40✅ | 2.80✅ | 1.88✅ | 2.48✅ | — | 3.10✅ | +| **u8** | 1.42✅ | 3.55✅ | 3.38✅ | 2.00✅ | 1.85✅ | 1.77✅ | 1.83✅ | 2.16✅ | 2.24✅ | 1.88✅ | 2.52✅ | 1.47✅ | 2.46✅ | — | 3.02✅ | +| **i8** | 1.31✅ | 4.32✅ | 2.73✅ | 1.70✅ | 1.89✅ | 1.82✅ | 1.83✅ | 2.24✅ | 2.26✅ | 1.73✅ | 2.63✅ | 1.65✅ | 2.37✅ | — | 3.10✅ | +| **i16** | 3.46✅ | 2.49✅ | 2.83✅ | 1.78✅ | 1.77✅ | 1.66✅ | 1.68✅ | 2.17✅ | 2.25✅ | 1.70✅ | 2.01✅ | 1.78✅ | 2.24✅ | — | 2.86✅ | +| **u16** | 3.61✅ | 3.01✅ | 3.11✅ | 1.76✅ | 1.62✅ | 1.68✅ | 1.75✅ | 2.43✅ | 2.22✅ | 1.69✅ | 2.02✅ | 1.43✅ | 2.38✅ | — | 2.92✅ | +| **i32** | 2.10✅ | 1.95✅ | 1.67✅ | 1.60✅ | 1.61✅ | 1.55✅ | 1.55✅ | 2.56✅ | 2.15✅ | 1.68✅ | 2.06✅ | 1.65✅ | 2.06✅ | — | 2.76✅ | +| **u32** | 2.08✅ | 1.60✅ | 1.53✅ | 1.59✅ | 1.58✅ | 1.48✅ | 1.61✅ | 2.46✅ | 2.30✅ | 1.63✅ | 2.02✅ | 1.47✅ | 2.34✅ | — | 2.84✅ | +| **i64** | 1.33✅ | 0.96🟡 | 0.97🟡 | 1.29✅ | 1.41✅ | 1.75✅ | 1.87✅ | 2.41✅ | 2.36✅ | 1.39✅ | 1.30✅ | 1.58✅ | 2.17✅ | — | 2.44✅ | +| **u64** | 1.26✅ | 1.01✅ | 0.93🟡 | 1.27✅ | 1.33✅ | 1.72✅ | 1.69✅ | 2.32✅ | 2.19✅ | 1.34✅ | 1.65✅ | 1.05✅ | 1.50✅ | — | 2.50✅ | +| **char** | 3.38✅ | 2.34✅ | 3.24✅ | 1.67✅ | 1.60✅ | 1.67✅ | 1.65✅ | 2.21✅ | 2.35✅ | 1.64✅ | 2.03✅ | 1.56✅ | 2.27✅ | — | 2.96✅ | +| **f16** | 4.65✅ | 1.73✅ | 1.73✅ | 1.74✅ | 1.76✅ | 2.23✅ | 1.33✅ | 2.07✅ | 0.89🟡 | 1.76✅ | 1.61✅ | 1.56✅ | 0.96🟡 | — | 1.51✅ | +| **f32** | 2.48✅ | 1.71✅ | 1.67✅ | 1.65✅ | 1.62✅ | 1.90✅ | 1.08✅ | 0.85🟡 | 0.81🟡 | 1.61✅ | 1.94✅ | 1.62✅ | 1.20✅ | — | 2.30✅ | +| **f64** | 1.45✅ | 1.12✅ | 1.10✅ | 1.33✅ | 1.38✅ | 1.75✅ | 1.10✅ | 0.83🟡 | 0.83🟡 | 1.39✅ | 1.19✅ | 1.65✅ | 2.67✅ | — | 2.68✅ | | **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | -| **c128** | 0.87🟡 | 0.89🟡 | 0.97🟡 | 1.22✅ | 1.08✅ | 1.51✅ | 0.98🟡 | 0.86🟡 | 0.79🟡 | 1.16✅ | 0.97🟡 | 1.10✅ | 1.53✅ | — | 2.07✅ | +| **c128** | 1.02✅ | 0.98🟡 | 1.08✅ | 1.19✅ | 1.10✅ | 1.54✅ | 1.16✅ | 0.98🟡 | 0.90🟡 | 1.22✅ | 0.95🟡 | 1.49✅ | 1.71✅ | — | 2.26✅ | ## Layout: strided (rows=src, cols=dst) | src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| **bool** | 1.71✅ | 3.24✅ | 3.65✅ | 1.76✅ | 1.96✅ | 1.22✅ | 1.28✅ | 1.88✅ | 1.77✅ | 2.21✅ | 2.49✅ | 1.44✅ | 1.86✅ | — | 2.43✅ | -| **u8** | 0.98🟡 | 2.62✅ | 2.02✅ | 1.71✅ | 1.33✅ | 1.41✅ | 1.37✅ | 2.09✅ | 2.00✅ | 1.80✅ | 2.38✅ | 1.18✅ | 2.00✅ | — | 2.60✅ | -| **i8** | 1.03✅ | 2.14✅ | 1.85✅ | 1.44✅ | 1.51✅ | 1.35✅ | 1.41✅ | 2.10✅ | 2.06✅ | 1.44✅ | 2.35✅ | 1.34✅ | 1.93✅ | — | 2.58✅ | -| **i16** | 2.48✅ | 1.61✅ | 1.68✅ | 1.65✅ | 1.77✅ | 1.43✅ | 1.33✅ | 1.93✅ | 1.92✅ | 1.62✅ | 1.96✅ | 1.34✅ | 1.98✅ | — | 2.37✅ | -| **u16** | 2.36✅ | 1.76✅ | 2.09✅ | 1.40✅ | 1.28✅ | 1.36✅ | 1.25✅ | 2.01✅ | 1.82✅ | 1.31✅ | 1.90✅ | 1.18✅ | 1.88✅ | — | 2.45✅ | -| **i32** | 1.77✅ | 1.15✅ | 1.40✅ | 1.15✅ | 1.18✅ | 1.23✅ | 1.17✅ | 1.89✅ | 1.95✅ | 1.18✅ | 1.89✅ | 1.19✅ | 2.05✅ | — | 2.53✅ | -| **u32** | 1.77✅ | 1.41✅ | 1.46✅ | 1.27✅ | 1.25✅ | 1.28✅ | 1.23✅ | 1.90✅ | 1.83✅ | 1.22✅ | 1.88✅ | 1.20✅ | 2.09✅ | — | 2.37✅ | -| **i64** | 1.16✅ | 1.00🟡 | 1.00✅ | 0.93🟡 | 0.94🟡 | 1.25✅ | 1.28✅ | 1.75✅ | 1.65✅ | 1.01✅ | 1.18✅ | 1.34✅ | 1.82✅ | — | 2.35✅ | -| **u64** | 1.13✅ | 0.97🟡 | 1.01✅ | 0.86🟡 | 0.98🟡 | 1.26✅ | 1.33✅ | 1.66✅ | 1.78✅ | 0.99🟡 | 1.44✅ | 0.92🟡 | 1.43✅ | — | 2.27✅ | -| **char** | 2.14✅ | 1.77✅ | 1.99✅ | 1.35✅ | 1.16✅ | 1.32✅ | 1.40✅ | 1.81✅ | 2.05✅ | 1.30✅ | 1.79✅ | 1.14✅ | 1.83✅ | — | 2.19✅ | -| **f16** | 4.00✅ | 1.51✅ | 1.50✅ | 1.60✅ | 1.60✅ | 1.77✅ | 1.14✅ | 1.95✅ | 0.90🟡 | 1.61✅ | 1.42✅ | 1.32✅ | 0.97🟡 | — | 1.46✅ | -| **f32** | 1.61✅ | 1.05✅ | 1.35✅ | 1.16✅ | 1.16✅ | 1.35✅ | 0.78🟡 | 0.91🟡 | 0.80🟡 | 1.32✅ | 1.76✅ | 1.15✅ | 1.26✅ | — | 2.34✅ | -| **f64** | 1.21✅ | 0.83🟡 | 1.13✅ | 0.94🟡 | 0.96🟡 | 1.28✅ | 1.03✅ | 0.96🟡 | 0.81🟡 | 0.96🟡 | 1.06✅ | 1.21✅ | 1.73✅ | — | 2.23✅ | +| **bool** | 2.26✅ | 3.65✅ | 3.30✅ | 3.63✅ | 3.72✅ | 1.18✅ | 1.12✅ | 1.74✅ | 1.77✅ | 2.09✅ | 2.71✅ | 1.41✅ | 1.75✅ | — | 2.30✅ | +| **u8** | 0.97🟡 | 2.00✅ | 2.27✅ | 2.28✅ | 1.89✅ | 1.30✅ | 1.27✅ | 2.06✅ | 1.84✅ | 1.90✅ | 2.55✅ | 1.13✅ | 1.78✅ | — | 2.61✅ | +| **i8** | 0.91🟡 | 2.13✅ | 2.46✅ | 1.91✅ | 1.77✅ | 1.35✅ | 1.55✅ | 2.04✅ | 1.89✅ | 1.86✅ | 2.45✅ | 1.33✅ | 1.92✅ | — | 2.34✅ | +| **i16** | 2.41✅ | 1.68✅ | 1.97✅ | 1.14✅ | 2.07✅ | 1.21✅ | 1.26✅ | 1.90✅ | 1.96✅ | 1.70✅ | 1.89✅ | 1.18✅ | 1.73✅ | — | 2.39✅ | +| **u16** | 2.39✅ | 1.77✅ | 1.76✅ | 1.37✅ | 1.41✅ | 1.29✅ | 1.32✅ | 1.89✅ | 1.90✅ | 1.52✅ | 1.90✅ | 1.04✅ | 1.81✅ | — | 2.32✅ | +| **i32** | 1.60✅ | 1.43✅ | 1.39✅ | 1.16✅ | 1.16✅ | 1.23✅ | 1.08✅ | 1.86✅ | 1.78✅ | 1.26✅ | 1.88✅ | 1.28✅ | 1.76✅ | — | 2.44✅ | +| **u32** | 1.74✅ | 1.22✅ | 1.18✅ | 1.10✅ | 1.25✅ | 1.13✅ | 1.14✅ | 1.86✅ | 1.84✅ | 1.18✅ | 1.95✅ | 1.13✅ | 1.77✅ | — | 2.24✅ | +| **i64** | 1.11✅ | 0.95🟡 | 0.94🟡 | 0.86🟡 | 0.94🟡 | 1.22✅ | 1.35✅ | 1.77✅ | 1.74✅ | 0.91🟡 | 1.19✅ | 1.26✅ | 1.74✅ | — | 2.43✅ | +| **u64** | 1.07✅ | 0.93🟡 | 0.94🟡 | 0.83🟡 | 1.01✅ | 1.20✅ | 1.27✅ | 1.58✅ | 1.70✅ | 1.03✅ | 1.48✅ | 0.74🟡 | 1.47✅ | — | 2.17✅ | +| **char** | 2.39✅ | 1.80✅ | 1.68✅ | 1.26✅ | 1.49✅ | 1.21✅ | 1.30✅ | 1.85✅ | 1.75✅ | 1.51✅ | 1.83✅ | 1.13✅ | 1.73✅ | — | 2.34✅ | +| **f16** | 2.62✅ | 1.49✅ | 1.52✅ | 1.59✅ | 1.66✅ | 1.76✅ | 1.14✅ | 1.85✅ | 0.89🟡 | 1.59✅ | 1.11✅ | 1.23✅ | 0.95🟡 | — | 1.44✅ | +| **f32** | 1.98✅ | 1.31✅ | 1.31✅ | 1.30✅ | 1.37✅ | 1.34✅ | 0.91🟡 | 0.94🟡 | 0.80🟡 | 1.30✅ | 1.78✅ | 1.16✅ | 1.20✅ | — | 2.43✅ | +| **f64** | 1.25✅ | 0.98🟡 | 0.97🟡 | 0.91🟡 | 0.86🟡 | 1.22✅ | 0.94🟡 | 0.89🟡 | 0.80🟡 | 0.94🟡 | 1.06✅ | 1.07✅ | 1.81✅ | — | 2.08✅ | | **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | -| **c128** | 0.82🟡 | 1.17✅ | 0.95🟡 | 0.91🟡 | 0.81🟡 | 1.14✅ | 0.87🟡 | 1.02✅ | 0.75🟡 | 0.82🟡 | 0.84🟡 | 1.11✅ | 1.43✅ | — | 1.56✅ | +| **c128** | 1.07✅ | 1.14✅ | 0.99🟡 | 0.93🟡 | 0.91🟡 | 1.15✅ | 1.03✅ | 0.97🟡 | 0.83🟡 | 0.94🟡 | 0.89🟡 | 1.08✅ | 1.59✅ | — | 1.66✅ | ## Layout: bcast (rows=src, cols=dst) | src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| -| **bool** | 0.22🟠 | 4.09✅ | 4.15✅ | 2.28✅ | 2.13✅ | 1.54✅ | 1.61✅ | 2.33✅ | 2.55✅ | 2.33✅ | 4.19✅ | 1.92✅ | 2.41✅ | — | 3.36✅ | -| **u8** | 4.89✅ | 0.23🟠 | 3.71✅ | 1.97✅ | 1.76✅ | 2.07✅ | 2.20✅ | 2.30✅ | 2.41✅ | 1.76✅ | 3.81✅ | 2.39✅ | 2.29✅ | — | 3.21✅ | -| **i8** | 3.86✅ | 2.95✅ | 0.18🔴 | 1.87✅ | 1.91✅ | 2.07✅ | 2.38✅ | 2.28✅ | 2.59✅ | 1.99✅ | 3.63✅ | 2.08✅ | 2.41✅ | — | 2.92✅ | -| **i16** | 5.51✅ | 4.13✅ | 3.91✅ | 1.76✅ | 1.71✅ | 1.97✅ | 2.15✅ | 2.39✅ | 2.39✅ | 1.81✅ | 3.68✅ | 2.37✅ | 2.53✅ | — | 2.96✅ | -| **u16** | 4.78✅ | 4.90✅ | 3.58✅ | 1.70✅ | 1.58✅ | 1.99✅ | 1.95✅ | 2.14✅ | 2.47✅ | 1.85✅ | 3.76✅ | 2.16✅ | 2.11✅ | — | 2.90✅ | -| **i32** | 3.52✅ | 3.21✅ | 2.47✅ | 1.82✅ | 1.87✅ | 2.26✅ | 2.04✅ | 2.36✅ | 2.40✅ | 1.69✅ | 3.80✅ | 1.99✅ | 2.12✅ | — | 2.87✅ | -| **u32** | 4.98✅ | 3.15✅ | 2.88✅ | 1.71✅ | 1.95✅ | 1.92✅ | 2.23✅ | 2.22✅ | 2.17✅ | 1.92✅ | 3.67✅ | 2.34✅ | 2.28✅ | — | 2.78✅ | -| **i64** | 2.38✅ | 2.10✅ | 2.21✅ | 1.79✅ | 1.75✅ | 2.08✅ | 2.02✅ | 2.49✅ | 2.38✅ | 1.86✅ | 1.81✅ | 2.01✅ | 2.30✅ | — | 2.74✅ | -| **u64** | 2.66✅ | 2.27✅ | 2.14✅ | 1.64✅ | 1.75✅ | 2.13✅ | 2.05✅ | 2.20✅ | 2.88✅ | 1.72✅ | 2.37✅ | 2.34✅ | 2.34✅ | — | 2.58✅ | -| **char** | 4.06✅ | 3.83✅ | 2.84✅ | 1.64✅ | 1.56✅ | 1.73✅ | 1.77✅ | 2.42✅ | 2.08✅ | 1.78✅ | 3.52✅ | 1.59✅ | 2.27✅ | — | 2.65✅ | -| **f16** | 5.14✅ | 5.61✅ | 5.48✅ | 4.15✅ | 3.79✅ | 3.96✅ | 2.10✅ | 2.90✅ | 1.11✅ | 3.91✅ | 1.56✅ | 2.92✅ | 0.99🟡 | — | 1.60✅ | -| **f32** | 5.24✅ | 2.78✅ | 3.44✅ | 1.83✅ | 1.80✅ | 2.13✅ | 1.45✅ | 2.38✅ | 0.87🟡 | 2.05✅ | 3.59✅ | 2.29✅ | 2.39✅ | — | 2.37✅ | -| **f64** | 2.77✅ | 2.12✅ | 1.89✅ | 1.85✅ | 1.88✅ | 2.00✅ | 1.54✅ | 2.30✅ | 0.91🟡 | 1.85✅ | 1.59✅ | 2.07✅ | 2.65✅ | — | 2.62✅ | +| **bool** | 1.26✅ | 5.38✅ | 6.24✅ | 2.28✅ | 2.24✅ | 1.51✅ | 1.46✅ | 2.32✅ | 2.18✅ | 2.26✅ | 3.97✅ | 1.91✅ | 2.37✅ | — | 3.14✅ | +| **u8** | 4.95✅ | 0.89🟡 | 4.29✅ | 1.87✅ | 1.88✅ | 2.08✅ | 2.14✅ | 2.52✅ | 2.39✅ | 1.87✅ | 3.76✅ | 2.32✅ | 2.15✅ | — | 2.95✅ | +| **i8** | 5.13✅ | 4.15✅ | 0.80🟡 | 1.82✅ | 1.87✅ | 2.12✅ | 2.43✅ | 2.55✅ | 2.38✅ | 1.91✅ | 3.81✅ | 2.16✅ | 2.28✅ | — | 3.14✅ | +| **i16** | 4.77✅ | 3.50✅ | 3.80✅ | 1.60✅ | 1.72✅ | 1.98✅ | 1.91✅ | 2.18✅ | 2.32✅ | 1.69✅ | 3.62✅ | 2.22✅ | 2.52✅ | — | 3.12✅ | +| **u16** | 4.57✅ | 4.20✅ | 3.20✅ | 1.85✅ | 1.63✅ | 1.98✅ | 1.89✅ | 2.26✅ | 2.27✅ | 1.66✅ | 3.72✅ | 2.08✅ | 2.26✅ | — | 2.70✅ | +| **i32** | 3.42✅ | 2.89✅ | 3.25✅ | 1.70✅ | 1.67✅ | 2.12✅ | 1.92✅ | 2.09✅ | 2.34✅ | 1.94✅ | 3.82✅ | 2.24✅ | 2.67✅ | — | 3.00✅ | +| **u32** | 3.64✅ | 3.72✅ | 2.42✅ | 1.66✅ | 1.77✅ | 2.02✅ | 1.99✅ | 2.23✅ | 2.31✅ | 1.90✅ | 3.73✅ | 2.29✅ | 2.27✅ | — | 2.94✅ | +| **i64** | 2.70✅ | 2.23✅ | 2.29✅ | 1.73✅ | 1.72✅ | 2.04✅ | 2.10✅ | 2.41✅ | 2.12✅ | 1.72✅ | 1.85✅ | 2.08✅ | 2.00✅ | — | 2.67✅ | +| **u64** | 2.57✅ | 2.40✅ | 2.16✅ | 1.60✅ | 1.66✅ | 2.08✅ | 1.92✅ | 2.24✅ | 2.29✅ | 1.75✅ | 2.43✅ | 2.26✅ | 2.14✅ | — | 2.72✅ | +| **char** | 4.78✅ | 3.81✅ | 3.84✅ | 1.74✅ | 1.50✅ | 1.65✅ | 1.66✅ | 2.17✅ | 2.36✅ | 1.65✅ | 3.64✅ | 1.54✅ | 2.36✅ | — | 2.75✅ | +| **f16** | 6.87✅ | 5.51✅ | 5.35✅ | 4.23✅ | 4.21✅ | 3.88✅ | 2.04✅ | 3.04✅ | 1.09✅ | 3.86✅ | 1.46✅ | 2.87✅ | 0.96🟡 | — | 1.55✅ | +| **f32** | 4.89✅ | 3.29✅ | 2.71✅ | 1.88✅ | 2.02✅ | 2.15✅ | 1.47✅ | 2.21✅ | 0.86🟡 | 1.84✅ | 3.65✅ | 2.09✅ | 2.11✅ | — | 2.29✅ | +| **f64** | 2.70✅ | 2.15✅ | 2.14✅ | 1.78✅ | 1.77✅ | 2.11✅ | 1.49✅ | 1.99✅ | 0.88🟡 | 1.81✅ | 1.58✅ | 2.04✅ | 2.51✅ | — | 2.66✅ | | **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | -| **c128** | 1.06✅ | 1.04✅ | 1.08✅ | 1.42✅ | 1.38✅ | 1.93✅ | 1.38✅ | 0.88🟡 | 0.79🟡 | 1.36✅ | 1.51✅ | 1.61✅ | 2.19✅ | — | 2.93✅ | +| **c128** | 1.09✅ | 1.06✅ | 1.05✅ | 1.38✅ | 1.40✅ | 1.75✅ | 1.35✅ | 0.84🟡 | 0.80🟡 | 1.39✅ | 1.50✅ | 1.56✅ | 2.01✅ | — | 2.94✅ | -## Lagging cells (<1.0) — the worklist (129 cells) +## Lagging cells (<1.0) — the worklist (118 cells) | key | NumSharp ms | NumPy ms | ratio | |---|---|---|---| -| bool\|C\|bool | 0.0930 | 0.0146 | 0.16 🔴 | -| u8\|T\|u8 | 0.0942 | 0.0150 | 0.16 🔴 | -| bool\|T\|bool | 0.0836 | 0.0148 | 0.18 🔴 | -| i8\|bcast\|i8 | 0.0741 | 0.0133 | 0.18 🔴 | -| bool\|sliced\|bool | 0.0866 | 0.0172 | 0.20 🔴 | -| i8\|T\|i8 | 0.0753 | 0.0153 | 0.20 🟠 | -| u8\|C\|u8 | 0.0719 | 0.0147 | 0.20 🟠 | -| u8\|sliced\|u8 | 0.0782 | 0.0170 | 0.22 🟠 | -| bool\|bcast\|bool | 0.0601 | 0.0134 | 0.22 🟠 | -| u8\|bcast\|u8 | 0.0580 | 0.0133 | 0.23 🟠 | -| u8\|negrow\|u8 | 0.0754 | 0.0174 | 0.23 🟠 | -| bool\|negrow\|bool | 0.0731 | 0.0172 | 0.24 🟠 | -| i8\|negrow\|i8 | 0.0732 | 0.0175 | 0.24 🟠 | -| i8\|C\|i8 | 0.0556 | 0.0146 | 0.26 🟠 | -| i8\|sliced\|i8 | 0.0596 | 0.0173 | 0.29 🟠 | -| c128\|strided\|u64 | 1.0409 | 0.7793 | 0.75 🟡 | -| c128\|sliced\|u64 | 1.8015 | 1.3784 | 0.77 🟡 | -| c128\|negrow\|u8 | 0.4046 | 0.3127 | 0.77 🟡 | -| f32\|strided\|u32 | 0.4345 | 0.3399 | 0.78 🟡 | -| c128\|negcol\|u64 | 1.7175 | 1.3574 | 0.79 🟡 | -| c128\|bcast\|u64 | 1.5889 | 1.2598 | 0.79 🟡 | -| f32\|strided\|u64 | 0.8030 | 0.6402 | 0.80 🟡 | -| f64\|sliced\|i32 | 0.9055 | 0.7249 | 0.80 🟡 | -| c128\|C\|u64 | 1.6541 | 1.3315 | 0.80 🟡 | -| c128\|negrow\|u64 | 1.7336 | 1.4027 | 0.81 🟡 | -| f64\|strided\|u64 | 0.8082 | 0.6566 | 0.81 🟡 | -| c128\|strided\|u16 | 0.3140 | 0.2553 | 0.81 🟡 | -| f32\|negcol\|u64 | 1.5682 | 1.2821 | 0.82 🟡 | -| c128\|strided\|bool | 0.3447 | 0.2829 | 0.82 🟡 | -| c128\|F\|u64 | 1.7086 | 1.4038 | 0.82 🟡 | -| f64\|negcol\|u64 | 1.5536 | 1.2785 | 0.82 🟡 | -| c128\|strided\|char | 0.3088 | 0.2547 | 0.82 🟡 | -| c128\|negrow\|bool | 0.5430 | 0.4512 | 0.83 🟡 | -| f64\|strided\|u8 | 0.1717 | 0.1430 | 0.83 🟡 | -| f32\|sliced\|i64 | 1.4652 | 1.2288 | 0.84 🟡 | -| c128\|strided\|f16 | 0.8931 | 0.7492 | 0.84 🟡 | -| f64\|C\|u8 | 0.2835 | 0.2387 | 0.84 🟡 | -| f32\|T\|i64 | 1.4811 | 1.2507 | 0.84 🟡 | -| f32\|F\|i64 | 1.4574 | 1.2362 | 0.85 🟡 | -| c128\|F\|u8 | 0.3452 | 0.2939 | 0.85 🟡 | -| f64\|negcol\|i64 | 1.4715 | 1.2532 | 0.85 🟡 | -| c128\|T\|u64 | 1.6769 | 1.4340 | 0.86 🟡 | -| c128\|sliced\|i64 | 1.5784 | 1.3516 | 0.86 🟡 | -| c128\|F\|i8 | 0.3486 | 0.2987 | 0.86 🟡 | -| f32\|C\|u64 | 1.4853 | 1.2784 | 0.86 🟡 | -| c128\|negcol\|i64 | 1.6269 | 1.4042 | 0.86 🟡 | -| f64\|sliced\|i64 | 1.4984 | 1.2939 | 0.86 🟡 | -| u64\|strided\|i16 | 0.1644 | 0.1420 | 0.86 🟡 | -| f32\|T\|u64 | 1.4626 | 1.2668 | 0.87 🟡 | -| c128\|strided\|u32 | 0.5487 | 0.4762 | 0.87 🟡 | -| f32\|negrow\|i64 | 1.4789 | 1.2839 | 0.87 🟡 | -| f64\|T\|i64 | 1.4482 | 1.2586 | 0.87 🟡 | -| f32\|C\|i64 | 1.4536 | 1.2668 | 0.87 🟡 | -| c128\|F\|i64 | 1.5922 | 1.3883 | 0.87 🟡 | -| f32\|bcast\|u64 | 1.4674 | 1.2816 | 0.87 🟡 | -| f64\|negrow\|i64 | 1.4964 | 1.3079 | 0.87 🟡 | -| c128\|negcol\|bool | 0.5195 | 0.4544 | 0.87 🟡 | -| c128\|bcast\|i64 | 1.4481 | 1.2677 | 0.88 🟡 | -| f32\|F\|u64 | 1.4896 | 1.3080 | 0.88 🟡 | -| f32\|negrow\|u64 | 1.4970 | 1.3187 | 0.88 🟡 | -| f64\|F\|i64 | 1.4520 | 1.2833 | 0.88 🟡 | -| f32\|negcol\|i64 | 1.4142 | 1.2505 | 0.88 🟡 | -| f64\|F\|u64 | 1.4627 | 1.2941 | 0.88 🟡 | -| c128\|T\|i8 | 0.3269 | 0.2902 | 0.89 🟡 | -| c128\|T\|i64 | 1.5379 | 1.3708 | 0.89 🟡 | -| c128\|negcol\|u8 | 0.3712 | 0.3314 | 0.89 🟡 | -| f64\|sliced\|u64 | 1.4568 | 1.3039 | 0.89 🟡 | -| f32\|sliced\|u64 | 1.4956 | 1.3400 | 0.90 🟡 | -| f64\|T\|u64 | 1.4262 | 1.2802 | 0.90 🟡 | -| f16\|negcol\|u64 | 2.0974 | 1.8845 | 0.90 🟡 | -| f64\|C\|i8 | 0.2900 | 0.2608 | 0.90 🟡 | -| u64\|C\|u8 | 0.2218 | 0.2000 | 0.90 🟡 | -| c128\|negrow\|i64 | 1.6017 | 1.4469 | 0.90 🟡 | -| f16\|strided\|u64 | 1.0325 | 0.9327 | 0.90 🟡 | -| f64\|C\|i64 | 1.4545 | 1.3151 | 0.90 🟡 | -| f64\|bcast\|u64 | 1.4342 | 1.3050 | 0.91 🟡 | -| f64\|negrow\|u64 | 1.4679 | 1.3358 | 0.91 🟡 | -| c128\|strided\|i16 | 0.2931 | 0.2677 | 0.91 🟡 | -| f32\|strided\|i64 | 0.7058 | 0.6456 | 0.91 🟡 | -| u64\|strided\|f32 | 0.4321 | 0.3956 | 0.92 🟡 | -| u64\|F\|i8 | 0.2140 | 0.1962 | 0.92 🟡 | -| c128\|sliced\|i8 | 0.3210 | 0.2949 | 0.92 🟡 | -| u64\|negcol\|i8 | 0.2499 | 0.2296 | 0.92 🟡 | -| c128\|T\|u8 | 0.3420 | 0.3149 | 0.92 🟡 | -| c128\|C\|i64 | 1.5669 | 1.4433 | 0.92 🟡 | -| f64\|C\|u64 | 1.4336 | 1.3246 | 0.92 🟡 | -| u64\|F\|u8 | 0.2147 | 0.1985 | 0.92 🟡 | -| c128\|F\|bool | 0.4381 | 0.4058 | 0.93 🟡 | -| i64\|strided\|i16 | 0.1542 | 0.1434 | 0.93 🟡 | -| f16\|C\|f64 | 1.5849 | 1.4744 | 0.93 🟡 | -| c128\|negrow\|i8 | 0.3334 | 0.3111 | 0.93 🟡 | -| u64\|T\|u8 | 0.2141 | 0.1998 | 0.93 🟡 | -| c128\|sliced\|u8 | 0.3155 | 0.2949 | 0.93 🟡 | -| i64\|C\|i8 | 0.2113 | 0.1976 | 0.94 🟡 | -| i64\|strided\|u16 | 0.1507 | 0.1417 | 0.94 🟡 | -| f64\|strided\|i16 | 0.1653 | 0.1562 | 0.94 🟡 | -| u64\|T\|i8 | 0.2094 | 0.1981 | 0.95 🟡 | -| c128\|strided\|i8 | 0.2675 | 0.2533 | 0.95 🟡 | -| i64\|negcol\|u8 | 0.2486 | 0.2356 | 0.95 🟡 | -| i64\|F\|i8 | 0.2086 | 0.1983 | 0.95 🟡 | -| i64\|negcol\|i8 | 0.2551 | 0.2430 | 0.95 🟡 | -| c128\|C\|bool | 0.4338 | 0.4158 | 0.96 🟡 | -| f64\|strided\|u16 | 0.1591 | 0.1527 | 0.96 🟡 | -| f64\|strided\|char | 0.1576 | 0.1517 | 0.96 🟡 | -| f64\|strided\|i64 | 0.7156 | 0.6903 | 0.96 🟡 | -| f16\|strided\|f64 | 0.7580 | 0.7321 | 0.97 🟡 | -| u64\|strided\|u8 | 0.1434 | 0.1386 | 0.97 🟡 | -| c128\|negcol\|i8 | 0.3582 | 0.3466 | 0.97 🟡 | -| c128\|sliced\|bool | 0.4285 | 0.4147 | 0.97 🟡 | -| c128\|negcol\|f16 | 1.8312 | 1.7743 | 0.97 🟡 | -| f16\|F\|f64 | 1.4863 | 1.4453 | 0.97 🟡 | -| f16\|C\|u64 | 1.8897 | 1.8418 | 0.97 🟡 | -| c128\|C\|u8 | 0.3243 | 0.3163 | 0.98 🟡 | -| f16\|negrow\|f64 | 1.5172 | 1.4849 | 0.98 🟡 | -| u8\|strided\|bool | 0.1541 | 0.1513 | 0.98 🟡 | -| c128\|negcol\|u32 | 0.7951 | 0.7816 | 0.98 🟡 | -| char\|C\|u16 | 0.2561 | 0.2517 | 0.98 🟡 | -| u64\|strided\|u16 | 0.1497 | 0.1474 | 0.98 🟡 | -| f64\|F\|u8 | 0.2394 | 0.2366 | 0.99 🟡 | -| f16\|T\|f64 | 1.4960 | 1.4799 | 0.99 🟡 | -| u64\|negrow\|f32 | 0.7876 | 0.7794 | 0.99 🟡 | -| u64\|C\|i8 | 0.2056 | 0.2036 | 0.99 🟡 | -| c128\|sliced\|u16 | 0.5149 | 0.5101 | 0.99 🟡 | -| u64\|strided\|char | 0.1500 | 0.1490 | 0.99 🟡 | -| f16\|bcast\|f64 | 1.4862 | 1.4782 | 0.99 🟡 | -| f16\|negcol\|f64 | 1.5169 | 1.5088 | 0.99 🟡 | -| i64\|strided\|u8 | 0.1367 | 0.1361 | 1.00 🟡 | -| i64\|T\|i8 | 0.2063 | 0.2055 | 1.00 🟡 | -| u64\|sliced\|f32 | 0.7792 | 0.7791 | 1.00 🟡 | - -_1568 comparable cells (1800 NumSharp rows; 129 lagging <1.0)._ +| bool\|F\|bool | 0.0595 | 0.0144 | 0.24 🟠 | +| f64\|sliced\|i32 | 0.9124 | 0.6414 | 0.70 🟡 | +| u64\|strided\|f32 | 0.5215 | 0.3885 | 0.74 🟡 | +| c128\|bcast\|u64 | 1.5413 | 1.2301 | 0.80 🟡 | +| f32\|strided\|u64 | 0.7987 | 0.6399 | 0.80 🟡 | +| i8\|bcast\|i8 | 0.0619 | 0.0496 | 0.80 🟡 | +| c128\|sliced\|u64 | 1.7479 | 1.4021 | 0.80 🟡 | +| f64\|strided\|u64 | 0.7758 | 0.6243 | 0.80 🟡 | +| f64\|negrow\|i64 | 1.5209 | 1.2241 | 0.80 🟡 | +| c128\|F\|u64 | 1.7023 | 1.3717 | 0.81 🟡 | +| f32\|negcol\|u64 | 1.5659 | 1.2627 | 0.81 🟡 | +| f64\|C\|i8 | 0.2927 | 0.2376 | 0.81 🟡 | +| f64\|C\|u8 | 0.2892 | 0.2350 | 0.81 🟡 | +| f32\|F\|i64 | 1.4588 | 1.1945 | 0.82 🟡 | +| c128\|strided\|u64 | 0.9366 | 0.7745 | 0.83 🟡 | +| u64\|strided\|i16 | 0.1726 | 0.1432 | 0.83 🟡 | +| f32\|C\|i64 | 1.4773 | 1.2276 | 0.83 🟡 | +| f64\|negcol\|u64 | 1.5574 | 1.2946 | 0.83 🟡 | +| f32\|T\|i64 | 1.4640 | 1.2208 | 0.83 🟡 | +| f64\|negcol\|i64 | 1.4625 | 1.2196 | 0.83 🟡 | +| c128\|C\|u64 | 1.6652 | 1.3911 | 0.84 🟡 | +| f32\|negrow\|i64 | 1.4768 | 1.2373 | 0.84 🟡 | +| f32\|F\|u64 | 1.4675 | 1.2315 | 0.84 🟡 | +| f64\|sliced\|i64 | 1.4533 | 1.2221 | 0.84 🟡 | +| c128\|bcast\|i64 | 1.4280 | 1.2013 | 0.84 🟡 | +| f64\|sliced\|u64 | 1.4453 | 1.2198 | 0.84 🟡 | +| i8\|T\|i8 | 0.0613 | 0.0519 | 0.85 🟡 | +| c128\|T\|u64 | 1.6154 | 1.3692 | 0.85 🟡 | +| f32\|negrow\|u64 | 1.4957 | 1.2720 | 0.85 🟡 | +| f32\|negcol\|i64 | 1.4173 | 1.2088 | 0.85 🟡 | +| f64\|C\|i64 | 1.4556 | 1.2468 | 0.86 🟡 | +| i64\|strided\|i16 | 0.1748 | 0.1499 | 0.86 🟡 | +| f32\|sliced\|i64 | 1.4408 | 1.2354 | 0.86 🟡 | +| f64\|strided\|u16 | 0.1706 | 0.1469 | 0.86 🟡 | +| f32\|T\|u64 | 1.4532 | 1.2546 | 0.86 🟡 | +| f32\|bcast\|u64 | 1.4571 | 1.2602 | 0.86 🟡 | +| c128\|negrow\|i64 | 1.5665 | 1.3619 | 0.87 🟡 | +| f64\|F\|i64 | 1.4506 | 1.2612 | 0.87 🟡 | +| f64\|T\|i64 | 1.4409 | 1.2540 | 0.87 🟡 | +| f32\|C\|u64 | 1.4695 | 1.2834 | 0.87 🟡 | +| f64\|bcast\|u64 | 1.4020 | 1.2277 | 0.88 🟡 | +| f64\|negrow\|u64 | 1.4430 | 1.2673 | 0.88 🟡 | +| c128\|F\|i8 | 0.3572 | 0.3148 | 0.88 🟡 | +| f32\|sliced\|u64 | 1.4888 | 1.3174 | 0.88 🟡 | +| f16\|negcol\|u64 | 2.1147 | 1.8765 | 0.89 🟡 | +| u8\|bcast\|u8 | 0.0587 | 0.0522 | 0.89 🟡 | +| f64\|strided\|i64 | 0.7057 | 0.6283 | 0.89 🟡 | +| f64\|C\|u64 | 1.4293 | 1.2761 | 0.89 🟡 | +| f16\|strided\|u64 | 1.0346 | 0.9241 | 0.89 🟡 | +| c128\|strided\|f16 | 0.8811 | 0.7874 | 0.89 🟡 | +| f64\|T\|u64 | 1.4221 | 1.2717 | 0.89 🟡 | +| c128\|T\|i64 | 1.5151 | 1.3650 | 0.90 🟡 | +| c128\|negcol\|u64 | 1.6586 | 1.4945 | 0.90 🟡 | +| f64\|F\|u64 | 1.4496 | 1.3097 | 0.90 🟡 | +| f32\|strided\|u32 | 0.3838 | 0.3480 | 0.91 🟡 | +| c128\|sliced\|char | 0.5601 | 0.5081 | 0.91 🟡 | +| f64\|strided\|i16 | 0.1641 | 0.1489 | 0.91 🟡 | +| c128\|F\|u8 | 0.3391 | 0.3083 | 0.91 🟡 | +| i64\|strided\|char | 0.1534 | 0.1397 | 0.91 🟡 | +| i8\|strided\|bool | 0.1632 | 0.1489 | 0.91 🟡 | +| i8\|C\|i8 | 0.0595 | 0.0543 | 0.91 🟡 | +| c128\|strided\|u16 | 0.3051 | 0.2789 | 0.91 🟡 | +| c128\|C\|i64 | 1.5153 | 1.3895 | 0.92 🟡 | +| u64\|F\|u8 | 0.2149 | 0.1979 | 0.92 🟡 | +| c128\|negrow\|u64 | 1.7009 | 1.5680 | 0.92 🟡 | +| i64\|F\|i8 | 0.2150 | 0.1982 | 0.92 🟡 | +| f16\|C\|f64 | 1.5894 | 1.4665 | 0.92 🟡 | +| u64\|C\|u8 | 0.2155 | 0.1988 | 0.92 🟡 | +| i8\|sliced\|i8 | 0.0575 | 0.0530 | 0.92 🟡 | +| c128\|strided\|i16 | 0.3130 | 0.2897 | 0.93 🟡 | +| c128\|C\|u8 | 0.3526 | 0.3283 | 0.93 🟡 | +| u64\|negcol\|i8 | 0.2475 | 0.2306 | 0.93 🟡 | +| i64\|T\|i8 | 0.2114 | 0.1973 | 0.93 🟡 | +| c128\|sliced\|i64 | 1.4869 | 1.3889 | 0.93 🟡 | +| u64\|strided\|u8 | 0.1409 | 0.1316 | 0.93 🟡 | +| c128\|strided\|char | 0.2944 | 0.2754 | 0.94 🟡 | +| f32\|strided\|i64 | 0.7048 | 0.6596 | 0.94 🟡 | +| c128\|C\|i8 | 0.3417 | 0.3201 | 0.94 🟡 | +| i64\|strided\|u16 | 0.1550 | 0.1454 | 0.94 🟡 | +| u64\|F\|i8 | 0.2121 | 0.1991 | 0.94 🟡 | +| u64\|strided\|i8 | 0.1389 | 0.1305 | 0.94 🟡 | +| u64\|T\|u8 | 0.2105 | 0.1978 | 0.94 🟡 | +| i64\|F\|u8 | 0.2204 | 0.2078 | 0.94 🟡 | +| c128\|negrow\|u8 | 0.3492 | 0.3294 | 0.94 🟡 | +| f64\|strided\|char | 0.1593 | 0.1504 | 0.94 🟡 | +| f64\|strided\|u32 | 0.3797 | 0.3587 | 0.94 🟡 | +| i64\|strided\|i8 | 0.1438 | 0.1358 | 0.94 🟡 | +| u64\|C\|i8 | 0.2096 | 0.1981 | 0.95 🟡 | +| f16\|strided\|f64 | 0.7709 | 0.7306 | 0.95 🟡 | +| f16\|F\|f64 | 1.5066 | 1.4286 | 0.95 🟡 | +| i64\|strided\|u8 | 0.1424 | 0.1351 | 0.95 🟡 | +| u64\|T\|i8 | 0.2094 | 0.1987 | 0.95 🟡 | +| c128\|negcol\|f16 | 1.8203 | 1.7315 | 0.95 🟡 | +| f16\|sliced\|f64 | 1.4890 | 1.4241 | 0.96 🟡 | +| c128\|F\|i64 | 1.5008 | 1.4359 | 0.96 🟡 | +| i64\|negcol\|u8 | 0.2439 | 0.2335 | 0.96 🟡 | +| f16\|bcast\|f64 | 1.5177 | 1.4532 | 0.96 🟡 | +| c128\|sliced\|i8 | 0.3253 | 0.3118 | 0.96 🟡 | +| i64\|C\|i8 | 0.2090 | 0.2004 | 0.96 🟡 | +| f16\|negcol\|f64 | 1.5238 | 1.4668 | 0.96 🟡 | +| char\|C\|u16 | 0.2540 | 0.2457 | 0.97 🟡 | +| c128\|sliced\|bool | 0.4498 | 0.4355 | 0.97 🟡 | +| c128\|strided\|i64 | 0.8006 | 0.7755 | 0.97 🟡 | +| u64\|sliced\|f32 | 0.7665 | 0.7425 | 0.97 🟡 | +| f16\|negrow\|f64 | 1.5293 | 1.4823 | 0.97 🟡 | +| f64\|strided\|i8 | 0.1454 | 0.1413 | 0.97 🟡 | +| i8\|negrow\|i8 | 0.0558 | 0.0542 | 0.97 🟡 | +| u8\|strided\|bool | 0.1537 | 0.1495 | 0.97 🟡 | +| i64\|negcol\|i8 | 0.2480 | 0.2414 | 0.97 🟡 | +| i64\|C\|u8 | 0.2108 | 0.2062 | 0.98 🟡 | +| c128\|negcol\|u8 | 0.3485 | 0.3423 | 0.98 🟡 | +| f64\|strided\|u8 | 0.1456 | 0.1431 | 0.98 🟡 | +| c128\|negcol\|i64 | 1.6104 | 1.5838 | 0.98 🟡 | +| f16\|C\|u64 | 1.8886 | 1.8634 | 0.99 🟡 | +| c128\|F\|bool | 0.4151 | 0.4098 | 0.99 🟡 | +| c128\|strided\|i8 | 0.2650 | 0.2633 | 0.99 🟡 | +| f16\|T\|f64 | 1.4800 | 1.4751 | 1.00 🟡 | +| u64\|negrow\|f32 | 0.7923 | 0.7904 | 1.00 🟡 | + +_1568 comparable cells (1800 NumSharp rows; 118 lagging <1.0)._ --- @@ -2636,33 +2624,33 @@ NumSharp — fused np.evaluate vs unfused np.* chains (4M elements, best-of-9; ( correctness cross-checks ok 4M float64, best of 9: - a*b+c fused 4.48 ms unfused 6.97 ms (1.56x) - (a-b)/(a+b) fused 3.26 ms unfused 13.54 ms (4.16x) - sum(a*b) fused 2.44 ms unfused 3.90 ms (1.60x) - sum(af*bf) fused 1.30 ms unfused 1.68 ms (1.29x) [f32] - a*b+c out= fused 3.77 ms [1-pass fused-into-out] - i4*2+f8 fused 2.93 ms unfused 4.18 ms (1.43x) + a*b+c fused 3.53 ms unfused 6.08 ms (1.72x) + (a-b)/(a+b) fused 3.16 ms unfused 12.32 ms (3.90x) + sum(a*b) fused 2.30 ms unfused 4.33 ms (1.88x) + sum(af*bf) fused 1.43 ms unfused 1.57 ms (1.10x) [f32] + a*b+c out= fused 3.56 ms [1-pass fused-into-out] + i4*2+f8 fused 2.78 ms unfused 4.04 ms (1.45x) a*b+c across operand layouts (2-D 2000x2000, all 3 operands same layout): - [C ] fused 3.68 ms unfused 6.43 ms (1.75x) - [F ] fused 3.60 ms unfused 6.67 ms (1.85x) - [T ] fused 3.67 ms unfused 6.37 ms (1.74x) - [strided] fused 3.49 ms unfused 4.75 ms (1.36x) - [bcast ] fused 1.11 ms unfused 3.99 ms (3.60x) + [C ] fused 3.43 ms unfused 6.05 ms (1.77x) + [F ] fused 3.52 ms unfused 6.18 ms (1.75x) + [T ] fused 3.50 ms unfused 6.27 ms (1.79x) + [strided] fused 3.31 ms unfused 4.72 ms (1.43x) + [bcast ] fused 1.06 ms unfused 3.75 ms (3.53x) NumPy — absolutes on the same box (context for the unfused column): numpy 2.4.2, 4M float64, best of 9: - a*b+c 12.93 ms - (a-b)/(a+b) 19.64 ms - sum(a*b) 8.45 ms - sum(af*bf) 4.19 ms [f32] - a*b+c out= 4.96 ms [two-pass with out=] - i4*2+f8 9.99 ms + a*b+c 12.35 ms + (a-b)/(a+b) 18.75 ms + sum(a*b) 8.60 ms + sum(af*bf) 4.14 ms [f32] + a*b+c out= 4.68 ms [two-pass with out=] + i4*2+f8 9.67 ms a*b+c across operand layouts (2-D 2000x2000, unfused): - [C ] 12.87 ms - [F ] 12.76 ms - [T ] 12.84 ms - [strided] 7.87 ms - [bcast ] 12.36 ms + [C ] 12.52 ms + [F ] 12.54 ms + [T ] 12.51 ms + [strided] 7.91 ms + [bcast ] 11.95 ms ``` diff --git a/benchmark/history/2026-06-29_2d16f477/MANIFEST.md b/benchmark/history/2026-06-29_2d16f477/MANIFEST.md new file mode 100644 index 00000000..7fdee9b8 --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/MANIFEST.md @@ -0,0 +1,55 @@ +# Benchmark snapshot — 2026-06-29 · 2d16f477 + +Official NumSharp-vs-NumPy 3-size comparison + the five matrix subsystems, persisted for provenance. Auto-generated by `benchmark/scripts/snapshot_history.py`. + +## Provenance +| | | +|---|---| +| Run timestamp | `20260629-083502` | +| Git HEAD | `2d16f477` — test(drawing): edge-case and data-loss/corruption coverage … | +| Working tree | clean (HEAD exactly) | +| Date | 2026-06-29 | + +## Environment +| | | +|---|---| +| CPU | 13th Gen Intel Core i9-13900K | +| OS | Windows 11 (10.0.26200) | +| .NET SDK | 10.0.101 (net10.0, Release) | +| Python | 3.12.12 | +| NumPy | 2.4.2 | + +## Convention +**Ratio = NumPy_ms ÷ NumSharp_ms (NPY/NS) → `>1.0×` = NumSharp faster** (higher is better). + +## Methodology +- **C#:** BenchmarkDotNet, `OfficialBenchmarkConfig` — InProcessEmit toolchain, 50 measured + iterations / 5 warmup, iteration time capped at 25 ms. MemoryDiagnoser on. +- **NumPy:** 50 timed iterations / 10 warmup per op (warm long-lived interpreter). +- **Sizes:** 1,000 / 100,000 / 10,000,000 elements. Same seeds both sides. +- Join keyed on (op, dtype, N). +- **Subsystems** appended to `benchmark-report.md`: NDIter, Layout, Operand, Cast, Fusion. + +## Headline — op-matrix geomean by size (NPY/NS, >1 = NumSharp faster) +| Size | geomean | %NumPy🕐 | ✅ / 🟡 / 🟠 / 🔴 | +|---|--:|--:|---| +| 1,000 | 1.13x | 89% | 116 / 58 / 35 / 11 | +| 100,000 | 0.98x | 102% | 295 / 135 / 124 / 35 | +| 10,000,000 | 1.39x | 72% | 427 / 136 / 24 / 4 | + +Overall op-matrix: **1851 ops | ✅ 838 | 🟡 329 | 🟠 183 | 🔴 50 | ▫ 388 | ⚪ 63**. + +NDIter: _HEADLINE — operation matrix: 1.20× geomean · 83%🕐 of NumPy's time · 77 win / 53 lose over 130 cells_ + +Cast: _**118 / 1568** comparable cells lag (<1.0); **1450** win (≥1.0)._ + +## Files +| file | what | +|---|---| +| `benchmark-report.md` | op-matrix (per-(op,dtype,N) ratio) + appended NDIter/Layout/Operand/Cast/Fusion | +| `benchmark-report.json` / `.csv` | unified machine-readable / spreadsheet form | +| `numpy-results.json` | raw NumPy timings (merge input) | +| `nditer_results.*` + `cards/` | iterator benchmark sheet + README cards | +| `layout_/operand_/cast_/fusion_results.*` | the four matrix-subsystem sheets | + +Raw BenchmarkDotNet per-class JSON (~tens of MB) is **not** persisted here (regenerable). Reproduce with `python benchmark/run_benchmark.py`. diff --git a/benchmark/history/2026-06-29_2d16f477/benchmark-report.csv b/benchmark/history/2026-06-29_2d16f477/benchmark-report.csv new file mode 100644 index 00000000..c66cf685 --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/benchmark-report.csv @@ -0,0 +1,1852 @@ +Operation,Suite,Category,DType,N,NumPy (ms),NumSharp (ms),Ratio (NumPy/NumSharp),%NumPy,Status +a + b (element-wise) (uint8),Arithmetic,Add,uint8,1000,0.0008,0.0019,0.432,231.6,negligible +"np.add(a, b) (uint8)",Arithmetic,Add,uint8,1000,0.0007,0.0019,0.362,276.2,negligible +a + scalar (uint8),Arithmetic,Add,uint8,1000,0.0008,0.0015,0.496,201.6,negligible +a + 5 (literal) (uint8),Arithmetic,Add,uint8,1000,0.0009,0.0036,0.256,390.2,negligible +a - b (element-wise) (uint8),Arithmetic,Subtract,uint8,1000,0.0007,0.0015,0.447,223.6,negligible +a - scalar (uint8),Arithmetic,Subtract,uint8,1000,0.0008,0.0008,0.95,105.3,negligible +scalar - a (uint8),Arithmetic,Subtract,uint8,1000,0.0008,0.0009,0.838,119.3,negligible +a * b (element-wise) (uint8),Arithmetic,Multiply,uint8,1000,0.0007,0.0017,0.389,257.4,negligible +a * a (square) (uint8),Arithmetic,Multiply,uint8,1000,0.0007,0.0014,0.462,216.2,negligible +a * scalar (uint8),Arithmetic,Multiply,uint8,1000,0.0008,0.0016,0.475,210.3,negligible +a * 2 (literal) (uint8),Arithmetic,Multiply,uint8,1000,0.0009,0.0038,0.234,427.9,negligible +a + b (element-wise) (int8),Arithmetic,Add,int8,1000,0.0007,0.0017,0.391,255.8,negligible +"np.add(a, b) (int8)",Arithmetic,Add,int8,1000,0.0007,0.0017,0.389,256.8,negligible +a + scalar (int8),Arithmetic,Add,int8,1000,0.0008,0.0016,0.493,203.0,negligible +a + 5 (literal) (int8),Arithmetic,Add,int8,1000,0.0009,0.0034,0.258,387.0,negligible +a - b (element-wise) (int8),Arithmetic,Subtract,int8,1000,0.0007,0.0014,0.456,219.3,negligible +a - scalar (int8),Arithmetic,Subtract,int8,1000,0.0008,0.0011,0.679,147.2,negligible +scalar - a (int8),Arithmetic,Subtract,int8,1000,0.0008,0.0009,0.904,110.7,negligible +a * b (element-wise) (int8),Arithmetic,Multiply,int8,1000,0.0006,0.0016,0.392,255.2,negligible +a * a (square) (int8),Arithmetic,Multiply,int8,1000,0.0006,0.0015,0.419,238.8,negligible +a * scalar (int8),Arithmetic,Multiply,int8,1000,0.0007,0.0016,0.448,223.1,negligible +a * 2 (literal) (int8),Arithmetic,Multiply,int8,1000,0.0008,0.0036,0.235,425.0,negligible +a + b (element-wise) (int16),Arithmetic,Add,int16,1000,0.0009,0.0018,0.47,212.8,negligible +"np.add(a, b) (int16)",Arithmetic,Add,int16,1000,0.0009,0.0017,0.511,195.8,negligible +a + scalar (int16),Arithmetic,Add,int16,1000,0.0009,0.0017,0.51,196.0,negligible +a + 5 (literal) (int16),Arithmetic,Add,int16,1000,0.001,0.0025,0.408,245.4,slower +a - b (element-wise) (int16),Arithmetic,Subtract,int16,1000,0.0008,0.0017,0.451,221.5,negligible +a - scalar (int16),Arithmetic,Subtract,int16,1000,0.0009,0.0013,0.703,142.3,negligible +scalar - a (int16),Arithmetic,Subtract,int16,1000,0.0009,0.0012,0.783,127.7,negligible +a * b (element-wise) (int16),Arithmetic,Multiply,int16,1000,0.0008,0.0019,0.415,240.9,negligible +a * a (square) (int16),Arithmetic,Multiply,int16,1000,0.0008,0.0018,0.414,241.5,negligible +a * scalar (int16),Arithmetic,Multiply,int16,1000,0.0009,0.0018,0.498,200.9,negligible +a * 2 (literal) (int16),Arithmetic,Multiply,int16,1000,0.001,0.0058,0.17,589.3,negligible +a + b (element-wise) (uint16),Arithmetic,Add,uint16,1000,0.0008,0.0021,0.391,255.8,negligible +"np.add(a, b) (uint16)",Arithmetic,Add,uint16,1000,0.0008,0.0018,0.425,235.3,negligible +a + scalar (uint16),Arithmetic,Add,uint16,1000,0.0009,0.0017,0.535,187.0,negligible +a + 5 (literal) (uint16),Arithmetic,Add,uint16,1000,0.001,0.0048,0.211,473.8,slower +a - b (element-wise) (uint16),Arithmetic,Subtract,uint16,1000,0.0008,0.0018,0.45,222.4,negligible +a - scalar (uint16),Arithmetic,Subtract,uint16,1000,0.0009,0.0012,0.768,130.3,negligible +scalar - a (uint16),Arithmetic,Subtract,uint16,1000,0.0009,0.0011,0.849,117.9,negligible +a * b (element-wise) (uint16),Arithmetic,Multiply,uint16,1000,0.0008,0.0019,0.393,254.1,negligible +a * a (square) (uint16),Arithmetic,Multiply,uint16,1000,0.0008,0.0017,0.448,223.2,negligible +a * scalar (uint16),Arithmetic,Multiply,uint16,1000,0.0009,0.0018,0.47,212.9,negligible +a * 2 (literal) (uint16),Arithmetic,Multiply,uint16,1000,0.001,0.0027,0.372,268.7,slower +a + b (element-wise) (int32),Arithmetic,Add,int32,1000,0.0008,0.0022,0.349,286.2,negligible +"np.add(a, b) (int32)",Arithmetic,Add,int32,1000,0.0008,0.0019,0.42,238.0,negligible +a + scalar (int32),Arithmetic,Add,int32,1000,0.0009,0.0019,0.462,216.6,negligible +a + 5 (literal) (int32),Arithmetic,Add,int32,1000,0.001,0.0033,0.312,320.6,slower +a - b (element-wise) (int32),Arithmetic,Subtract,int32,1000,0.0008,0.002,0.395,253.4,negligible +a - scalar (int32),Arithmetic,Subtract,int32,1000,0.0009,0.0016,0.577,173.3,negligible +scalar - a 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(2D),Manipulation,Transpose,float64,100000,0.0001,,,,no_data +np.transpose (2D),Manipulation,Transpose,float64,100000,0.0004,,,,no_data +np.ravel,Manipulation,Flatten,float64,100000,0.0003,0.0005,0.689,145.2,negligible +a.flatten,Manipulation,Flatten,float64,100000,0.0113,0.0883,0.128,782.0,much_slower +np.concatenate,Manipulation,Stack,float64,100000,0.3154,,,,no_data +np.stack,Manipulation,Stack,float64,100000,0.313,,,,no_data +reshape 1D->2D,Manipulation,Reshape,float64,10000000,0.0002,,,,no_data +reshape 2D->1D,Manipulation,Reshape,float64,10000000,0.0002,0.0006,0.315,317.4,negligible +a.T (2D),Manipulation,Transpose,float64,10000000,0.0001,,,,no_data +np.transpose (2D),Manipulation,Transpose,float64,10000000,0.0004,,,,no_data +np.ravel,Manipulation,Flatten,float64,10000000,0.0003,0.0005,0.616,162.4,negligible +a.flatten,Manipulation,Flatten,float64,10000000,13.0852,11.4613,1.142,87.6,faster +np.concatenate,Manipulation,Stack,float64,10000000,32.8142,,,,no_data +np.stack,Manipulation,Stack,float64,10000000,33.9601,,,,no_data +a[100:1000] (contiguous),Slicing,Create,float64,1000,0.0002,,,,no_data +a[::2] (strided),Slicing,Create,float64,1000,0.0002,,,,no_data +a[::-1] (reversed),Slicing,Create,float64,1000,0.0001,,,,no_data +np.sum(contiguous_slice),Slicing,SumSlice,float64,900,0.0017,,,,no_data +np.sum(strided_slice),Slicing,SumSlice,float64,500,0.0016,,,,no_data +a[100:1000] (contiguous),Slicing,Create,float64,100000,0.0001,,,,no_data +a[::2] (strided),Slicing,Create,float64,100000,0.0001,,,,no_data +a[::-1] (reversed),Slicing,Create,float64,100000,0.0002,0.0012,0.128,781.3,negligible +np.sum(contiguous_slice),Slicing,SumSlice,float64,900,0.0017,,,,no_data +np.sum(strided_slice),Slicing,SumSlice,float64,50000,0.0096,,,,no_data +a[100:1000] (contiguous),Slicing,Create,float64,10000000,0.0001,,,,no_data +a[::2] (strided),Slicing,Create,float64,10000000,0.0001,,,,no_data +a[::-1] (reversed),Slicing,Create,float64,10000000,0.0002,0.0012,0.127,788.7,negligible +np.sum(contiguous_slice),Slicing,SumSlice,float64,900,0.0016,,,,no_data +np.sum(strided_slice),Slicing,SumSlice,float64,5000000,4.6738,,,,no_data +a == b (int32),Comparison,Comparison,int32,1000,0.0004,0.0007,0.598,167.2,negligible +a != b (int32),Comparison,Comparison,int32,1000,0.0004,0.0008,0.537,186.1,negligible +a < b (int32),Comparison,Comparison,int32,1000,0.0004,0.0007,0.619,161.7,negligible +a > b (int32),Comparison,Comparison,int32,1000,0.0004,0.0008,0.538,185.8,negligible +a <= b (int32),Comparison,Comparison,int32,1000,0.0004,0.0007,0.578,173.0,negligible +a >= b (int32),Comparison,Comparison,int32,1000,0.0004,0.0008,0.565,177.0,negligible +a == b (int64),Comparison,Comparison,int64,1000,0.0004,0.0008,0.58,172.5,negligible +a != b (int64),Comparison,Comparison,int64,1000,0.0005,0.0008,0.603,165.9,negligible +a < b (int64),Comparison,Comparison,int64,1000,0.0005,0.0007,0.73,137.1,negligible +a > b (int64),Comparison,Comparison,int64,1000,0.0005,0.0007,0.745,134.2,negligible +a <= b (int64),Comparison,Comparison,int64,1000,0.0005,0.0007,0.722,138.4,negligible +a >= b (int64),Comparison,Comparison,int64,1000,0.0008,0.0008,1.003,99.7,negligible +a == b (float32),Comparison,Comparison,float32,1000,0.0005,0.0007,0.613,163.1,negligible +a != b (float32),Comparison,Comparison,float32,1000,0.0004,0.0007,0.602,166.0,negligible +a < b (float32),Comparison,Comparison,float32,1000,0.0004,0.0007,0.56,178.6,negligible +a > b (float32),Comparison,Comparison,float32,1000,0.0004,0.0007,0.562,177.8,negligible +a <= b (float32),Comparison,Comparison,float32,1000,0.0006,0.0007,0.796,125.6,negligible +a >= b (float32),Comparison,Comparison,float32,1000,0.0004,0.0007,0.607,164.7,negligible +a == b (float64),Comparison,Comparison,float64,1000,0.0004,0.0007,0.581,172.2,negligible +a != b (float64),Comparison,Comparison,float64,1000,0.0005,0.0008,0.616,162.4,negligible +a < b (float64),Comparison,Comparison,float64,1000,0.0005,0.0008,0.616,162.3,negligible +a > b (float64),Comparison,Comparison,float64,1000,0.0004,0.0008,0.585,171.0,negligible +a <= b (float64),Comparison,Comparison,float64,1000,0.0004,0.0007,0.6,166.6,negligible +a >= b (float64),Comparison,Comparison,float64,1000,0.0005,0.0007,0.604,165.7,negligible +a == b (int32),Comparison,Comparison,int32,100000,0.0072,0.0183,0.395,253.0,slower +a != b (int32),Comparison,Comparison,int32,100000,0.0069,0.0201,0.343,291.4,slower +a < b (int32),Comparison,Comparison,int32,100000,0.0071,0.0187,0.378,264.8,slower +a > b (int32),Comparison,Comparison,int32,100000,0.007,0.0195,0.358,279.6,slower +a <= b (int32),Comparison,Comparison,int32,100000,0.007,0.0192,0.367,272.7,slower +a >= b (int32),Comparison,Comparison,int32,100000,0.0069,0.0191,0.361,276.9,slower +a == b (int64),Comparison,Comparison,int64,100000,0.0131,0.0248,0.53,188.8,close +a != b 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(float64),Comparison,Comparison,float64,100000,0.0114,0.0279,0.41,244.0,slower +a != b (float64),Comparison,Comparison,float64,100000,0.011,0.0263,0.419,238.9,slower +a < b (float64),Comparison,Comparison,float64,100000,0.0106,0.0279,0.379,263.7,slower +a > b (float64),Comparison,Comparison,float64,100000,0.0105,0.0262,0.402,248.5,slower +a <= b (float64),Comparison,Comparison,float64,100000,0.0104,0.0283,0.368,272.0,slower +a >= b (float64),Comparison,Comparison,float64,100000,0.0118,0.0241,0.492,203.4,slower +a == b (int32),Comparison,Comparison,int32,10000000,4.1736,3.3406,1.249,80.0,faster +a != b (int32),Comparison,Comparison,int32,10000000,4.2916,3.4612,1.24,80.6,faster +a < b (int32),Comparison,Comparison,int32,10000000,4.3674,3.2941,1.326,75.4,faster +a > b (int32),Comparison,Comparison,int32,10000000,4.1882,3.3117,1.265,79.1,faster +a <= b (int32),Comparison,Comparison,int32,10000000,4.2354,3.3183,1.276,78.3,faster +a >= b (int32),Comparison,Comparison,int32,10000000,4.5128,3.3235,1.358,73.6,faster +a == b (int64),Comparison,Comparison,int64,10000000,7.1546,5.73,1.249,80.1,faster +a != b (int64),Comparison,Comparison,int64,10000000,7.2974,5.8513,1.247,80.2,faster +a < b (int64),Comparison,Comparison,int64,10000000,7.0769,5.6821,1.245,80.3,faster +a > b (int64),Comparison,Comparison,int64,10000000,7.6396,5.7329,1.333,75.0,faster +a <= b (int64),Comparison,Comparison,int64,10000000,8.2513,5.987,1.378,72.6,faster +a >= b (int64),Comparison,Comparison,int64,10000000,7.8543,5.9554,1.319,75.8,faster +a == b (float32),Comparison,Comparison,float32,10000000,3.9574,3.2149,1.231,81.2,faster +a != b (float32),Comparison,Comparison,float32,10000000,3.9247,3.3366,1.176,85.0,faster +a < b (float32),Comparison,Comparison,float32,10000000,3.8362,3.2974,1.163,86.0,faster +a > b (float32),Comparison,Comparison,float32,10000000,3.8956,3.3476,1.164,85.9,faster +a <= b (float32),Comparison,Comparison,float32,10000000,3.8528,3.2691,1.179,84.8,faster +a >= b (float32),Comparison,Comparison,float32,10000000,3.8402,3.2664,1.176,85.1,faster +a == b (float64),Comparison,Comparison,float64,10000000,7.1028,5.6882,1.249,80.1,faster +a != b (float64),Comparison,Comparison,float64,10000000,6.7684,5.847,1.158,86.4,faster +a < b (float64),Comparison,Comparison,float64,10000000,6.5961,5.7503,1.147,87.2,faster +a > b (float64),Comparison,Comparison,float64,10000000,6.6876,5.7415,1.165,85.9,faster +a <= b (float64),Comparison,Comparison,float64,10000000,6.4781,5.7355,1.129,88.5,faster +a >= b (float64),Comparison,Comparison,float64,10000000,7.1505,5.6739,1.26,79.3,faster +a & b (bool),Bitwise,Bitwise,bool,1000,0.0004,0.0016,0.245,408.9,negligible +a | b (bool),Bitwise,Bitwise,bool,1000,0.0004,0.0015,0.307,325.9,negligible +a ^ b (bool),Bitwise,Bitwise,bool,1000,0.0004,0.0017,0.224,447.0,negligible +np.invert(a) (bool),Bitwise,Bitwise,bool,1000,0.0004,0.0015,0.245,408.6,negligible +"np.left_shift(a, 2) (bool)",Bitwise,Bitwise,bool,1000,0.0015,0.0125,0.118,845.4,much_slower +"np.right_shift(a, 2) (bool)",Bitwise,Bitwise,bool,1000,0.0016,0.0135,0.118,844.0,much_slower +a & b (uint8),Bitwise,Bitwise,uint8,1000,0.0006,0.001,0.619,161.6,negligible +a | b (uint8),Bitwise,Bitwise,uint8,1000,0.0007,0.0012,0.565,177.0,negligible +a ^ b (uint8),Bitwise,Bitwise,uint8,1000,0.0006,0.0012,0.534,187.3,negligible +np.invert(a) (uint8),Bitwise,Bitwise,uint8,1000,0.0006,0.0014,0.463,216.1,negligible +"np.left_shift(a, 2) (uint8)",Bitwise,Bitwise,uint8,1000,0.0009,0.0023,0.401,249.7,negligible +"np.right_shift(a, 2) (uint8)",Bitwise,Bitwise,uint8,1000,0.0009,0.0021,0.441,226.8,negligible +a & b (int8),Bitwise,Bitwise,int8,1000,0.0007,0.0013,0.489,204.5,negligible +a | b (int8),Bitwise,Bitwise,int8,1000,0.0007,0.0014,0.477,209.8,negligible +a ^ b (int8),Bitwise,Bitwise,int8,1000,0.0007,0.0012,0.524,190.7,negligible +np.invert(a) (int8),Bitwise,Bitwise,int8,1000,0.0006,0.0014,0.458,218.3,negligible +"np.left_shift(a, 2) (int8)",Bitwise,Bitwise,int8,1000,0.0009,0.0019,0.491,203.7,negligible +"np.right_shift(a, 2) (int8)",Bitwise,Bitwise,int8,1000,0.001,0.0019,0.54,185.1,close +a & b (int16),Bitwise,Bitwise,int16,1000,0.0008,0.0027,0.286,349.0,negligible +a | b (int16),Bitwise,Bitwise,int16,1000,0.0008,0.0023,0.348,287.7,negligible +a ^ b (int16),Bitwise,Bitwise,int16,1000,0.0008,0.0026,0.305,327.9,negligible +np.invert(a) (int16),Bitwise,Bitwise,int16,1000,0.0007,0.0018,0.404,247.3,negligible +"np.left_shift(a, 2) (int16)",Bitwise,Bitwise,int16,1000,0.001,0.0031,0.331,302.4,slower +"np.right_shift(a, 2) (int16)",Bitwise,Bitwise,int16,1000,0.0011,0.0026,0.443,225.7,slower +a & b (uint16),Bitwise,Bitwise,uint16,1000,0.0008,0.0034,0.228,439.5,negligible +a | b (uint16),Bitwise,Bitwise,uint16,1000,0.0008,0.0019,0.426,234.5,negligible +a ^ b (uint16),Bitwise,Bitwise,uint16,1000,0.0008,0.0028,0.277,360.9,negligible +np.invert(a) (uint16),Bitwise,Bitwise,uint16,1000,0.0007,0.0022,0.331,302.0,negligible +"np.left_shift(a, 2) (uint16)",Bitwise,Bitwise,uint16,1000,0.0011,0.0023,0.451,221.7,slower +"np.right_shift(a, 2) (uint16)",Bitwise,Bitwise,uint16,1000,0.0011,0.0029,0.364,274.7,slower +a & b (int32),Bitwise,Bitwise,int32,1000,0.0008,0.006,0.128,778.8,negligible +a | b (int32),Bitwise,Bitwise,int32,1000,0.0008,0.0074,0.105,950.5,negligible +a ^ b (int32),Bitwise,Bitwise,int32,1000,0.0008,0.0047,0.163,615.0,negligible +np.invert(a) (int32),Bitwise,Bitwise,int32,1000,0.0008,0.0058,0.132,758.0,negligible +"np.left_shift(a, 2) (int32)",Bitwise,Bitwise,int32,1000,0.001,0.0057,0.176,569.3,negligible +"np.right_shift(a, 2) (int32)",Bitwise,Bitwise,int32,1000,0.0011,0.0038,0.282,354.2,slower +a & b (uint32),Bitwise,Bitwise,uint32,1000,0.0008,0.0039,0.203,491.6,negligible +a | b (uint32),Bitwise,Bitwise,uint32,1000,0.0008,0.0035,0.224,445.7,negligible +a ^ b (uint32),Bitwise,Bitwise,uint32,1000,0.0008,0.0048,0.164,610.3,negligible +np.invert(a) (uint32),Bitwise,Bitwise,uint32,1000,0.0008,0.0036,0.218,459.1,negligible +"np.left_shift(a, 2) (uint32)",Bitwise,Bitwise,uint32,1000,0.001,0.0045,0.219,455.7,negligible +"np.right_shift(a, 2) (uint32)",Bitwise,Bitwise,uint32,1000,0.001,0.0036,0.272,368.1,negligible +a & b (int64),Bitwise,Bitwise,int64,1000,0.0008,0.0069,0.112,891.2,negligible +a | b (int64),Bitwise,Bitwise,int64,1000,0.0008,0.0079,0.1,1003.2,negligible +a ^ b (int64),Bitwise,Bitwise,int64,1000,0.0008,0.004,0.196,510.6,negligible +np.invert(a) (int64),Bitwise,Bitwise,int64,1000,0.0007,0.0059,0.126,795.7,negligible +"np.left_shift(a, 2) (int64)",Bitwise,Bitwise,int64,1000,0.0009,0.0038,0.238,419.5,negligible +"np.right_shift(a, 2) (int64)",Bitwise,Bitwise,int64,1000,0.0013,0.0039,0.324,308.9,slower +a & b (uint64),Bitwise,Bitwise,uint64,1000,0.0008,0.0024,0.322,310.1,negligible +a | b (uint64),Bitwise,Bitwise,uint64,1000,0.0008,0.0022,0.354,282.3,negligible +a ^ b (uint64),Bitwise,Bitwise,uint64,1000,0.0009,0.0111,0.078,1287.0,negligible +np.invert(a) (uint64),Bitwise,Bitwise,uint64,1000,0.0007,0.0035,0.207,483.9,negligible +"np.left_shift(a, 2) (uint64)",Bitwise,Bitwise,uint64,1000,0.001,0.0096,0.1,1000.2,negligible +"np.right_shift(a, 2) (uint64)",Bitwise,Bitwise,uint64,1000,0.001,0.0108,0.091,1099.6,negligible +a & b (bool),Bitwise,Bitwise,bool,100000,0.0028,0.0215,0.131,763.1,much_slower +a | b (bool),Bitwise,Bitwise,bool,100000,0.0025,0.0232,0.11,911.6,much_slower +a ^ b (bool),Bitwise,Bitwise,bool,100000,0.0026,0.0214,0.122,821.8,much_slower +np.invert(a) (bool),Bitwise,Bitwise,bool,100000,0.0021,0.023,0.092,1082.9,much_slower +"np.left_shift(a, 2) (bool)",Bitwise,Bitwise,bool,100000,0.043,0.0779,0.552,181.2,close +"np.right_shift(a, 2) (bool)",Bitwise,Bitwise,bool,100000,0.0529,0.075,0.706,141.7,close +a & b (uint8),Bitwise,Bitwise,uint8,100000,0.0296,0.0103,2.877,34.8,faster +a | b (uint8),Bitwise,Bitwise,uint8,100000,0.0296,0.0105,2.827,35.4,faster +a ^ b (uint8),Bitwise,Bitwise,uint8,100000,0.0288,0.0119,2.424,41.3,faster +np.invert(a) (uint8),Bitwise,Bitwise,uint8,100000,0.0268,0.011,2.446,40.9,faster +"np.left_shift(a, 2) (uint8)",Bitwise,Bitwise,uint8,100000,0.0292,0.0131,2.222,45.0,faster +"np.right_shift(a, 2) (uint8)",Bitwise,Bitwise,uint8,100000,0.0296,0.0095,3.117,32.1,faster +a & b (int8),Bitwise,Bitwise,int8,100000,0.0286,0.01,2.858,35.0,faster +a | b (int8),Bitwise,Bitwise,int8,100000,0.03,0.0116,2.58,38.8,faster +a ^ b (int8),Bitwise,Bitwise,int8,100000,0.0295,0.0102,2.898,34.5,faster +np.invert(a) (int8),Bitwise,Bitwise,int8,100000,0.0293,0.0108,2.719,36.8,faster +"np.left_shift(a, 2) (int8)",Bitwise,Bitwise,int8,100000,0.0305,0.0085,3.576,28.0,faster +"np.right_shift(a, 2) (int8)",Bitwise,Bitwise,int8,100000,0.0436,0.0108,4.019,24.9,faster +a & b (int16),Bitwise,Bitwise,int16,100000,0.029,0.0169,1.718,58.2,faster +a | b (int16),Bitwise,Bitwise,int16,100000,0.0285,0.0178,1.603,62.4,faster +a ^ b (int16),Bitwise,Bitwise,int16,100000,0.0285,0.0149,1.915,52.2,faster +np.invert(a) (int16),Bitwise,Bitwise,int16,100000,0.0257,0.014,1.83,54.6,faster +"np.left_shift(a, 2) (int16)",Bitwise,Bitwise,int16,100000,0.0285,0.015,1.898,52.7,faster +"np.right_shift(a, 2) (int16)",Bitwise,Bitwise,int16,100000,0.0377,0.0158,2.384,41.9,faster +a & b (uint16),Bitwise,Bitwise,uint16,100000,0.0301,0.018,1.676,59.7,faster +a | b (uint16),Bitwise,Bitwise,uint16,100000,0.0291,0.0168,1.733,57.7,faster +a ^ b (uint16),Bitwise,Bitwise,uint16,100000,0.0287,0.0163,1.766,56.6,faster +np.invert(a) (uint16),Bitwise,Bitwise,uint16,100000,0.0258,0.0206,1.25,80.0,faster +"np.left_shift(a, 2) (uint16)",Bitwise,Bitwise,uint16,100000,0.029,0.0163,1.78,56.2,faster +"np.right_shift(a, 2) (uint16)",Bitwise,Bitwise,uint16,100000,0.0283,0.017,1.667,60.0,faster +a & b (int32),Bitwise,Bitwise,int32,100000,0.0286,0.0342,0.837,119.5,close +a | b (int32),Bitwise,Bitwise,int32,100000,0.0286,0.038,0.752,133.0,close +a ^ b (int32),Bitwise,Bitwise,int32,100000,0.0288,0.0321,0.9,111.1,close +np.invert(a) (int32),Bitwise,Bitwise,int32,100000,0.0264,0.0305,0.865,115.6,close +"np.left_shift(a, 2) (int32)",Bitwise,Bitwise,int32,100000,0.019,0.0373,0.511,195.8,close +"np.right_shift(a, 2) (int32)",Bitwise,Bitwise,int32,100000,0.0288,0.0378,0.762,131.3,close +a & b (uint32),Bitwise,Bitwise,uint32,100000,0.0339,0.04,0.846,118.1,close +a | b (uint32),Bitwise,Bitwise,uint32,100000,0.0285,0.0334,0.855,117.0,close +a ^ b (uint32),Bitwise,Bitwise,uint32,100000,0.0285,0.0315,0.905,110.5,close +np.invert(a) (uint32),Bitwise,Bitwise,uint32,100000,0.0332,0.029,1.147,87.2,faster +"np.left_shift(a, 2) (uint32)",Bitwise,Bitwise,uint32,100000,0.0192,0.0272,0.703,142.2,close +"np.right_shift(a, 2) (uint32)",Bitwise,Bitwise,uint32,100000,0.0205,0.0284,0.723,138.4,close +a & b (int64),Bitwise,Bitwise,int64,100000,0.0338,0.0634,0.533,187.6,close +a | b (int64),Bitwise,Bitwise,int64,100000,0.0339,0.0707,0.479,208.6,slower +a ^ b (int64),Bitwise,Bitwise,int64,100000,0.0337,0.0666,0.506,197.6,close +np.invert(a) (int64),Bitwise,Bitwise,int64,100000,0.0263,0.0661,0.398,251.0,slower +"np.left_shift(a, 2) (int64)",Bitwise,Bitwise,int64,100000,0.019,0.0672,0.283,352.8,slower +"np.right_shift(a, 2) (int64)",Bitwise,Bitwise,int64,100000,0.0287,0.0702,0.409,244.4,slower +a & b (uint64),Bitwise,Bitwise,uint64,100000,0.0356,0.0739,0.482,207.4,slower +a | b (uint64),Bitwise,Bitwise,uint64,100000,0.0353,0.0677,0.522,191.7,close +a ^ b (uint64),Bitwise,Bitwise,uint64,100000,0.0341,0.0681,0.5,199.8,close +np.invert(a) (uint64),Bitwise,Bitwise,uint64,100000,0.0263,0.0652,0.404,247.8,slower +"np.left_shift(a, 2) (uint64)",Bitwise,Bitwise,uint64,100000,0.0194,0.0697,0.278,359.4,slower +"np.right_shift(a, 2) (uint64)",Bitwise,Bitwise,uint64,100000,0.0212,0.075,0.283,354.0,slower +a & b (bool),Bitwise,Bitwise,bool,10000000,1.8589,3.0929,0.601,166.4,close +a | b (bool),Bitwise,Bitwise,bool,10000000,1.8887,2.8568,0.661,151.3,close +a ^ b (bool),Bitwise,Bitwise,bool,10000000,1.862,3.0807,0.604,165.5,close +np.invert(a) (bool),Bitwise,Bitwise,bool,10000000,1.6689,2.5576,0.653,153.2,close +"np.left_shift(a, 2) (bool)",Bitwise,Bitwise,bool,10000000,15.2024,10.3241,1.473,67.9,faster +"np.right_shift(a, 2) (bool)",Bitwise,Bitwise,bool,10000000,15.2744,9.3866,1.627,61.5,faster +a & b (uint8),Bitwise,Bitwise,uint8,10000000,3.8191,1.5272,2.501,40.0,faster +a | b (uint8),Bitwise,Bitwise,uint8,10000000,4.1042,1.4855,2.763,36.2,faster +a ^ b (uint8),Bitwise,Bitwise,uint8,10000000,3.8171,1.4591,2.616,38.2,faster +np.invert(a) (uint8),Bitwise,Bitwise,uint8,10000000,3.5162,1.3311,2.642,37.9,faster +"np.left_shift(a, 2) (uint8)",Bitwise,Bitwise,uint8,10000000,3.6979,1.2844,2.879,34.7,faster +"np.right_shift(a, 2) (uint8)",Bitwise,Bitwise,uint8,10000000,3.7353,1.2943,2.886,34.7,faster +a & b (int8),Bitwise,Bitwise,int8,10000000,3.8854,1.5338,2.533,39.5,faster +a | b (int8),Bitwise,Bitwise,int8,10000000,4.1649,1.5132,2.752,36.3,faster +a ^ b (int8),Bitwise,Bitwise,int8,10000000,3.8764,1.5503,2.501,40.0,faster +np.invert(a) (int8),Bitwise,Bitwise,int8,10000000,3.539,1.3133,2.695,37.1,faster +"np.left_shift(a, 2) (int8)",Bitwise,Bitwise,int8,10000000,3.7477,1.2738,2.942,34.0,faster +"np.right_shift(a, 2) (int8)",Bitwise,Bitwise,int8,10000000,4.7856,1.2716,3.763,26.6,faster +a & b (int16),Bitwise,Bitwise,int16,10000000,5.2942,2.9661,1.785,56.0,faster +a | b (int16),Bitwise,Bitwise,int16,10000000,5.2268,2.9832,1.752,57.1,faster +a ^ b (int16),Bitwise,Bitwise,int16,10000000,5.2041,2.9252,1.779,56.2,faster +np.invert(a) (int16),Bitwise,Bitwise,int16,10000000,4.767,2.5132,1.897,52.7,faster +"np.left_shift(a, 2) (int16)",Bitwise,Bitwise,int16,10000000,4.9855,2.6983,1.848,54.1,faster +"np.right_shift(a, 2) (int16)",Bitwise,Bitwise,int16,10000000,5.7282,2.6585,2.155,46.4,faster +a & b (uint16),Bitwise,Bitwise,uint16,10000000,5.2834,3.0212,1.749,57.2,faster +a | b (uint16),Bitwise,Bitwise,uint16,10000000,5.1942,2.8924,1.796,55.7,faster +a ^ b (uint16),Bitwise,Bitwise,uint16,10000000,5.2129,2.9215,1.784,56.0,faster +np.invert(a) (uint16),Bitwise,Bitwise,uint16,10000000,4.7281,2.4541,1.927,51.9,faster +"np.left_shift(a, 2) (uint16)",Bitwise,Bitwise,uint16,10000000,5.3571,2.5057,2.138,46.8,faster +"np.right_shift(a, 2) (uint16)",Bitwise,Bitwise,uint16,10000000,5.1786,2.5878,2.001,50.0,faster +a & b (int32),Bitwise,Bitwise,int32,10000000,9.1881,5.3408,1.72,58.1,faster +a | b (int32),Bitwise,Bitwise,int32,10000000,9.0217,5.4574,1.653,60.5,faster +a ^ b (int32),Bitwise,Bitwise,int32,10000000,8.9067,5.3599,1.662,60.2,faster +np.invert(a) 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"numsharp_ms": 14.3503, + "ratio": 1.402, + "pct_numpy": 71.3, + "status": "faster" + }, + { + "operation": "np.searchsorted(a, v) (int32)", + "suite": "Sorting", + "category": "Sorting", + "dtype": "int32", + "n": 10000000, + "numpy_ms": 390.1343, + "numsharp_ms": 243.3194, + "ratio": 1.603, + "pct_numpy": 62.4, + "status": "faster" + }, + { + "operation": "np.argsort(a) (int64)", + "suite": "Sorting", + "category": "Sorting", + "dtype": "int64", + "n": 10000000, + "numpy_ms": 310.5208, + "numsharp_ms": 188.5734, + "ratio": 1.647, + "pct_numpy": 60.7, + "status": "faster" + }, + { + "operation": "np.nonzero(a) (int64)", + "suite": "Sorting", + "category": "Sorting", + "dtype": "int64", + "n": 10000000, + "numpy_ms": 23.7474, + "numsharp_ms": 18.256, + "ratio": 1.301, + "pct_numpy": 76.9, + "status": "faster" + }, + { + "operation": "np.searchsorted(a, v) (int64)", + "suite": "Sorting", + "category": "Sorting", + "dtype": "int64", + "n": 10000000, + "numpy_ms": 568.2151, + "numsharp_ms": 243.9343, + "ratio": 2.329, + "pct_numpy": 42.9, + "status": "faster" + }, + { + "operation": "np.argsort(a) (float32)", + "suite": "Sorting", + "category": "Sorting", + "dtype": "float32", + "n": 10000000, + "numpy_ms": 1348.273, + "numsharp_ms": 132.4664, + "ratio": 10.178, + "pct_numpy": 9.8, + "status": "faster" + }, + { + "operation": "np.nonzero(a) (float32)", + "suite": "Sorting", + "category": "Sorting", + "dtype": "float32", + "n": 10000000, + "numpy_ms": 39.8712, + "numsharp_ms": 14.1945, + "ratio": 2.809, + "pct_numpy": 35.6, + "status": "faster" + }, + { + "operation": "np.searchsorted(a, v) (float32)", + "suite": "Sorting", + "category": "Sorting", + "dtype": "float32", + "n": 10000000, + "numpy_ms": 312.1823, + "numsharp_ms": 191.5647, + "ratio": 1.63, + "pct_numpy": 61.4, + "status": "faster" + }, + { + "operation": "np.argsort(a) (float64)", + "suite": "Sorting", + "category": "Sorting", + "dtype": "float64", + "n": 10000000, + "numpy_ms": 1768.7035, + "numsharp_ms": 251.488, + "ratio": 7.033, + "pct_numpy": 14.2, + "status": "faster" + }, + { + "operation": "np.nonzero(a) (float64)", + "suite": "Sorting", + "category": "Sorting", + "dtype": "float64", + "n": 10000000, + "numpy_ms": 42.6329, + "numsharp_ms": 17.6206, + "ratio": 2.419, + "pct_numpy": 41.3, + "status": "faster" + }, + { + "operation": "np.searchsorted(a, v) (float64)", + "suite": "Sorting", + "category": "Sorting", + "dtype": "float64", + "n": 10000000, + "numpy_ms": 310.2307, + "numsharp_ms": 192.5449, + "ratio": 1.611, + "pct_numpy": 62.1, + "status": "faster" + }, + { + "operation": "np.dot(a, b) (float64)", + "suite": "LinearAlgebra", + "category": "LinearAlgebra", + "dtype": "float64", + "n": 1000, + "numpy_ms": 0.0007, + "numsharp_ms": 0.0007, + "ratio": 1.06, + "pct_numpy": 94.3, + "status": "negligible" + }, + { + "operation": "np.outer(a, b) (float64)", + "suite": "LinearAlgebra", + "category": "LinearAlgebra", + "dtype": "float64", + "n": 1000, + "numpy_ms": 0.0022, + "numsharp_ms": 0.0061, + "ratio": 0.352, + "pct_numpy": 284.4, + "status": "slower" + }, + { + "operation": "np.matmul(A, B) (float64)", + "suite": "LinearAlgebra", + "category": "LinearAlgebra", + "dtype": "float64", + "n": 1000, + "numpy_ms": 0.0027, + "numsharp_ms": 0.006, + "ratio": 0.448, + "pct_numpy": 223.2, + "status": "slower" + }, + { + "operation": "np.dot(a, b) (float64)", + "suite": "LinearAlgebra", + "category": "LinearAlgebra", + "dtype": "float64", + "n": 100000, + "numpy_ms": 0.1123, + "numsharp_ms": 0.0093, + "ratio": 12.052, + "pct_numpy": 8.3, + "status": "faster" + }, + { + "operation": "np.outer(a, b) (float64)", + "suite": "LinearAlgebra", + "category": "LinearAlgebra", + "dtype": "float64", + "n": 100000, + "numpy_ms": 0.038, + "numsharp_ms": 0.0708, + "ratio": 0.537, + "pct_numpy": 186.1, + "status": "close" + }, + { + "operation": "np.matmul(A, B) (float64)", + "suite": "LinearAlgebra", + "category": "LinearAlgebra", + "dtype": "float64", + "n": 100000, + "numpy_ms": 0.5771, + "numsharp_ms": 2.7815, + "ratio": 0.207, + "pct_numpy": 482.0, + "status": "slower" + }, + { + "operation": "np.dot(a, b) (float64)", + "suite": "LinearAlgebra", + "category": "LinearAlgebra", + "dtype": "float64", + "n": 10000000, + "numpy_ms": 0.869, + "numsharp_ms": 3.0303, + "ratio": 0.287, + "pct_numpy": 348.7, + "status": "slower" + }, + { + "operation": "np.outer(a, b) (float64)", + "suite": "LinearAlgebra", + "category": "LinearAlgebra", + "dtype": "float64", + "n": 10000000, + "numpy_ms": 13.4954, + "numsharp_ms": 11.7542, + "ratio": 1.148, + "pct_numpy": 87.1, + "status": "faster" + }, + { + "operation": "np.matmul(A, B) (float64)", + "suite": "LinearAlgebra", + "category": "LinearAlgebra", + "dtype": "float64", + "n": 10000000, + "numpy_ms": 0.6896, + "numsharp_ms": 4.4742, + "ratio": 0.154, + "pct_numpy": 648.8, + "status": "much_slower" + }, + { + "operation": "np.where(cond, a, b) (float64)", + "suite": "Selection", + "category": "Selection", + "dtype": "float64", + "n": 1000, + "numpy_ms": 0.0017, + "numsharp_ms": 0.002, + "ratio": 0.855, + "pct_numpy": 116.9, + "status": "close" + }, + { + "operation": "np.where(cond) (float64)", + "suite": "Selection", + "category": "Selection", + "dtype": "float64", + "n": 1000, + "numpy_ms": 0.0009, + "numsharp_ms": 0.0014, + "ratio": 0.657, + "pct_numpy": 152.3, + "status": "negligible" + }, + { + "operation": "np.where(cond, a, b) (float64)", + "suite": "Selection", + "category": "Selection", + "dtype": "float64", + "n": 100000, + "numpy_ms": 0.0399, + "numsharp_ms": 0.0688, + "ratio": 0.579, + "pct_numpy": 172.7, + "status": "close" + }, + { + "operation": "np.where(cond) (float64)", + "suite": "Selection", + "category": "Selection", + "dtype": "float64", + "n": 100000, + "numpy_ms": 0.0294, + "numsharp_ms": 0.0981, + "ratio": 0.3, + "pct_numpy": 333.2, + "status": "slower" + }, + { + "operation": "np.where(cond, a, b) (float64)", + "suite": "Selection", + "category": "Selection", + "dtype": "float64", + "n": 10000000, + "numpy_ms": 17.1294, + "numsharp_ms": 14.8189, + "ratio": 1.156, + "pct_numpy": 86.5, + "status": "faster" + }, + { + "operation": "np.where(cond) (float64)", + "suite": "Selection", + "category": "Selection", + "dtype": "float64", + "n": 10000000, + "numpy_ms": 7.45, + "numsharp_ms": 6.9913, + "ratio": 1.066, + "pct_numpy": 93.8, + "status": "faster" + } +] \ No newline at end of file diff --git a/benchmark/history/2026-06-29_2d16f477/benchmark-report.md b/benchmark/history/2026-06-29_2d16f477/benchmark-report.md new file mode 100644 index 00000000..798922e3 --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/benchmark-report.md @@ -0,0 +1,2656 @@ +# NumSharp vs NumPy Performance + +**Baseline:** NumPy · measured across all array sizes (per-(op, dtype, N)) + +**Ratio** = NumPy ÷ NumSharp → Higher is better (>1.0× = NumSharp faster) + +**%NumPy🕐** = NumSharp ÷ NumPy × 100 = the share of NumPy's time NumSharp uses (30% = NumSharp takes only 30% of the time NumPy would; <100% = faster). + +| | Status | Ratio | %NumPy🕐 | Meaning | +|:-:|--------|:-----:|:------:|---------| +|✅| Faster | ≥1.0× | ≤100% | NumSharp ≥ NumPy speed | +|🟡| Close | 0.5–1.0× | 100–200% | within 2× slower | +|🟠| Slower | 0.2–0.5× | 200–500% | optimization target | +|🔴| Slow | <0.2× | >500% | priority fix | +|▫| Negligible | <1µs / >20× | — | too fast to compare — excluded from rankings | +|⚪| Pending | - | — | C# benchmark not run | + +--- + +**Summary:** 1851 ops | ✅ 838 | 🟡 329 | 🟠 183 | 🔴 50 | ▫ 388 | ⚪ 63 + +## Summary by size + +| N | ops | ✅ faster | 🟡 close | 🟠 slower | 🔴 much | ▫ negl | ⚪ n/a | geomean | %NP🕐 | +|---:|----:|--------:|--------:|---------:|------:|-----:|-----:|--------:|------:| +| 500 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | - | - | +| 900 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | - | - | +| 1,000 | 615 | 116 | 58 | 35 | 11 | 372 | 23 | 1.13x | 89% | +| 50,000 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | - | - | +| 100,000 | 615 | 295 | 135 | 124 | 35 | 7 | 19 | 0.98x | 102% | +| 5,000,000 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | - | - | +| 10,000,000 | 615 | 427 | 136 | 24 | 4 | 9 | 15 | 1.39x | 72% | + +--- + +### 🏆 Top 15 Best (NumSharp fastest vs NumPy) + +_Ranked over 1400 credible comparisons (both sides ≥1µs, within 20×); 388 negligible rows excluded as non-comparable (▫). Ratio = NumPy ÷ NumSharp — above 1.0× = NumSharp faster · %NumPy🕐 = share of NumPy's time NumSharp uses._ + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|✅| np.prod (float64) | float64 | 100,000 | 2.331 | 0.170 | 13.71× | 7% | +|✅| np.nanprod(a) (float32) | float32 | 100,000 | 0.160 | 0.012 | 13.53× | 7% | +|✅| np.sum axis=1 (int8) | int8 | 10,000,000 | 3.511 | 0.264 | 13.30× | 8% | +|✅| np.nanvar(a) (float64) | float64 | 1,000 | 0.020 | 0.002 | 13.06× | 8% | +|✅| np.nanquantile(a, 0.5) (float32) | float32 | 1,000 | 0.028 | 0.002 | 12.60× | 8% | +|✅| np.nanstd(a) (float64) | float64 | 1,000 | 0.019 | 0.002 | 12.52× | 8% | +|✅| np.nanstd(a) (float16) | float16 | 1,000 | 0.034 | 0.003 | 12.26× | 8% | +|✅| np.prod axis=1 (float64) | float64 | 100,000 | 0.060 | 0.005 | 12.13× | 8% | +|✅| np.sum axis=1 (uint8) | uint8 | 10,000,000 | 3.206 | 0.265 | 12.11× | 8% | +|✅| np.mean (uint64) | uint64 | 100,000 | 0.054 | 0.004 | 12.08× | 8% | +|✅| np.dot(a, b) (float64) | float64 | 100,000 | 0.112 | 0.009 | 12.05× | 8% | +|✅| np.nanpercentile(a, 50) (float32) | float32 | 1,000 | 0.027 | 0.002 | 11.99× | 8% | +|✅| np.nanstd(a) (float32) | float32 | 1,000 | 0.020 | 0.002 | 11.92× | 8% | +|✅| np.nanvar(a) (float16) | float16 | 1,000 | 0.032 | 0.003 | 11.72× | 8% | +|✅| np.sum axis=0 (uint8) | uint8 | 100,000 | 0.051 | 0.004 | 11.69× | 9% | + +### 🔻 Top 15 Worst (Optimization priorities) + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|🔴| np.zeros_like (int64) | int64 | 1,000 | 0.001 | 0.013 | 0.079× | 1261% | +|🔴| np.sum (float64) | float64 | 100,000 | 0.017 | 0.209 | 0.079× | 1260% | +|🔴| np.isnan(a) (float32) | float32 | 100,000 | 0.004 | 0.048 | 0.092× | 1091% | +|🔴| np.invert(a) (bool) | bool | 100,000 | 0.002 | 0.023 | 0.092× | 1083% | +|🔴| np.zeros_like (float64) | float64 | 1,000 | 0.001 | 0.013 | 0.092× | 1086% | +|🔴| a | b (bool) | bool | 100,000 | 0.003 | 0.023 | 0.11× | 912% | +|🔴| a + scalar (float64) | float64 | 100,000 | 0.012 | 0.104 | 0.11× | 902% | +|🔴| a * 2 (literal) (float64) | float64 | 100,000 | 0.012 | 0.106 | 0.11× | 887% | +|🔴| a + 5 (literal) (float64) | float64 | 100,000 | 0.012 | 0.103 | 0.11× | 884% | +|🔴| np.zeros_like (float32) | float32 | 1,000 | 0.001 | 0.009 | 0.12× | 861% | +|🔴| a * a (square) (float64) | float64 | 100,000 | 0.011 | 0.098 | 0.12× | 863% | +|🔴| a * scalar (float64) | float64 | 100,000 | 0.012 | 0.100 | 0.12× | 857% | +|🔴| np.right_shift(a, 2) (bool) | bool | 1,000 | 0.002 | 0.013 | 0.12× | 844% | +|🔴| np.left_shift(a, 2) (bool) | bool | 1,000 | 0.002 | 0.013 | 0.12× | 845% | +|🔴| np.zeros_like (int32) | int32 | 1,000 | 0.001 | 0.009 | 0.12× | 842% | + +--- + +### Arithmetic + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|🟡| a % 7 (literal) (float32) | float32 | 1,000 | 0.0143 | 0.0167 | 0.86× | 116% | +|🟡| a % 7 (literal) (float32) | float32 | 100,000 | 1.6323 | 1.8289 | 0.89× | 112% | +|🟡| a % 7 (literal) (float32) | float32 | 10,000,000 | 166.6333 | 176.1757 | 0.95× | 106% | +|🟡| a % 7 (literal) (float64) | float64 | 1,000 | 0.0112 | 0.0175 | 0.64× | 156% | +|🟡| a % 7 (literal) (float64) | float64 | 100,000 | 1.4461 | 1.6302 | 0.89× | 113% | +|🟡| a % 7 (literal) (float64) | float64 | 10,000,000 | 159.2176 | 162.5277 | 0.98× | 102% | +|🟡| a % 7 (literal) (int32) | int32 | 1,000 | 0.0022 | 0.0028 | 0.78× | 129% | +|🟡| a % 7 (literal) (int32) | int32 | 100,000 | 0.3957 | 0.6590 | 0.60× | 166% | +|🟡| a % 7 (literal) (int32) | int32 | 10,000,000 | 49.0203 | 66.9383 | 0.73× | 137% | +|🟡| a % 7 (literal) (int64) | int64 | 1,000 | 0.0044 | 0.0069 | 0.64× | 156% | +|🟡| a % 7 (literal) (int64) | int64 | 100,000 | 0.4155 | 0.6703 | 0.62× | 161% | +|🟡| a % 7 (literal) (int64) | int64 | 10,000,000 | 50.7135 | 75.8665 | 0.67× | 150% | +|✅| a % b (element-wise) (float32) | float32 | 1,000 | 0.0121 | 0.0108 | 1.12× | 90% | +|🟡| a % b (element-wise) (float32) | float32 | 100,000 | 1.5318 | 1.5713 | 0.97× | 103% | +|✅| a % b (element-wise) (float32) | float32 | 10,000,000 | 156.9058 | 152.7181 | 1.03× | 97% | +|✅| a % b (element-wise) (float64) | float64 | 1,000 | 0.0100 | 0.0090 | 1.12× | 89% | +|🟡| a % b (element-wise) (float64) | float64 | 100,000 | 1.2891 | 1.3568 | 0.95× | 105% | +|🟡| a % b (element-wise) (float64) | float64 | 10,000,000 | 143.9662 | 145.6373 | 0.99× | 101% | +|🟡| a % b (element-wise) (int32) | int32 | 1,000 | 0.0021 | 0.0028 | 0.76× | 132% | +|🟡| a % b (element-wise) (int32) | int32 | 100,000 | 0.3784 | 0.6093 | 0.62× | 161% | +|🟡| a % b (element-wise) (int32) | int32 | 10,000,000 | 47.9091 | 58.8577 | 0.81× | 123% | +|🟡| a % b (element-wise) (int64) | int64 | 1,000 | 0.0035 | 0.0040 | 0.88× | 114% | +|🟡| a % b (element-wise) (int64) | int64 | 100,000 | 0.3756 | 0.6058 | 0.62× | 161% | +|🟡| a % b (element-wise) (int64) | int64 | 10,000,000 | 49.5215 | 66.8191 | 0.74× | 135% | +|🟠| a * 2 (literal) (complex128) | complex128 | 1,000 | 0.0011 | 0.0045 | 0.25× | 402% | +|✅| a * 2 (literal) (complex128) | complex128 | 100,000 | 0.3633 | 0.3155 | 1.15× | 87% | +|🟡| a * 2 (literal) (complex128) | complex128 | 10,000,000 | 31.3365 | 57.0611 | 0.55× | 182% | +|🟡| a * 2 (literal) (float16) | float16 | 1,000 | 0.0066 | 0.0103 | 0.63× | 158% | +|🟠| a * 2 (literal) (float16) | float16 | 100,000 | 0.3439 | 0.8963 | 0.38× | 261% | +|🟠| a * 2 (literal) (float16) | float16 | 10,000,000 | 31.2284 | 86.0293 | 0.36× | 276% | +|▫| a * 2 (literal) (float32) | float32 | 1,000 | 0.0008 | 0.0053 | 0.14× | 691% | +|🔴| a * 2 (literal) (float32) | float32 | 100,000 | 0.0066 | 0.0516 | 0.13× | 780% | +|🟡| a * 2 (literal) (float32) | float32 | 10,000,000 | 7.8824 | 9.4240 | 0.84× | 120% | +|▫| a * 2 (literal) (float64) | float64 | 1,000 | 0.0008 | 0.0062 | 0.13× | 773% | +|🔴| a * 2 (literal) (float64) | float64 | 100,000 | 0.0119 | 0.1056 | 0.11× | 887% | +|🟡| a * 2 (literal) (float64) | float64 | 10,000,000 | 20.0350 | 22.1709 | 0.90× | 111% | +|▫| a * 2 (literal) (int16) | int16 | 1,000 | 0.0010 | 0.0058 | 0.17× | 589% | +|🟡| a * 2 (literal) (int16) | int16 | 100,000 | 0.0233 | 0.0306 | 0.76× | 131% | +|✅| a * 2 (literal) (int16) | int16 | 10,000,000 | 6.0238 | 4.4309 | 1.36× | 74% | +|🟠| a * 2 (literal) (int32) | int32 | 1,000 | 0.0010 | 0.0034 | 0.30× | 334% | +|🟠| a * 2 (literal) (int32) | int32 | 100,000 | 0.0230 | 0.0521 | 0.44× | 226% | +|✅| a * 2 (literal) (int32) | int32 | 10,000,000 | 12.3132 | 7.7215 | 1.59× | 63% | +|▫| a * 2 (literal) (int64) | int64 | 1,000 | 0.0009 | 0.0056 | 0.16× | 614% | +|🟠| a * 2 (literal) (int64) | int64 | 100,000 | 0.0216 | 0.1059 | 0.20× | 491% | +|🟡| a * 2 (literal) (int64) | int64 | 10,000,000 | 16.4530 | 31.0792 | 0.53× | 189% | +|▫| a * 2 (literal) (int8) | int8 | 1,000 | 0.0008 | 0.0036 | 0.23× | 425% | +|🟡| a * 2 (literal) (int8) | int8 | 100,000 | 0.0226 | 0.0233 | 0.97× | 103% | +|✅| a * 2 (literal) (int8) | int8 | 10,000,000 | 3.7464 | 2.1072 | 1.78× | 56% | +|🟠| a * 2 (literal) (uint16) | uint16 | 1,000 | 0.0010 | 0.0027 | 0.37× | 269% | +|🟡| a * 2 (literal) (uint16) | uint16 | 100,000 | 0.0229 | 0.0291 | 0.78× | 128% | +|✅| a * 2 (literal) (uint16) | uint16 | 10,000,000 | 6.6073 | 4.8579 | 1.36× | 74% | +|🔴| a * 2 (literal) (uint32) | uint32 | 1,000 | 0.0010 | 0.0054 | 0.19× | 520% | +|🟠| a * 2 (literal) (uint32) | uint32 | 100,000 | 0.0225 | 0.0519 | 0.43× | 231% | +|✅| a * 2 (literal) (uint32) | uint32 | 10,000,000 | 7.8930 | 7.6778 | 1.03× | 97% | +|▫| a * 2 (literal) (uint64) | uint64 | 1,000 | 0.0009 | 0.0030 | 0.31× | 323% | +|🟠| a * 2 (literal) (uint64) | uint64 | 100,000 | 0.0222 | 0.1066 | 0.21× | 481% | +|🟡| a * 2 (literal) (uint64) | uint64 | 10,000,000 | 15.0621 | 29.8983 | 0.50× | 198% | +|▫| a * 2 (literal) (uint8) | uint8 | 1,000 | 0.0009 | 0.0038 | 0.23× | 428% | +|✅| a * 2 (literal) (uint8) | uint8 | 100,000 | 0.0229 | 0.0216 | 1.06× | 95% | +|✅| a * 2 (literal) (uint8) | uint8 | 10,000,000 | 4.1643 | 2.1224 | 1.96× | 51% | +|▫| a * a (square) (complex128) | complex128 | 1,000 | 0.0008 | 0.0042 | 0.19× | 520% | +|🟡| a * a (square) (complex128) | complex128 | 100,000 | 0.3485 | 0.4268 | 0.82× | 122% | +|🟡| a * a (square) (complex128) | complex128 | 10,000,000 | 30.2303 | 43.0242 | 0.70× | 142% | +|🟡| a * a (square) (float16) | float16 | 1,000 | 0.0057 | 0.0093 | 0.61× | 162% | +|🟠| a * a (square) (float16) | float16 | 100,000 | 0.2934 | 0.8294 | 0.35× | 283% | +|🟠| a * a (square) (float16) | float16 | 10,000,000 | 30.7340 | 84.9605 | 0.36× | 276% | +|▫| a * a (square) (float32) | float32 | 1,000 | 0.0005 | 0.0019 | 0.28× | 356% | +|🔴| a * a (square) (float32) | float32 | 100,000 | 0.0059 | 0.0493 | 0.12× | 834% | +|🟡| a * a (square) (float32) | float32 | 10,000,000 | 7.8671 | 8.1233 | 0.97× | 103% | +|▫| a * a (square) (float64) | float64 | 1,000 | 0.0005 | 0.0027 | 0.20× | 506% | +|🔴| a * a (square) (float64) | float64 | 100,000 | 0.0113 | 0.0977 | 0.12× | 863% | +|🟡| a * a (square) (float64) | float64 | 10,000,000 | 19.3973 | 20.3482 | 0.95× | 105% | +|▫| a * a (square) (int16) | int16 | 1,000 | 0.0008 | 0.0018 | 0.41× | 242% | +|✅| a * a (square) (int16) | int16 | 100,000 | 0.0287 | 0.0251 | 1.14× | 88% | +|✅| a * a (square) (int16) | int16 | 10,000,000 | 7.2866 | 5.2338 | 1.39× | 72% | +|▫| a * a (square) (int32) | int32 | 1,000 | 0.0008 | 0.0020 | 0.39× | 258% | +|🟡| a * a (square) (int32) | int32 | 100,000 | 0.0300 | 0.0553 | 0.54× | 184% | +|✅| a * a (square) (int32) | int32 | 10,000,000 | 12.7934 | 7.8908 | 1.62× | 62% | +|▫| a * a (square) (int64) | int64 | 1,000 | 0.0008 | 0.0029 | 0.27× | 376% | +|🟠| a * a (square) (int64) | int64 | 100,000 | 0.0289 | 0.1056 | 0.27× | 366% | +|🟡| a * a (square) (int64) | int64 | 10,000,000 | 15.6967 | 30.4188 | 0.52× | 194% | +|▫| a * a (square) (int8) | int8 | 1,000 | 0.0006 | 0.0015 | 0.42× | 239% | +|✅| a * a (square) (int8) | int8 | 100,000 | 0.0280 | 0.0158 | 1.77× | 57% | +|✅| a * a (square) (int8) | int8 | 10,000,000 | 4.4945 | 2.8519 | 1.58× | 64% | +|▫| a * a (square) (uint16) | uint16 | 1,000 | 0.0008 | 0.0017 | 0.45× | 223% | +|✅| a * a (square) (uint16) | uint16 | 100,000 | 0.0347 | 0.0258 | 1.34× | 74% | +|✅| a * a (square) (uint16) | uint16 | 10,000,000 | 7.9808 | 5.3188 | 1.50× | 67% | +|▫| a * a (square) (uint32) | uint32 | 1,000 | 0.0008 | 0.0021 | 0.37× | 269% | +|🟡| a * a (square) (uint32) | uint32 | 100,000 | 0.0285 | 0.0490 | 0.58× | 172% | +|🟡| a * a (square) (uint32) | uint32 | 10,000,000 | 7.9914 | 8.4603 | 0.94× | 106% | +|▫| a * a (square) (uint64) | uint64 | 1,000 | 0.0007 | 0.0028 | 0.26× | 384% | +|🟠| a * a (square) (uint64) | uint64 | 100,000 | 0.0293 | 0.1078 | 0.27× | 368% | +|🟡| a * a (square) (uint64) | uint64 | 10,000,000 | 15.1757 | 29.8093 | 0.51× | 196% | +|▫| a * a (square) (uint8) | uint8 | 1,000 | 0.0007 | 0.0014 | 0.46× | 216% | +|✅| a * a (square) (uint8) | uint8 | 100,000 | 0.0290 | 0.0155 | 1.88× | 53% | +|✅| a * a (square) (uint8) | uint8 | 10,000,000 | 4.8089 | 2.8618 | 1.68× | 60% | +|▫| a * b (element-wise) (complex128) | complex128 | 1,000 | 0.0009 | 0.0043 | 0.22× | 453% | +|🟡| a * b (element-wise) (complex128) | complex128 | 100,000 | 0.3579 | 0.4442 | 0.81× | 124% | +|🟠| a * b (element-wise) (complex128) | complex128 | 10,000,000 | 34.4053 | 73.3371 | 0.47× | 213% | +|🟠| a * b (element-wise) (float16) | float16 | 1,000 | 0.0034 | 0.0090 | 0.37× | 268% | +|🟠| a * b (element-wise) (float16) | float16 | 100,000 | 0.3186 | 0.8505 | 0.38× | 267% | +|🟠| a * b (element-wise) (float16) | float16 | 10,000,000 | 30.9166 | 88.9610 | 0.35× | 288% | +|▫| a * b (element-wise) (float32) | float32 | 1,000 | 0.0006 | 0.0019 | 0.30× | 336% | +|🔴| a * b (element-wise) (float32) | float32 | 100,000 | 0.0071 | 0.0514 | 0.14× | 722% | +|🟡| a * b (element-wise) (float32) | float32 | 10,000,000 | 8.4965 | 10.2234 | 0.83× | 120% | +|▫| a * b (element-wise) (float64) | float64 | 1,000 | 0.0005 | 0.0025 | 0.20× | 489% | +|🟠| a * b (element-wise) (float64) | float64 | 100,000 | 0.0274 | 0.1030 | 0.27× | 376% | +|🟡| a * b (element-wise) (float64) | float64 | 10,000,000 | 21.0075 | 33.0143 | 0.64× | 157% | +|▫| a * b (element-wise) (int16) | int16 | 1,000 | 0.0008 | 0.0019 | 0.41× | 241% | +|✅| a * b (element-wise) (int16) | int16 | 100,000 | 0.0282 | 0.0266 | 1.06× | 95% | +|✅| a * b (element-wise) (int16) | int16 | 10,000,000 | 7.7338 | 6.8196 | 1.13× | 88% | +|▫| a * b (element-wise) (int32) | int32 | 1,000 | 0.0008 | 0.0020 | 0.39× | 257% | +|🟡| a * b (element-wise) (int32) | int32 | 100,000 | 0.0287 | 0.0519 | 0.55× | 181% | +|✅| a * b (element-wise) (int32) | int32 | 10,000,000 | 13.0207 | 10.9911 | 1.19× | 84% | +|▫| a * b (element-wise) (int64) | int64 | 1,000 | 0.0008 | 0.0030 | 0.26× | 386% | +|🟠| a * b (element-wise) (int64) | int64 | 100,000 | 0.0386 | 0.1068 | 0.36× | 277% | +|🟠| a * b (element-wise) (int64) | int64 | 10,000,000 | 17.0533 | 38.5984 | 0.44× | 226% | +|▫| a * b (element-wise) (int8) | int8 | 1,000 | 0.0006 | 0.0016 | 0.39× | 255% | +|✅| a * b (element-wise) (int8) | int8 | 100,000 | 0.0282 | 0.0162 | 1.74× | 57% | +|✅| a * b (element-wise) (int8) | int8 | 10,000,000 | 5.3373 | 3.6339 | 1.47× | 68% | +|▫| a * b (element-wise) (uint16) | uint16 | 1,000 | 0.0008 | 0.0019 | 0.39× | 254% | +|✅| a * b (element-wise) (uint16) | uint16 | 100,000 | 0.0308 | 0.0267 | 1.15× | 87% | +|✅| a * b (element-wise) (uint16) | uint16 | 10,000,000 | 7.7433 | 6.7987 | 1.14× | 88% | +|▫| a * b (element-wise) (uint32) | uint32 | 1,000 | 0.0008 | 0.0020 | 0.40× | 250% | +|🟡| a * b (element-wise) (uint32) | uint32 | 100,000 | 0.0278 | 0.0486 | 0.57× | 175% | +|🟡| a * b (element-wise) (uint32) | uint32 | 10,000,000 | 9.8085 | 11.0138 | 0.89× | 112% | +|▫| a * b (element-wise) (uint64) | uint64 | 1,000 | 0.0007 | 0.0027 | 0.27× | 377% | +|🟠| a * b (element-wise) (uint64) | uint64 | 100,000 | 0.0361 | 0.1090 | 0.33× | 302% | +|🟠| a * b (element-wise) (uint64) | uint64 | 10,000,000 | 16.9583 | 37.9021 | 0.45× | 224% | +|▫| a * b (element-wise) (uint8) | uint8 | 1,000 | 0.0007 | 0.0017 | 0.39× | 257% | +|✅| a * b (element-wise) (uint8) | uint8 | 100,000 | 0.0295 | 0.0157 | 1.88× | 53% | +|✅| a * b (element-wise) (uint8) | uint8 | 10,000,000 | 4.5011 | 3.6771 | 1.22× | 82% | +|▫| a * scalar (complex128) | complex128 | 1,000 | 0.0009 | 0.0043 | 0.22× | 458% | +|✅| a * scalar (complex128) | complex128 | 100,000 | 0.3458 | 0.3076 | 1.12× | 89% | +|🟡| a * scalar (complex128) | complex128 | 10,000,000 | 30.9297 | 45.1226 | 0.69× | 146% | +|🟡| a * scalar (float16) | float16 | 1,000 | 0.0062 | 0.0092 | 0.68× | 148% | +|🟠| a * scalar (float16) | float16 | 100,000 | 0.3472 | 0.8794 | 0.40× | 253% | +|🟠| a * scalar (float16) | float16 | 10,000,000 | 35.1625 | 88.3826 | 0.40× | 251% | +|▫| a * scalar (float32) | float32 | 1,000 | 0.0007 | 0.0020 | 0.36× | 281% | +|🔴| a * scalar (float32) | float32 | 100,000 | 0.0068 | 0.0488 | 0.14× | 722% | +|🟡| a * scalar (float32) | float32 | 10,000,000 | 8.0629 | 8.9608 | 0.90× | 111% | +|▫| a * scalar (float64) | float64 | 1,000 | 0.0009 | 0.0028 | 0.31× | 325% | +|🔴| a * scalar (float64) | float64 | 100,000 | 0.0117 | 0.0999 | 0.12× | 857% | +|🟡| a * scalar (float64) | float64 | 10,000,000 | 19.9081 | 22.8588 | 0.87× | 115% | +|▫| a * scalar (int16) | int16 | 1,000 | 0.0009 | 0.0018 | 0.50× | 201% | +|🟡| a * scalar (int16) | int16 | 100,000 | 0.0233 | 0.0262 | 0.89× | 112% | +|✅| a * scalar (int16) | int16 | 10,000,000 | 7.0592 | 4.6609 | 1.51× | 66% | +|▫| a * scalar (int32) | int32 | 1,000 | 0.0009 | 0.0019 | 0.46× | 217% | +|🟠| a * scalar (int32) | int32 | 100,000 | 0.0231 | 0.0508 | 0.46× | 220% | +|✅| a * scalar (int32) | int32 | 10,000,000 | 12.6141 | 9.1360 | 1.38× | 72% | +|▫| a * scalar (int64) | int64 | 1,000 | 0.0009 | 0.0029 | 0.30× | 332% | +|🟠| a * scalar (int64) | int64 | 100,000 | 0.0226 | 0.1076 | 0.21× | 476% | +|🟠| a * scalar (int64) | int64 | 10,000,000 | 15.4341 | 31.3975 | 0.49× | 203% | +|▫| a * scalar (int8) | int8 | 1,000 | 0.0007 | 0.0016 | 0.45× | 223% | +|✅| a * scalar (int8) | int8 | 100,000 | 0.0223 | 0.0161 | 1.39× | 72% | +|✅| a * scalar (int8) | int8 | 10,000,000 | 3.7610 | 2.0651 | 1.82× | 55% | +|▫| a * scalar (uint16) | uint16 | 1,000 | 0.0009 | 0.0018 | 0.47× | 213% | +|🟡| a * scalar (uint16) | uint16 | 100,000 | 0.0262 | 0.0269 | 0.97× | 103% | +|✅| a * scalar (uint16) | uint16 | 10,000,000 | 7.0635 | 4.6785 | 1.51× | 66% | +|▫| a * scalar (uint32) | uint32 | 1,000 | 0.0009 | 0.0020 | 0.46× | 219% | +|🟠| a * scalar (uint32) | uint32 | 100,000 | 0.0227 | 0.0507 | 0.45× | 223% | +|🟡| a * scalar (uint32) | uint32 | 10,000,000 | 7.8165 | 9.2370 | 0.85× | 118% | +|▫| a * scalar (uint64) | uint64 | 1,000 | 0.0008 | 0.0028 | 0.28× | 351% | +|🟠| a * scalar (uint64) | uint64 | 100,000 | 0.0222 | 0.1048 | 0.21× | 471% | +|🟡| a * scalar (uint64) | uint64 | 10,000,000 | 15.1079 | 30.0570 | 0.50× | 199% | +|▫| a * scalar (uint8) | uint8 | 1,000 | 0.0008 | 0.0016 | 0.47× | 210% | +|✅| a * scalar (uint8) | uint8 | 100,000 | 0.0238 | 0.0159 | 1.50× | 67% | +|✅| a * scalar (uint8) | uint8 | 10,000,000 | 4.0195 | 2.0901 | 1.92× | 52% | +|🔴| a + 5 (literal) (complex128) | complex128 | 1,000 | 0.0011 | 0.0076 | 0.14× | 691% | +|✅| a + 5 (literal) (complex128) | complex128 | 100,000 | 0.3558 | 0.3115 | 1.14× | 88% | +|✅| a + 5 (literal) (complex128) | complex128 | 10,000,000 | 42.7672 | 39.9933 | 1.07× | 94% | +|🟡| a + 5 (literal) (float16) | float16 | 1,000 | 0.0064 | 0.0106 | 0.60× | 166% | +|🟠| a + 5 (literal) (float16) | float16 | 100,000 | 0.3642 | 0.8592 | 0.42× | 236% | +|🟠| a + 5 (literal) (float16) | float16 | 10,000,000 | 31.0643 | 84.9236 | 0.37× | 273% | +|▫| a + 5 (literal) (float32) | float32 | 1,000 | 0.0009 | 0.0049 | 0.18× | 568% | +|🔴| a + 5 (literal) (float32) | float32 | 100,000 | 0.0065 | 0.0543 | 0.12× | 841% | +|🟡| a + 5 (literal) (float32) | float32 | 10,000,000 | 8.0704 | 8.3566 | 0.97× | 104% | +|▫| a + 5 (literal) (float64) | float64 | 1,000 | 0.0008 | 0.0056 | 0.14× | 708% | +|🔴| a + 5 (literal) (float64) | float64 | 100,000 | 0.0117 | 0.1033 | 0.11× | 884% | +|✅| a + 5 (literal) (float64) | float64 | 10,000,000 | 19.9822 | 19.3898 | 1.03× | 97% | +|🟠| a + 5 (literal) (int16) | int16 | 1,000 | 0.0010 | 0.0025 | 0.41× | 245% | +|🟡| a + 5 (literal) (int16) | int16 | 100,000 | 0.0256 | 0.0297 | 0.86× | 116% | +|✅| a + 5 (literal) (int16) | int16 | 10,000,000 | 6.8921 | 4.3178 | 1.60× | 63% | +|🟠| a + 5 (literal) (int32) | int32 | 1,000 | 0.0010 | 0.0033 | 0.31× | 321% | +|🟠| a + 5 (literal) (int32) | int32 | 100,000 | 0.0250 | 0.0530 | 0.47× | 212% | +|✅| a + 5 (literal) (int32) | int32 | 10,000,000 | 12.6603 | 8.4146 | 1.50× | 66% | +|▫| a + 5 (literal) (int64) | int64 | 1,000 | 0.0010 | 0.0058 | 0.17× | 598% | +|🟠| a + 5 (literal) (int64) | int64 | 100,000 | 0.0233 | 0.1024 | 0.23× | 439% | +|🟡| a + 5 (literal) (int64) | int64 | 10,000,000 | 15.3142 | 19.3520 | 0.79× | 126% | +|▫| a + 5 (literal) (int8) | int8 | 1,000 | 0.0009 | 0.0034 | 0.26× | 387% | +|✅| a + 5 (literal) (int8) | int8 | 100,000 | 0.0369 | 0.0230 | 1.60× | 62% | +|✅| a + 5 (literal) (int8) | int8 | 10,000,000 | 4.1932 | 1.9965 | 2.10× | 48% | +|🟠| a + 5 (literal) (uint16) | uint16 | 1,000 | 0.0010 | 0.0048 | 0.21× | 474% | +|🟡| a + 5 (literal) (uint16) | uint16 | 100,000 | 0.0250 | 0.0308 | 0.81× | 123% | +|✅| a + 5 (literal) (uint16) | uint16 | 10,000,000 | 6.9032 | 4.2530 | 1.62× | 62% | +|🔴| a + 5 (literal) (uint32) | uint32 | 1,000 | 0.0011 | 0.0056 | 0.19× | 530% | +|🟠| a + 5 (literal) (uint32) | uint32 | 100,000 | 0.0234 | 0.0493 | 0.47× | 211% | +|✅| a + 5 (literal) (uint32) | uint32 | 10,000,000 | 10.0916 | 8.5435 | 1.18× | 85% | +|🔴| a + 5 (literal) (uint64) | uint64 | 1,000 | 0.0010 | 0.0054 | 0.19× | 526% | +|🟠| a + 5 (literal) (uint64) | uint64 | 100,000 | 0.0236 | 0.1073 | 0.22× | 454% | +|🟡| a + 5 (literal) (uint64) | uint64 | 10,000,000 | 15.2161 | 20.0614 | 0.76× | 132% | +|▫| a + 5 (literal) (uint8) | uint8 | 1,000 | 0.0009 | 0.0036 | 0.26× | 390% | +|✅| a + 5 (literal) (uint8) | uint8 | 100,000 | 0.0248 | 0.0205 | 1.21× | 83% | +|✅| a + 5 (literal) (uint8) | uint8 | 10,000,000 | 4.1679 | 1.9455 | 2.14× | 47% | +|🟠| a + b (element-wise) (complex128) | complex128 | 1,000 | 0.0010 | 0.0046 | 0.23× | 442% | +|🟡| a + b (element-wise) (complex128) | complex128 | 100,000 | 0.3515 | 0.4716 | 0.74× | 134% | +|🟡| a + b (element-wise) (complex128) | complex128 | 10,000,000 | 42.3859 | 58.1610 | 0.73× | 137% | +|🟡| a + b (element-wise) (float16) | float16 | 1,000 | 0.0055 | 0.0091 | 0.60× | 165% | +|🟠| a + b (element-wise) (float16) | float16 | 100,000 | 0.2982 | 0.8560 | 0.35× | 287% | +|🟠| a + b (element-wise) (float16) | float16 | 10,000,000 | 31.3075 | 87.5987 | 0.36× | 280% | +|▫| a + b (element-wise) (float32) | float32 | 1,000 | 0.0007 | 0.0011 | 0.67× | 149% | +|🔴| a + b (element-wise) (float32) | float32 | 100,000 | 0.0069 | 0.0546 | 0.13× | 792% | +|🟡| a + b (element-wise) (float32) | float32 | 10,000,000 | 8.7877 | 10.3685 | 0.85× | 118% | +|▫| a + b (element-wise) (float64) | float64 | 1,000 | 0.0005 | 0.0018 | 0.28× | 358% | +|🟠| a + b (element-wise) (float64) | float64 | 100,000 | 0.0287 | 0.1183 | 0.24× | 413% | +|🟡| a + b (element-wise) (float64) | float64 | 10,000,000 | 17.1247 | 32.2672 | 0.53× | 188% | +|▫| a + b (element-wise) (int16) | int16 | 1,000 | 0.0009 | 0.0018 | 0.47× | 213% | +|🟡| a + b (element-wise) (int16) | int16 | 100,000 | 0.0306 | 0.0341 | 0.90× | 111% | +|✅| a + b (element-wise) (int16) | int16 | 10,000,000 | 7.6408 | 7.1288 | 1.07× | 93% | +|▫| a + b (element-wise) (int32) | int32 | 1,000 | 0.0008 | 0.0022 | 0.35× | 286% | +|🟡| a + b (element-wise) (int32) | int32 | 100,000 | 0.0296 | 0.0569 | 0.52× | 192% | +|✅| a + b (element-wise) (int32) | int32 | 10,000,000 | 13.4533 | 10.0724 | 1.34× | 75% | +|▫| a + b (element-wise) (int64) | int64 | 1,000 | 0.0010 | 0.0032 | 0.30× | 330% | +|🟠| a + b (element-wise) (int64) | int64 | 100,000 | 0.0351 | 0.1211 | 0.29× | 345% | +|🟡| a + b (element-wise) (int64) | int64 | 10,000,000 | 16.9982 | 32.1148 | 0.53× | 189% | +|▫| a + b (element-wise) (int8) | int8 | 1,000 | 0.0007 | 0.0017 | 0.39× | 256% | +|✅| a + b (element-wise) (int8) | int8 | 100,000 | 0.0289 | 0.0158 | 1.83× | 55% | +|✅| a + b (element-wise) (int8) | int8 | 10,000,000 | 4.7377 | 3.2841 | 1.44× | 69% | +|▫| a + b (element-wise) (uint16) | uint16 | 1,000 | 0.0008 | 0.0021 | 0.39× | 256% | +|✅| a + b (element-wise) (uint16) | uint16 | 100,000 | 0.0305 | 0.0303 | 1.00× | 100% | +|✅| a + b (element-wise) (uint16) | uint16 | 10,000,000 | 7.7579 | 7.1286 | 1.09× | 92% | +|🟡| a + b (element-wise) (uint32) | uint32 | 1,000 | 0.0011 | 0.0017 | 0.68× | 148% | +|🟡| a + b (element-wise) (uint32) | uint32 | 100,000 | 0.0292 | 0.0565 | 0.52× | 194% | +|✅| a + b (element-wise) (uint32) | uint32 | 10,000,000 | 13.4492 | 9.8607 | 1.36× | 73% | +|▫| a + b (element-wise) (uint64) | uint64 | 1,000 | 0.0008 | 0.0027 | 0.29× | 348% | +|🟠| a + b (element-wise) (uint64) | uint64 | 100,000 | 0.0529 | 0.1072 | 0.49× | 203% | +|🟠| a + b (element-wise) (uint64) | uint64 | 10,000,000 | 17.2194 | 35.4223 | 0.49× | 206% | +|▫| a + b (element-wise) (uint8) | uint8 | 1,000 | 0.0008 | 0.0019 | 0.43× | 232% | +|✅| a + b (element-wise) (uint8) | uint8 | 100,000 | 0.0305 | 0.0154 | 1.98× | 50% | +|✅| a + b (element-wise) (uint8) | uint8 | 10,000,000 | 4.9889 | 3.3759 | 1.48× | 68% | +|▫| a + scalar (complex128) | complex128 | 1,000 | 0.0009 | 0.0042 | 0.23× | 442% | +|✅| a + scalar (complex128) | complex128 | 100,000 | 0.3392 | 0.3087 | 1.10× | 91% | +|🟡| a + scalar (complex128) | complex128 | 10,000,000 | 39.4817 | 41.1306 | 0.96× | 104% | +|🟡| a + scalar (float16) | float16 | 1,000 | 0.0066 | 0.0091 | 0.73× | 138% | +|🟠| a + scalar (float16) | float16 | 100,000 | 0.3632 | 0.8631 | 0.42× | 238% | +|🟠| a + scalar (float16) | float16 | 10,000,000 | 30.8505 | 85.6472 | 0.36× | 278% | +|▫| a + scalar (float32) | float32 | 1,000 | 0.0007 | 0.0020 | 0.35× | 283% | +|🔴| a + scalar (float32) | float32 | 100,000 | 0.0062 | 0.0514 | 0.12× | 829% | +|🟡| a + scalar (float32) | float32 | 10,000,000 | 7.9325 | 8.3761 | 0.95× | 106% | +|▫| a + scalar (float64) | float64 | 1,000 | 0.0007 | 0.0027 | 0.24× | 416% | +|🔴| a + scalar (float64) | float64 | 100,000 | 0.0116 | 0.1045 | 0.11× | 902% | +|🟡| a + scalar (float64) | float64 | 10,000,000 | 19.7762 | 20.4638 | 0.97× | 104% | +|▫| a + scalar (int16) | int16 | 1,000 | 0.0009 | 0.0017 | 0.51× | 196% | +|🟡| a + scalar (int16) | int16 | 100,000 | 0.0249 | 0.0262 | 0.95× | 105% | +|✅| a + scalar (int16) | int16 | 10,000,000 | 7.1782 | 4.4382 | 1.62× | 62% | +|▫| a + scalar (int32) | int32 | 1,000 | 0.0009 | 0.0019 | 0.46× | 217% | +|🟠| a + scalar (int32) | int32 | 100,000 | 0.0245 | 0.0532 | 0.46× | 217% | +|✅| a + scalar (int32) | int32 | 10,000,000 | 12.6205 | 8.3589 | 1.51× | 66% | +|▫| a + scalar (int64) | int64 | 1,000 | 0.0009 | 0.0027 | 0.33× | 304% | +|🟠| a + scalar (int64) | int64 | 100,000 | 0.0236 | 0.1014 | 0.23× | 429% | +|🟡| a + scalar (int64) | int64 | 10,000,000 | 14.8466 | 19.9072 | 0.75× | 134% | +|▫| a + scalar (int8) | int8 | 1,000 | 0.0008 | 0.0016 | 0.49× | 203% | +|✅| a + scalar (int8) | int8 | 100,000 | 0.0251 | 0.0149 | 1.69× | 59% | +|✅| a + scalar (int8) | int8 | 10,000,000 | 4.2152 | 1.9671 | 2.14× | 47% | +|▫| a + scalar (uint16) | uint16 | 1,000 | 0.0009 | 0.0017 | 0.54× | 187% | +|🟡| a + scalar (uint16) | uint16 | 100,000 | 0.0245 | 0.0256 | 0.96× | 105% | +|✅| a + scalar (uint16) | uint16 | 10,000,000 | 6.9794 | 4.3583 | 1.60× | 62% | +|▫| a + scalar (uint32) | uint32 | 1,000 | 0.0009 | 0.0019 | 0.49× | 205% | +|🟠| a + scalar (uint32) | uint32 | 100,000 | 0.0247 | 0.0503 | 0.49× | 204% | +|✅| a + scalar (uint32) | uint32 | 10,000,000 | 10.8927 | 8.4266 | 1.29× | 77% | +|▫| a + scalar (uint64) | uint64 | 1,000 | 0.0009 | 0.0025 | 0.37× | 273% | +|🟠| a + scalar (uint64) | uint64 | 100,000 | 0.0235 | 0.1018 | 0.23× | 433% | +|🟡| a + scalar (uint64) | uint64 | 10,000,000 | 15.0579 | 20.0299 | 0.75× | 133% | +|▫| a + scalar (uint8) | uint8 | 1,000 | 0.0008 | 0.0015 | 0.50× | 202% | +|✅| a + scalar (uint8) | uint8 | 100,000 | 0.0248 | 0.0148 | 1.67× | 60% | +|✅| a + scalar (uint8) | uint8 | 10,000,000 | 4.4127 | 1.9329 | 2.28× | 44% | +|▫| a - b (element-wise) (complex128) | complex128 | 1,000 | 0.0009 | 0.0040 | 0.22× | 458% | +|✅| a - b (element-wise) (complex128) | complex128 | 100,000 | 0.3505 | 0.2610 | 1.34× | 74% | +|✅| a - b (element-wise) (complex128) | complex128 | 10,000,000 | 41.6707 | 35.7057 | 1.17× | 86% | +|🟠| a - b (element-wise) (float16) | float16 | 1,000 | 0.0041 | 0.0092 | 0.44× | 225% | +|🟡| a - b (element-wise) (float16) | float16 | 100,000 | 0.3042 | 0.4791 | 0.64× | 158% | +|🟡| a - b (element-wise) (float16) | float16 | 10,000,000 | 31.1711 | 48.4343 | 0.64× | 155% | +|▫| a - b (element-wise) (float32) | float32 | 1,000 | 0.0006 | 0.0020 | 0.28× | 359% | +|🔴| a - b (element-wise) (float32) | float32 | 100,000 | 0.0069 | 0.0482 | 0.14× | 698% | +|✅| a - b (element-wise) (float32) | float32 | 10,000,000 | 8.6157 | 5.6899 | 1.51× | 66% | +|▫| a - b (element-wise) (float64) | float64 | 1,000 | 0.0005 | 0.0018 | 0.29× | 340% | +|🟠| a - b (element-wise) (float64) | float64 | 100,000 | 0.0331 | 0.1072 | 0.31× | 324% | +|✅| a - b (element-wise) (float64) | float64 | 10,000,000 | 21.1427 | 16.8826 | 1.25× | 80% | +|▫| a - b (element-wise) (int16) | int16 | 1,000 | 0.0008 | 0.0017 | 0.45× | 222% | +|✅| a - b (element-wise) (int16) | int16 | 100,000 | 0.0293 | 0.0250 | 1.17× | 85% | +|✅| a - b (element-wise) (int16) | int16 | 10,000,000 | 7.8362 | 3.1173 | 2.51× | 40% | +|▫| a - b (element-wise) (int32) | int32 | 1,000 | 0.0008 | 0.0020 | 0.40× | 253% | +|🟡| a - b (element-wise) (int32) | int32 | 100,000 | 0.0287 | 0.0524 | 0.55× | 182% | +|✅| a - b (element-wise) (int32) | int32 | 10,000,000 | 13.7810 | 6.3717 | 2.16× | 46% | +|▫| a - b (element-wise) (int64) | int64 | 1,000 | 0.0008 | 0.0028 | 0.27× | 367% | +|🟠| a - b (element-wise) (int64) | int64 | 100,000 | 0.0352 | 0.1044 | 0.34× | 297% | +|🟡| a - b (element-wise) (int64) | int64 | 10,000,000 | 16.8594 | 20.7569 | 0.81× | 123% | +|▫| a - b (element-wise) (int8) | int8 | 1,000 | 0.0007 | 0.0014 | 0.46× | 219% | +|✅| a - b (element-wise) (int8) | int8 | 100,000 | 0.0355 | 0.0155 | 2.29× | 44% | +|✅| a - b (element-wise) (int8) | int8 | 10,000,000 | 4.7577 | 1.7145 | 2.77× | 36% | +|▫| a - b (element-wise) (uint16) | uint16 | 1,000 | 0.0008 | 0.0018 | 0.45× | 222% | +|✅| a - b (element-wise) (uint16) | uint16 | 100,000 | 0.0299 | 0.0263 | 1.14× | 88% | +|✅| a - b (element-wise) (uint16) | uint16 | 10,000,000 | 8.2884 | 3.1096 | 2.67× | 38% | +|▫| a - b (element-wise) (uint32) | uint32 | 1,000 | 0.0008 | 0.0020 | 0.41× | 242% | +|🟡| a - b (element-wise) (uint32) | uint32 | 100,000 | 0.0294 | 0.0506 | 0.58× | 172% | +|✅| a - b (element-wise) (uint32) | uint32 | 10,000,000 | 10.8540 | 6.0006 | 1.81× | 55% | +|▫| a - b (element-wise) (uint64) | uint64 | 1,000 | 0.0008 | 0.0029 | 0.26× | 385% | +|🟠| a - b (element-wise) (uint64) | uint64 | 100,000 | 0.0385 | 0.1017 | 0.38× | 264% | +|🟡| a - b (element-wise) (uint64) | uint64 | 10,000,000 | 16.7811 | 20.9601 | 0.80× | 125% | +|▫| a - b (element-wise) (uint8) | uint8 | 1,000 | 0.0007 | 0.0015 | 0.45× | 224% | +|✅| a - b (element-wise) (uint8) | uint8 | 100,000 | 0.0288 | 0.0152 | 1.90× | 53% | +|✅| a - b (element-wise) (uint8) | uint8 | 10,000,000 | 5.0513 | 1.6363 | 3.09× | 32% | +|▫| a - scalar (complex128) | complex128 | 1,000 | 0.0009 | 0.0030 | 0.31× | 319% | +|✅| a - scalar (complex128) | complex128 | 100,000 | 0.3509 | 0.2223 | 1.58× | 63% | +|✅| a - scalar (complex128) | complex128 | 10,000,000 | 39.6368 | 25.1620 | 1.57× | 64% | +|✅| a - scalar (float16) | float16 | 1,000 | 0.0061 | 0.0052 | 1.18× | 85% | +|🟡| a - scalar (float16) | float16 | 100,000 | 0.2950 | 0.4792 | 0.62× | 162% | +|🟡| a - scalar (float16) | float16 | 10,000,000 | 31.0622 | 48.9935 | 0.63× | 158% | +|▫| a - scalar (float32) | float32 | 1,000 | 0.0007 | 0.0013 | 0.53× | 190% | +|🟠| a - scalar (float32) | float32 | 100,000 | 0.0070 | 0.0293 | 0.24× | 417% | +|✅| a - scalar (float32) | float32 | 10,000,000 | 8.0175 | 5.3684 | 1.49× | 67% | +|▫| a - scalar (float64) | float64 | 1,000 | 0.0006 | 0.0019 | 0.33× | 302% | +|🔴| a - scalar (float64) | float64 | 100,000 | 0.0118 | 0.0691 | 0.17× | 586% | +|✅| a - scalar (float64) | float64 | 10,000,000 | 19.9346 | 14.0728 | 1.42× | 71% | +|▫| a - scalar (int16) | int16 | 1,000 | 0.0009 | 0.0013 | 0.70× | 142% | +|✅| a - scalar (int16) | int16 | 100,000 | 0.0239 | 0.0141 | 1.70× | 59% | +|✅| a - scalar (int16) | int16 | 10,000,000 | 7.1495 | 2.7330 | 2.62× | 38% | +|▫| a - scalar (int32) | int32 | 1,000 | 0.0009 | 0.0016 | 0.58× | 173% | +|🟡| a - scalar (int32) | int32 | 100,000 | 0.0237 | 0.0278 | 0.85× | 117% | +|✅| a - scalar (int32) | int32 | 10,000,000 | 13.2645 | 5.2875 | 2.51× | 40% | +|▫| a - scalar (int64) | int64 | 1,000 | 0.0009 | 0.0015 | 0.62× | 162% | +|🟠| a - scalar (int64) | int64 | 100,000 | 0.0241 | 0.0677 | 0.36× | 280% | +|✅| a - scalar (int64) | int64 | 10,000,000 | 15.1015 | 14.4570 | 1.04× | 96% | +|▫| a - scalar (int8) | int8 | 1,000 | 0.0008 | 0.0011 | 0.68× | 147% | +|✅| a - scalar (int8) | int8 | 100,000 | 0.0319 | 0.0079 | 4.02× | 25% | +|✅| a - scalar (int8) | int8 | 10,000,000 | 4.4975 | 1.3821 | 3.25× | 31% | +|▫| a - scalar (uint16) | uint16 | 1,000 | 0.0009 | 0.0012 | 0.77× | 130% | +|✅| a - scalar (uint16) | uint16 | 100,000 | 0.0244 | 0.0148 | 1.65× | 61% | +|✅| a - scalar (uint16) | uint16 | 10,000,000 | 6.7651 | 2.6065 | 2.60× | 38% | +|🟡| a - scalar (uint32) | uint32 | 1,000 | 0.0011 | 0.0012 | 0.88× | 114% | +|🟡| a - scalar (uint32) | uint32 | 100,000 | 0.0233 | 0.0264 | 0.89× | 113% | +|✅| a - scalar (uint32) | uint32 | 10,000,000 | 10.2434 | 5.3189 | 1.93× | 52% | +|▫| a - scalar (uint64) | uint64 | 1,000 | 0.0009 | 0.0016 | 0.56× | 177% | +|🟠| a - scalar (uint64) | uint64 | 100,000 | 0.0241 | 0.0652 | 0.37× | 271% | +|✅| a - scalar (uint64) | uint64 | 10,000,000 | 15.1815 | 14.5668 | 1.04× | 96% | +|▫| a - scalar (uint8) | uint8 | 1,000 | 0.0008 | 0.0008 | 0.95× | 105% | +|✅| a - scalar (uint8) | uint8 | 100,000 | 0.0374 | 0.0079 | 4.71× | 21% | +|✅| a - scalar (uint8) | uint8 | 10,000,000 | 4.1241 | 1.3972 | 2.95× | 34% | +|▫| a / b (element-wise) (float32) | float32 | 1,000 | 0.0005 | 0.0023 | 0.22× | 458% | +|🟠| a / b (element-wise) (float32) | float32 | 100,000 | 0.0134 | 0.0623 | 0.21× | 466% | +|🟡| a / b (element-wise) (float32) | float32 | 10,000,000 | 8.4590 | 10.2825 | 0.82× | 122% | +|▫| a / b (element-wise) (float64) | float64 | 1,000 | 0.0007 | 0.0034 | 0.22× | 452% | +|🟠| a / b (element-wise) (float64) | float64 | 100,000 | 0.0382 | 0.1801 | 0.21× | 472% | +|🟡| a / b (element-wise) (float64) | float64 | 10,000,000 | 20.4780 | 25.4700 | 0.80× | 124% | +|🟠| a / b (element-wise) (int32) | int32 | 1,000 | 0.0024 | 0.0053 | 0.47× | 215% | +|🟠| a / b (element-wise) (int32) | int32 | 100,000 | 0.0909 | 0.1953 | 0.47× | 215% | +|✅| a / b (element-wise) (int32) | int32 | 10,000,000 | 31.1185 | 23.9718 | 1.30× | 77% | +|🟠| a / b (element-wise) (int64) | int64 | 1,000 | 0.0019 | 0.0055 | 0.35× | 289% | +|🟠| a / b (element-wise) (int64) | int64 | 100,000 | 0.0785 | 0.1896 | 0.41× | 242% | +|🟡| a / b (element-wise) (int64) | int64 | 10,000,000 | 24.7535 | 40.7339 | 0.61× | 165% | +|▫| a / scalar (float32) | float32 | 1,000 | 0.0007 | 0.0020 | 0.32× | 308% | +|🟠| a / scalar (float32) | float32 | 100,000 | 0.0123 | 0.0595 | 0.21× | 484% | +|🟡| a / scalar (float32) | float32 | 10,000,000 | 7.8511 | 8.8399 | 0.89× | 113% | +|▫| a / scalar (float64) | float64 | 1,000 | 0.0009 | 0.0034 | 0.27× | 376% | +|🟠| a / scalar (float64) | float64 | 100,000 | 0.0372 | 0.1696 | 0.22× | 456% | +|🟡| a / scalar (float64) | float64 | 10,000,000 | 19.0548 | 23.8037 | 0.80× | 125% | +|🟠| a / scalar (int32) | int32 | 1,000 | 0.0018 | 0.0041 | 0.43× | 234% | +|🟠| a / scalar (int32) | int32 | 100,000 | 0.0622 | 0.1883 | 0.33× | 302% | +|✅| a / scalar (int32) | int32 | 10,000,000 | 26.4799 | 23.0389 | 1.15× | 87% | +|🟠| a / scalar (int64) | int64 | 1,000 | 0.0017 | 0.0074 | 0.23× | 436% | +|🟠| a / scalar (int64) | int64 | 100,000 | 0.0575 | 0.1885 | 0.30× | 328% | +|🟡| a / scalar (int64) | int64 | 10,000,000 | 20.5211 | 29.8992 | 0.69× | 146% | +|▫| np.add(a, b) (complex128) | complex128 | 1,000 | 0.0008 | 0.0046 | 0.17× | 572% | +|🟡| np.add(a, b) (complex128) | complex128 | 100,000 | 0.3569 | 0.4892 | 0.73× | 137% | +|🟡| np.add(a, b) (complex128) | complex128 | 10,000,000 | 41.8396 | 58.6023 | 0.71× | 140% | +|🟡| np.add(a, b) (float16) | float16 | 1,000 | 0.0057 | 0.0088 | 0.65× | 155% | +|🟠| np.add(a, b) (float16) | float16 | 100,000 | 0.2995 | 0.8369 | 0.36× | 280% | +|🟠| np.add(a, b) (float16) | float16 | 10,000,000 | 31.2511 | 85.9844 | 0.36× | 275% | +|▫| np.add(a, b) (float32) | float32 | 1,000 | 0.0008 | 0.0019 | 0.41× | 245% | +|🔴| np.add(a, b) (float32) | float32 | 100,000 | 0.0068 | 0.0542 | 0.12× | 798% | +|🟡| np.add(a, b) (float32) | float32 | 10,000,000 | 8.6159 | 10.2348 | 0.84× | 119% | +|▫| np.add(a, b) (float64) | float64 | 1,000 | 0.0005 | 0.0025 | 0.21× | 475% | +|🟠| np.add(a, b) (float64) | float64 | 100,000 | 0.0287 | 0.1135 | 0.25× | 396% | +|🟡| np.add(a, b) (float64) | float64 | 10,000,000 | 23.3169 | 25.7561 | 0.91× | 110% | +|▫| np.add(a, b) (int16) | int16 | 1,000 | 0.0009 | 0.0017 | 0.51× | 196% | +|✅| np.add(a, b) (int16) | int16 | 100,000 | 0.0312 | 0.0273 | 1.14× | 87% | +|✅| np.add(a, b) (int16) | int16 | 10,000,000 | 7.8320 | 7.1303 | 1.10× | 91% | +|▫| np.add(a, b) (int32) | int32 | 1,000 | 0.0008 | 0.0019 | 0.42× | 238% | +|🟡| np.add(a, b) (int32) | int32 | 100,000 | 0.0299 | 0.0546 | 0.55× | 183% | +|✅| np.add(a, b) (int32) | int32 | 10,000,000 | 14.4326 | 12.7485 | 1.13× | 88% | +|▫| np.add(a, b) (int64) | int64 | 1,000 | 0.0008 | 0.0028 | 0.29× | 341% | +|🟠| np.add(a, b) (int64) | int64 | 100,000 | 0.0359 | 0.1337 | 0.27× | 372% | +|🟠| np.add(a, b) (int64) | int64 | 10,000,000 | 17.0171 | 34.3239 | 0.50× | 202% | +|▫| np.add(a, b) (int8) | int8 | 1,000 | 0.0007 | 0.0017 | 0.39× | 257% | +|✅| np.add(a, b) (int8) | int8 | 100,000 | 0.0293 | 0.0152 | 1.93× | 52% | +|✅| np.add(a, b) (int8) | int8 | 10,000,000 | 4.8360 | 3.1341 | 1.54× | 65% | +|▫| np.add(a, b) (uint16) | uint16 | 1,000 | 0.0008 | 0.0018 | 0.42× | 235% | +|🟡| np.add(a, b) (uint16) | uint16 | 100,000 | 0.0294 | 0.0296 | 0.99× | 101% | +|✅| np.add(a, b) (uint16) | uint16 | 10,000,000 | 8.1075 | 6.8350 | 1.19× | 84% | +|▫| np.add(a, b) (uint32) | uint32 | 1,000 | 0.0010 | 0.0019 | 0.51× | 195% | +|🟡| np.add(a, b) (uint32) | uint32 | 100,000 | 0.0324 | 0.0542 | 0.60× | 167% | +|✅| np.add(a, b) (uint32) | uint32 | 10,000,000 | 10.4639 | 9.8564 | 1.06× | 94% | +|▫| np.add(a, b) (uint64) | uint64 | 1,000 | 0.0008 | 0.0028 | 0.30× | 330% | +|🟠| np.add(a, b) (uint64) | uint64 | 100,000 | 0.0405 | 0.1101 | 0.37× | 272% | +|🟠| np.add(a, b) (uint64) | uint64 | 10,000,000 | 16.7988 | 34.5621 | 0.49× | 206% | +|▫| np.add(a, b) (uint8) | uint8 | 1,000 | 0.0007 | 0.0019 | 0.36× | 276% | +|✅| np.add(a, b) (uint8) | uint8 | 100,000 | 0.0287 | 0.0154 | 1.86× | 54% | +|✅| np.add(a, b) (uint8) | uint8 | 10,000,000 | 4.7543 | 3.2277 | 1.47× | 68% | +|▫| scalar - a (complex128) | complex128 | 1,000 | 0.0010 | 0.0027 | 0.36× | 280% | +|✅| scalar - a (complex128) | complex128 | 100,000 | 0.3545 | 0.2210 | 1.60× | 62% | +|✅| scalar - a (complex128) | complex128 | 10,000,000 | 37.5427 | 25.1481 | 1.49× | 67% | +|✅| scalar - a (float16) | float16 | 1,000 | 0.0054 | 0.0051 | 1.05× | 95% | +|🟡| scalar - a (float16) | float16 | 100,000 | 0.3008 | 0.4746 | 0.63× | 158% | +|🟡| scalar - a (float16) | float16 | 10,000,000 | 31.1065 | 47.0491 | 0.66× | 151% | +|▫| scalar - a (float32) | float32 | 1,000 | 0.0007 | 0.0014 | 0.52× | 191% | +|🟠| scalar - a (float32) | float32 | 100,000 | 0.0067 | 0.0325 | 0.20× | 487% | +|✅| scalar - a (float32) | float32 | 10,000,000 | 7.8766 | 5.3211 | 1.48× | 68% | +|▫| scalar - a (float64) | float64 | 1,000 | 0.0007 | 0.0020 | 0.33× | 300% | +|🟠| scalar - a (float64) | float64 | 100,000 | 0.0138 | 0.0582 | 0.24× | 422% | +|✅| scalar - a (float64) | float64 | 10,000,000 | 20.1742 | 14.0006 | 1.44× | 69% | +|▫| scalar - a (int16) | int16 | 1,000 | 0.0009 | 0.0012 | 0.78× | 128% | +|✅| scalar - a (int16) | int16 | 100,000 | 0.0243 | 0.0148 | 1.64× | 61% | +|✅| scalar - a (int16) | int16 | 10,000,000 | 7.1811 | 2.7354 | 2.62× | 38% | +|▫| scalar - a (int32) | int32 | 1,000 | 0.0009 | 0.0013 | 0.72× | 139% | +|🟡| scalar - a (int32) | int32 | 100,000 | 0.0240 | 0.0290 | 0.83× | 121% | +|✅| scalar - a (int32) | int32 | 10,000,000 | 13.3685 | 5.4031 | 2.47× | 40% | +|🟡| scalar - a (int64) | int64 | 1,000 | 0.0011 | 0.0018 | 0.63× | 159% | +|🟠| scalar - a (int64) | int64 | 100,000 | 0.0250 | 0.0667 | 0.38× | 267% | +|✅| scalar - a (int64) | int64 | 10,000,000 | 15.5954 | 14.9929 | 1.04× | 96% | +|▫| scalar - a (int8) | int8 | 1,000 | 0.0008 | 0.0009 | 0.90× | 111% | +|✅| scalar - a (int8) | int8 | 100,000 | 0.0254 | 0.0080 | 3.18× | 31% | +|✅| scalar - a (int8) | int8 | 10,000,000 | 4.5431 | 1.3785 | 3.30× | 30% | +|▫| scalar - a (uint16) | uint16 | 1,000 | 0.0009 | 0.0011 | 0.85× | 118% | +|✅| scalar - a (uint16) | uint16 | 100,000 | 0.0313 | 0.0147 | 2.12× | 47% | +|✅| scalar - a (uint16) | uint16 | 10,000,000 | 7.1706 | 2.6431 | 2.71× | 37% | +|▫| scalar - a (uint32) | uint32 | 1,000 | 0.0009 | 0.0012 | 0.80× | 124% | +|🟡| scalar - a (uint32) | uint32 | 100,000 | 0.0239 | 0.0299 | 0.80× | 125% | +|✅| scalar - a (uint32) | uint32 | 10,000,000 | 9.9808 | 5.3724 | 1.86× | 54% | +|▫| scalar - a (uint64) | uint64 | 1,000 | 0.0009 | 0.0018 | 0.51× | 198% | +|🟠| scalar - a (uint64) | uint64 | 100,000 | 0.0246 | 0.0594 | 0.41× | 242% | +|🟡| scalar - a (uint64) | uint64 | 10,000,000 | 14.8288 | 14.8342 | 1.00× | 100% | +|▫| scalar - a (uint8) | uint8 | 1,000 | 0.0008 | 0.0009 | 0.84× | 119% | +|✅| scalar - a (uint8) | uint8 | 100,000 | 0.0243 | 0.0082 | 2.98× | 34% | +|✅| scalar - a (uint8) | uint8 | 10,000,000 | 4.0556 | 1.4050 | 2.89× | 35% | +|▫| scalar / a (float32) | float32 | 1,000 | 0.0007 | 0.0020 | 0.33× | 301% | +|🟠| scalar / a (float32) | float32 | 100,000 | 0.0127 | 0.0612 | 0.21× | 483% | +|✅| scalar / a (float32) | float32 | 10,000,000 | 7.9570 | 7.2244 | 1.10× | 91% | +|▫| scalar / a (float64) | float64 | 1,000 | 0.0009 | 0.0035 | 0.26× | 385% | +|🟠| scalar / a (float64) | float64 | 100,000 | 0.0372 | 0.1857 | 0.20× | 499% | +|🟡| scalar / a (float64) | float64 | 10,000,000 | 19.3997 | 22.5621 | 0.86× | 116% | +|🟠| scalar / a (int32) | int32 | 1,000 | 0.0018 | 0.0070 | 0.26× | 391% | +|🟠| scalar / a (int32) | int32 | 100,000 | 0.0638 | 0.1794 | 0.35× | 281% | +|🟡| scalar / a (int32) | int32 | 10,000,000 | 22.0961 | 25.4827 | 0.87× | 115% | +|🟠| scalar / a (int64) | int64 | 1,000 | 0.0018 | 0.0069 | 0.26× | 383% | +|🟠| scalar / a (int64) | int64 | 100,000 | 0.0627 | 0.1881 | 0.33× | 300% | +|🟡| scalar / a (int64) | int64 | 10,000,000 | 19.6435 | 30.8085 | 0.64× | 157% | + +### Unary + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|▫| np.abs (float16) | float16 | 1,000 | 0.0008 | 0.0015 | 0.54× | 184% | +|✅| np.abs (float16) | float16 | 100,000 | 0.0264 | 0.0234 | 1.13× | 89% | +|✅| np.abs (float16) | float16 | 10,000,000 | 4.7982 | 2.9688 | 1.62× | 62% | +|▫| np.abs (float32) | float32 | 1,000 | 0.0005 | 0.0015 | 0.34× | 291% | +|🔴| np.abs (float32) | float32 | 100,000 | 0.0064 | 0.0352 | 0.18× | 553% | +|✅| np.abs (float32) | float32 | 10,000,000 | 7.1189 | 4.2161 | 1.69× | 59% | +|▫| np.abs (float64) | float64 | 1,000 | 0.0006 | 0.0028 | 0.20× | 503% | +|🔴| np.abs (float64) | float64 | 100,000 | 0.0112 | 0.0686 | 0.16× | 612% | +|✅| np.abs (float64) | float64 | 10,000,000 | 14.6849 | 13.9245 | 1.05× | 95% | +|🟡| np.cbrt(a) (float16) | float16 | 1,000 | 0.0101 | 0.0111 | 0.91× | 110% | +|🟡| np.cbrt(a) (float16) | float16 | 100,000 | 1.1991 | 1.3766 | 0.87× | 115% | +|🟡| np.cbrt(a) (float16) | float16 | 10,000,000 | 121.4253 | 136.3973 | 0.89× | 112% | +|✅| np.cbrt(a) (float32) | float32 | 1,000 | 0.0066 | 0.0060 | 1.10× | 91% | +|✅| np.cbrt(a) (float32) | float32 | 100,000 | 0.8943 | 0.8882 | 1.01× | 99% | +|✅| np.cbrt(a) (float32) | float32 | 10,000,000 | 94.5195 | 86.5058 | 1.09× | 92% | +|✅| np.cbrt(a) (float64) | float64 | 1,000 | 0.0098 | 0.0093 | 1.05× | 95% | +|✅| np.cbrt(a) (float64) | float64 | 100,000 | 1.0970 | 1.0793 | 1.02× | 98% | +|✅| np.cbrt(a) (float64) | float64 | 10,000,000 | 115.3869 | 109.5524 | 1.05× | 95% | +|✅| np.ceil (float16) | float16 | 1,000 | 0.0050 | 0.0040 | 1.26× | 79% | +|✅| np.ceil (float16) | float16 | 100,000 | 0.4660 | 0.3448 | 1.35× | 74% | +|✅| np.ceil (float16) | float16 | 10,000,000 | 43.5339 | 33.7644 | 1.29× | 78% | +|▫| np.ceil (float32) | float32 | 1,000 | 0.0005 | 0.0014 | 0.38× | 263% | +|🟠| np.ceil (float32) | float32 | 100,000 | 0.0066 | 0.0305 | 0.22× | 460% | +|✅| np.ceil (float32) | float32 | 10,000,000 | 7.2732 | 4.4002 | 1.65× | 60% | +|🟠| np.ceil (float64) | float64 | 1,000 | 0.0014 | 0.0036 | 0.37× | 270% | +|🔴| np.ceil (float64) | float64 | 100,000 | 0.0120 | 0.0606 | 0.20× | 503% | +|🟡| np.ceil (float64) | float64 | 10,000,000 | 14.6298 | 16.0489 | 0.91× | 110% | +|✅| np.clip(a, -10, 10) (float16) | float16 | 1,000 | 0.0087 | 0.0059 | 1.47× | 68% | +|✅| np.clip(a, -10, 10) (float16) | float16 | 100,000 | 0.9296 | 0.7061 | 1.32× | 76% | +|✅| np.clip(a, -10, 10) (float16) | float16 | 10,000,000 | 93.7915 | 68.7791 | 1.36× | 73% | +|🟠| np.clip(a, -10, 10) (float32) | float32 | 1,000 | 0.0021 | 0.0064 | 0.32× | 308% | +|🟠| np.clip(a, -10, 10) (float32) | float32 | 100,000 | 0.0082 | 0.0345 | 0.24× | 421% | +|✅| np.clip(a, -10, 10) (float32) | float32 | 10,000,000 | 7.5158 | 4.1400 | 1.81× | 55% | +|🟠| np.clip(a, -10, 10) (float64) | float64 | 1,000 | 0.0019 | 0.0047 | 0.40× | 252% | +|🟠| np.clip(a, -10, 10) (float64) | float64 | 100,000 | 0.0166 | 0.0700 | 0.24× | 421% | +|✅| np.clip(a, -10, 10) (float64) | float64 | 10,000,000 | 18.6971 | 13.3810 | 1.40× | 72% | +|🟡| np.cos (float16) | float16 | 1,000 | 0.0051 | 0.0081 | 0.63× | 158% | +|🟡| np.cos (float16) | float16 | 100,000 | 0.7102 | 1.1049 | 0.64× | 156% | +|🟡| np.cos (float16) | float16 | 10,000,000 | 80.1757 | 109.6838 | 0.73× | 137% | +|✅| np.cos (float32) | float32 | 1,000 | 0.0051 | 0.0038 | 1.35× | 74% | +|🟡| np.cos (float32) | float32 | 100,000 | 0.7030 | 0.7111 | 0.99× | 101% | +|✅| np.cos (float32) | float32 | 10,000,000 | 82.6791 | 69.0830 | 1.20× | 84% | +|✅| np.cos (float64) | float64 | 1,000 | 0.0050 | 0.0040 | 1.22× | 82% | +|🟡| np.cos (float64) | float64 | 100,000 | 0.6990 | 0.7313 | 0.96× | 105% | +|✅| np.cos (float64) | float64 | 10,000,000 | 81.9906 | 77.3710 | 1.06× | 94% | +|🟡| np.exp (float16) | float16 | 1,000 | 0.0052 | 0.0066 | 0.79× | 127% | +|🟡| np.exp (float16) | float16 | 100,000 | 0.4392 | 0.6077 | 0.72× | 138% | +|🟡| np.exp (float16) | float16 | 10,000,000 | 44.3829 | 58.6632 | 0.76× | 132% | +|🔴| np.exp (float32) | float32 | 1,000 | 0.0010 | 0.0056 | 0.19× | 539% | +|🟠| np.exp (float32) | float32 | 100,000 | 0.0553 | 0.1841 | 0.30× | 333% | +|🟡| np.exp (float32) | float32 | 10,000,000 | 10.4285 | 17.6747 | 0.59× | 170% | +|🟡| np.exp (float64) | float64 | 1,000 | 0.0030 | 0.0040 | 0.75× | 134% | +|🟡| np.exp (float64) | float64 | 100,000 | 0.2581 | 0.2792 | 0.93× | 108% | +|✅| np.exp (float64) | float64 | 10,000,000 | 33.4427 | 31.7361 | 1.05× | 95% | +|🟠| np.exp2 (float16) | float16 | 1,000 | 0.0047 | 0.0099 | 0.48× | 210% | +|🟠| np.exp2 (float16) | float16 | 100,000 | 0.4598 | 0.9896 | 0.47× | 215% | +|🟠| np.exp2 (float16) | float16 | 10,000,000 | 48.1931 | 97.0656 | 0.50× | 201% | +|🟠| np.exp2 (float32) | float32 | 1,000 | 0.0022 | 0.0100 | 0.22× | 461% | +|🔴| np.exp2 (float32) | float32 | 100,000 | 0.1760 | 0.8987 | 0.20× | 511% | +|🟠| np.exp2 (float32) | float32 | 10,000,000 | 22.9673 | 90.4267 | 0.25× | 394% | +|🟠| np.exp2 (float64) | float64 | 1,000 | 0.0025 | 0.0096 | 0.26× | 389% | +|🟠| np.exp2 (float64) | float64 | 100,000 | 0.2137 | 0.8559 | 0.25× | 400% | +|🟠| np.exp2 (float64) | float64 | 10,000,000 | 37.0195 | 90.0413 | 0.41× | 243% | +|🟡| np.expm1 (float16) | float16 | 1,000 | 0.0058 | 0.0091 | 0.64× | 157% | +|🟡| np.expm1 (float16) | float16 | 100,000 | 0.5474 | 0.8722 | 0.63× | 159% | +|🟡| np.expm1 (float16) | float16 | 10,000,000 | 55.0439 | 85.4215 | 0.64× | 155% | +|🟡| np.expm1 (float32) | float32 | 1,000 | 0.0032 | 0.0046 | 0.70× | 142% | +|✅| np.expm1 (float32) | float32 | 100,000 | 0.2728 | 0.1902 | 1.43× | 70% | +|✅| np.expm1 (float32) | float32 | 10,000,000 | 31.7384 | 18.4160 | 1.72× | 58% | +|🟡| np.expm1 (float64) | float64 | 1,000 | 0.0038 | 0.0040 | 0.95× | 106% | +|✅| np.expm1 (float64) | float64 | 100,000 | 0.3590 | 0.2864 | 1.25× | 80% | +|✅| np.expm1 (float64) | float64 | 10,000,000 | 47.0453 | 33.2374 | 1.42× | 71% | +|✅| np.floor (float16) | float16 | 1,000 | 0.0051 | 0.0039 | 1.30× | 77% | +|✅| np.floor (float16) | float16 | 100,000 | 0.4407 | 0.3400 | 1.30× | 77% | +|✅| np.floor (float16) | float16 | 10,000,000 | 43.3912 | 33.7248 | 1.29× | 78% | +|▫| np.floor (float32) | float32 | 1,000 | 0.0006 | 0.0015 | 0.38× | 267% | +|🔴| np.floor (float32) | float32 | 100,000 | 0.0064 | 0.0319 | 0.20× | 501% | +|✅| np.floor (float32) | float32 | 10,000,000 | 7.3030 | 5.1858 | 1.41× | 71% | +|▫| np.floor (float64) | float64 | 1,000 | 0.0006 | 0.0032 | 0.17× | 575% | +|🟠| np.floor (float64) | float64 | 100,000 | 0.0151 | 0.0632 | 0.24× | 419% | +|🟡| np.floor (float64) | float64 | 10,000,000 | 14.8331 | 19.6220 | 0.76× | 132% | +|🟡| np.log (float16) | float16 | 1,000 | 0.0049 | 0.0069 | 0.71× | 141% | +|🟡| np.log (float16) | float16 | 100,000 | 0.4263 | 0.6316 | 0.68× | 148% | +|🟡| np.log (float16) | float16 | 10,000,000 | 46.0282 | 63.6713 | 0.72× | 138% | +|🟠| np.log (float32) | float32 | 1,000 | 0.0013 | 0.0051 | 0.26× | 384% | +|🟠| np.log (float32) | float32 | 100,000 | 0.0869 | 0.2148 | 0.40× | 247% | +|🟡| np.log (float32) | float32 | 10,000,000 | 13.1800 | 20.9525 | 0.63× | 159% | +|🟡| np.log (float64) | float64 | 1,000 | 0.0028 | 0.0038 | 0.73× | 136% | +|🟡| np.log (float64) | float64 | 100,000 | 0.2424 | 0.2668 | 0.91× | 110% | +|✅| np.log (float64) | float64 | 10,000,000 | 31.9128 | 30.1149 | 1.06× | 94% | +|🟡| np.log10 (float16) | float16 | 1,000 | 0.0052 | 0.0070 | 0.74× | 135% | +|🟡| np.log10 (float16) | float16 | 100,000 | 0.4465 | 0.6426 | 0.69× | 144% | +|🟡| np.log10 (float16) | float16 | 10,000,000 | 47.8418 | 62.9499 | 0.76× | 132% | +|🟡| np.log10 (float32) | float32 | 1,000 | 0.0035 | 0.0050 | 0.70× | 143% | +|🟡| np.log10 (float32) | float32 | 100,000 | 0.1985 | 0.2140 | 0.93× | 108% | +|✅| np.log10 (float32) | float32 | 10,000,000 | 23.5444 | 20.6616 | 1.14× | 88% | +|🟡| np.log10 (float64) | float64 | 1,000 | 0.0029 | 0.0040 | 0.73× | 138% | +|🟡| np.log10 (float64) | float64 | 100,000 | 0.2625 | 0.2698 | 0.97× | 103% | +|✅| np.log10 (float64) | float64 | 10,000,000 | 32.9855 | 30.7610 | 1.07× | 93% | +|🟡| np.log1p (float16) | float16 | 1,000 | 0.0062 | 0.0086 | 0.72× | 139% | +|🟡| np.log1p (float16) | float16 | 100,000 | 0.5885 | 0.7878 | 0.75× | 134% | +|🟡| np.log1p (float16) | float16 | 10,000,000 | 60.5869 | 77.8953 | 0.78× | 129% | +|🟡| np.log1p (float32) | float32 | 1,000 | 0.0039 | 0.0040 | 0.98× | 102% | +|✅| np.log1p (float32) | float32 | 100,000 | 0.3058 | 0.2337 | 1.31× | 76% | +|✅| np.log1p (float32) | float32 | 10,000,000 | 33.3514 | 22.3477 | 1.49× | 67% | +|✅| np.log1p (float64) | float64 | 1,000 | 0.0039 | 0.0033 | 1.19× | 84% | +|✅| np.log1p (float64) | float64 | 100,000 | 0.3412 | 0.2752 | 1.24× | 81% | +|✅| np.log1p (float64) | float64 | 10,000,000 | 48.2083 | 31.5835 | 1.53× | 66% | +|🟡| np.log2 (float16) | float16 | 1,000 | 0.0050 | 0.0070 | 0.72× | 139% | +|🟡| np.log2 (float16) | float16 | 100,000 | 0.4545 | 0.6414 | 0.71× | 141% | +|🟡| np.log2 (float16) | float16 | 10,000,000 | 48.9339 | 62.7084 | 0.78× | 128% | +|🟡| np.log2 (float32) | float32 | 1,000 | 0.0027 | 0.0052 | 0.51× | 195% | +|✅| np.log2 (float32) | float32 | 100,000 | 0.2008 | 0.2006 | 1.00× | 100% | +|✅| np.log2 (float32) | float32 | 10,000,000 | 23.4387 | 19.2540 | 1.22× | 82% | +|🟡| np.log2 (float64) | float64 | 1,000 | 0.0042 | 0.0044 | 0.95× | 106% | +|🟡| np.log2 (float64) | float64 | 100,000 | 0.3739 | 0.3882 | 0.96× | 104% | +|✅| np.log2 (float64) | float64 | 10,000,000 | 50.1019 | 42.1909 | 1.19× | 84% | +|▫| np.negative(a) (float16) | float16 | 1,000 | 0.0007 | 0.0014 | 0.54× | 185% | +|✅| np.negative(a) (float16) | float16 | 100,000 | 0.0278 | 0.0234 | 1.19× | 84% | +|✅| np.negative(a) (float16) | float16 | 10,000,000 | 4.7690 | 2.8837 | 1.65× | 60% | +|▫| np.negative(a) (float32) | float32 | 1,000 | 0.0005 | 0.0015 | 0.35× | 289% | +|🟠| np.negative(a) (float32) | float32 | 100,000 | 0.0066 | 0.0261 | 0.25× | 394% | +|✅| np.negative(a) (float32) | float32 | 10,000,000 | 7.7826 | 4.1048 | 1.90× | 53% | +|▫| np.negative(a) (float64) | float64 | 1,000 | 0.0005 | 0.0036 | 0.15× | 679% | +|🟠| np.negative(a) (float64) | float64 | 100,000 | 0.0121 | 0.0539 | 0.22× | 447% | +|✅| np.negative(a) (float64) | float64 | 10,000,000 | 16.0651 | 13.8831 | 1.16× | 86% | +|▫| np.positive(a) (float16) | float16 | 1,000 | 0.0007 | 0.0010 | 0.69× | 144% | +|✅| np.positive(a) (float16) | float16 | 100,000 | 0.0217 | 0.0130 | 1.67× | 60% | +|✅| np.positive(a) (float16) | float16 | 10,000,000 | 4.2319 | 1.6180 | 2.62× | 38% | +|▫| np.positive(a) (float32) | float32 | 1,000 | 0.0007 | 0.0020 | 0.32× | 310% | +|🟡| np.positive(a) (float32) | float32 | 100,000 | 0.0194 | 0.0250 | 0.78× | 129% | +|✅| np.positive(a) (float32) | float32 | 10,000,000 | 7.7620 | 3.3803 | 2.30× | 44% | +|▫| np.positive(a) (float64) | float64 | 1,000 | 0.0007 | 0.0028 | 0.24× | 409% | +|🟠| np.positive(a) (float64) | float64 | 100,000 | 0.0206 | 0.0520 | 0.40× | 252% | +|✅| np.positive(a) (float64) | float64 | 10,000,000 | 15.1241 | 10.8534 | 1.39× | 72% | +|✅| np.power(a, 0.5) (float16) | float16 | 1,000 | 0.0092 | 0.0057 | 1.62× | 62% | +|✅| np.power(a, 0.5) (float16) | float16 | 100,000 | 0.8324 | 0.3398 | 2.45× | 41% | +|✅| np.power(a, 0.5) (float16) | float16 | 10,000,000 | 84.2019 | 32.9557 | 2.56× | 39% | +|✅| np.power(a, 0.5) (float32) | float32 | 1,000 | 0.0020 | 0.0019 | 1.06× | 94% | +|✅| np.power(a, 0.5) (float32) | float32 | 100,000 | 0.1267 | 0.0285 | 4.45× | 22% | +|✅| np.power(a, 0.5) (float32) | float32 | 10,000,000 | 16.0019 | 4.1327 | 3.87× | 26% | +|🟡| np.power(a, 0.5) (float64) | float64 | 1,000 | 0.0018 | 0.0032 | 0.55× | 181% | +|✅| np.power(a, 0.5) (float64) | float64 | 100,000 | 0.1231 | 0.0638 | 1.93× | 52% | +|✅| np.power(a, 0.5) (float64) | float64 | 10,000,000 | 25.4013 | 13.6356 | 1.86× | 54% | +|✅| np.power(a, 2) (float16) | float16 | 1,000 | 0.0103 | 0.0054 | 1.90× | 53% | +|✅| np.power(a, 2) (float16) | float16 | 100,000 | 1.0690 | 0.4762 | 2.25× | 44% | +|✅| np.power(a, 2) (float16) | float16 | 10,000,000 | 106.8632 | 46.7294 | 2.29× | 44% | +|✅| np.power(a, 2) (float32) | float32 | 1,000 | 0.0024 | 0.0017 | 1.36× | 73% | +|✅| np.power(a, 2) (float32) | float32 | 100,000 | 0.1544 | 0.0283 | 5.46× | 18% | +|✅| np.power(a, 2) (float32) | float32 | 10,000,000 | 18.9400 | 4.1649 | 4.55× | 22% | +|🟡| np.power(a, 2) (float64) | float64 | 1,000 | 0.0022 | 0.0031 | 0.71× | 141% | +|✅| np.power(a, 2) (float64) | float64 | 100,000 | 0.1542 | 0.0590 | 2.62× | 38% | +|✅| np.power(a, 2) (float64) | float64 | 10,000,000 | 28.4405 | 13.5219 | 2.10× | 48% | +|🟡| np.power(a, 3) (float16) | float16 | 1,000 | 0.0126 | 0.0176 | 0.72× | 140% | +|🟡| np.power(a, 3) (float16) | float16 | 100,000 | 1.5343 | 2.4828 | 0.62× | 162% | +|🟡| np.power(a, 3) (float16) | float16 | 10,000,000 | 150.3564 | 250.3069 | 0.60× | 166% | +|🟡| np.power(a, 3) (float32) | float32 | 1,000 | 0.0059 | 0.0082 | 0.73× | 137% | +|🟡| np.power(a, 3) (float32) | float32 | 100,000 | 0.6578 | 0.6780 | 0.97× | 103% | +|✅| np.power(a, 3) (float32) | float32 | 10,000,000 | 72.0628 | 66.4589 | 1.08× | 92% | +|🟡| np.power(a, 3) (float64) | float64 | 1,000 | 0.0098 | 0.0121 | 0.81× | 123% | +|🟡| np.power(a, 3) (float64) | float64 | 100,000 | 1.0724 | 1.0959 | 0.98× | 102% | +|✅| np.power(a, 3) (float64) | float64 | 10,000,000 | 124.6532 | 111.9286 | 1.11× | 90% | +|🟡| np.reciprocal(a) (float16) | float16 | 1,000 | 0.0036 | 0.0045 | 0.79× | 127% | +|🟡| np.reciprocal(a) (float16) | float16 | 100,000 | 0.2193 | 0.4080 | 0.54× | 186% | +|🟡| np.reciprocal(a) (float16) | float16 | 10,000,000 | 23.1324 | 40.0357 | 0.58× | 173% | +|▫| np.reciprocal(a) (float32) | float32 | 1,000 | 0.0006 | 0.0014 | 0.44× | 225% | +|🟡| np.reciprocal(a) (float32) | float32 | 100,000 | 0.0143 | 0.0263 | 0.54× | 184% | +|✅| np.reciprocal(a) (float32) | float32 | 10,000,000 | 7.1620 | 4.0578 | 1.76× | 57% | +|▫| np.reciprocal(a) (float64) | float64 | 1,000 | 0.0008 | 0.0019 | 0.42× | 237% | +|🟡| np.reciprocal(a) (float64) | float64 | 100,000 | 0.0421 | 0.0535 | 0.79× | 127% | +|✅| np.reciprocal(a) (float64) | float64 | 10,000,000 | 14.7266 | 13.7636 | 1.07× | 94% | +|✅| np.round (float16) | float16 | 1,000 | 0.0058 | 0.0046 | 1.24× | 81% | +|✅| np.round (float16) | float16 | 100,000 | 0.4702 | 0.4240 | 1.11× | 90% | +|✅| np.round (float16) | float16 | 10,000,000 | 42.8027 | 40.9331 | 1.05× | 96% | +|🟡| np.round (float32) | float32 | 1,000 | 0.0011 | 0.0015 | 0.73× | 136% | +|🟠| np.round (float32) | float32 | 100,000 | 0.0070 | 0.0281 | 0.25× | 403% | +|✅| np.round (float32) | float32 | 10,000,000 | 7.3586 | 4.6823 | 1.57× | 64% | +|🟠| np.round (float64) | float64 | 1,000 | 0.0012 | 0.0024 | 0.49× | 204% | +|🟠| np.round (float64) | float64 | 100,000 | 0.0128 | 0.0572 | 0.22× | 447% | +|🟡| np.round (float64) | float64 | 10,000,000 | 15.0627 | 15.7393 | 0.96× | 104% | +|🟠| np.sign (float16) | float16 | 1,000 | 0.0014 | 0.0042 | 0.33× | 304% | +|🔴| np.sign (float16) | float16 | 100,000 | 0.0887 | 0.6603 | 0.13× | 744% | +|🔴| np.sign (float16) | float16 | 10,000,000 | 10.5588 | 63.8912 | 0.17× | 605% | +|🟠| np.sign (float32) | float32 | 1,000 | 0.0011 | 0.0038 | 0.29× | 339% | +|🟡| np.sign (float32) | float32 | 100,000 | 0.2971 | 0.4006 | 0.74× | 135% | +|🟡| np.sign (float32) | float32 | 10,000,000 | 36.0421 | 39.7051 | 0.91× | 110% | +|🟠| np.sign (float64) | float64 | 1,000 | 0.0010 | 0.0047 | 0.22× | 448% | +|🟡| np.sign (float64) | float64 | 100,000 | 0.2936 | 0.4000 | 0.73× | 136% | +|🟡| np.sign (float64) | float64 | 10,000,000 | 40.6151 | 45.1700 | 0.90× | 111% | +|🟡| np.sin (float16) | float16 | 1,000 | 0.0057 | 0.0080 | 0.72× | 139% | +|🟡| np.sin (float16) | float16 | 100,000 | 0.7108 | 1.1057 | 0.64× | 156% | +|🟡| np.sin (float16) | float16 | 10,000,000 | 79.6291 | 110.4160 | 0.72× | 139% | +|✅| np.sin (float32) | float32 | 1,000 | 0.0060 | 0.0038 | 1.57× | 64% | +|✅| np.sin (float32) | float32 | 100,000 | 0.7167 | 0.7077 | 1.01× | 99% | +|✅| np.sin (float32) | float32 | 10,000,000 | 80.7643 | 70.4978 | 1.15× | 87% | +|✅| np.sin (float64) | float64 | 1,000 | 0.0049 | 0.0040 | 1.22× | 82% | +|🟡| np.sin (float64) | float64 | 100,000 | 0.7140 | 0.7415 | 0.96× | 104% | +|✅| np.sin (float64) | float64 | 10,000,000 | 82.0560 | 78.2388 | 1.05× | 95% | +|✅| np.sqrt (float16) | float16 | 1,000 | 0.0046 | 0.0039 | 1.18× | 85% | +|✅| np.sqrt (float16) | float16 | 100,000 | 0.4264 | 0.3413 | 1.25× | 80% | +|✅| np.sqrt (float16) | float16 | 10,000,000 | 40.8006 | 33.6967 | 1.21× | 83% | +|▫| np.sqrt (float32) | float32 | 1,000 | 0.0006 | 0.0015 | 0.42× | 238% | +|🟡| np.sqrt (float32) | float32 | 100,000 | 0.0149 | 0.0291 | 0.51× | 195% | +|✅| np.sqrt (float32) | float32 | 10,000,000 | 7.0955 | 4.2557 | 1.67× | 60% | +|🟠| np.sqrt (float64) | float64 | 1,000 | 0.0012 | 0.0025 | 0.47× | 214% | +|🟡| np.sqrt (float64) | float64 | 100,000 | 0.0557 | 0.0656 | 0.85× | 118% | +|✅| np.sqrt (float64) | float64 | 10,000,000 | 15.1785 | 13.7551 | 1.10× | 91% | +|🟡| np.square(a) (float16) | float16 | 1,000 | 0.0034 | 0.0048 | 0.71× | 141% | +|🟠| np.square(a) (float16) | float16 | 100,000 | 0.2122 | 0.4399 | 0.48× | 207% | +|🟡| np.square(a) (float16) | float16 | 10,000,000 | 23.1199 | 42.7721 | 0.54× | 185% | +|▫| np.square(a) (float32) | float32 | 1,000 | 0.0005 | 0.0013 | 0.39× | 258% | +|🟠| np.square(a) (float32) | float32 | 100,000 | 0.0058 | 0.0262 | 0.22× | 448% | +|✅| np.square(a) (float32) | float32 | 10,000,000 | 7.1833 | 4.0587 | 1.77× | 56% | +|▫| np.square(a) (float64) | float64 | 1,000 | 0.0005 | 0.0030 | 0.17× | 596% | +|🟠| np.square(a) (float64) | float64 | 100,000 | 0.0122 | 0.0533 | 0.23× | 436% | +|✅| np.square(a) (float64) | float64 | 10,000,000 | 14.9158 | 13.8405 | 1.08× | 93% | +|🟡| np.tan (float16) | float16 | 1,000 | 0.0045 | 0.0083 | 0.55× | 183% | +|🟡| np.tan (float16) | float16 | 100,000 | 0.8245 | 1.1120 | 0.74× | 135% | +|🟡| np.tan (float16) | float16 | 10,000,000 | 92.5062 | 110.2014 | 0.84× | 119% | +|✅| np.tan (float32) | float32 | 1,000 | 0.0047 | 0.0038 | 1.24× | 81% | +|✅| np.tan (float32) | float32 | 100,000 | 0.8414 | 0.6851 | 1.23× | 81% | +|✅| np.tan (float32) | float32 | 10,000,000 | 92.7529 | 67.5651 | 1.37× | 73% | +|🟡| np.tan (float64) | float64 | 1,000 | 0.0050 | 0.0050 | 0.99× | 101% | +|🟡| np.tan (float64) | float64 | 100,000 | 0.8055 | 0.8447 | 0.95× | 105% | +|✅| np.tan (float64) | float64 | 10,000,000 | 94.5311 | 88.7915 | 1.06× | 94% | +|✅| np.trunc(a) (float16) | float16 | 1,000 | 0.0054 | 0.0038 | 1.40× | 71% | +|✅| np.trunc(a) (float16) | float16 | 100,000 | 0.4827 | 0.3346 | 1.44× | 69% | +|✅| np.trunc(a) (float16) | float16 | 10,000,000 | 42.0396 | 32.4934 | 1.29× | 77% | +|▫| np.trunc(a) (float32) | float32 | 1,000 | 0.0005 | 0.0016 | 0.34× | 290% | +|🟠| np.trunc(a) (float32) | float32 | 100,000 | 0.0058 | 0.0257 | 0.22× | 445% | +|✅| np.trunc(a) (float32) | float32 | 10,000,000 | 7.1817 | 4.0632 | 1.77× | 57% | +|▫| np.trunc(a) (float64) | float64 | 1,000 | 0.0005 | 0.0020 | 0.27× | 373% | +|🟠| np.trunc(a) (float64) | float64 | 100,000 | 0.0115 | 0.0513 | 0.22× | 446% | +|✅| np.trunc(a) (float64) | float64 | 10,000,000 | 14.6674 | 13.6305 | 1.08× | 93% | + +### Reduction + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|✅| np.amax (complex128) | complex128 | 1,000 | 0.0030 | 0.0018 | 1.64× | 61% | +|✅| np.amax (complex128) | complex128 | 100,000 | 0.1573 | 0.1142 | 1.38× | 73% | +|✅| np.amax (complex128) | complex128 | 10,000,000 | 16.6240 | 13.6265 | 1.22× | 82% | +|✅| np.amax (float16) | float16 | 1,000 | 0.0037 | 0.0011 | 3.48× | 29% | +|✅| np.amax (float16) | float16 | 100,000 | 0.5066 | 0.3200 | 1.58× | 63% | +|✅| np.amax (float16) | float16 | 10,000,000 | 50.6659 | 33.1861 | 1.53× | 66% | +|▫| np.amax (float32) | float32 | 1,000 | 0.0017 | 0.0006 | 2.73× | 37% | +|✅| np.amax (float32) | float32 | 100,000 | 0.0059 | 0.0038 | 1.54× | 65% | +|✅| np.amax (float32) | float32 | 10,000,000 | 1.5947 | 1.2905 | 1.24× | 81% | +|▫| np.amax (float64) | float64 | 1,000 | 0.0018 | 0.0008 | 2.34× | 43% | +|✅| np.amax (float64) | float64 | 100,000 | 0.0099 | 0.0062 | 1.60× | 62% | +|✅| np.amax (float64) | float64 | 10,000,000 | 3.3475 | 3.2005 | 1.05× | 96% | +|▫| np.amax (int16) | int16 | 1,000 | 0.0015 | 0.0007 | 2.14× | 47% | +|✅| np.amax (int16) | int16 | 100,000 | 0.0031 | 0.0015 | 2.04× | 49% | +|✅| np.amax (int16) | int16 | 10,000,000 | 0.4359 | 0.3359 | 1.30× | 77% | +|▫| np.amax (int32) | int32 | 1,000 | 0.0016 | 0.0007 | 2.28× | 44% | +|✅| np.amax (int32) | int32 | 100,000 | 0.0044 | 0.0023 | 1.94× | 52% | +|✅| np.amax (int32) | int32 | 10,000,000 | 1.2908 | 1.0195 | 1.27× | 79% | +|▫| np.amax (int64) | int64 | 1,000 | 0.0017 | 0.0006 | 2.62× | 38% | +|✅| np.amax (int64) | int64 | 100,000 | 0.0089 | 0.0078 | 1.14× | 88% | +|🟡| np.amax (int64) | int64 | 10,000,000 | 3.4074 | 3.5508 | 0.96× | 104% | +|▫| np.amax (int8) | int8 | 1,000 | 0.0016 | 0.0007 | 2.08× | 48% | +|✅| np.amax (int8) | int8 | 100,000 | 0.0024 | 0.0012 | 2.00× | 50% | +|✅| np.amax (int8) | int8 | 10,000,000 | 0.1508 | 0.1463 | 1.03× | 97% | +|▫| np.amax (uint16) | uint16 | 1,000 | 0.0016 | 0.0008 | 2.12× | 47% | +|✅| np.amax (uint16) | uint16 | 100,000 | 0.0032 | 0.0015 | 2.13× | 47% | +|🟡| np.amax (uint16) | uint16 | 10,000,000 | 0.2938 | 0.3342 | 0.88× | 114% | +|▫| np.amax (uint32) | uint32 | 1,000 | 0.0016 | 0.0007 | 2.21× | 45% | +|✅| np.amax (uint32) | uint32 | 100,000 | 0.0049 | 0.0032 | 1.53× | 65% | +|✅| np.amax (uint32) | uint32 | 10,000,000 | 1.4205 | 1.0043 | 1.41× | 71% | +|▫| np.amax (uint64) | uint64 | 1,000 | 0.0017 | 0.0008 | 2.14× | 47% | +|✅| np.amax (uint64) | uint64 | 100,000 | 0.0129 | 0.0100 | 1.29× | 78% | +|🟡| np.amax (uint64) | uint64 | 10,000,000 | 3.6692 | 3.6868 | 0.99× | 100% | +|▫| np.amax (uint8) | uint8 | 1,000 | 0.0016 | 0.0008 | 2.06× | 49% | +|✅| np.amax (uint8) | uint8 | 100,000 | 0.0024 | 0.0012 | 2.06× | 49% | +|🟡| np.amax (uint8) | uint8 | 10,000,000 | 0.1351 | 0.1468 | 0.92× | 109% | +|✅| np.amax axis=0 (complex128) | complex128 | 1,000 | 0.0030 | 0.0030 | 1.00× | 100% | +|✅| np.amax axis=0 (complex128) | complex128 | 100,000 | 0.1550 | 0.1176 | 1.32× | 76% | +|✅| np.amax axis=0 (complex128) | complex128 | 10,000,000 | 16.0042 | 13.0433 | 1.23× | 82% | +|✅| np.amax axis=0 (float16) | float16 | 1,000 | 0.0042 | 0.0019 | 2.18× | 46% | +|✅| np.amax axis=0 (float16) | float16 | 100,000 | 0.5075 | 0.5048 | 1.00× | 100% | +|🟡| np.amax axis=0 (float16) | float16 | 10,000,000 | 49.7420 | 78.1538 | 0.64× | 157% | +|▫| np.amax axis=0 (float32) | float32 | 1,000 | 0.0021 | 0.0008 | 2.66× | 38% | +|🟡| np.amax axis=0 (float32) | float32 | 100,000 | 0.0091 | 0.0100 | 0.92× | 109% | +|🟡| np.amax axis=0 (float32) | float32 | 10,000,000 | 1.6926 | 1.8598 | 0.91× | 110% | +|▫| np.amax axis=0 (float64) | float64 | 1,000 | 0.0021 | 0.0009 | 2.33× | 43% | +|🟡| np.amax axis=0 (float64) | float64 | 100,000 | 0.0145 | 0.0175 | 0.83× | 120% | +|🟡| np.amax axis=0 (float64) | float64 | 10,000,000 | 3.9104 | 4.2597 | 0.92× | 109% | +|▫| np.amax axis=0 (int16) | int16 | 1,000 | 0.0020 | 0.0007 | 2.94× | 34% | +|✅| np.amax axis=0 (int16) | int16 | 100,000 | 0.0063 | 0.0037 | 1.73× | 58% | +|✅| np.amax axis=0 (int16) | int16 | 10,000,000 | 0.5073 | 0.3767 | 1.35× | 74% | +|▫| np.amax axis=0 (int32) | int32 | 1,000 | 0.0020 | 0.0007 | 2.84× | 35% | +|✅| np.amax axis=0 (int32) | int32 | 100,000 | 0.0082 | 0.0055 | 1.48× | 67% | +|✅| np.amax axis=0 (int32) | int32 | 10,000,000 | 1.7469 | 1.3193 | 1.32× | 76% | +|▫| np.amax axis=0 (int64) | int64 | 1,000 | 0.0021 | 0.0008 | 2.62× | 38% | +|✅| np.amax axis=0 (int64) | int64 | 100,000 | 0.0153 | 0.0118 | 1.30× | 77% | +|✅| np.amax axis=0 (int64) | int64 | 10,000,000 | 4.1410 | 3.6647 | 1.13× | 88% | +|▫| np.amax axis=0 (int8) | int8 | 1,000 | 0.0020 | 0.0007 | 2.79× | 36% | +|✅| np.amax axis=0 (int8) | int8 | 100,000 | 0.0076 | 0.0039 | 1.96× | 51% | +|✅| np.amax axis=0 (int8) | int8 | 10,000,000 | 0.3964 | 0.1724 | 2.30× | 44% | +|▫| np.amax axis=0 (uint16) | uint16 | 1,000 | 0.0020 | 0.0007 | 2.97× | 34% | +|✅| np.amax axis=0 (uint16) | uint16 | 100,000 | 0.0065 | 0.0036 | 1.79× | 56% | +|✅| np.amax axis=0 (uint16) | uint16 | 10,000,000 | 0.4804 | 0.3808 | 1.26× | 79% | +|▫| np.amax axis=0 (uint32) | uint32 | 1,000 | 0.0019 | 0.0007 | 2.81× | 36% | +|✅| np.amax axis=0 (uint32) | uint32 | 100,000 | 0.0082 | 0.0055 | 1.49× | 67% | +|✅| np.amax axis=0 (uint32) | uint32 | 10,000,000 | 1.6177 | 1.2662 | 1.28× | 78% | +|▫| np.amax axis=0 (uint64) | uint64 | 1,000 | 0.0020 | 0.0009 | 2.24× | 45% | +|✅| np.amax axis=0 (uint64) | uint64 | 100,000 | 0.0185 | 0.0138 | 1.34× | 74% | +|✅| np.amax axis=0 (uint64) | uint64 | 10,000,000 | 4.3907 | 3.8229 | 1.15× | 87% | +|▫| np.amax axis=0 (uint8) | uint8 | 1,000 | 0.0019 | 0.0007 | 2.85× | 35% | +|✅| np.amax axis=0 (uint8) | uint8 | 100,000 | 0.0081 | 0.0031 | 2.65× | 38% | +|✅| np.amax axis=0 (uint8) | uint8 | 10,000,000 | 0.2207 | 0.1718 | 1.28× | 78% | +|✅| np.amin (complex128) | complex128 | 1,000 | 0.0031 | 0.0016 | 1.93× | 52% | +|✅| np.amin (complex128) | complex128 | 100,000 | 0.1527 | 0.1141 | 1.34× | 75% | +|✅| np.amin (complex128) | complex128 | 10,000,000 | 16.4378 | 13.5639 | 1.21× | 82% | +|✅| np.amin (float16) | float16 | 1,000 | 0.0041 | 0.0012 | 3.53× | 28% | +|✅| np.amin (float16) | float16 | 100,000 | 0.5120 | 0.3020 | 1.70× | 59% | +|✅| np.amin (float16) | float16 | 10,000,000 | 51.8815 | 31.4161 | 1.65× | 61% | +|▫| np.amin (float32) | float32 | 1,000 | 0.0023 | 0.0008 | 2.97× | 34% | +|✅| np.amin (float32) | float32 | 100,000 | 0.0058 | 0.0035 | 1.68× | 60% | +|✅| np.amin (float32) | float32 | 10,000,000 | 1.4447 | 1.3328 | 1.08× | 92% | +|▫| np.amin (float64) | float64 | 1,000 | 0.0017 | 0.0008 | 2.21× | 45% | +|✅| np.amin (float64) | float64 | 100,000 | 0.0104 | 0.0073 | 1.42× | 70% | +|✅| np.amin (float64) | float64 | 10,000,000 | 3.3498 | 3.2187 | 1.04× | 96% | +|▫| np.amin (int16) | int16 | 1,000 | 0.0016 | 0.0007 | 2.27× | 44% | +|✅| np.amin (int16) | int16 | 100,000 | 0.0035 | 0.0015 | 2.34× | 43% | +|✅| np.amin (int16) | int16 | 10,000,000 | 0.7445 | 0.3330 | 2.24× | 45% | +|▫| np.amin (int32) | int32 | 1,000 | 0.0016 | 0.0007 | 2.25× | 44% | +|✅| np.amin (int32) | int32 | 100,000 | 0.0045 | 0.0023 | 1.98× | 50% | +|✅| np.amin (int32) | int32 | 10,000,000 | 1.2150 | 1.0174 | 1.19× | 84% | +|▫| np.amin (int64) | int64 | 1,000 | 0.0017 | 0.0008 | 2.13× | 47% | +|✅| np.amin (int64) | int64 | 100,000 | 0.0106 | 0.0074 | 1.44× | 70% | +|🟡| np.amin (int64) | int64 | 10,000,000 | 3.3282 | 3.5000 | 0.95× | 105% | +|▫| np.amin (int8) | int8 | 1,000 | 0.0017 | 0.0007 | 2.30× | 44% | +|✅| np.amin (int8) | int8 | 100,000 | 0.0028 | 0.0012 | 2.38× | 42% | +|🟡| np.amin (int8) | int8 | 10,000,000 | 0.1435 | 0.1467 | 0.98× | 102% | +|▫| np.amin (uint16) | uint16 | 1,000 | 0.0016 | 0.0007 | 2.16× | 46% | +|✅| np.amin (uint16) | uint16 | 100,000 | 0.0033 | 0.0015 | 2.21× | 45% | +|🟡| np.amin (uint16) | uint16 | 10,000,000 | 0.3225 | 0.3309 | 0.97× | 103% | +|▫| np.amin (uint32) | uint32 | 1,000 | 0.0016 | 0.0007 | 2.30× | 44% | +|✅| np.amin (uint32) | uint32 | 100,000 | 0.0044 | 0.0032 | 1.36× | 74% | +|✅| np.amin (uint32) | uint32 | 10,000,000 | 1.0859 | 1.0190 | 1.07× | 94% | +|▫| np.amin (uint64) | uint64 | 1,000 | 0.0017 | 0.0008 | 2.12× | 47% | +|✅| np.amin (uint64) | uint64 | 100,000 | 0.0126 | 0.0097 | 1.29× | 77% | +|🟡| np.amin (uint64) | uint64 | 10,000,000 | 3.6037 | 3.7516 | 0.96× | 104% | +|▫| np.amin (uint8) | uint8 | 1,000 | 0.0016 | 0.0007 | 2.26× | 44% | +|✅| np.amin (uint8) | uint8 | 100,000 | 0.0028 | 0.0012 | 2.41× | 42% | +|🟡| np.amin (uint8) | uint8 | 10,000,000 | 0.1350 | 0.1465 | 0.92× | 108% | +|🟡| np.amin axis=0 (complex128) | complex128 | 1,000 | 0.0029 | 0.0030 | 0.99× | 101% | +|🟡| np.amin axis=0 (complex128) | complex128 | 100,000 | 0.1409 | 0.1480 | 0.95× | 105% | +|✅| np.amin axis=0 (complex128) | complex128 | 10,000,000 | 15.7182 | 15.2730 | 1.03× | 97% | +|✅| np.amin axis=0 (float16) | float16 | 1,000 | 0.0040 | 0.0019 | 2.11× | 47% | +|🟡| np.amin axis=0 (float16) | float16 | 100,000 | 0.4771 | 0.5006 | 0.95× | 105% | +|🟡| np.amin axis=0 (float16) | float16 | 10,000,000 | 47.3591 | 77.6081 | 0.61× | 164% | +|▫| np.amin axis=0 (float32) | float32 | 1,000 | 0.0021 | 0.0008 | 2.67× | 37% | +|✅| np.amin axis=0 (float32) | float32 | 100,000 | 0.0116 | 0.0098 | 1.18× | 85% | +|🟡| np.amin axis=0 (float32) | float32 | 10,000,000 | 1.7252 | 1.8255 | 0.94× | 106% | +|▫| np.amin axis=0 (float64) | float64 | 1,000 | 0.0021 | 0.0009 | 2.41× | 42% | +|🟡| np.amin axis=0 (float64) | float64 | 100,000 | 0.0157 | 0.0165 | 0.95× | 105% | +|🟡| np.amin axis=0 (float64) | float64 | 10,000,000 | 4.0124 | 4.1873 | 0.96× | 104% | +|▫| np.amin axis=0 (int16) | int16 | 1,000 | 0.0020 | 0.0007 | 2.90× | 34% | +|✅| np.amin axis=0 (int16) | int16 | 100,000 | 0.0065 | 0.0037 | 1.74× | 58% | +|✅| np.amin axis=0 (int16) | int16 | 10,000,000 | 0.5065 | 0.3754 | 1.35× | 74% | +|▫| np.amin axis=0 (int32) | int32 | 1,000 | 0.0023 | 0.0007 | 3.32× | 30% | +|✅| np.amin axis=0 (int32) | int32 | 100,000 | 0.0088 | 0.0055 | 1.60× | 62% | +|✅| np.amin axis=0 (int32) | int32 | 10,000,000 | 2.0366 | 1.2825 | 1.59× | 63% | +|▫| np.amin axis=0 (int64) | int64 | 1,000 | 0.0027 | 0.0008 | 3.32× | 30% | +|✅| np.amin axis=0 (int64) | int64 | 100,000 | 0.0193 | 0.0118 | 1.63× | 61% | +|✅| np.amin axis=0 (int64) | int64 | 10,000,000 | 4.3888 | 3.6438 | 1.20× | 83% | +|▫| np.amin axis=0 (int8) | int8 | 1,000 | 0.0019 | 0.0006 | 2.99× | 33% | +|✅| np.amin axis=0 (int8) | int8 | 100,000 | 0.0077 | 0.0041 | 1.88× | 53% | +|✅| np.amin axis=0 (int8) | int8 | 10,000,000 | 0.2767 | 0.1726 | 1.60× | 62% | +|▫| np.amin axis=0 (uint16) | uint16 | 1,000 | 0.0020 | 0.0007 | 2.98× | 34% | +|✅| np.amin axis=0 (uint16) | uint16 | 100,000 | 0.0065 | 0.0036 | 1.79× | 56% | +|✅| np.amin axis=0 (uint16) | uint16 | 10,000,000 | 0.4421 | 0.3803 | 1.16× | 86% | +|▫| np.amin axis=0 (uint32) | uint32 | 1,000 | 0.0019 | 0.0007 | 2.77× | 36% | +|✅| np.amin axis=0 (uint32) | uint32 | 100,000 | 0.0084 | 0.0055 | 1.53× | 65% | +|✅| np.amin axis=0 (uint32) | uint32 | 10,000,000 | 1.8904 | 1.2875 | 1.47× | 68% | +|▫| np.amin axis=0 (uint64) | uint64 | 1,000 | 0.0020 | 0.0008 | 2.38× | 42% | +|✅| np.amin axis=0 (uint64) | uint64 | 100,000 | 0.0177 | 0.0137 | 1.29× | 77% | +|✅| np.amin axis=0 (uint64) | uint64 | 10,000,000 | 4.4047 | 3.8707 | 1.14× | 88% | +|▫| np.amin axis=0 (uint8) | uint8 | 1,000 | 0.0019 | 0.0007 | 2.86× | 35% | +|✅| np.amin axis=0 (uint8) | uint8 | 100,000 | 0.0076 | 0.0051 | 1.49× | 67% | +|✅| np.amin axis=0 (uint8) | uint8 | 10,000,000 | 0.2090 | 0.1721 | 1.22× | 82% | +|✅| np.argmax (complex128) | complex128 | 1,000 | 0.0020 | 0.0018 | 1.14× | 88% | +|🟡| np.argmax (complex128) | complex128 | 100,000 | 0.1132 | 0.1141 | 0.99× | 101% | +|✅| np.argmax (complex128) | complex128 | 10,000,000 | 14.1968 | 13.5590 | 1.05× | 96% | +|✅| np.argmax (float16) | float16 | 1,000 | 0.0029 | 0.0021 | 1.41× | 71% | +|✅| np.argmax (float16) | float16 | 100,000 | 0.4331 | 0.1412 | 3.07× | 33% | +|✅| np.argmax (float16) | float16 | 10,000,000 | 43.8538 | 14.1652 | 3.10× | 32% | +|▫| np.argmax (float32) | float32 | 1,000 | 0.0009 | 0.0012 | 0.78× | 129% | +|🔴| np.argmax (float32) | float32 | 100,000 | 0.0087 | 0.0564 | 0.15× | 648% | +|🟠| np.argmax (float32) | float32 | 10,000,000 | 2.3137 | 5.7594 | 0.40× | 249% | +|▫| np.argmax (float64) | float64 | 1,000 | 0.0010 | 0.0012 | 0.82× | 122% | +|🟠| np.argmax (float64) | float64 | 100,000 | 0.0193 | 0.0568 | 0.34× | 294% | +|🟡| np.argmax (float64) | float64 | 10,000,000 | 4.1180 | 6.8003 | 0.61× | 165% | +|▫| np.argmax (int16) | int16 | 1,000 | 0.0009 | 0.0007 | 1.23× | 82% | +|✅| np.argmax (int16) | int16 | 100,000 | 0.0034 | 0.0017 | 2.02× | 50% | +|✅| np.argmax (int16) | int16 | 10,000,000 | 0.6951 | 0.3548 | 1.96× | 51% | +|▫| np.argmax (int32) | int32 | 1,000 | 0.0009 | 0.0007 | 1.23× | 82% | +|✅| np.argmax (int32) | int32 | 100,000 | 0.0056 | 0.0027 | 2.10× | 48% | +|✅| np.argmax (int32) | int32 | 10,000,000 | 1.8979 | 1.2026 | 1.58× | 63% | +|▫| np.argmax (int64) | int64 | 1,000 | 0.0010 | 0.0009 | 1.11× | 90% | +|🟡| np.argmax (int64) | int64 | 100,000 | 0.0146 | 0.0284 | 0.51× | 195% | +|🟡| np.argmax (int64) | int64 | 10,000,000 | 4.1056 | 4.5513 | 0.90× | 111% | +|▫| np.argmax (int8) | int8 | 1,000 | 0.0009 | 0.0007 | 1.25× | 80% | +|✅| np.argmax (int8) | int8 | 100,000 | 0.0023 | 0.0012 | 1.88× | 53% | +|✅| np.argmax (int8) | int8 | 10,000,000 | 0.1717 | 0.1469 | 1.17× | 86% | +|▫| np.argmax (uint16) | uint16 | 1,000 | 0.0009 | 0.0007 | 1.27× | 79% | +|✅| np.argmax (uint16) | uint16 | 100,000 | 0.0049 | 0.0017 | 2.96× | 34% | +|✅| np.argmax (uint16) | uint16 | 10,000,000 | 0.5085 | 0.3549 | 1.43× | 70% | +|▫| np.argmax (uint32) | uint32 | 1,000 | 0.0009 | 0.0007 | 1.30× | 77% | +|✅| np.argmax (uint32) | uint32 | 100,000 | 0.0104 | 0.0036 | 2.91× | 34% | +|✅| np.argmax (uint32) | uint32 | 10,000,000 | 1.8851 | 1.2169 | 1.55× | 65% | +|▫| np.argmax (uint64) | uint64 | 1,000 | 0.0010 | 0.0009 | 1.04× | 96% | +|🟡| np.argmax (uint64) | uint64 | 100,000 | 0.0171 | 0.0332 | 0.52× | 194% | +|🟡| np.argmax (uint64) | uint64 | 10,000,000 | 4.3878 | 4.9463 | 0.89× | 113% | +|▫| np.argmax (uint8) | uint8 | 1,000 | 0.0013 | 0.0007 | 1.95× | 51% | +|✅| np.argmax (uint8) | uint8 | 100,000 | 0.0032 | 0.0012 | 2.60× | 38% | +|✅| np.argmax (uint8) | uint8 | 10,000,000 | 0.2246 | 0.1465 | 1.53× | 65% | +|✅| np.argmin (complex128) | complex128 | 1,000 | 0.0019 | 0.0015 | 1.25× | 80% | +|🟡| np.argmin (complex128) | complex128 | 100,000 | 0.1139 | 0.1141 | 1.00× | 100% | +|✅| np.argmin (complex128) | complex128 | 10,000,000 | 13.8711 | 13.6034 | 1.02× | 98% | +|✅| np.argmin (float16) | float16 | 1,000 | 0.0029 | 0.0020 | 1.42× | 70% | +|✅| np.argmin (float16) | float16 | 100,000 | 0.4248 | 0.1413 | 3.01× | 33% | +|✅| np.argmin (float16) | float16 | 10,000,000 | 42.5861 | 14.1619 | 3.01× | 33% | +|▫| np.argmin (float32) | float32 | 1,000 | 0.0009 | 0.0012 | 0.80× | 125% | +|🔴| np.argmin (float32) | float32 | 100,000 | 0.0087 | 0.0564 | 0.15× | 650% | +|🟠| np.argmin (float32) | float32 | 10,000,000 | 2.7401 | 5.7656 | 0.47× | 210% | +|▫| np.argmin (float64) | float64 | 1,000 | 0.0010 | 0.0012 | 0.83× | 121% | +|🟠| np.argmin (float64) | float64 | 100,000 | 0.0276 | 0.0569 | 0.48× | 206% | +|🟡| np.argmin (float64) | float64 | 10,000,000 | 4.2847 | 6.7686 | 0.63× | 158% | +|▫| np.argmin (int16) | int16 | 1,000 | 0.0011 | 0.0007 | 1.52× | 66% | +|✅| np.argmin (int16) | int16 | 100,000 | 0.0034 | 0.0016 | 2.06× | 48% | +|✅| np.argmin (int16) | int16 | 10,000,000 | 0.7167 | 0.3613 | 1.98× | 50% | +|▫| np.argmin (int32) | int32 | 1,000 | 0.0010 | 0.0007 | 1.38× | 73% | +|✅| np.argmin (int32) | int32 | 100,000 | 0.0055 | 0.0026 | 2.09× | 48% | +|✅| np.argmin (int32) | int32 | 10,000,000 | 1.7646 | 1.1707 | 1.51× | 66% | +|▫| np.argmin (int64) | int64 | 1,000 | 0.0010 | 0.0009 | 1.11× | 90% | +|🟡| np.argmin (int64) | int64 | 100,000 | 0.0142 | 0.0283 | 0.50× | 199% | +|🟡| np.argmin (int64) | int64 | 10,000,000 | 4.0644 | 4.5666 | 0.89× | 112% | +|▫| np.argmin (int8) | int8 | 1,000 | 0.0009 | 0.0007 | 1.28× | 78% | +|✅| np.argmin (int8) | int8 | 100,000 | 0.0023 | 0.0012 | 1.86× | 54% | +|✅| np.argmin (int8) | int8 | 10,000,000 | 0.1626 | 0.1468 | 1.11× | 90% | +|▫| np.argmin (uint16) | uint16 | 1,000 | 0.0009 | 0.0007 | 1.32× | 76% | +|✅| np.argmin (uint16) | uint16 | 100,000 | 0.0050 | 0.0016 | 3.04× | 33% | +|✅| np.argmin (uint16) | uint16 | 10,000,000 | 0.5632 | 0.3583 | 1.57× | 64% | +|▫| np.argmin (uint32) | uint32 | 1,000 | 0.0010 | 0.0007 | 1.36× | 73% | +|✅| np.argmin (uint32) | uint32 | 100,000 | 0.0101 | 0.0035 | 2.86× | 35% | +|✅| np.argmin (uint32) | uint32 | 10,000,000 | 1.8480 | 1.1772 | 1.57× | 64% | +|▫| np.argmin (uint64) | uint64 | 1,000 | 0.0010 | 0.0009 | 1.03× | 97% | +|🟡| np.argmin (uint64) | uint64 | 100,000 | 0.0180 | 0.0331 | 0.54× | 184% | +|🟡| np.argmin (uint64) | uint64 | 10,000,000 | 4.2831 | 4.9246 | 0.87× | 115% | +|▫| np.argmin (uint8) | uint8 | 1,000 | 0.0009 | 0.0007 | 1.28× | 78% | +|✅| np.argmin (uint8) | uint8 | 100,000 | 0.0033 | 0.0012 | 2.70× | 37% | +|✅| np.argmin (uint8) | uint8 | 10,000,000 | 0.2237 | 0.1465 | 1.53× | 66% | +|🟡| np.cumprod(a) (float16) | float16 | 1,000 | 0.0061 | 0.0097 | 0.63× | 159% | +|🟠| np.cumprod(a) (float16) | float16 | 100,000 | 0.4103 | 0.9506 | 0.43× | 232% | +|🟠| np.cumprod(a) (float16) | float16 | 10,000,000 | 43.5457 | 94.2260 | 0.46× | 216% | +|✅| np.cumprod(a) (float32) | float32 | 1,000 | 0.0046 | 0.0028 | 1.65× | 61% | +|✅| np.cumprod(a) (float32) | float32 | 100,000 | 0.1700 | 0.1114 | 1.53× | 66% | +|✅| np.cumprod(a) (float32) | float32 | 10,000,000 | 20.8437 | 10.1707 | 2.05× | 49% | +|✅| np.cumprod(a) (float64) | float64 | 1,000 | 0.0046 | 0.0034 | 1.33× | 75% | +|✅| np.cumprod(a) (float64) | float64 | 100,000 | 0.1694 | 0.1446 | 1.17× | 85% | +|✅| np.cumprod(a) (float64) | float64 | 10,000,000 | 25.4983 | 14.0728 | 1.81× | 55% | +|🟡| np.cumsum (complex128) | complex128 | 1,000 | 0.0030 | 0.0037 | 0.82× | 122% | +|✅| np.cumsum (complex128) | complex128 | 100,000 | 0.3769 | 0.2333 | 1.61× | 62% | +|✅| np.cumsum (complex128) | complex128 | 10,000,000 | 41.3487 | 24.9780 | 1.66× | 60% | +|🟡| np.cumsum (float16) | float16 | 1,000 | 0.0071 | 0.0094 | 0.75× | 134% | +|🟡| np.cumsum (float16) | float16 | 100,000 | 0.4728 | 0.9144 | 0.52× | 193% | +|🟡| np.cumsum (float16) | float16 | 10,000,000 | 49.3071 | 90.7463 | 0.54× | 184% | +|✅| np.cumsum (float32) | float32 | 1,000 | 0.0029 | 0.0018 | 1.65× | 61% | +|✅| np.cumsum (float32) | float32 | 100,000 | 0.1656 | 0.0789 | 2.10× | 48% | +|✅| np.cumsum (float32) | float32 | 10,000,000 | 20.1855 | 7.3471 | 2.75× | 36% | +|✅| np.cumsum (float64) | float64 | 1,000 | 0.0028 | 0.0022 | 1.31× | 76% | +|✅| np.cumsum (float64) | float64 | 100,000 | 0.1698 | 0.1249 | 1.36× | 74% | +|✅| np.cumsum (float64) | float64 | 10,000,000 | 24.6822 | 13.3431 | 1.85× | 54% | +|✅| np.cumsum (int16) | int16 | 1,000 | 0.0025 | 0.0020 | 1.23× | 81% | +|✅| np.cumsum (int16) | int16 | 100,000 | 0.3253 | 0.1209 | 2.69× | 37% | +|✅| np.cumsum (int16) | int16 | 10,000,000 | 48.4266 | 10.6129 | 4.56× | 22% | +|✅| np.cumsum (int32) | int32 | 1,000 | 0.0024 | 0.0019 | 1.27× | 79% | +|✅| np.cumsum (int32) | int32 | 100,000 | 0.3303 | 0.1226 | 2.69× | 37% | +|✅| np.cumsum (int32) | int32 | 10,000,000 | 32.3880 | 11.1306 | 2.91× | 34% | +|🟡| np.cumsum (int64) | int64 | 1,000 | 0.0017 | 0.0019 | 0.92× | 109% | +|🟠| np.cumsum (int64) | int64 | 100,000 | 0.0315 | 0.1231 | 0.26× | 391% | +|✅| np.cumsum (int64) | int64 | 10,000,000 | 16.3798 | 12.7464 | 1.28× | 78% | +|✅| np.cumsum (int8) | int8 | 1,000 | 0.0025 | 0.0017 | 1.49× | 67% | +|✅| np.cumsum (int8) | int8 | 100,000 | 0.3124 | 0.1187 | 2.63× | 38% | +|✅| np.cumsum (int8) | int8 | 10,000,000 | 41.0555 | 10.2543 | 4.00× | 25% | +|✅| np.cumsum (uint16) | uint16 | 1,000 | 0.0026 | 0.0020 | 1.26× | 80% | +|✅| np.cumsum (uint16) | uint16 | 100,000 | 0.3256 | 0.1199 | 2.72× | 37% | +|✅| np.cumsum (uint16) | uint16 | 10,000,000 | 34.5917 | 10.5051 | 3.29× | 30% | +|✅| np.cumsum (uint32) | uint32 | 1,000 | 0.0024 | 0.0021 | 1.17× | 85% | +|✅| np.cumsum (uint32) | uint32 | 100,000 | 0.3374 | 0.1227 | 2.75× | 36% | +|✅| np.cumsum (uint32) | uint32 | 10,000,000 | 29.7223 | 11.0895 | 2.68× | 37% | +|🟡| np.cumsum (uint64) | uint64 | 1,000 | 0.0017 | 0.0020 | 0.82× | 121% | +|🟠| np.cumsum (uint64) | uint64 | 100,000 | 0.0312 | 0.1218 | 0.26× | 391% | +|✅| np.cumsum (uint64) | uint64 | 10,000,000 | 16.2415 | 12.7570 | 1.27× | 78% | +|✅| np.cumsum (uint8) | uint8 | 1,000 | 0.0025 | 0.0019 | 1.27× | 79% | +|✅| np.cumsum (uint8) | uint8 | 100,000 | 0.3198 | 0.1205 | 2.65× | 38% | +|✅| np.cumsum (uint8) | uint8 | 10,000,000 | 28.9566 | 10.2799 | 2.82× | 36% | +|▫| np.mean (complex128) | complex128 | 1,000 | 0.0027 | 0.0009 | 2.85× | 35% | +|✅| np.mean (complex128) | complex128 | 100,000 | 0.0305 | 0.0101 | 3.01× | 33% | +|✅| np.mean (complex128) | complex128 | 10,000,000 | 8.5581 | 6.4565 | 1.33× | 75% | +|✅| np.mean (float16) | float16 | 1,000 | 0.0047 | 0.0012 | 3.86× | 26% | +|✅| np.mean (float16) | float16 | 100,000 | 0.1088 | 0.0802 | 1.36× | 74% | +|✅| np.mean (float16) | float16 | 10,000,000 | 10.4935 | 8.0190 | 1.31× | 76% | +|▫| np.mean (float32) | float32 | 1,000 | 0.0040 | 0.0007 | 5.42× | 18% | +|✅| np.mean (float32) | float32 | 100,000 | 0.0178 | 0.0032 | 5.55× | 18% | +|✅| np.mean (float32) | float32 | 10,000,000 | 3.6083 | 1.0039 | 3.59× | 28% | +|▫| np.mean (float64) | float64 | 1,000 | 0.0024 | 0.0007 | 3.25× | 31% | +|✅| np.mean (float64) | float64 | 100,000 | 0.0169 | 0.0040 | 4.21× | 24% | +|✅| np.mean (float64) | float64 | 10,000,000 | 4.6544 | 2.7943 | 1.67× | 60% | +|▫| np.mean (int16) | int16 | 1,000 | 0.0031 | 0.0008 | 3.92× | 26% | +|✅| np.mean (int16) | int16 | 100,000 | 0.0532 | 0.0189 | 2.81× | 36% | +|✅| np.mean (int16) | int16 | 10,000,000 | 6.3750 | 1.9609 | 3.25× | 31% | +|▫| np.mean (int32) | int32 | 1,000 | 0.0030 | 0.0008 | 3.71× | 27% | +|✅| np.mean (int32) | int32 | 100,000 | 0.0429 | 0.0191 | 2.24× | 45% | +|✅| np.mean (int32) | int32 | 10,000,000 | 4.9575 | 2.6752 | 1.85× | 54% | +|▫| np.mean (int64) | int64 | 1,000 | 0.0029 | 0.0008 | 3.71× | 27% | +|✅| np.mean (int64) | int64 | 100,000 | 0.0344 | 0.0045 | 7.72× | 13% | +|✅| np.mean (int64) | int64 | 10,000,000 | 6.3414 | 2.7730 | 2.29× | 44% | +|▫| np.mean (int8) | int8 | 1,000 | 0.0030 | 0.0008 | 3.58× | 28% | +|✅| np.mean (int8) | int8 | 100,000 | 0.0567 | 0.0186 | 3.04× | 33% | +|✅| np.mean (int8) | int8 | 10,000,000 | 5.6945 | 1.8461 | 3.08× | 32% | +|▫| np.mean (uint16) | uint16 | 1,000 | 0.0031 | 0.0008 | 3.90× | 26% | +|✅| np.mean (uint16) | uint16 | 100,000 | 0.0526 | 0.0189 | 2.78× | 36% | +|✅| np.mean (uint16) | uint16 | 10,000,000 | 5.1914 | 1.9498 | 2.66× | 38% | +|▫| np.mean (uint32) | uint32 | 1,000 | 0.0030 | 0.0008 | 3.70× | 27% | +|✅| np.mean (uint32) | uint32 | 100,000 | 0.0402 | 0.0191 | 2.11× | 47% | +|✅| np.mean (uint32) | uint32 | 10,000,000 | 4.6035 | 2.6686 | 1.73× | 58% | +|▫| np.mean (uint64) | uint64 | 1,000 | 0.0032 | 0.0008 | 4.16× | 24% | +|✅| np.mean (uint64) | uint64 | 100,000 | 0.0539 | 0.0045 | 12.08× | 8% | +|✅| np.mean (uint64) | uint64 | 10,000,000 | 7.3769 | 2.7725 | 2.66× | 38% | +|▫| np.mean (uint8) | uint8 | 1,000 | 0.0031 | 0.0008 | 3.81× | 26% | +|✅| np.mean (uint8) | uint8 | 100,000 | 0.0541 | 0.0187 | 2.90× | 34% | +|✅| np.mean (uint8) | uint8 | 10,000,000 | 5.0561 | 1.8459 | 2.74× | 36% | +|✅| np.mean axis=0 (complex128) | complex128 | 1,000 | 0.0033 | 0.0025 | 1.30× | 77% | +|✅| np.mean axis=0 (complex128) | complex128 | 100,000 | 0.0286 | 0.0241 | 1.19× | 84% | +|✅| np.mean axis=0 (complex128) | complex128 | 10,000,000 | 7.5298 | 7.0381 | 1.07× | 94% | +|✅| np.mean axis=0 (float16) | float16 | 1,000 | 0.0061 | 0.0035 | 1.75× | 57% | +|🟡| np.mean axis=0 (float16) | float16 | 100,000 | 0.0790 | 0.1492 | 0.53× | 189% | +|🟡| np.mean axis=0 (float16) | float16 | 10,000,000 | 7.3133 | 14.1963 | 0.52× | 194% | +|✅| np.mean axis=0 (float32) | float32 | 1,000 | 0.0039 | 0.0022 | 1.82× | 55% | +|🟡| np.mean axis=0 (float32) | float32 | 100,000 | 0.0086 | 0.0099 | 0.87× | 115% | +|✅| np.mean axis=0 (float32) | float32 | 10,000,000 | 1.4901 | 1.3911 | 1.07× | 93% | +|✅| np.mean axis=0 (float64) | float64 | 1,000 | 0.0030 | 0.0023 | 1.29× | 78% | +|✅| np.mean axis=0 (float64) | float64 | 100,000 | 0.0166 | 0.0148 | 1.12× | 89% | +|🟡| np.mean axis=0 (float64) | float64 | 10,000,000 | 3.3836 | 3.4775 | 0.97× | 103% | +|▫| np.mean axis=0 (int16) | int16 | 1,000 | 0.0038 | 0.0008 | 4.83× | 21% | +|✅| np.mean axis=0 (int16) | int16 | 100,000 | 0.0550 | 0.0084 | 6.53× | 15% | +|✅| np.mean axis=0 (int16) | int16 | 10,000,000 | 5.3822 | 0.9630 | 5.59× | 18% | +|▫| np.mean axis=0 (int32) | int32 | 1,000 | 0.0044 | 0.0008 | 5.78× | 17% | +|✅| np.mean axis=0 (int32) | int32 | 100,000 | 0.0359 | 0.0078 | 4.60× | 22% | +|✅| np.mean axis=0 (int32) | int32 | 10,000,000 | 4.5299 | 1.7225 | 2.63× | 38% | +|✅| np.mean axis=0 (int64) | int64 | 1,000 | 0.0043 | 0.0013 | 3.25× | 31% | +|🟡| np.mean axis=0 (int64) | int64 | 100,000 | 0.0320 | 0.0609 | 0.53× | 190% | +|🔴| np.mean axis=0 (int64) | int64 | 10,000,000 | 5.9854 | 37.4022 | 0.16× | 625% | +|▫| np.mean axis=0 (int8) | int8 | 1,000 | 0.0038 | 0.0008 | 4.56× | 22% | +|✅| np.mean axis=0 (int8) | int8 | 100,000 | 0.0534 | 0.0082 | 6.49× | 15% | +|✅| np.mean axis=0 (int8) | int8 | 10,000,000 | 6.4497 | 0.8079 | 7.98× | 12% | +|▫| np.mean axis=0 (uint16) | uint16 | 1,000 | 0.0037 | 0.0007 | 5.00× | 20% | +|✅| np.mean axis=0 (uint16) | uint16 | 100,000 | 0.0516 | 0.0084 | 6.10× | 16% | +|✅| np.mean axis=0 (uint16) | uint16 | 10,000,000 | 5.8879 | 0.9392 | 6.27× | 16% | +|▫| np.mean axis=0 (uint32) | uint32 | 1,000 | 0.0044 | 0.0008 | 5.64× | 18% | +|✅| np.mean axis=0 (uint32) | uint32 | 100,000 | 0.0471 | 0.0093 | 5.04× | 20% | +|✅| np.mean axis=0 (uint32) | uint32 | 10,000,000 | 4.9815 | 2.0165 | 2.47× | 40% | +|✅| np.mean axis=0 (uint64) | uint64 | 1,000 | 0.0037 | 0.0017 | 2.20× | 46% | +|🟡| np.mean axis=0 (uint64) | uint64 | 100,000 | 0.0479 | 0.0887 | 0.54× | 185% | +|🔴| np.mean axis=0 (uint64) | uint64 | 10,000,000 | 7.1495 | 43.0435 | 0.17× | 602% | +|▫| np.mean axis=0 (uint8) | uint8 | 1,000 | 0.0039 | 0.0007 | 5.20× | 19% | +|✅| np.mean axis=0 (uint8) | uint8 | 100,000 | 0.0545 | 0.0082 | 6.63× | 15% | +|✅| np.mean axis=0 (uint8) | uint8 | 10,000,000 | 5.3637 | 0.7883 | 6.80× | 15% | +|✅| np.mean axis=1 (complex128) | complex128 | 1,000 | 0.0032 | 0.0025 | 1.24× | 81% | +|✅| np.mean axis=1 (complex128) | complex128 | 100,000 | 0.0443 | 0.0348 | 1.27× | 79% | +|✅| np.mean axis=1 (complex128) | complex128 | 10,000,000 | 8.4055 | 8.0660 | 1.04× | 96% | +|✅| np.mean axis=1 (float16) | float16 | 1,000 | 0.0052 | 0.0039 | 1.31× | 76% | +|🟡| np.mean axis=1 (float16) | float16 | 100,000 | 0.0952 | 0.1325 | 0.72× | 139% | +|🟡| np.mean axis=1 (float16) | float16 | 10,000,000 | 7.9626 | 12.5326 | 0.64× | 157% | +|✅| np.mean axis=1 (float32) | float32 | 1,000 | 0.0036 | 0.0022 | 1.67× | 60% | +|✅| np.mean axis=1 (float32) | float32 | 100,000 | 0.0183 | 0.0127 | 1.45× | 69% | +|✅| np.mean axis=1 (float32) | float32 | 10,000,000 | 2.9697 | 1.9066 | 1.56× | 64% | +|✅| np.mean axis=1 (float64) | float64 | 1,000 | 0.0029 | 0.0022 | 1.33× | 75% | +|✅| np.mean axis=1 (float64) | float64 | 100,000 | 0.0198 | 0.0144 | 1.37× | 73% | +|✅| np.mean axis=1 (float64) | float64 | 10,000,000 | 5.1086 | 3.6854 | 1.39× | 72% | +|▫| np.mean axis=1 (int16) | int16 | 1,000 | 0.0037 | 0.0008 | 4.55× | 22% | +|✅| np.mean axis=1 (int16) | int16 | 100,000 | 0.0573 | 0.0080 | 7.19× | 14% | +|✅| np.mean axis=1 (int16) | int16 | 10,000,000 | 6.0592 | 0.8322 | 7.28× | 14% | +|▫| np.mean axis=1 (int32) | int32 | 1,000 | 0.0037 | 0.0007 | 5.00× | 20% | +|✅| np.mean axis=1 (int32) | int32 | 100,000 | 0.0428 | 0.0049 | 8.79× | 11% | +|✅| np.mean axis=1 (int32) | int32 | 10,000,000 | 4.8185 | 1.4394 | 3.35× | 30% | +|✅| np.mean axis=1 (int64) | int64 | 1,000 | 0.0034 | 0.0013 | 2.55× | 39% | +|🟡| np.mean axis=1 (int64) | int64 | 100,000 | 0.0394 | 0.0596 | 0.66× | 151% | +|🟡| np.mean axis=1 (int64) | int64 | 10,000,000 | 6.3625 | 6.9185 | 0.92× | 109% | +|▫| np.mean axis=1 (int8) | int8 | 1,000 | 0.0036 | 0.0008 | 4.50× | 22% | +|✅| np.mean axis=1 (int8) | int8 | 100,000 | 0.0571 | 0.0079 | 7.21× | 14% | +|✅| np.mean axis=1 (int8) | int8 | 10,000,000 | 5.5023 | 0.7110 | 7.74× | 13% | +|▫| np.mean axis=1 (uint16) | uint16 | 1,000 | 0.0038 | 0.0008 | 4.68× | 21% | +|✅| np.mean axis=1 (uint16) | uint16 | 100,000 | 0.0629 | 0.0080 | 7.90× | 13% | +|✅| np.mean axis=1 (uint16) | uint16 | 10,000,000 | 5.4615 | 0.8406 | 6.50× | 15% | +|▫| np.mean axis=1 (uint32) | uint32 | 1,000 | 0.0034 | 0.0008 | 4.12× | 24% | +|✅| np.mean axis=1 (uint32) | uint32 | 100,000 | 0.0467 | 0.0091 | 5.12× | 20% | +|✅| np.mean axis=1 (uint32) | uint32 | 10,000,000 | 4.9555 | 1.8041 | 2.75× | 36% | +|✅| np.mean axis=1 (uint64) | uint64 | 1,000 | 0.0037 | 0.0016 | 2.23× | 45% | +|🟡| np.mean axis=1 (uint64) | uint64 | 100,000 | 0.0569 | 0.0860 | 0.66× | 151% | +|🟡| np.mean axis=1 (uint64) | uint64 | 10,000,000 | 7.3817 | 9.0110 | 0.82× | 122% | +|▫| np.mean axis=1 (uint8) | uint8 | 1,000 | 0.0035 | 0.0008 | 4.50× | 22% | +|✅| np.mean axis=1 (uint8) | uint8 | 100,000 | 0.0587 | 0.0079 | 7.42× | 14% | +|✅| np.mean axis=1 (uint8) | uint8 | 10,000,000 | 5.2924 | 0.7077 | 7.48× | 13% | +|✅| np.nanmax(a) (float16) | float16 | 1,000 | 0.0054 | 0.0011 | 4.87× | 21% | +|✅| np.nanmax(a) (float16) | float16 | 100,000 | 0.5409 | 0.3172 | 1.71× | 59% | +|✅| np.nanmax(a) (float16) | float16 | 10,000,000 | 51.2440 | 32.5673 | 1.57× | 64% | +|▫| np.nanmax(a) (float32) | float32 | 1,000 | 0.0030 | 0.0009 | 3.29× | 30% | +|🟠| np.nanmax(a) (float32) | float32 | 100,000 | 0.0081 | 0.0287 | 0.28× | 356% | +|🟠| np.nanmax(a) (float32) | float32 | 10,000,000 | 1.5124 | 3.2798 | 0.46× | 217% | +|✅| np.nanmax(a) (float64) | float64 | 1,000 | 0.0030 | 0.0012 | 2.52× | 40% | +|🔴| np.nanmax(a) (float64) | float64 | 100,000 | 0.0115 | 0.0590 | 0.20× | 512% | +|🟡| np.nanmax(a) (float64) | float64 | 10,000,000 | 3.5081 | 6.8740 | 0.51× | 196% | +|✅| np.nanmean(a) (float16) | float16 | 1,000 | 0.0127 | 0.0017 | 7.37× | 14% | +|✅| np.nanmean(a) (float16) | float16 | 100,000 | 0.3723 | 0.1054 | 3.53× | 28% | +|✅| np.nanmean(a) (float16) | float16 | 10,000,000 | 37.8624 | 10.5382 | 3.59× | 28% | +|▫| np.nanmean(a) (float32) | float32 | 1,000 | 0.0102 | 0.0008 | 12.61× | 8% | +|✅| np.nanmean(a) (float32) | float32 | 100,000 | 0.0751 | 0.0383 | 1.96× | 51% | +|✅| np.nanmean(a) (float32) | float32 | 10,000,000 | 19.3182 | 4.1701 | 4.63× | 22% | +|✅| np.nanmean(a) (float64) | float64 | 1,000 | 0.0088 | 0.0012 | 7.49× | 13% | +|✅| np.nanmean(a) (float64) | float64 | 100,000 | 0.3101 | 0.0378 | 8.21× | 12% | +|✅| np.nanmean(a) (float64) | float64 | 10,000,000 | 29.7719 | 5.5602 | 5.35× | 19% | +|✅| np.nanmedian(a) (float16) | float16 | 1,000 | 0.0171 | 0.0043 | 3.93× | 26% | +|🟡| np.nanmedian(a) (float16) | float16 | 100,000 | 0.9889 | 1.3085 | 0.76× | 132% | +|✅| np.nanmedian(a) (float16) | float16 | 10,000,000 | 114.7082 | 92.8901 | 1.24× | 81% | +|✅| np.nanmedian(a) (float32) | float32 | 1,000 | 0.0132 | 0.0022 | 5.94× | 17% | +|🟡| np.nanmedian(a) (float32) | float32 | 100,000 | 0.4888 | 0.6965 | 0.70× | 142% | +|🟡| np.nanmedian(a) (float32) | float32 | 10,000,000 | 77.3793 | 80.5553 | 0.96× | 104% | +|✅| np.nanmedian(a) (float64) | float64 | 1,000 | 0.0127 | 0.0023 | 5.54× | 18% | +|🟡| np.nanmedian(a) (float64) | float64 | 100,000 | 0.5210 | 0.7022 | 0.74× | 135% | +|✅| np.nanmedian(a) (float64) | float64 | 10,000,000 | 92.4864 | 89.4458 | 1.03× | 97% | +|✅| np.nanmin(a) (float16) | float16 | 1,000 | 0.0053 | 0.0011 | 4.77× | 21% | +|✅| np.nanmin(a) (float16) | float16 | 100,000 | 0.5322 | 0.3019 | 1.76× | 57% | +|✅| np.nanmin(a) (float16) | float16 | 10,000,000 | 51.4324 | 31.1142 | 1.65× | 60% | +|▫| np.nanmin(a) (float32) | float32 | 1,000 | 0.0030 | 0.0009 | 3.24× | 31% | +|🟠| np.nanmin(a) (float32) | float32 | 100,000 | 0.0072 | 0.0287 | 0.25× | 400% | +|🟠| np.nanmin(a) (float32) | float32 | 10,000,000 | 1.5001 | 3.3155 | 0.45× | 221% | +|▫| np.nanmin(a) (float64) | float64 | 1,000 | 0.0042 | 0.0009 | 4.51× | 22% | +|🔴| np.nanmin(a) (float64) | float64 | 100,000 | 0.0114 | 0.0589 | 0.19× | 519% | +|🟡| np.nanmin(a) (float64) | float64 | 10,000,000 | 3.6863 | 6.8141 | 0.54× | 185% | +|✅| np.nanpercentile(a, 50) (float16) | float16 | 1,000 | 0.0365 | 0.0043 | 8.45× | 12% | +|✅| np.nanpercentile(a, 50) (float16) | float16 | 100,000 | 1.8857 | 1.3044 | 1.45× | 69% | +|✅| np.nanpercentile(a, 50) (float16) | float16 | 10,000,000 | 120.9498 | 92.9097 | 1.30× | 77% | +|✅| np.nanpercentile(a, 50) (float32) | float32 | 1,000 | 0.0267 | 0.0022 | 11.99× | 8% | +|✅| np.nanpercentile(a, 50) (float32) | float32 | 100,000 | 0.7602 | 0.6960 | 1.09× | 92% | +|🟡| np.nanpercentile(a, 50) (float32) | float32 | 10,000,000 | 51.8789 | 80.6571 | 0.64× | 156% | +|✅| np.nanpercentile(a, 50) (float64) | float64 | 1,000 | 0.0265 | 0.0023 | 11.56× | 9% | +|✅| np.nanpercentile(a, 50) (float64) | float64 | 100,000 | 0.7628 | 0.7034 | 1.08× | 92% | +|🟡| np.nanpercentile(a, 50) (float64) | float64 | 10,000,000 | 63.5626 | 89.5666 | 0.71× | 141% | +|✅| np.nanprod(a) (float16) | float16 | 1,000 | 0.0061 | 0.0013 | 4.73× | 21% | +|✅| np.nanprod(a) (float16) | float16 | 100,000 | 0.1701 | 0.0886 | 1.92× | 52% | +|✅| np.nanprod(a) (float16) | float16 | 10,000,000 | 20.5179 | 8.9271 | 2.30× | 44% | +|▫| np.nanprod(a) (float32) | float32 | 1,000 | 0.0052 | 0.0008 | 6.31× | 16% | +|✅| np.nanprod(a) (float32) | float32 | 100,000 | 0.1604 | 0.0119 | 13.53× | 7% | +|✅| np.nanprod(a) (float32) | float32 | 10,000,000 | 18.0353 | 1.7540 | 10.28× | 10% | +|▫| np.nanprod(a) (float64) | float64 | 1,000 | 0.0055 | 0.0008 | 6.96× | 14% | +|✅| np.nanprod(a) (float64) | float64 | 100,000 | 0.1079 | 0.0235 | 4.60× | 22% | +|✅| np.nanprod(a) (float64) | float64 | 10,000,000 | 28.6688 | 3.9531 | 7.25× | 14% | +|✅| np.nanquantile(a, 0.5) (float16) | float16 | 1,000 | 0.0336 | 0.0043 | 7.73× | 13% | +|✅| np.nanquantile(a, 0.5) (float16) | float16 | 100,000 | 1.8626 | 1.3071 | 1.43× | 70% | +|✅| np.nanquantile(a, 0.5) (float16) | float16 | 10,000,000 | 121.1817 | 92.8534 | 1.30× | 77% | +|✅| np.nanquantile(a, 0.5) (float32) | float32 | 1,000 | 0.0280 | 0.0022 | 12.60× | 8% | +|✅| np.nanquantile(a, 0.5) (float32) | float32 | 100,000 | 0.7231 | 0.6973 | 1.04× | 96% | +|🟡| np.nanquantile(a, 0.5) (float32) | float32 | 10,000,000 | 51.1057 | 80.6773 | 0.63× | 158% | +|✅| np.nanquantile(a, 0.5) (float64) | float64 | 1,000 | 0.0257 | 0.0023 | 11.24× | 9% | +|✅| np.nanquantile(a, 0.5) (float64) | float64 | 100,000 | 0.7484 | 0.7037 | 1.06× | 94% | +|🟡| np.nanquantile(a, 0.5) (float64) | float64 | 10,000,000 | 64.0584 | 89.4386 | 0.72× | 140% | +|✅| np.nanstd(a) (float16) | float16 | 1,000 | 0.0341 | 0.0028 | 12.26× | 8% | +|✅| np.nanstd(a) (float16) | float16 | 100,000 | 1.2279 | 0.2159 | 5.69× | 18% | +|✅| np.nanstd(a) (float16) | float16 | 10,000,000 | 119.9407 | 21.6231 | 5.55× | 18% | +|✅| np.nanstd(a) (float32) | float32 | 1,000 | 0.0202 | 0.0017 | 11.92× | 8% | +|✅| np.nanstd(a) (float32) | float32 | 100,000 | 0.1683 | 0.0864 | 1.95× | 51% | +|✅| np.nanstd(a) (float32) | float32 | 10,000,000 | 31.9536 | 9.2060 | 3.47× | 29% | +|✅| np.nanstd(a) (float64) | float64 | 1,000 | 0.0190 | 0.0015 | 12.52× | 8% | +|✅| np.nanstd(a) (float64) | float64 | 100,000 | 0.4497 | 0.0761 | 5.91× | 17% | +|✅| np.nanstd(a) (float64) | float64 | 10,000,000 | 49.6062 | 11.3264 | 4.38× | 23% | +|✅| np.nansum(a) (float16) | float16 | 1,000 | 0.0063 | 0.0013 | 4.84× | 21% | +|✅| np.nansum(a) (float16) | float16 | 100,000 | 0.2885 | 0.0901 | 3.20× | 31% | +|✅| np.nansum(a) (float16) | float16 | 10,000,000 | 31.6077 | 8.9086 | 3.55× | 28% | +|▫| np.nansum(a) (float32) | float32 | 1,000 | 0.0037 | 0.0008 | 4.83× | 21% | +|✅| np.nansum(a) (float32) | float32 | 100,000 | 0.0321 | 0.0049 | 6.51× | 15% | +|✅| np.nansum(a) (float32) | float32 | 10,000,000 | 13.3987 | 1.4131 | 9.48× | 10% | +|▫| np.nansum(a) (float64) | float64 | 1,000 | 0.0037 | 0.0008 | 4.61× | 22% | +|✅| np.nansum(a) (float64) | float64 | 100,000 | 0.0471 | 0.0097 | 4.88× | 20% | +|✅| np.nansum(a) (float64) | float64 | 10,000,000 | 23.8754 | 3.4851 | 6.85× | 15% | +|✅| np.nanvar(a) (float16) | float16 | 1,000 | 0.0324 | 0.0028 | 11.72× | 8% | +|✅| np.nanvar(a) (float16) | float16 | 100,000 | 1.2270 | 0.2160 | 5.68× | 18% | +|✅| np.nanvar(a) (float16) | float16 | 10,000,000 | 120.0573 | 21.5187 | 5.58× | 18% | +|✅| np.nanvar(a) (float32) | float32 | 1,000 | 0.0195 | 0.0017 | 11.49× | 9% | +|✅| np.nanvar(a) (float32) | float32 | 100,000 | 0.1645 | 0.0864 | 1.90× | 52% | +|✅| np.nanvar(a) (float32) | float32 | 10,000,000 | 32.0612 | 9.2119 | 3.48× | 29% | +|✅| np.nanvar(a) (float64) | float64 | 1,000 | 0.0198 | 0.0015 | 13.06× | 8% | +|✅| np.nanvar(a) (float64) | float64 | 100,000 | 0.4356 | 0.0762 | 5.72× | 18% | +|✅| np.nanvar(a) (float64) | float64 | 10,000,000 | 51.1759 | 11.2532 | 4.55× | 22% | +|▫| np.prod (float64) | float64 | 1,000 | 0.0022 | 0.0008 | 2.89× | 35% | +|✅| np.prod (float64) | float64 | 100,000 | 2.3310 | 0.1700 | 13.71× | 7% | +|✅| np.prod (float64) | float64 | 10,000,000 | 239.6474 | 60.4092 | 3.97× | 25% | +|▫| np.prod (int64) | int64 | 1,000 | 0.0021 | 0.0007 | 2.87× | 35% | +|✅| np.prod (int64) | int64 | 100,000 | 0.0572 | 0.0143 | 4.00× | 25% | +|✅| np.prod (int64) | int64 | 10,000,000 | 6.3161 | 3.8044 | 1.66× | 60% | +|▫| np.prod axis=0 (float64) | float64 | 1,000 | 0.0019 | 0.0007 | 2.75× | 36% | +|✅| np.prod axis=0 (float64) | float64 | 100,000 | 0.0136 | 0.0089 | 1.54× | 65% | +|🟡| np.prod axis=0 (float64) | float64 | 10,000,000 | 19.8072 | 19.8419 | 1.00× | 100% | +|▫| np.prod axis=0 (int64) | int64 | 1,000 | 0.0020 | 0.0009 | 2.24× | 45% | +|✅| np.prod axis=0 (int64) | int64 | 100,000 | 0.0272 | 0.0152 | 1.79× | 56% | +|✅| np.prod axis=0 (int64) | int64 | 10,000,000 | 5.0107 | 4.1147 | 1.22× | 82% | +|▫| np.prod axis=1 (float64) | float64 | 1,000 | 0.0019 | 0.0008 | 2.47× | 40% | +|✅| np.prod axis=1 (float64) | float64 | 100,000 | 0.0601 | 0.0050 | 12.13× | 8% | +|▫| np.prod axis=1 (float64) | float64 | 10,000,000 | 69.9249 | 3.0274 | 23.10× | 4% | +|▫| np.prod axis=1 (int64) | int64 | 1,000 | 0.0019 | 0.0010 | 1.96× | 51% | +|✅| np.prod axis=1 (int64) | int64 | 100,000 | 0.0458 | 0.0171 | 2.68× | 37% | +|✅| np.prod axis=1 (int64) | int64 | 10,000,000 | 6.2759 | 3.9188 | 1.60× | 62% | +|✅| np.std (float16) | float16 | 1,000 | 0.0203 | 0.0021 | 9.63× | 10% | +|✅| np.std (float16) | float16 | 100,000 | 0.9085 | 0.1830 | 4.96× | 20% | +|✅| np.std (float16) | float16 | 10,000,000 | 90.8149 | 18.0508 | 5.03× | 20% | +|▫| np.std (float32) | float32 | 1,000 | 0.0086 | 0.0007 | 12.74× | 8% | +|✅| np.std (float32) | float32 | 100,000 | 0.0450 | 0.0096 | 4.67× | 21% | +|✅| np.std (float32) | float32 | 10,000,000 | 17.8322 | 2.6281 | 6.79× | 15% | +|▫| np.std (float64) | float64 | 1,000 | 0.0068 | 0.0004 | 15.61× | 6% | +|✅| np.std (float64) | float64 | 100,000 | 0.0570 | 0.0190 | 3.00× | 33% | +|✅| np.std (float64) | float64 | 10,000,000 | 30.3403 | 6.6087 | 4.59× | 22% | +|✅| np.std axis=0 (float16) | float16 | 1,000 | 0.0214 | 0.0045 | 4.78× | 21% | +|✅| np.std axis=0 (float16) | float16 | 100,000 | 1.1043 | 0.3567 | 3.10× | 32% | +|✅| np.std axis=0 (float16) | float16 | 10,000,000 | 110.0006 | 83.8826 | 1.31× | 76% | +|✅| np.std axis=0 (float32) | float32 | 1,000 | 0.0087 | 0.0016 | 5.57× | 18% | +|✅| np.std axis=0 (float32) | float32 | 100,000 | 0.0377 | 0.0231 | 1.63× | 61% | +|✅| np.std axis=0 (float32) | float32 | 10,000,000 | 14.2545 | 4.6258 | 3.08× | 32% | +|▫| np.std axis=0 (float64) | float64 | 1,000 | 0.0076 | 0.0009 | 8.36× | 12% | +|✅| np.std axis=0 (float64) | float64 | 100,000 | 0.0637 | 0.0263 | 2.42× | 41% | +|✅| np.std axis=0 (float64) | float64 | 10,000,000 | 30.0409 | 7.5189 | 4.00× | 25% | +|▫| np.sum (complex128) | complex128 | 1,000 | 0.0018 | 0.0009 | 2.02× | 50% | +|✅| np.sum (complex128) | complex128 | 100,000 | 0.0294 | 0.0101 | 2.91× | 34% | +|✅| np.sum (complex128) | complex128 | 10,000,000 | 8.9925 | 6.4940 | 1.39× | 72% | +|✅| np.sum (float16) | float16 | 1,000 | 0.0040 | 0.0012 | 3.33× | 30% | +|✅| np.sum (float16) | float16 | 100,000 | 0.1981 | 0.0802 | 2.47× | 40% | +|✅| np.sum (float16) | float16 | 10,000,000 | 19.9449 | 8.0365 | 2.48× | 40% | +|▫| np.sum (float32) | float32 | 1,000 | 0.0017 | 0.0008 | 2.26× | 44% | +|✅| np.sum (float32) | float32 | 100,000 | 0.0171 | 0.0032 | 5.40× | 18% | +|✅| np.sum (float32) | float32 | 10,000,000 | 3.0579 | 1.0257 | 2.98× | 34% | +|▫| np.sum (float64) | float64 | 1,000 | 0.0017 | 0.0008 | 2.24× | 45% | +|🔴| np.sum (float64) | float64 | 100,000 | 0.0166 | 0.2090 | 0.079× | 1260% | +|✅| np.sum (float64) | float64 | 10,000,000 | 4.8117 | 2.8908 | 1.66× | 60% | +|▫| np.sum (int16) | int16 | 1,000 | 0.0020 | 0.0008 | 2.55× | 39% | +|✅| np.sum (int16) | int16 | 100,000 | 0.0339 | 0.0189 | 1.79× | 56% | +|✅| np.sum (int16) | int16 | 10,000,000 | 3.5547 | 1.9468 | 1.83× | 55% | +|▫| np.sum (int32) | int32 | 1,000 | 0.0023 | 0.0008 | 2.85× | 35% | +|✅| np.sum (int32) | int32 | 100,000 | 0.0345 | 0.0190 | 1.82× | 55% | +|✅| np.sum (int32) | int32 | 10,000,000 | 4.7348 | 2.6909 | 1.76× | 57% | +|▫| np.sum (int64) | int64 | 1,000 | 0.0018 | 0.0008 | 2.27× | 44% | +|✅| np.sum (int64) | int64 | 100,000 | 0.0153 | 0.0045 | 3.42× | 29% | +|✅| np.sum (int64) | int64 | 10,000,000 | 4.1490 | 2.8310 | 1.47× | 68% | +|▫| np.sum (int8) | int8 | 1,000 | 0.0022 | 0.0008 | 2.77× | 36% | +|✅| np.sum (int8) | int8 | 100,000 | 0.0342 | 0.0186 | 1.84× | 54% | +|✅| np.sum (int8) | int8 | 10,000,000 | 3.2333 | 1.8404 | 1.76× | 57% | +|▫| np.sum (uint16) | uint16 | 1,000 | 0.0022 | 0.0008 | 2.84× | 35% | +|✅| np.sum (uint16) | uint16 | 100,000 | 0.0334 | 0.0189 | 1.77× | 57% | +|✅| np.sum (uint16) | uint16 | 10,000,000 | 3.7579 | 1.9368 | 1.94× | 52% | +|▫| np.sum (uint32) | uint32 | 1,000 | 0.0023 | 0.0008 | 2.83× | 35% | +|✅| np.sum (uint32) | uint32 | 100,000 | 0.0332 | 0.0190 | 1.75× | 57% | +|✅| np.sum (uint32) | uint32 | 10,000,000 | 4.0560 | 2.6922 | 1.51× | 66% | +|▫| np.sum (uint64) | uint64 | 1,000 | 0.0018 | 0.0008 | 2.28× | 44% | +|✅| np.sum (uint64) | uint64 | 100,000 | 0.0200 | 0.0044 | 4.52× | 22% | +|✅| np.sum (uint64) | uint64 | 10,000,000 | 4.2549 | 2.9226 | 1.46× | 69% | +|▫| np.sum (uint8) | uint8 | 1,000 | 0.0024 | 0.0008 | 3.07× | 32% | +|✅| np.sum (uint8) | uint8 | 100,000 | 0.0348 | 0.0186 | 1.87× | 54% | +|✅| np.sum (uint8) | uint8 | 10,000,000 | 3.2589 | 1.8460 | 1.76× | 57% | +|✅| np.sum axis=0 (complex128) | complex128 | 1,000 | 0.0020 | 0.0017 | 1.16× | 86% | +|✅| np.sum axis=0 (complex128) | complex128 | 100,000 | 0.0309 | 0.0204 | 1.51× | 66% | +|✅| np.sum axis=0 (complex128) | complex128 | 10,000,000 | 7.4501 | 7.3478 | 1.01× | 99% | +|✅| np.sum axis=0 (float16) | float16 | 1,000 | 0.0053 | 0.0040 | 1.32× | 76% | +|✅| np.sum axis=0 (float16) | float16 | 100,000 | 0.2950 | 0.1473 | 2.00× | 50% | +|✅| np.sum axis=0 (float16) | float16 | 10,000,000 | 29.0440 | 14.1979 | 2.05× | 49% | +|✅| np.sum axis=0 (float32) | float32 | 1,000 | 0.0022 | 0.0019 | 1.18× | 85% | +|✅| np.sum axis=0 (float32) | float32 | 100,000 | 0.0083 | 0.0080 | 1.04× | 96% | +|✅| np.sum axis=0 (float32) | float32 | 10,000,000 | 1.8895 | 1.3370 | 1.41× | 71% | +|✅| np.sum axis=0 (float64) | float64 | 1,000 | 0.0020 | 0.0019 | 1.03× | 97% | +|✅| np.sum axis=0 (float64) | float64 | 100,000 | 0.0142 | 0.0115 | 1.23× | 81% | +|✅| np.sum axis=0 (float64) | float64 | 10,000,000 | 3.4762 | 3.4200 | 1.02× | 98% | +|▫| np.sum axis=0 (int16) | int16 | 1,000 | 0.0025 | 0.0009 | 2.70× | 37% | +|✅| np.sum axis=0 (int16) | int16 | 100,000 | 0.0474 | 0.0047 | 10.08× | 10% | +|✅| np.sum axis=0 (int16) | int16 | 10,000,000 | 5.0614 | 0.5369 | 9.43× | 11% | +|▫| np.sum axis=0 (int32) | int32 | 1,000 | 0.0028 | 0.0007 | 3.80× | 26% | +|✅| np.sum axis=0 (int32) | int32 | 100,000 | 0.0485 | 0.0086 | 5.62× | 18% | +|✅| np.sum axis=0 (int32) | int32 | 10,000,000 | 5.6694 | 1.8929 | 3.00× | 33% | +|▫| np.sum axis=0 (int64) | int64 | 1,000 | 0.0020 | 0.0007 | 2.74× | 36% | +|✅| np.sum axis=0 (int64) | int64 | 100,000 | 0.0278 | 0.0104 | 2.67× | 37% | +|✅| np.sum axis=0 (int64) | int64 | 10,000,000 | 5.6011 | 3.2402 | 1.73× | 58% | +|▫| np.sum axis=0 (int8) | int8 | 1,000 | 0.0024 | 0.0009 | 2.52× | 40% | +|✅| np.sum axis=0 (int8) | int8 | 100,000 | 0.0474 | 0.0044 | 10.75× | 9% | +|✅| np.sum axis=0 (int8) | int8 | 10,000,000 | 4.6440 | 0.5247 | 8.85× | 11% | +|▫| np.sum axis=0 (uint16) | uint16 | 1,000 | 0.0025 | 0.0009 | 2.71× | 37% | +|✅| np.sum axis=0 (uint16) | uint16 | 100,000 | 0.0479 | 0.0053 | 9.02× | 11% | +|✅| np.sum axis=0 (uint16) | uint16 | 10,000,000 | 4.7415 | 0.6972 | 6.80× | 15% | +|▫| np.sum axis=0 (uint32) | uint32 | 1,000 | 0.0028 | 0.0007 | 3.86× | 26% | +|✅| np.sum axis=0 (uint32) | uint32 | 100,000 | 0.0506 | 0.0086 | 5.87× | 17% | +|✅| np.sum axis=0 (uint32) | uint32 | 10,000,000 | 5.5471 | 1.7971 | 3.09× | 32% | +|▫| np.sum axis=0 (uint64) | uint64 | 1,000 | 0.0020 | 0.0007 | 2.73× | 37% | +|✅| np.sum axis=0 (uint64) | uint64 | 100,000 | 0.0274 | 0.0104 | 2.64× | 38% | +|✅| np.sum axis=0 (uint64) | uint64 | 10,000,000 | 5.1406 | 3.2350 | 1.59× | 63% | +|▫| np.sum axis=0 (uint8) | uint8 | 1,000 | 0.0026 | 0.0009 | 2.85× | 35% | +|✅| np.sum axis=0 (uint8) | uint8 | 100,000 | 0.0513 | 0.0044 | 11.69× | 9% | +|✅| np.sum axis=0 (uint8) | uint8 | 10,000,000 | 4.5577 | 0.5260 | 8.66× | 12% | +|✅| np.sum axis=1 (complex128) | complex128 | 1,000 | 0.0021 | 0.0018 | 1.17× | 86% | +|✅| np.sum axis=1 (complex128) | complex128 | 100,000 | 0.0340 | 0.0306 | 1.11× | 90% | +|✅| np.sum axis=1 (complex128) | complex128 | 10,000,000 | 9.2453 | 8.6134 | 1.07× | 93% | +|✅| np.sum axis=1 (float16) | float16 | 1,000 | 0.0039 | 0.0038 | 1.04× | 96% | +|✅| np.sum axis=1 (float16) | float16 | 100,000 | 0.1987 | 0.1303 | 1.52× | 66% | +|✅| np.sum axis=1 (float16) | float16 | 10,000,000 | 19.7912 | 12.4799 | 1.59× | 63% | +|✅| np.sum axis=1 (float32) | float32 | 1,000 | 0.0030 | 0.0019 | 1.53× | 65% | +|✅| np.sum axis=1 (float32) | float32 | 100,000 | 0.0162 | 0.0104 | 1.55× | 64% | +|✅| np.sum axis=1 (float32) | float32 | 10,000,000 | 3.3850 | 1.8916 | 1.79× | 56% | +|✅| np.sum axis=1 (float64) | float64 | 1,000 | 0.0019 | 0.0016 | 1.21× | 83% | +|✅| np.sum axis=1 (float64) | float64 | 100,000 | 0.0200 | 0.0127 | 1.58× | 63% | +|✅| np.sum axis=1 (float64) | float64 | 10,000,000 | 5.1036 | 3.6624 | 1.39× | 72% | +|▫| np.sum axis=1 (int16) | int16 | 1,000 | 0.0023 | 0.0007 | 3.51× | 28% | +|✅| np.sum axis=1 (int16) | int16 | 100,000 | 0.0376 | 0.0038 | 9.79× | 10% | +|✅| np.sum axis=1 (int16) | int16 | 10,000,000 | 3.6422 | 0.4546 | 8.01× | 12% | +|▫| np.sum axis=1 (int32) | int32 | 1,000 | 0.0025 | 0.0007 | 3.66× | 27% | +|✅| np.sum axis=1 (int32) | int32 | 100,000 | 0.0396 | 0.0053 | 7.49× | 13% | +|✅| np.sum axis=1 (int32) | int32 | 10,000,000 | 4.2337 | 1.6246 | 2.61× | 38% | +|▫| np.sum axis=1 (int64) | int64 | 1,000 | 0.0019 | 0.0007 | 2.60× | 38% | +|✅| np.sum axis=1 (int64) | int64 | 100,000 | 0.0175 | 0.0075 | 2.33× | 43% | +|✅| np.sum axis=1 (int64) | int64 | 10,000,000 | 4.7220 | 2.8722 | 1.64× | 61% | +|▫| np.sum axis=1 (int8) | int8 | 1,000 | 0.0023 | 0.0007 | 3.54× | 28% | +|✅| np.sum axis=1 (int8) | int8 | 100,000 | 0.0379 | 0.0039 | 9.63× | 10% | +|✅| np.sum axis=1 (int8) | int8 | 10,000,000 | 3.5111 | 0.2640 | 13.30× | 8% | +|▫| np.sum axis=1 (uint16) | uint16 | 1,000 | 0.0027 | 0.0008 | 3.44× | 29% | +|✅| np.sum axis=1 (uint16) | uint16 | 100,000 | 0.0375 | 0.0037 | 10.00× | 10% | +|✅| np.sum axis=1 (uint16) | uint16 | 10,000,000 | 3.8775 | 0.4583 | 8.46× | 12% | +|▫| np.sum axis=1 (uint32) | uint32 | 1,000 | 0.0026 | 0.0007 | 3.63× | 28% | +|✅| np.sum axis=1 (uint32) | uint32 | 100,000 | 0.0413 | 0.0053 | 7.79× | 13% | +|✅| np.sum axis=1 (uint32) | uint32 | 10,000,000 | 4.2817 | 1.6385 | 2.61× | 38% | +|▫| np.sum axis=1 (uint64) | uint64 | 1,000 | 0.0019 | 0.0007 | 2.69× | 37% | +|✅| np.sum axis=1 (uint64) | uint64 | 100,000 | 0.0173 | 0.0075 | 2.30× | 44% | +|✅| np.sum axis=1 (uint64) | uint64 | 10,000,000 | 4.6457 | 2.8809 | 1.61× | 62% | +|▫| np.sum axis=1 (uint8) | uint8 | 1,000 | 0.0025 | 0.0007 | 3.81× | 26% | +|✅| np.sum axis=1 (uint8) | uint8 | 100,000 | 0.0402 | 0.0038 | 10.47× | 10% | +|✅| np.sum axis=1 (uint8) | uint8 | 10,000,000 | 3.2063 | 0.2647 | 12.11× | 8% | +|✅| np.var (float16) | float16 | 1,000 | 0.0200 | 0.0021 | 9.51× | 10% | +|✅| np.var (float16) | float16 | 100,000 | 0.9060 | 0.1822 | 4.97× | 20% | +|✅| np.var (float16) | float16 | 10,000,000 | 91.4660 | 18.1038 | 5.05× | 20% | +|▫| np.var (float32) | float32 | 1,000 | 0.0083 | 0.0007 | 11.92× | 8% | +|✅| np.var (float32) | float32 | 100,000 | 0.0451 | 0.0096 | 4.69× | 21% | +|✅| np.var (float32) | float32 | 10,000,000 | 17.1259 | 2.6208 | 6.54× | 15% | +|▫| np.var (float64) | float64 | 1,000 | 0.0067 | 0.0008 | 8.40× | 12% | +|✅| np.var (float64) | float64 | 100,000 | 0.0573 | 0.0191 | 3.00× | 33% | +|✅| np.var (float64) | float64 | 10,000,000 | 30.5902 | 6.6728 | 4.58× | 22% | +|✅| np.var axis=0 (float16) | float16 | 1,000 | 0.0200 | 0.0044 | 4.51× | 22% | +|✅| np.var axis=0 (float16) | float16 | 100,000 | 1.1366 | 0.3566 | 3.19× | 31% | +|✅| np.var axis=0 (float16) | float16 | 10,000,000 | 109.8825 | 83.9268 | 1.31× | 76% | +|✅| np.var axis=0 (float32) | float32 | 1,000 | 0.0086 | 0.0017 | 5.19× | 19% | +|✅| np.var axis=0 (float32) | float32 | 100,000 | 0.0371 | 0.0226 | 1.64× | 61% | +|✅| np.var axis=0 (float32) | float32 | 10,000,000 | 14.0847 | 4.7475 | 2.97× | 34% | +|▫| np.var axis=0 (float64) | float64 | 1,000 | 0.0075 | 0.0009 | 7.97× | 12% | +|✅| np.var axis=0 (float64) | float64 | 100,000 | 0.0680 | 0.0252 | 2.70× | 37% | +|✅| np.var axis=0 (float64) | float64 | 10,000,000 | 28.5127 | 7.5344 | 3.78× | 26% | + +### Broadcasting + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|⚪| matrix + col_vector (N,M)+(N,1) | float64 | 1,000 | 0.0013 | - | - | - | +|⚪| matrix + col_vector (N,M)+(N,1) | float64 | 100,000 | 0.1968 | - | - | - | +|✅| matrix + col_vector (N,M)+(N,1) | float64 | 10,000,000 | 16.0261 | 13.9778 | 1.15× | 87% | +|⚪| matrix + row_vector (N,M)+(M,) | float64 | 1,000 | 0.0012 | - | - | - | +|⚪| matrix + row_vector (N,M)+(M,) | float64 | 100,000 | 0.1942 | - | - | - | +|✅| matrix + row_vector (N,M)+(M,) | float64 | 10,000,000 | 16.3420 | 13.3161 | 1.23× | 82% | +|⚪| matrix + scalar | float64 | 1,000 | 0.0007 | - | - | - | +|⚪| matrix + scalar | float64 | 100,000 | 0.0142 | - | - | - | +|✅| matrix + scalar | float64 | 10,000,000 | 15.6849 | 13.5809 | 1.16× | 87% | +|⚪| np.broadcast_to(row, (N,M)) | float64 | 1,000 | 0.0018 | - | - | - | +|⚪| np.broadcast_to(row, (N,M)) | float64 | 100,000 | 0.0021 | - | - | - | +|▫| np.broadcast_to(row, (N,M)) | float64 | 10,000,000 | 0.0018 | 0.0006 | 3.22× | 31% | + +### Creation + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|▫| np.copy (float32) | float32 | 1,000 | 0.0006 | 0.0117 | 0.050× | 1983% | +|🟡| np.copy (float32) | float32 | 100,000 | 0.0061 | 0.0071 | 0.86× | 117% | +|✅| np.copy (float32) | float32 | 10,000,000 | 6.4782 | 1.8708 | 3.46× | 29% | +|▫| np.copy (float64) | float64 | 1,000 | 0.0006 | 0.0198 | 0.031× | 3272% | +|🟡| np.copy (float64) | float64 | 100,000 | 0.0113 | 0.0135 | 0.83× | 120% | +|✅| np.copy (float64) | float64 | 10,000,000 | 13.0404 | 12.7447 | 1.02× | 98% | +|▫| np.copy (int32) | int32 | 1,000 | 0.0006 | 0.0116 | 0.051× | 1958% | +|🟡| np.copy (int32) | int32 | 100,000 | 0.0060 | 0.0069 | 0.88× | 114% | +|✅| np.copy (int32) | int32 | 10,000,000 | 6.3440 | 1.8390 | 3.45× | 29% | +|▫| np.copy (int64) | int64 | 1,000 | 0.0006 | 0.0133 | 0.046× | 2177% | +|🟡| np.copy (int64) | int64 | 100,000 | 0.0115 | 0.0140 | 0.82× | 122% | +|✅| np.copy (int64) | int64 | 10,000,000 | 13.2030 | 12.7437 | 1.04× | 96% | +|▫| np.empty (float32) | float32 | 1,000 | 0.0003 | 0.0085 | 0.039× | 2532% | +|▫| np.empty (float32) | float32 | 100,000 | 0.0003 | 0.0141 | 0.024× | 4090% | +|🟡| np.empty (float32) | float32 | 10,000,000 | 0.0087 | 0.0143 | 0.61× | 165% | +|▫| np.empty (float64) | float64 | 1,000 | 0.0005 | 0.0139 | 0.038× | 2598% | +|▫| np.empty (float64) | float64 | 100,000 | 0.0003 | 0.0140 | 0.021× | 4722% | +|🟡| np.empty (float64) | float64 | 10,000,000 | 0.0080 | 0.0138 | 0.58× | 172% | +|▫| np.empty (int32) | int32 | 1,000 | 0.0003 | 0.0089 | 0.038× | 2635% | +|▫| np.empty (int32) | int32 | 100,000 | 0.0003 | 0.0133 | 0.026× | 3857% | +|🟡| np.empty (int32) | int32 | 10,000,000 | 0.0108 | 0.0140 | 0.77× | 130% | +|▫| np.empty (int64) | int64 | 1,000 | 0.0003 | 0.0150 | 0.021× | 4695% | +|▫| np.empty (int64) | int64 | 100,000 | 0.0003 | 0.0152 | 0.022× | 4622% | +|🟡| np.empty (int64) | int64 | 10,000,000 | 0.0119 | 0.0141 | 0.85× | 118% | +|▫| np.full (float32) | float32 | 1,000 | 0.0010 | 0.0117 | 0.084× | 1196% | +|🟠| np.full (float32) | float32 | 100,000 | 0.0053 | 0.0126 | 0.42× | 239% | +|✅| np.full (float32) | float32 | 10,000,000 | 7.3482 | 1.8309 | 4.01× | 25% | +|▫| np.full (float64) | float64 | 1,000 | 0.0010 | 0.0142 | 0.068× | 1466% | +|🟡| np.full (float64) | float64 | 100,000 | 0.0098 | 0.0148 | 0.66× | 151% | +|✅| np.full (float64) | float64 | 10,000,000 | 14.6927 | 12.3279 | 1.19× | 84% | +|▫| np.full (int32) | int32 | 1,000 | 0.0009 | 0.0104 | 0.088× | 1138% | +|🟠| np.full (int32) | int32 | 100,000 | 0.0054 | 0.0123 | 0.44× | 228% | +|✅| np.full (int32) | int32 | 10,000,000 | 7.3322 | 1.9121 | 3.83× | 26% | +|▫| np.full (int64) | int64 | 1,000 | 0.0009 | 0.0135 | 0.064× | 1571% | +|🟡| np.full (int64) | int64 | 100,000 | 0.0098 | 0.0150 | 0.65× | 153% | +|✅| np.full (int64) | int64 | 10,000,000 | 14.7304 | 12.3185 | 1.20× | 84% | +|▫| np.ones (float32) | float32 | 1,000 | 0.0010 | 0.0094 | 0.10× | 953% | +|🟠| np.ones (float32) | float32 | 100,000 | 0.0054 | 0.0124 | 0.43× | 232% | +|✅| np.ones (float32) | float32 | 10,000,000 | 7.3143 | 1.8034 | 4.06× | 25% | +|▫| np.ones (float64) | float64 | 1,000 | 0.0009 | 0.0119 | 0.073× | 1370% | +|🟡| np.ones (float64) | float64 | 100,000 | 0.0098 | 0.0150 | 0.66× | 152% | +|✅| np.ones (float64) | float64 | 10,000,000 | 14.5911 | 12.4361 | 1.17× | 85% | +|▫| np.ones (int32) | int32 | 1,000 | 0.0009 | 0.0111 | 0.080× | 1257% | +|🟠| np.ones (int32) | int32 | 100,000 | 0.0053 | 0.0124 | 0.43× | 234% | +|✅| np.ones (int32) | int32 | 10,000,000 | 7.1344 | 2.0772 | 3.44× | 29% | +|▫| np.ones (int64) | int64 | 1,000 | 0.0009 | 0.0145 | 0.059× | 1685% | +|🟡| np.ones (int64) | int64 | 100,000 | 0.0099 | 0.0148 | 0.67× | 149% | +|✅| np.ones (int64) | int64 | 10,000,000 | 14.7038 | 12.3182 | 1.19× | 84% | +|▫| np.zeros (float32) | float32 | 1,000 | 0.0004 | 0.0107 | 0.039× | 2555% | +|✅| np.zeros (float32) | float32 | 100,000 | 0.0048 | 0.0020 | 2.37× | 42% | +|✅| np.zeros (float32) | float32 | 10,000,000 | 0.0090 | 0.0020 | 4.51× | 22% | +|▫| np.zeros (float64) | float64 | 1,000 | 0.0004 | 0.0137 | 0.026× | 3875% | +|✅| np.zeros (float64) | float64 | 100,000 | 0.0093 | 0.0026 | 3.62× | 28% | +|✅| np.zeros (float64) | float64 | 10,000,000 | 0.0083 | 0.0022 | 3.70× | 27% | +|▫| np.zeros (int32) | int32 | 1,000 | 0.0004 | 0.0107 | 0.036× | 2816% | +|✅| np.zeros (int32) | int32 | 100,000 | 0.0049 | 0.0020 | 2.45× | 41% | +|✅| np.zeros (int32) | int32 | 10,000,000 | 0.0111 | 0.0065 | 1.71× | 58% | +|▫| np.zeros (int64) | int64 | 1,000 | 0.0004 | 0.0140 | 0.027× | 3638% | +|✅| np.zeros (int64) | int64 | 100,000 | 0.0094 | 0.0026 | 3.70× | 27% | +|✅| np.zeros (int64) | int64 | 10,000,000 | 0.0114 | 0.0057 | 1.99× | 50% | +|🔴| np.zeros_like (float32) | float32 | 1,000 | 0.0011 | 0.0091 | 0.12× | 861% | +|✅| np.zeros_like (float32) | float32 | 100,000 | 0.0055 | 0.0021 | 2.63× | 38% | +|▫| np.zeros_like (float32) | float32 | 10,000,000 | 7.1806 | 0.0021 | 3422.48× | 0% | +|🔴| np.zeros_like (float64) | float64 | 1,000 | 0.0012 | 0.0135 | 0.092× | 1086% | +|✅| np.zeros_like (float64) | float64 | 100,000 | 0.0099 | 0.0027 | 3.70× | 27% | +|▫| np.zeros_like (float64) | float64 | 10,000,000 | 14.8132 | 0.0023 | 6363.47× | 0% | +|🔴| np.zeros_like (int32) | int32 | 1,000 | 0.0011 | 0.0089 | 0.12× | 842% | +|✅| np.zeros_like (int32) | int32 | 100,000 | 0.0057 | 0.0020 | 2.78× | 36% | +|▫| np.zeros_like (int32) | int32 | 10,000,000 | 7.2566 | 0.0066 | 1091.90× | 0% | +|🔴| np.zeros_like (int64) | int64 | 1,000 | 0.0010 | 0.0132 | 0.079× | 1261% | +|✅| np.zeros_like (int64) | int64 | 100,000 | 0.0100 | 0.0026 | 3.79× | 26% | +|▫| np.zeros_like (int64) | int64 | 10,000,000 | 14.7069 | 0.0056 | 2623.48× | 0% | + +### Manipulation + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|⚪| a.T (2D) | float64 | 1,000 | 0.0001 | - | - | - | +|⚪| a.T (2D) | float64 | 100,000 | 0.0001 | - | - | - | +|⚪| a.T (2D) | float64 | 10,000,000 | 0.0001 | - | - | - | +|⚪| a.flatten | float64 | 1,000 | 0.0005 | - | - | - | +|🔴| a.flatten | float64 | 100,000 | 0.0113 | 0.0883 | 0.13× | 782% | +|✅| a.flatten | float64 | 10,000,000 | 13.0852 | 11.4613 | 1.14× | 88% | +|⚪| np.concatenate | float64 | 1,000 | 0.0010 | - | - | - | +|⚪| np.concatenate | float64 | 100,000 | 0.3154 | - | - | - | +|⚪| np.concatenate | float64 | 10,000,000 | 32.8142 | - | - | - | +|⚪| np.ravel | float64 | 1,000 | 0.0004 | - | - | - | +|▫| np.ravel | float64 | 100,000 | 0.0003 | 0.0005 | 0.69× | 145% | +|▫| np.ravel | float64 | 10,000,000 | 0.0003 | 0.0005 | 0.62× | 162% | +|⚪| np.stack | float64 | 1,000 | 0.0022 | - | - | - | +|⚪| np.stack | float64 | 100,000 | 0.3130 | - | - | - | +|⚪| np.stack | float64 | 10,000,000 | 33.9601 | - | - | - | +|⚪| np.transpose (2D) | float64 | 1,000 | 0.0004 | - | - | - | +|⚪| np.transpose (2D) | float64 | 100,000 | 0.0004 | - | - | - | +|⚪| np.transpose (2D) | float64 | 10,000,000 | 0.0004 | - | - | - | +|⚪| reshape 1D->2D | float64 | 1,000 | 0.0002 | - | - | - | +|⚪| reshape 1D->2D | float64 | 100,000 | 0.0002 | - | - | - | +|⚪| reshape 1D->2D | float64 | 10,000,000 | 0.0002 | - | - | - | +|⚪| reshape 2D->1D | float64 | 1,000 | 0.0002 | - | - | - | +|▫| reshape 2D->1D | float64 | 100,000 | 0.0002 | 0.0005 | 0.33× | 304% | +|▫| reshape 2D->1D | float64 | 10,000,000 | 0.0002 | 0.0006 | 0.32× | 317% | + +### Slicing + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|⚪| a[100:1000] (contiguous) | float64 | 1,000 | 0.0002 | - | - | - | +|⚪| a[100:1000] (contiguous) | float64 | 100,000 | 0.0001 | - | - | - | +|⚪| a[100:1000] (contiguous) | float64 | 10,000,000 | 0.0001 | - | - | - | +|⚪| a[::-1] (reversed) | float64 | 1,000 | 0.0001 | - | - | - | +|▫| a[::-1] (reversed) | float64 | 100,000 | 0.0002 | 0.0012 | 0.13× | 781% | +|▫| a[::-1] (reversed) | float64 | 10,000,000 | 0.0002 | 0.0012 | 0.13× | 789% | +|⚪| a[::2] (strided) | float64 | 1,000 | 0.0002 | - | - | - | +|⚪| a[::2] (strided) | float64 | 100,000 | 0.0001 | - | - | - | +|⚪| a[::2] (strided) | float64 | 10,000,000 | 0.0001 | - | - | - | +|⚪| np.sum(contiguous_slice) | float64 | 900 | 0.0017 | - | - | - | +|⚪| np.sum(contiguous_slice) | float64 | 900 | 0.0017 | - | - | - | +|⚪| np.sum(contiguous_slice) | float64 | 900 | 0.0016 | - | - | - | +|⚪| np.sum(strided_slice) | float64 | 500 | 0.0016 | - | - | - | +|⚪| np.sum(strided_slice) | float64 | 50,000 | 0.0096 | - | - | - | +|⚪| np.sum(strided_slice) | float64 | 5,000,000 | 4.6738 | - | - | - | + +### Comparison + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|▫| a != b (float32) | float32 | 1,000 | 0.0004 | 0.0007 | 0.60× | 166% | +|🟠| a != b (float32) | float32 | 100,000 | 0.0059 | 0.0188 | 0.31× | 321% | +|✅| a != b (float32) | float32 | 10,000,000 | 3.9247 | 3.3366 | 1.18× | 85% | +|▫| a != b (float64) | float64 | 1,000 | 0.0005 | 0.0008 | 0.62× | 162% | +|🟠| a != b (float64) | float64 | 100,000 | 0.0110 | 0.0263 | 0.42× | 239% | +|✅| a != b (float64) | float64 | 10,000,000 | 6.7684 | 5.8470 | 1.16× | 86% | +|▫| a != b (int32) | int32 | 1,000 | 0.0004 | 0.0008 | 0.54× | 186% | +|🟠| a != b (int32) | int32 | 100,000 | 0.0069 | 0.0201 | 0.34× | 291% | +|✅| a != b (int32) | int32 | 10,000,000 | 4.2916 | 3.4612 | 1.24× | 81% | +|▫| a != b (int64) | int64 | 1,000 | 0.0005 | 0.0008 | 0.60× | 166% | +|🟡| a != b (int64) | int64 | 100,000 | 0.0140 | 0.0260 | 0.54× | 186% | +|✅| a != b (int64) | int64 | 10,000,000 | 7.2974 | 5.8513 | 1.25× | 80% | +|▫| a < b (float32) | float32 | 1,000 | 0.0004 | 0.0007 | 0.56× | 179% | +|🟠| a < b (float32) | float32 | 100,000 | 0.0056 | 0.0189 | 0.29× | 339% | +|✅| a < b (float32) | float32 | 10,000,000 | 3.8362 | 3.2974 | 1.16× | 86% | +|▫| a < b (float64) | float64 | 1,000 | 0.0005 | 0.0008 | 0.62× | 162% | +|🟠| a < b (float64) | float64 | 100,000 | 0.0106 | 0.0279 | 0.38× | 264% | +|✅| a < b (float64) | float64 | 10,000,000 | 6.5961 | 5.7503 | 1.15× | 87% | +|▫| a < b (int32) | int32 | 1,000 | 0.0004 | 0.0007 | 0.62× | 162% | +|🟠| a < b (int32) | int32 | 100,000 | 0.0071 | 0.0187 | 0.38× | 265% | +|✅| a < b (int32) | int32 | 10,000,000 | 4.3674 | 3.2941 | 1.33× | 75% | +|▫| a < b (int64) | int64 | 1,000 | 0.0005 | 0.0007 | 0.73× | 137% | +|🟡| a < b (int64) | int64 | 100,000 | 0.0182 | 0.0250 | 0.73× | 137% | +|✅| a < b (int64) | int64 | 10,000,000 | 7.0769 | 5.6821 | 1.25× | 80% | +|▫| a <= b (float32) | float32 | 1,000 | 0.0006 | 0.0007 | 0.80× | 126% | +|🟠| a <= b (float32) | float32 | 100,000 | 0.0063 | 0.0181 | 0.35× | 289% | +|✅| a <= b (float32) | float32 | 10,000,000 | 3.8528 | 3.2691 | 1.18× | 85% | +|▫| a <= b (float64) | float64 | 1,000 | 0.0004 | 0.0007 | 0.60× | 167% | +|🟠| a <= b (float64) | float64 | 100,000 | 0.0104 | 0.0283 | 0.37× | 272% | +|✅| a <= b (float64) | float64 | 10,000,000 | 6.4781 | 5.7355 | 1.13× | 88% | +|▫| a <= b (int32) | int32 | 1,000 | 0.0004 | 0.0007 | 0.58× | 173% | +|🟠| a <= b (int32) | int32 | 100,000 | 0.0070 | 0.0192 | 0.37× | 273% | +|✅| a <= b (int32) | int32 | 10,000,000 | 4.2354 | 3.3183 | 1.28× | 78% | +|▫| a <= b (int64) | int64 | 1,000 | 0.0005 | 0.0007 | 0.72× | 138% | +|🟡| a <= b (int64) | int64 | 100,000 | 0.0186 | 0.0268 | 0.69× | 144% | +|✅| a <= b (int64) | int64 | 10,000,000 | 8.2513 | 5.9870 | 1.38× | 73% | +|▫| a == b (float32) | float32 | 1,000 | 0.0005 | 0.0007 | 0.61× | 163% | +|🟠| a == b (float32) | float32 | 100,000 | 0.0059 | 0.0199 | 0.29× | 339% | +|✅| a == b (float32) | float32 | 10,000,000 | 3.9574 | 3.2149 | 1.23× | 81% | +|▫| a == b (float64) | float64 | 1,000 | 0.0004 | 0.0007 | 0.58× | 172% | +|🟠| a == b (float64) | float64 | 100,000 | 0.0114 | 0.0279 | 0.41× | 244% | +|✅| a == b (float64) | float64 | 10,000,000 | 7.1028 | 5.6882 | 1.25× | 80% | +|▫| a == b (int32) | int32 | 1,000 | 0.0004 | 0.0007 | 0.60× | 167% | +|🟠| a == b (int32) | int32 | 100,000 | 0.0072 | 0.0183 | 0.40× | 253% | +|✅| a == b (int32) | int32 | 10,000,000 | 4.1736 | 3.3406 | 1.25× | 80% | +|▫| a == b (int64) | int64 | 1,000 | 0.0004 | 0.0008 | 0.58× | 172% | +|🟡| a == b (int64) | int64 | 100,000 | 0.0131 | 0.0248 | 0.53× | 189% | +|✅| a == b (int64) | int64 | 10,000,000 | 7.1546 | 5.7300 | 1.25× | 80% | +|▫| a > b (float32) | float32 | 1,000 | 0.0004 | 0.0007 | 0.56× | 178% | +|🟠| a > b (float32) | float32 | 100,000 | 0.0056 | 0.0185 | 0.31× | 327% | +|✅| a > b (float32) | float32 | 10,000,000 | 3.8956 | 3.3476 | 1.16× | 86% | +|▫| a > b (float64) | float64 | 1,000 | 0.0004 | 0.0008 | 0.58× | 171% | +|🟠| a > b (float64) | float64 | 100,000 | 0.0105 | 0.0262 | 0.40× | 248% | +|✅| a > b (float64) | float64 | 10,000,000 | 6.6876 | 5.7415 | 1.17× | 86% | +|▫| a > b (int32) | int32 | 1,000 | 0.0004 | 0.0008 | 0.54× | 186% | +|🟠| a > b (int32) | int32 | 100,000 | 0.0070 | 0.0195 | 0.36× | 280% | +|✅| a > b (int32) | int32 | 10,000,000 | 4.1882 | 3.3117 | 1.26× | 79% | +|▫| a > b (int64) | int64 | 1,000 | 0.0005 | 0.0007 | 0.74× | 134% | +|🟡| a > b (int64) | int64 | 100,000 | 0.0231 | 0.0256 | 0.90× | 111% | +|✅| a > b (int64) | int64 | 10,000,000 | 7.6396 | 5.7329 | 1.33× | 75% | +|▫| a >= b (float32) | float32 | 1,000 | 0.0004 | 0.0007 | 0.61× | 165% | +|🟠| a >= b (float32) | float32 | 100,000 | 0.0063 | 0.0186 | 0.34× | 295% | +|✅| a >= b (float32) | float32 | 10,000,000 | 3.8402 | 3.2664 | 1.18× | 85% | +|▫| a >= b (float64) | float64 | 1,000 | 0.0005 | 0.0007 | 0.60× | 166% | +|🟠| a >= b (float64) | float64 | 100,000 | 0.0118 | 0.0241 | 0.49× | 203% | +|✅| a >= b (float64) | float64 | 10,000,000 | 7.1505 | 5.6739 | 1.26× | 79% | +|▫| a >= b (int32) | int32 | 1,000 | 0.0004 | 0.0008 | 0.56× | 177% | +|🟠| a >= b (int32) | int32 | 100,000 | 0.0069 | 0.0191 | 0.36× | 277% | +|✅| a >= b (int32) | int32 | 10,000,000 | 4.5128 | 3.3235 | 1.36× | 74% | +|▫| a >= b (int64) | int64 | 1,000 | 0.0008 | 0.0008 | 1.00× | 100% | +|🟡| a >= b (int64) | int64 | 100,000 | 0.0188 | 0.0266 | 0.71× | 142% | +|✅| a >= b (int64) | int64 | 10,000,000 | 7.8543 | 5.9554 | 1.32× | 76% | + +### Bitwise + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|▫| a & b (bool) | bool | 1,000 | 0.0004 | 0.0016 | 0.24× | 409% | +|🔴| a & b (bool) | bool | 100,000 | 0.0028 | 0.0215 | 0.13× | 763% | +|🟡| a & b (bool) | bool | 10,000,000 | 1.8589 | 3.0929 | 0.60× | 166% | +|▫| a & b (int16) | int16 | 1,000 | 0.0008 | 0.0027 | 0.29× | 349% | +|✅| a & b (int16) | int16 | 100,000 | 0.0290 | 0.0169 | 1.72× | 58% | +|✅| a & b (int16) | int16 | 10,000,000 | 5.2942 | 2.9661 | 1.78× | 56% | +|▫| a & b (int32) | int32 | 1,000 | 0.0008 | 0.0060 | 0.13× | 779% | +|🟡| a & b (int32) | int32 | 100,000 | 0.0286 | 0.0342 | 0.84× | 120% | +|✅| a & b (int32) | int32 | 10,000,000 | 9.1881 | 5.3408 | 1.72× | 58% | +|▫| a & b (int64) | int64 | 1,000 | 0.0008 | 0.0069 | 0.11× | 891% | +|🟡| a & b (int64) | int64 | 100,000 | 0.0338 | 0.0634 | 0.53× | 188% | +|✅| a & b (int64) | int64 | 10,000,000 | 17.1008 | 16.6525 | 1.03× | 97% | +|▫| a & b (int8) | int8 | 1,000 | 0.0007 | 0.0013 | 0.49× | 204% | +|✅| a & b (int8) | int8 | 100,000 | 0.0286 | 0.0100 | 2.86× | 35% | +|✅| a & b (int8) | int8 | 10,000,000 | 3.8854 | 1.5338 | 2.53× | 40% | +|▫| a & b (uint16) | uint16 | 1,000 | 0.0008 | 0.0034 | 0.23× | 440% | +|✅| a & b (uint16) | uint16 | 100,000 | 0.0301 | 0.0180 | 1.68× | 60% | +|✅| a & b (uint16) | uint16 | 10,000,000 | 5.2834 | 3.0212 | 1.75× | 57% | +|▫| a & b (uint32) | uint32 | 1,000 | 0.0008 | 0.0039 | 0.20× | 492% | +|🟡| a & b (uint32) | uint32 | 100,000 | 0.0339 | 0.0400 | 0.85× | 118% | +|✅| a & b (uint32) | uint32 | 10,000,000 | 8.5821 | 6.0168 | 1.43× | 70% | +|▫| a & b (uint64) | uint64 | 1,000 | 0.0008 | 0.0024 | 0.32× | 310% | +|🟠| a & b (uint64) | uint64 | 100,000 | 0.0356 | 0.0739 | 0.48× | 207% | +|✅| a & b (uint64) | uint64 | 10,000,000 | 17.0644 | 16.1487 | 1.06× | 95% | +|▫| a & b (uint8) | uint8 | 1,000 | 0.0006 | 0.0010 | 0.62× | 162% | +|✅| a & b (uint8) | uint8 | 100,000 | 0.0296 | 0.0103 | 2.88× | 35% | +|✅| a & b (uint8) | uint8 | 10,000,000 | 3.8191 | 1.5272 | 2.50× | 40% | +|▫| a ^ b (bool) | bool | 1,000 | 0.0004 | 0.0017 | 0.22× | 447% | +|🔴| a ^ b (bool) | bool | 100,000 | 0.0026 | 0.0214 | 0.12× | 822% | +|🟡| a ^ b (bool) | bool | 10,000,000 | 1.8620 | 3.0807 | 0.60× | 166% | +|▫| a ^ b (int16) | int16 | 1,000 | 0.0008 | 0.0026 | 0.30× | 328% | +|✅| a ^ b (int16) | int16 | 100,000 | 0.0285 | 0.0149 | 1.92× | 52% | +|✅| a ^ b (int16) | int16 | 10,000,000 | 5.2041 | 2.9252 | 1.78× | 56% | +|▫| a ^ b (int32) | int32 | 1,000 | 0.0008 | 0.0047 | 0.16× | 615% | +|🟡| a ^ b (int32) | int32 | 100,000 | 0.0288 | 0.0321 | 0.90× | 111% | +|✅| a ^ b (int32) | int32 | 10,000,000 | 8.9067 | 5.3599 | 1.66× | 60% | +|▫| a ^ b (int64) | int64 | 1,000 | 0.0008 | 0.0040 | 0.20× | 511% | +|🟡| a ^ b (int64) | int64 | 100,000 | 0.0337 | 0.0666 | 0.51× | 198% | +|✅| a ^ b (int64) | int64 | 10,000,000 | 17.2489 | 15.6910 | 1.10× | 91% | +|▫| a ^ b (int8) | int8 | 1,000 | 0.0007 | 0.0012 | 0.52× | 191% | +|✅| a ^ b (int8) | int8 | 100,000 | 0.0295 | 0.0102 | 2.90× | 34% | +|✅| a ^ b (int8) | int8 | 10,000,000 | 3.8764 | 1.5503 | 2.50× | 40% | +|▫| a ^ b (uint16) | uint16 | 1,000 | 0.0008 | 0.0028 | 0.28× | 361% | +|✅| a ^ b (uint16) | uint16 | 100,000 | 0.0287 | 0.0163 | 1.77× | 57% | +|✅| a ^ b (uint16) | uint16 | 10,000,000 | 5.2129 | 2.9215 | 1.78× | 56% | +|▫| a ^ b (uint32) | uint32 | 1,000 | 0.0008 | 0.0048 | 0.16× | 610% | +|🟡| a ^ b (uint32) | uint32 | 100,000 | 0.0285 | 0.0315 | 0.91× | 110% | +|✅| a ^ b (uint32) | uint32 | 10,000,000 | 8.4267 | 5.7479 | 1.47× | 68% | +|▫| a ^ b (uint64) | uint64 | 1,000 | 0.0009 | 0.0111 | 0.078× | 1287% | +|🟡| a ^ b (uint64) | uint64 | 100,000 | 0.0341 | 0.0681 | 0.50× | 200% | +|✅| a ^ b (uint64) | uint64 | 10,000,000 | 16.9004 | 16.6136 | 1.02× | 98% | +|▫| a ^ b (uint8) | uint8 | 1,000 | 0.0006 | 0.0012 | 0.53× | 187% | +|✅| a ^ b (uint8) | uint8 | 100,000 | 0.0288 | 0.0119 | 2.42× | 41% | +|✅| a ^ b (uint8) | uint8 | 10,000,000 | 3.8171 | 1.4591 | 2.62× | 38% | +|▫| a | b (bool) | bool | 1,000 | 0.0004 | 0.0015 | 0.31× | 326% | +|🔴| a | b (bool) | bool | 100,000 | 0.0025 | 0.0232 | 0.11× | 912% | +|🟡| a | b (bool) | bool | 10,000,000 | 1.8887 | 2.8568 | 0.66× | 151% | +|▫| a | b (int16) | int16 | 1,000 | 0.0008 | 0.0023 | 0.35× | 288% | +|✅| a | b (int16) | int16 | 100,000 | 0.0285 | 0.0178 | 1.60× | 62% | +|✅| a | b (int16) | int16 | 10,000,000 | 5.2268 | 2.9832 | 1.75× | 57% | +|▫| a | b (int32) | int32 | 1,000 | 0.0008 | 0.0074 | 0.10× | 950% | +|🟡| a | b (int32) | int32 | 100,000 | 0.0286 | 0.0380 | 0.75× | 133% | +|✅| a | b (int32) | int32 | 10,000,000 | 9.0217 | 5.4574 | 1.65× | 60% | +|▫| a | b (int64) | int64 | 1,000 | 0.0008 | 0.0079 | 0.10× | 1003% | +|🟠| a | b (int64) | int64 | 100,000 | 0.0339 | 0.0707 | 0.48× | 209% | +|✅| a | b (int64) | int64 | 10,000,000 | 17.1245 | 15.6955 | 1.09× | 92% | +|▫| a | b (int8) | int8 | 1,000 | 0.0007 | 0.0014 | 0.48× | 210% | +|✅| a | b (int8) | int8 | 100,000 | 0.0300 | 0.0116 | 2.58× | 39% | +|✅| a | b (int8) | int8 | 10,000,000 | 4.1649 | 1.5132 | 2.75× | 36% | +|▫| a | b (uint16) | uint16 | 1,000 | 0.0008 | 0.0019 | 0.43× | 234% | +|✅| a | b (uint16) | uint16 | 100,000 | 0.0291 | 0.0168 | 1.73× | 58% | +|✅| a | b (uint16) | uint16 | 10,000,000 | 5.1942 | 2.8924 | 1.80× | 56% | +|▫| a | b (uint32) | uint32 | 1,000 | 0.0008 | 0.0035 | 0.22× | 446% | +|🟡| a | b (uint32) | uint32 | 100,000 | 0.0285 | 0.0334 | 0.85× | 117% | +|✅| a | b (uint32) | uint32 | 10,000,000 | 8.5824 | 5.4787 | 1.57× | 64% | +|▫| a | b (uint64) | uint64 | 1,000 | 0.0008 | 0.0022 | 0.35× | 282% | +|🟡| a | b (uint64) | uint64 | 100,000 | 0.0353 | 0.0677 | 0.52× | 192% | +|✅| a | b (uint64) | uint64 | 10,000,000 | 16.8283 | 16.1102 | 1.04× | 96% | +|▫| a | b (uint8) | uint8 | 1,000 | 0.0007 | 0.0012 | 0.56× | 177% | +|✅| a | b (uint8) | uint8 | 100,000 | 0.0296 | 0.0105 | 2.83× | 35% | +|✅| a | b (uint8) | uint8 | 10,000,000 | 4.1042 | 1.4855 | 2.76× | 36% | +|▫| np.invert(a) (bool) | bool | 1,000 | 0.0004 | 0.0015 | 0.24× | 409% | +|🔴| np.invert(a) (bool) | bool | 100,000 | 0.0021 | 0.0230 | 0.092× | 1083% | +|🟡| np.invert(a) (bool) | bool | 10,000,000 | 1.6689 | 2.5576 | 0.65× | 153% | +|▫| np.invert(a) (int16) | int16 | 1,000 | 0.0007 | 0.0018 | 0.40× | 247% | +|✅| np.invert(a) (int16) | int16 | 100,000 | 0.0257 | 0.0140 | 1.83× | 55% | +|✅| np.invert(a) (int16) | int16 | 10,000,000 | 4.7670 | 2.5132 | 1.90× | 53% | +|▫| np.invert(a) (int32) | int32 | 1,000 | 0.0008 | 0.0058 | 0.13× | 758% | +|🟡| np.invert(a) (int32) | int32 | 100,000 | 0.0264 | 0.0305 | 0.86× | 116% | +|✅| np.invert(a) (int32) | int32 | 10,000,000 | 8.1792 | 4.7721 | 1.71× | 58% | +|▫| np.invert(a) (int64) | int64 | 1,000 | 0.0007 | 0.0059 | 0.13× | 796% | +|🟠| np.invert(a) (int64) | int64 | 100,000 | 0.0263 | 0.0661 | 0.40× | 251% | +|✅| np.invert(a) (int64) | int64 | 10,000,000 | 15.1634 | 14.2955 | 1.06× | 94% | +|▫| np.invert(a) (int8) | int8 | 1,000 | 0.0006 | 0.0014 | 0.46× | 218% | +|✅| np.invert(a) (int8) | int8 | 100,000 | 0.0293 | 0.0108 | 2.72× | 37% | +|✅| np.invert(a) (int8) | int8 | 10,000,000 | 3.5390 | 1.3133 | 2.69× | 37% | +|▫| np.invert(a) (uint16) | uint16 | 1,000 | 0.0007 | 0.0022 | 0.33× | 302% | +|✅| np.invert(a) (uint16) | uint16 | 100,000 | 0.0258 | 0.0206 | 1.25× | 80% | +|✅| np.invert(a) (uint16) | uint16 | 10,000,000 | 4.7281 | 2.4541 | 1.93× | 52% | +|▫| np.invert(a) (uint32) | uint32 | 1,000 | 0.0008 | 0.0036 | 0.22× | 459% | +|✅| np.invert(a) (uint32) | uint32 | 100,000 | 0.0332 | 0.0290 | 1.15× | 87% | +|✅| np.invert(a) (uint32) | uint32 | 10,000,000 | 7.8329 | 4.8460 | 1.62× | 62% | +|▫| np.invert(a) (uint64) | uint64 | 1,000 | 0.0007 | 0.0035 | 0.21× | 484% | +|🟠| np.invert(a) (uint64) | uint64 | 100,000 | 0.0263 | 0.0652 | 0.40× | 248% | +|🟡| np.invert(a) (uint64) | uint64 | 10,000,000 | 15.2896 | 15.3374 | 1.00× | 100% | +|▫| np.invert(a) (uint8) | uint8 | 1,000 | 0.0006 | 0.0014 | 0.46× | 216% | +|✅| np.invert(a) (uint8) | uint8 | 100,000 | 0.0268 | 0.0110 | 2.45× | 41% | +|✅| np.invert(a) (uint8) | uint8 | 10,000,000 | 3.5162 | 1.3311 | 2.64× | 38% | +|🔴| np.left_shift(a, 2) (bool) | bool | 1,000 | 0.0015 | 0.0125 | 0.12× | 845% | +|🟡| np.left_shift(a, 2) (bool) | bool | 100,000 | 0.0430 | 0.0779 | 0.55× | 181% | +|✅| np.left_shift(a, 2) (bool) | bool | 10,000,000 | 15.2024 | 10.3241 | 1.47× | 68% | +|🟠| np.left_shift(a, 2) (int16) | int16 | 1,000 | 0.0010 | 0.0031 | 0.33× | 302% | +|✅| np.left_shift(a, 2) (int16) | int16 | 100,000 | 0.0285 | 0.0150 | 1.90× | 53% | +|✅| np.left_shift(a, 2) (int16) | int16 | 10,000,000 | 4.9855 | 2.6983 | 1.85× | 54% | +|▫| np.left_shift(a, 2) (int32) | int32 | 1,000 | 0.0010 | 0.0057 | 0.18× | 569% | +|🟡| np.left_shift(a, 2) (int32) | int32 | 100,000 | 0.0190 | 0.0373 | 0.51× | 196% | +|✅| np.left_shift(a, 2) (int32) | int32 | 10,000,000 | 7.6421 | 4.5654 | 1.67× | 60% | +|▫| np.left_shift(a, 2) (int64) | int64 | 1,000 | 0.0009 | 0.0038 | 0.24× | 420% | +|🟠| np.left_shift(a, 2) (int64) | int64 | 100,000 | 0.0190 | 0.0672 | 0.28× | 353% | +|✅| np.left_shift(a, 2) (int64) | int64 | 10,000,000 | 15.2053 | 14.9107 | 1.02× | 98% | +|▫| np.left_shift(a, 2) (int8) | int8 | 1,000 | 0.0009 | 0.0019 | 0.49× | 204% | +|✅| np.left_shift(a, 2) (int8) | int8 | 100,000 | 0.0305 | 0.0085 | 3.58× | 28% | +|✅| np.left_shift(a, 2) (int8) | int8 | 10,000,000 | 3.7477 | 1.2738 | 2.94× | 34% | +|🟠| np.left_shift(a, 2) (uint16) | uint16 | 1,000 | 0.0011 | 0.0023 | 0.45× | 222% | +|✅| np.left_shift(a, 2) (uint16) | uint16 | 100,000 | 0.0290 | 0.0163 | 1.78× | 56% | +|✅| np.left_shift(a, 2) (uint16) | uint16 | 10,000,000 | 5.3571 | 2.5057 | 2.14× | 47% | +|▫| np.left_shift(a, 2) (uint32) | uint32 | 1,000 | 0.0010 | 0.0045 | 0.22× | 456% | +|🟡| np.left_shift(a, 2) (uint32) | uint32 | 100,000 | 0.0192 | 0.0272 | 0.70× | 142% | +|✅| np.left_shift(a, 2) (uint32) | uint32 | 10,000,000 | 7.9134 | 5.0757 | 1.56× | 64% | +|▫| np.left_shift(a, 2) (uint64) | uint64 | 1,000 | 0.0010 | 0.0096 | 0.10× | 1000% | +|🟠| np.left_shift(a, 2) (uint64) | uint64 | 100,000 | 0.0194 | 0.0697 | 0.28× | 359% | +|🟡| np.left_shift(a, 2) (uint64) | uint64 | 10,000,000 | 15.2926 | 15.7026 | 0.97× | 103% | +|▫| np.left_shift(a, 2) (uint8) | uint8 | 1,000 | 0.0009 | 0.0023 | 0.40× | 250% | +|✅| np.left_shift(a, 2) (uint8) | uint8 | 100,000 | 0.0292 | 0.0131 | 2.22× | 45% | +|✅| np.left_shift(a, 2) (uint8) | uint8 | 10,000,000 | 3.6979 | 1.2844 | 2.88× | 35% | +|🔴| np.right_shift(a, 2) (bool) | bool | 1,000 | 0.0016 | 0.0135 | 0.12× | 844% | +|🟡| np.right_shift(a, 2) (bool) | bool | 100,000 | 0.0529 | 0.0750 | 0.71× | 142% | +|✅| np.right_shift(a, 2) (bool) | bool | 10,000,000 | 15.2744 | 9.3866 | 1.63× | 62% | +|🟠| np.right_shift(a, 2) (int16) | int16 | 1,000 | 0.0011 | 0.0026 | 0.44× | 226% | +|✅| np.right_shift(a, 2) (int16) | int16 | 100,000 | 0.0377 | 0.0158 | 2.38× | 42% | +|✅| np.right_shift(a, 2) (int16) | int16 | 10,000,000 | 5.7282 | 2.6585 | 2.15× | 46% | +|🟠| np.right_shift(a, 2) (int32) | int32 | 1,000 | 0.0011 | 0.0038 | 0.28× | 354% | +|🟡| np.right_shift(a, 2) (int32) | int32 | 100,000 | 0.0288 | 0.0378 | 0.76× | 131% | +|✅| np.right_shift(a, 2) (int32) | int32 | 10,000,000 | 7.8870 | 4.3972 | 1.79× | 56% | +|🟠| np.right_shift(a, 2) (int64) | int64 | 1,000 | 0.0013 | 0.0039 | 0.32× | 309% | +|🟠| np.right_shift(a, 2) (int64) | int64 | 100,000 | 0.0287 | 0.0702 | 0.41× | 244% | +|✅| np.right_shift(a, 2) (int64) | int64 | 10,000,000 | 15.0917 | 14.2202 | 1.06× | 94% | +|🟡| np.right_shift(a, 2) (int8) | int8 | 1,000 | 0.0010 | 0.0019 | 0.54× | 185% | +|✅| np.right_shift(a, 2) (int8) | int8 | 100,000 | 0.0436 | 0.0108 | 4.02× | 25% | +|✅| np.right_shift(a, 2) (int8) | int8 | 10,000,000 | 4.7856 | 1.2716 | 3.76× | 27% | +|🟠| np.right_shift(a, 2) (uint16) | uint16 | 1,000 | 0.0011 | 0.0029 | 0.36× | 275% | +|✅| np.right_shift(a, 2) (uint16) | uint16 | 100,000 | 0.0283 | 0.0170 | 1.67× | 60% | +|✅| np.right_shift(a, 2) (uint16) | uint16 | 10,000,000 | 5.1786 | 2.5878 | 2.00× | 50% | +|▫| np.right_shift(a, 2) (uint32) | uint32 | 1,000 | 0.0010 | 0.0036 | 0.27× | 368% | +|🟡| np.right_shift(a, 2) (uint32) | uint32 | 100,000 | 0.0205 | 0.0284 | 0.72× | 138% | +|✅| np.right_shift(a, 2) (uint32) | uint32 | 10,000,000 | 7.7130 | 4.7119 | 1.64× | 61% | +|▫| np.right_shift(a, 2) (uint64) | uint64 | 1,000 | 0.0010 | 0.0108 | 0.091× | 1100% | +|🟠| np.right_shift(a, 2) (uint64) | uint64 | 100,000 | 0.0212 | 0.0750 | 0.28× | 354% | +|🟡| np.right_shift(a, 2) (uint64) | uint64 | 10,000,000 | 14.9083 | 16.6474 | 0.90× | 112% | +|▫| np.right_shift(a, 2) (uint8) | uint8 | 1,000 | 0.0009 | 0.0021 | 0.44× | 227% | +|✅| np.right_shift(a, 2) (uint8) | uint8 | 100,000 | 0.0296 | 0.0095 | 3.12× | 32% | +|✅| np.right_shift(a, 2) (uint8) | uint8 | 10,000,000 | 3.7353 | 1.2943 | 2.89× | 35% | + +### Logic + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|⚪| np.all(a) (bool) | bool | 1,000 | 0.0015 | - | - | - | +|⚪| np.all(a) (bool) | bool | 100,000 | 0.0014 | - | - | - | +|⚪| np.all(a) (bool) | bool | 10,000,000 | 0.0014 | - | - | - | +|⚪| np.allclose(a, b) (float16) | float16 | 1,000 | 0.0305 | - | - | - | +|⚪| np.allclose(a, b) (float16) | float16 | 100,000 | 1.7794 | - | - | - | +|⚪| np.allclose(a, b) (float16) | float16 | 10,000,000 | 192.9692 | - | - | - | +|⚪| np.allclose(a, b) (float32) | float32 | 1,000 | 0.0132 | - | - | - | +|⚪| np.allclose(a, b) (float32) | float32 | 100,000 | 0.3660 | - | - | - | +|⚪| np.allclose(a, b) (float32) | float32 | 10,000,000 | 55.8239 | - | - | - | +|⚪| np.allclose(a, b) (float64) | float64 | 1,000 | 0.0165 | - | - | - | +|⚪| np.allclose(a, b) (float64) | float64 | 100,000 | 0.6299 | - | - | - | +|⚪| np.allclose(a, b) (float64) | float64 | 10,000,000 | 107.5215 | - | - | - | +|⚪| np.any(a) (bool) | bool | 1,000 | 0.0015 | - | - | - | +|⚪| np.any(a) (bool) | bool | 100,000 | 0.0017 | - | - | - | +|⚪| np.any(a) (bool) | bool | 10,000,000 | 0.0014 | - | - | - | +|✅| np.array_equal(a, b) (float16) | float16 | 1,000 | 0.0025 | 0.0013 | 1.90× | 53% | +|✅| np.array_equal(a, b) (float16) | float16 | 100,000 | 0.0873 | 0.0697 | 1.25× | 80% | +|✅| np.array_equal(a, b) (float16) | float16 | 10,000,000 | 9.5077 | 6.1828 | 1.54× | 65% | +|▫| np.array_equal(a, b) (float32) | float32 | 1,000 | 0.0016 | 0.0008 | 1.95× | 51% | +|🟠| np.array_equal(a, b) (float32) | float32 | 100,000 | 0.0067 | 0.0186 | 0.36× | 276% | +|✅| np.array_equal(a, b) (float32) | float32 | 10,000,000 | 3.9686 | 3.4029 | 1.17× | 86% | +|▫| np.array_equal(a, b) (float64) | float64 | 1,000 | 0.0018 | 0.0007 | 2.36× | 42% | +|🟠| np.array_equal(a, b) (float64) | float64 | 100,000 | 0.0121 | 0.0248 | 0.49× | 204% | +|✅| np.array_equal(a, b) (float64) | float64 | 10,000,000 | 6.8297 | 5.8386 | 1.17× | 86% | +|⚪| np.isclose(a, b) (float16) | float16 | 1,000 | 0.0300 | - | - | - | +|⚪| np.isclose(a, b) (float16) | float16 | 100,000 | 1.7764 | - | - | - | +|⚪| np.isclose(a, b) (float16) | float16 | 10,000,000 | 195.6207 | - | - | - | +|⚪| np.isclose(a, b) (float32) | float32 | 1,000 | 0.0113 | - | - | - | +|⚪| np.isclose(a, b) (float32) | float32 | 100,000 | 0.3666 | - | - | - | +|⚪| np.isclose(a, b) (float32) | float32 | 10,000,000 | 55.1472 | - | - | - | +|⚪| np.isclose(a, b) (float64) | float64 | 1,000 | 0.0116 | - | - | - | +|⚪| np.isclose(a, b) (float64) | float64 | 100,000 | 0.6275 | - | - | - | +|⚪| np.isclose(a, b) (float64) | float64 | 10,000,000 | 104.7217 | - | - | - | +|▫| np.isfinite(a) (float16) | float16 | 1,000 | 0.0008 | 0.0015 | 0.57× | 175% | +|✅| np.isfinite(a) (float16) | float16 | 100,000 | 0.0496 | 0.0388 | 1.28× | 78% | +|✅| np.isfinite(a) (float16) | float16 | 10,000,000 | 5.8734 | 3.1098 | 1.89× | 53% | +|▫| np.isfinite(a) (float32) | float32 | 1,000 | 0.0004 | 0.0012 | 0.33× | 302% | +|🔴| np.isfinite(a) (float32) | float32 | 100,000 | 0.0059 | 0.0364 | 0.16× | 617% | +|🟡| np.isfinite(a) (float32) | float32 | 10,000,000 | 3.3272 | 3.8128 | 0.87× | 115% | +|▫| np.isfinite(a) (float64) | float64 | 1,000 | 0.0005 | 0.0014 | 0.32× | 314% | +|🟠| np.isfinite(a) (float64) | float64 | 100,000 | 0.0098 | 0.0365 | 0.27× | 374% | +|🟡| np.isfinite(a) (float64) | float64 | 10,000,000 | 5.3098 | 5.6920 | 0.93× | 107% | +|▫| np.isinf(a) (float16) | float16 | 1,000 | 0.0009 | 0.0012 | 0.76× | 132% | +|✅| np.isinf(a) (float16) | float16 | 100,000 | 0.0506 | 0.0386 | 1.31× | 76% | +|✅| np.isinf(a) (float16) | float16 | 10,000,000 | 6.0644 | 3.1035 | 1.95× | 51% | +|▫| np.isinf(a) (float32) | float32 | 1,000 | 0.0006 | 0.0013 | 0.41× | 241% | +|🔴| np.isinf(a) (float32) | float32 | 100,000 | 0.0058 | 0.0416 | 0.14× | 721% | +|🟡| np.isinf(a) (float32) | float32 | 10,000,000 | 3.0154 | 3.9983 | 0.75× | 133% | +|▫| np.isinf(a) (float64) | float64 | 1,000 | 0.0005 | 0.0015 | 0.32× | 312% | +|🟠| np.isinf(a) (float64) | float64 | 100,000 | 0.0101 | 0.0433 | 0.23× | 429% | +|🟡| np.isinf(a) (float64) | float64 | 10,000,000 | 5.2606 | 5.9503 | 0.88× | 113% | +|✅| np.isnan(a) (float16) | float16 | 1,000 | 0.0014 | 0.0012 | 1.12× | 89% | +|✅| np.isnan(a) (float16) | float16 | 100,000 | 0.0692 | 0.0397 | 1.74× | 57% | +|✅| np.isnan(a) (float16) | float16 | 10,000,000 | 7.7981 | 3.1130 | 2.50× | 40% | +|▫| np.isnan(a) (float32) | float32 | 1,000 | 0.0005 | 0.0016 | 0.34× | 298% | +|🔴| np.isnan(a) (float32) | float32 | 100,000 | 0.0044 | 0.0482 | 0.092× | 1091% | +|🟡| np.isnan(a) (float32) | float32 | 10,000,000 | 2.9244 | 4.7104 | 0.62× | 161% | +|▫| np.isnan(a) (float64) | float64 | 1,000 | 0.0004 | 0.0013 | 0.33× | 300% | +|🔴| np.isnan(a) (float64) | float64 | 100,000 | 0.0085 | 0.0483 | 0.18× | 569% | +|🟡| np.isnan(a) (float64) | float64 | 10,000,000 | 4.9310 | 6.0302 | 0.82× | 122% | +|✅| np.maximum(a, b) (float16) | float16 | 1,000 | 0.0031 | 0.0030 | 1.02× | 98% | +|✅| np.maximum(a, b) (float16) | float16 | 100,000 | 0.7598 | 0.6663 | 1.14× | 88% | +|✅| np.maximum(a, b) (float16) | float16 | 10,000,000 | 80.9858 | 66.1452 | 1.22× | 82% | +|▫| np.maximum(a, b) (float32) | float32 | 1,000 | 0.0006 | 0.0013 | 0.45× | 223% | +|🟠| np.maximum(a, b) (float32) | float32 | 100,000 | 0.0084 | 0.0343 | 0.25× | 407% | +|✅| np.maximum(a, b) (float32) | float32 | 10,000,000 | 8.3101 | 4.3746 | 1.90× | 53% | +|▫| np.maximum(a, b) (float64) | float64 | 1,000 | 0.0006 | 0.0018 | 0.31× | 323% | +|🟠| np.maximum(a, b) (float64) | float64 | 100,000 | 0.0293 | 0.0803 | 0.36× | 274% | +|🟡| np.maximum(a, b) (float64) | float64 | 10,000,000 | 16.5750 | 17.2376 | 0.96× | 104% | +|✅| np.minimum(a, b) (float16) | float16 | 1,000 | 0.0032 | 0.0031 | 1.03× | 97% | +|✅| np.minimum(a, b) (float16) | float16 | 100,000 | 0.7620 | 0.6658 | 1.14× | 87% | +|✅| np.minimum(a, b) (float16) | float16 | 10,000,000 | 81.4032 | 65.4062 | 1.25× | 80% | +|▫| np.minimum(a, b) (float32) | float32 | 1,000 | 0.0005 | 0.0013 | 0.42× | 240% | +|🟠| np.minimum(a, b) (float32) | float32 | 100,000 | 0.0084 | 0.0345 | 0.24× | 411% | +|✅| np.minimum(a, b) (float32) | float32 | 10,000,000 | 8.4413 | 4.4356 | 1.90× | 52% | +|▫| np.minimum(a, b) (float64) | float64 | 1,000 | 0.0006 | 0.0013 | 0.44× | 228% | +|🟠| np.minimum(a, b) (float64) | float64 | 100,000 | 0.0290 | 0.0866 | 0.34× | 298% | +|🟡| np.minimum(a, b) (float64) | float64 | 10,000,000 | 16.8958 | 20.5756 | 0.82× | 122% | + +### Statistics + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|✅| np.average(a) (float16) | float16 | 1,000 | 0.0054 | 0.0012 | 4.48× | 22% | +|✅| np.average(a) (float16) | float16 | 100,000 | 0.1147 | 0.0798 | 1.44× | 70% | +|✅| np.average(a) (float16) | float16 | 10,000,000 | 10.5817 | 8.0311 | 1.32× | 76% | +|▫| np.average(a) (float32) | float32 | 1,000 | 0.0042 | 0.0007 | 5.80× | 17% | +|✅| np.average(a) (float32) | float32 | 100,000 | 0.0177 | 0.0032 | 5.57× | 18% | +|✅| np.average(a) (float32) | float32 | 10,000,000 | 2.8186 | 0.9734 | 2.90× | 34% | +|▫| np.average(a) (float64) | float64 | 1,000 | 0.0029 | 0.0007 | 3.89× | 26% | +|✅| np.average(a) (float64) | float64 | 100,000 | 0.0171 | 0.0040 | 4.26× | 23% | +|✅| np.average(a) (float64) | float64 | 10,000,000 | 4.6529 | 2.8474 | 1.63× | 61% | +|▫| np.count_nonzero(a) (float16) | float16 | 1,000 | 0.0018 | 0.0008 | 2.29× | 44% | +|✅| np.count_nonzero(a) (float16) | float16 | 100,000 | 0.1447 | 0.0436 | 3.32× | 30% | +|✅| np.count_nonzero(a) (float16) | float16 | 10,000,000 | 15.7587 | 4.3062 | 3.66× | 27% | +|▫| np.count_nonzero(a) (float32) | float32 | 1,000 | 0.0006 | 0.0003 | 1.87× | 53% | +|✅| np.count_nonzero(a) (float32) | float32 | 100,000 | 0.0438 | 0.0050 | 8.69× | 12% | +|✅| np.count_nonzero(a) (float32) | float32 | 10,000,000 | 4.2117 | 1.6445 | 2.56× | 39% | +|▫| np.count_nonzero(a) (float64) | float64 | 1,000 | 0.0007 | 0.0004 | 1.82× | 55% | +|✅| np.count_nonzero(a) (float64) | float64 | 100,000 | 0.0376 | 0.0090 | 4.19× | 24% | +|✅| np.count_nonzero(a) (float64) | float64 | 10,000,000 | 5.6444 | 3.9133 | 1.44× | 69% | +|✅| np.median(a) (float16) | float16 | 1,000 | 0.0144 | 0.0041 | 3.52× | 28% | +|🟡| np.median(a) (float16) | float16 | 100,000 | 0.8796 | 1.2503 | 0.70× | 142% | +|✅| np.median(a) (float16) | float16 | 10,000,000 | 105.7899 | 88.7294 | 1.19× | 84% | +|✅| np.median(a) (float32) | float32 | 1,000 | 0.0110 | 0.0023 | 4.80× | 21% | +|🟡| np.median(a) (float32) | float32 | 100,000 | 0.4686 | 0.6988 | 0.67× | 149% | +|🟡| np.median(a) (float32) | float32 | 10,000,000 | 73.7480 | 79.7326 | 0.93× | 108% | +|✅| np.median(a) (float64) | float64 | 1,000 | 0.0098 | 0.0023 | 4.23× | 24% | +|🟡| np.median(a) (float64) | float64 | 100,000 | 0.4653 | 0.7089 | 0.66× | 152% | +|🟡| np.median(a) (float64) | float64 | 10,000,000 | 86.4309 | 88.8375 | 0.97× | 103% | +|✅| np.percentile(a, 50) (float16) | float16 | 1,000 | 0.0288 | 0.0041 | 6.97× | 14% | +|✅| np.percentile(a, 50) (float16) | float16 | 100,000 | 1.7825 | 1.2489 | 1.43× | 70% | +|✅| np.percentile(a, 50) (float16) | float16 | 10,000,000 | 113.4806 | 88.7652 | 1.28× | 78% | +|✅| np.percentile(a, 50) (float32) | float32 | 1,000 | 0.0243 | 0.0023 | 10.61× | 9% | +|✅| np.percentile(a, 50) (float32) | float32 | 100,000 | 0.7026 | 0.7007 | 1.00× | 100% | +|🟡| np.percentile(a, 50) (float32) | float32 | 10,000,000 | 47.5374 | 79.7748 | 0.60× | 168% | +|✅| np.percentile(a, 50) (float64) | float64 | 1,000 | 0.0258 | 0.0023 | 11.00× | 9% | +|🟡| np.percentile(a, 50) (float64) | float64 | 100,000 | 0.7045 | 0.7086 | 0.99× | 101% | +|🟡| np.percentile(a, 50) (float64) | float64 | 10,000,000 | 58.5689 | 88.9709 | 0.66× | 152% | +|✅| np.ptp(a) (float16) | float16 | 1,000 | 0.0077 | 0.0037 | 2.10× | 48% | +|✅| np.ptp(a) (float16) | float16 | 100,000 | 1.0004 | 0.6489 | 1.54× | 65% | +|✅| np.ptp(a) (float16) | float16 | 10,000,000 | 103.1114 | 67.0357 | 1.54× | 65% | +|✅| np.ptp(a) (float32) | float32 | 1,000 | 0.0035 | 0.0022 | 1.59× | 63% | +|✅| np.ptp(a) (float32) | float32 | 100,000 | 0.0114 | 0.0080 | 1.44× | 70% | +|✅| np.ptp(a) (float32) | float32 | 10,000,000 | 2.6949 | 2.4569 | 1.10× | 91% | +|✅| np.ptp(a) (float64) | float64 | 1,000 | 0.0037 | 0.0020 | 1.88× | 53% | +|✅| np.ptp(a) (float64) | float64 | 100,000 | 0.0200 | 0.0134 | 1.49× | 67% | +|✅| np.ptp(a) (float64) | float64 | 10,000,000 | 6.9463 | 6.2250 | 1.12× | 90% | +|✅| np.quantile(a, 0.5) (float16) | float16 | 1,000 | 0.0283 | 0.0041 | 6.89× | 14% | +|✅| np.quantile(a, 0.5) (float16) | float16 | 100,000 | 1.7668 | 1.2481 | 1.42× | 71% | +|✅| np.quantile(a, 0.5) (float16) | float16 | 10,000,000 | 112.7790 | 88.6407 | 1.27× | 79% | +|✅| np.quantile(a, 0.5) (float32) | float32 | 1,000 | 0.0237 | 0.0023 | 10.32× | 10% | +|🟡| np.quantile(a, 0.5) (float32) | float32 | 100,000 | 0.6938 | 0.7016 | 0.99× | 101% | +|🟡| np.quantile(a, 0.5) (float32) | float32 | 10,000,000 | 47.6198 | 79.7894 | 0.60× | 168% | +|✅| np.quantile(a, 0.5) (float64) | float64 | 1,000 | 0.0239 | 0.0023 | 10.21× | 10% | +|🟡| np.quantile(a, 0.5) (float64) | float64 | 100,000 | 0.7004 | 0.7083 | 0.99× | 101% | +|🟡| np.quantile(a, 0.5) (float64) | float64 | 10,000,000 | 58.1853 | 89.0162 | 0.65× | 153% | + +### Sorting + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|🟡| np.argsort(a) (float32) | float32 | 1,000 | 0.0120 | 0.0223 | 0.54× | 186% | +|✅| np.argsort(a) (float32) | float32 | 100,000 | 1.5922 | 1.0142 | 1.57× | 64% | +|✅| np.argsort(a) (float32) | float32 | 10,000,000 | 1348.2730 | 132.4664 | 10.18× | 10% | +|🟡| np.argsort(a) (float64) | float64 | 1,000 | 0.0104 | 0.0177 | 0.59× | 171% | +|🟡| np.argsort(a) (float64) | float64 | 100,000 | 1.4280 | 2.5193 | 0.57× | 176% | +|✅| np.argsort(a) (float64) | float64 | 10,000,000 | 1768.7035 | 251.4880 | 7.03× | 14% | +|🟡| np.argsort(a) (int32) | int32 | 1,000 | 0.0113 | 0.0221 | 0.51× | 196% | +|🟠| np.argsort(a) (int32) | int32 | 100,000 | 0.4031 | 0.8504 | 0.47× | 211% | +|✅| np.argsort(a) (int32) | int32 | 10,000,000 | 174.0979 | 107.5057 | 1.62× | 62% | +|🟡| np.argsort(a) (int64) | int64 | 1,000 | 0.0131 | 0.0167 | 0.78× | 128% | +|🟠| np.argsort(a) (int64) | int64 | 100,000 | 0.4917 | 2.2549 | 0.22× | 459% | +|✅| np.argsort(a) (int64) | int64 | 10,000,000 | 310.5208 | 188.5734 | 1.65× | 61% | +|✅| np.nonzero(a) (float32) | float32 | 1,000 | 0.0027 | 0.0021 | 1.25× | 80% | +|✅| np.nonzero(a) (float32) | float32 | 100,000 | 0.1988 | 0.1946 | 1.02× | 98% | +|✅| np.nonzero(a) (float32) | float32 | 10,000,000 | 39.8712 | 14.1945 | 2.81× | 36% | +|✅| np.nonzero(a) (float64) | float64 | 1,000 | 0.0029 | 0.0020 | 1.44× | 70% | +|🟡| np.nonzero(a) (float64) | float64 | 100,000 | 0.1923 | 0.2002 | 0.96× | 104% | +|✅| np.nonzero(a) (float64) | float64 | 10,000,000 | 42.6329 | 17.6206 | 2.42× | 41% | +|🟡| np.nonzero(a) (int32) | int32 | 1,000 | 0.0017 | 0.0028 | 0.61× | 164% | +|🟡| np.nonzero(a) (int32) | int32 | 100,000 | 0.1051 | 0.1932 | 0.54× | 184% | +|✅| np.nonzero(a) (int32) | int32 | 10,000,000 | 20.1149 | 14.3503 | 1.40× | 71% | +|🟡| np.nonzero(a) (int64) | int64 | 1,000 | 0.0018 | 0.0026 | 0.69× | 144% | +|🟡| np.nonzero(a) (int64) | int64 | 100,000 | 0.1073 | 0.2034 | 0.53× | 190% | +|✅| np.nonzero(a) (int64) | int64 | 10,000,000 | 23.7474 | 18.2560 | 1.30× | 77% | +|✅| np.searchsorted(a, v) (float32) | float32 | 1,000 | 0.0083 | 0.0053 | 1.56× | 64% | +|✅| np.searchsorted(a, v) (float32) | float32 | 100,000 | 2.0222 | 1.7299 | 1.17× | 86% | +|✅| np.searchsorted(a, v) (float32) | float32 | 10,000,000 | 312.1823 | 191.5647 | 1.63× | 61% | +|✅| np.searchsorted(a, v) (float64) | float64 | 1,000 | 0.0083 | 0.0053 | 1.56× | 64% | +|✅| np.searchsorted(a, v) (float64) | float64 | 100,000 | 2.0961 | 1.7424 | 1.20× | 83% | +|✅| np.searchsorted(a, v) (float64) | float64 | 10,000,000 | 310.2307 | 192.5449 | 1.61× | 62% | +|✅| np.searchsorted(a, v) (int32) | int32 | 1,000 | 0.0190 | 0.0075 | 2.54× | 39% | +|✅| np.searchsorted(a, v) (int32) | int32 | 100,000 | 2.8983 | 2.2559 | 1.28× | 78% | +|✅| np.searchsorted(a, v) (int32) | int32 | 10,000,000 | 390.1343 | 243.3194 | 1.60× | 62% | +|✅| np.searchsorted(a, v) (int64) | int64 | 1,000 | 0.0186 | 0.0071 | 2.61× | 38% | +|✅| np.searchsorted(a, v) (int64) | int64 | 100,000 | 2.9016 | 2.2381 | 1.30× | 77% | +|✅| np.searchsorted(a, v) (int64) | int64 | 10,000,000 | 568.2151 | 243.9343 | 2.33× | 43% | + +### LinearAlgebra + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|▫| np.dot(a, b) (float64) | float64 | 1,000 | 0.0007 | 0.0007 | 1.06× | 94% | +|✅| np.dot(a, b) (float64) | float64 | 100,000 | 0.1123 | 0.0093 | 12.05× | 8% | +|🟠| np.dot(a, b) (float64) | float64 | 10,000,000 | 0.8690 | 3.0303 | 0.29× | 349% | +|🟠| np.matmul(A, B) (float64) | float64 | 1,000 | 0.0027 | 0.0060 | 0.45× | 223% | +|🟠| np.matmul(A, B) (float64) | float64 | 100,000 | 0.5771 | 2.7815 | 0.21× | 482% | +|🔴| np.matmul(A, B) (float64) | float64 | 10,000,000 | 0.6896 | 4.4742 | 0.15× | 649% | +|🟠| np.outer(a, b) (float64) | float64 | 1,000 | 0.0022 | 0.0061 | 0.35× | 284% | +|🟡| np.outer(a, b) (float64) | float64 | 100,000 | 0.0380 | 0.0708 | 0.54× | 186% | +|✅| np.outer(a, b) (float64) | float64 | 10,000,000 | 13.4954 | 11.7542 | 1.15× | 87% | + +### Selection + +| | Operation | Type | N | NumPy (ms) | NumSharp (ms) | Ratio | %NumPy🕐 | +|:-:|-----------|:----:|----:|----------:|-------------:|------:|--------:| +|▫| np.where(cond) (float64) | float64 | 1,000 | 0.0009 | 0.0014 | 0.66× | 152% | +|🟠| np.where(cond) (float64) | float64 | 100,000 | 0.0294 | 0.0981 | 0.30× | 333% | +|✅| np.where(cond) (float64) | float64 | 10,000,000 | 7.4500 | 6.9913 | 1.07× | 94% | +|🟡| np.where(cond, a, b) (float64) | float64 | 1,000 | 0.0017 | 0.0020 | 0.85× | 117% | +|🟡| np.where(cond, a, b) (float64) | float64 | 100,000 | 0.0399 | 0.0688 | 0.58× | 173% | +|✅| np.where(cond, a, b) (float64) | float64 | 10,000,000 | 17.1294 | 14.8189 | 1.16× | 86% | + +--- + +## NDIter iterator benchmark + +_Complementary harness: measures the iterator machinery itself (construction, traversal, reductions, selection, dtypes, pathologies, dividends) across cache tiers — not part of the op/dtype/N matrix above. speedup = NumPy / NumSharp; NA = section ignored due to a known intermittent NumSharp AccessViolation._ + +``` +NumSharp NDIter — canonical benchmark · 2026-06-29 · speedup = NumPy ÷ NumSharp (>1.0× = NumSharp faster) +198 measured pairs (35 NA) · best-of-rounds, Release · matched kernels/ids +%NumPy🕐 = NumSharp ÷ NumPy × 100 = share of NumPy's time NumSharp uses (8% = takes only 8% as long; <100% = faster) + +AV POLICY — a NumSharp section that crashes all retries (known intermittent +AccessViolation, an unmanaged-storage lifetime bug) is reported NA / IGNORED +and excluded from every geomean below. THIS RUN: NA across selection. + +HEADLINE — operation matrix: 1.20× geomean · 83%🕐 of NumPy's time · 77 win / 53 lose over 130 cells + +OPERATIONS — BY SIZE TIER (geomean over all families) + slower ◄───────── 1.0 (parity) ─────────► faster +scalar ████████████▌ ...... 1.26× 79%🕐 ( 17 win / 9 lose) +1K ███████████▌ ....... 1.16× 86%🕐 ( 15 win / 11 lose) +100K ██████████▋ ........ 1.07× 94%🕐 ( 12 win / 14 lose) +1M ████████████▉ ...... 1.30× 77%🕐 ( 17 win / 9 lose) +10M ████████████▎ ...... 1.23× 82%🕐 ( 16 win / 10 lose) +ALL ███████████▉ ....... 1.20× 83%🕐 ( 77 win / 53 lose) + +OPERATIONS — BY CATEGORY (geomean over its families, all sizes) + slower ◄───────── 1.0 (parity) ─────────► faster +elementwise████████████▊ ...... 1.28× 78%🕐 ( 31 win / 9 lose) +reductions █████████████████▍ 1.74× 57%🕐 ( 29 win / 11 lose) +selection (no data) +copy/cast ███████▎ ........... 0.73× 138%🕐 ( 9 win / 16 lose) ◄ SLOWER +index-math ███████▋ ........... 0.77× 130%🕐 ( 4 win / 6 lose) ◄ SLOWER +dtypes ███████████▌ ....... 1.16× 86%🕐 ( 4 win / 11 lose) + +CATEGORY × TIER geomean +category scalar 1K 100K 1M 10M +elementwise 1.65× 1.54× 1.14× 1.10× 1.08× +reductions 2.68× 2.00× 1.51× 1.43× 1.38× +selection - - - - - +copy/cast 0.59× 0.53× 0.41× 1.40× 1.12× +index-math 0.34× 0.51× 0.99× 1.23× 1.26× +dtypes 0.70× 0.81× 1.87× 1.39× 1.44× + +PER-FAMILY × TIER (NumPy ÷ NumSharp; >1.0 = NumSharp faster) +family scalar 1K 100K 1M 10M geomean +-- elementwise + add 1.61× 1.43× 0.96× 1.00× 1.02× 1.18× + sqrt 1.67× 1.16× 1.01× 1.00× 1.01× 1.15× + copy 1.67× 2.35× 1.62× 1.39× 1.64× 1.71× + strided 1.65× 1.28× 0.93× 1.01× 0.99× 1.15× + bcast 1.68× 1.32× 0.89× 0.95× 0.95× 1.12× + reversed 1.65× 1.26× 0.93× 0.99× 0.95× 1.13× + castbuf 1.87× 2.20× 1.63× 1.36× 1.12× 1.59× + mixbuf 1.42× 1.72× 1.41× 1.20× 1.07× 1.35× +-- reductions + sum 1.92× 1.85× 2.58× 1.76× 1.60× 1.92× + sum ax0 1.71× 0.86× 1.10× 0.96× 0.96× 1.09× + sum ax1 1.81× 0.92× 1.52× 1.79× 1.58× 1.48× + sum dt= 1.89× 1.35× 0.48× 0.46× 0.54× 0.79× + amin 1.69× 1.62× 0.71× 0.71× 0.76× 1.01× + cumsum 1.35× 1.13× 1.07× 1.87× 1.65× 1.38× + any(F) 10.04× 8.39× 2.00× 1.23× 1.00× 2.90× + any(hit) 10.25× 8.49× 8.50× 7.88× 7.98× 8.58× +-- selection + where NA NA NA NA NA + a[mask] NA NA NA NA NA + a[mask]= NA NA NA NA NA + count_nz NA NA NA NA NA + argwhere NA NA NA NA NA + a[idx] NA NA NA NA NA + a[idx]= NA NA NA NA NA +-- copy/cast + flatten 0.41× 0.33× 0.17× 2.21× 1.13× 0.56× + astype 0.31× 0.53× 0.54× 1.94× 1.89× 0.80× + ravel.T 0.50× 0.58× 0.53× 2.22× 1.09× 0.82× + in-place 1.45× 0.77× 0.96× 1.03× 1.04× 1.03× + less->b 0.81× 0.52× 0.25× 0.55× 0.75× 0.53× +-- index-math + unravel 0.36× 0.50× 0.96× 1.00× 1.04× 0.71× + ravel_mi 0.32× 0.53× 1.01× 1.53× 1.54× 0.83× +-- dtypes + complex 0.71× 0.58× 0.97× 0.77× 0.93× 0.78× + float16 0.72× 0.64× 0.58× 0.56× 0.57× 0.61× + int8 0.66× 1.43× 11.53× 6.16× 5.66× 3.28× + +CONSTRUCTION — iterator build+dispose vs np.nditer (size-invariant, 1K) + slower ◄───────── 1.0 (parity) ─────────► faster +1op █████████▋ ......... 0.97× 103%🕐 ( 0 win / 1 lose) ◄ SLOWER +3op_exl ███████████████████▶ 2.44× 41%🕐 ( 1 win / 0 lose) +ufunc ███████████████████▶ 2.91× 34%🕐 ( 1 win / 0 lose) +bufcast ███████████████████▶ 2.56× 39%🕐 ( 1 win / 0 lose) +multiindex █████████████▉ ..... 1.40× 72%🕐 ( 1 win / 0 lose) +8op ███████████████████▶ 3.67× 27%🕐 ( 1 win / 0 lose) +4d █████████████████▊ 1.78× 56%🕐 ( 1 win / 0 lose) +8d ██████████████████▌ 1.85× 54%🕐 ( 1 win / 0 lose) +strided2d ███████████████████▌ 1.95× 51%🕐 ( 1 win / 0 lose) +geomean ███████████████████▶ 2.03× 49%🕐 ( 8 win / 1 lose) + +CHUNK-WIDTH dispatch — strided rows, 2M total, inner width w (NumPy = np.positive) + slower ◄───────── 1.0 (parity) ─────────► faster +w=4 ██████▉ ............ 0.69× 145%🕐 ( 0 win / 1 lose) ◄ SLOWER +w=16 █████████▊ ......... 0.98× 102%🕐 ( 0 win / 1 lose) ◄ PARITY +w=64 ██████████▏ ........ 1.02× 98%🕐 ( 1 win / 0 lose) ◄ PARITY +w=256 ██████████████▌ .... 1.45× 69%🕐 ( 1 win / 0 lose) +w=1024 █████████████ ...... 1.31× 76%🕐 ( 1 win / 0 lose) + +PATHOLOGY canaries — known taxes/losses to track (NumPy ÷ NumSharp) + bcast_reduce 522.42× (522.4× faster, faster) + allocate 1.06× (1.1× faster, faster) + overlap_copy 1.77× (1.8× faster, faster) + forder_out 1.17× (1.2× faster, faster) + zerodim 1.55× (1.6× faster, faster) + +DIVIDENDS — NumSharp-only machinery (NumPy baseline = closest it can do) + scalar 1K 100K 1M 10M note +fuse7 12.55× 3.86× 1.44× 1.67× 2.09× vs chained 6× add +reuse 6.06× 6.12× 1.07× 1.00× 1.00× vs rebuild each call +par8 - 0.67× 2.78× 3.95× 5.40× vs single-thread + +biggest NumSharp wins: i8@100K 11.53× · anyeh@1 10.25× · anyff@1 10.04× · anyeh@100K 8.50× · anyeh@1K 8.49× +most behind: flatten@100K 0.17× · lessbool@100K 0.25× · astype@1 0.31× · ravelmi@1 0.32× · flatten@1K 0.33× +``` + + +--- + +## Layout suite — reduction / copy / elementwise × memory layout × dtype + +ratio = NumPy_ms / NumSharp_ms — **>1.0 = NumSharp faster**. ✅≥1.0 🟡≥0.5 🟠≥0.2 🔴<0.2. +Layouts (8, harmonized with the cast subsystem): `C`, `F` (Fortran), `T` (transpose), `strided` `[:, ::2]`, `sliced` (offset), `negrow` `[::-1,:]`, `negcol` `[:,::-1]`, `bcast` (stride-0). Fills the op-matrix's blind spot (it measures C-contiguous only). 100K + 1M elements, best-of-rounds. + +### Reduction (sum/min/max/prod, both axes) + +**Geomean by lay** + +| size | C | F | T | strided | negrow | negcol | sliced | bcast | +|---|---|---|---|---|---|---|---|---| +| 100K | 0.99 🟡 | 1.02 ✅ | 1.02 ✅ | 0.55 🟡 | 0.70 🟡 | 0.70 🟡 | 0.72 🟡 | 0.74 🟡 | +| 1M | 0.95 🟡 | 0.94 🟡 | 0.95 🟡 | 0.65 🟡 | 0.87 🟡 | 0.73 🟡 | 0.75 🟡 | 0.68 🟡 | + +**Geomean by dt** + +| size | f64 | f32 | c128 | dec | f16 | i32 | i64 | +|---|---|---|---|---|---|---|---| +| 100K | 0.87 🟡 | 1.00 🟡 | 0.98 🟡 | 0.08 🔴 | 1.03 ✅ | 1.33 ✅ | 1.14 ✅ | +| 1M | 1.12 ✅ | 1.15 ✅ | 1.04 ✅ | 0.07 🔴 | 1.02 ✅ | 1.19 ✅ | 1.02 ✅ | + +**Geomean by op** + +| size | sum | min | max | prod | +|---|---|---|---|---| +| 100K | 0.80 🟡 | 0.66 🟡 | 0.66 🟡 | 1.19 ✅ | +| 1M | 0.84 🟡 | 0.65 🟡 | 0.64 🟡 | 1.28 ✅ | + +**Worst 15 cells (NumSharp slowest vs NumPy)** + +| key | NumSharp ms | NumPy ms | ratio | +|---|---|---|---| +| 1M\|dec\|bcast\|sum\|ax0 | 5.5269 | 0.0655 | 0.01 🔴 | +| 100K\|dec\|bcast\|sum\|ax0 | 0.5555 | 0.0087 | 0.02 🔴 | +| 100K\|dec\|F\|sum\|ax1 | 0.5585 | 0.0094 | 0.02 🔴 | +| 100K\|dec\|C\|sum\|ax0 | 0.5615 | 0.0111 | 0.02 🔴 | +| 100K\|dec\|T\|sum\|ax1 | 0.5582 | 0.0111 | 0.02 🔴 | +| 100K\|dec\|negrow\|sum\|ax0 | 0.5592 | 0.0114 | 0.02 🔴 | +| 100K\|dec\|sliced\|sum\|ax0 | 0.5517 | 0.0115 | 0.02 🔴 | +| 1M\|dec\|sliced\|sum\|ax0 | 5.5802 | 0.1240 | 0.02 🔴 | +| 1M\|dec\|negrow\|sum\|ax0 | 5.5707 | 0.1242 | 0.02 🔴 | +| 1M\|dec\|F\|sum\|ax1 | 5.6157 | 0.1254 | 0.02 🔴 | +| 1M\|dec\|T\|sum\|ax1 | 5.5849 | 0.1278 | 0.02 🔴 | +| 1M\|dec\|C\|sum\|ax0 | 5.5736 | 0.1524 | 0.03 🔴 | +| 100K\|dec\|C\|min\|ax0 | 0.3021 | 0.0141 | 0.05 🔴 | +| 1M\|dec\|negcol\|sum\|ax0 | 5.6678 | 0.2866 | 0.05 🔴 | +| 100K\|dec\|negcol\|sum\|ax0 | 0.5594 | 0.0302 | 0.05 🔴 | + +### Copy / identity-ufunc (np.positive) + +**Geomean by lay** + +| size | C | F | T | strided | sliced | negrow | negcol | bcast | +|---|---|---|---|---|---|---|---|---| +| 100K | 1.22 ✅ | 1.51 ✅ | 1.28 ✅ | 0.83 🟡 | 1.54 ✅ | 1.48 ✅ | 2.20 ✅ | 1.62 ✅ | +| 1M | 2.82 ✅ | 2.89 ✅ | 2.88 ✅ | 2.08 ✅ | 2.58 ✅ | 2.64 ✅ | 3.16 ✅ | 2.66 ✅ | + +**Geomean by dt** + +| size | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| 100K | 0.92 🟡 | 1.03 ✅ | 1.51 ✅ | 1.99 ✅ | 1.02 ✅ | 1.09 ✅ | 1.65 ✅ | 1.08 ✅ | 2.56 ✅ | 2.47 ✅ | 1.15 ✅ | 0.85 🟡 | 2.56 ✅ | +| 1M | 4.60 ✅ | 4.87 ✅ | 2.13 ✅ | 2.14 ✅ | 2.11 ✅ | 2.12 ✅ | 2.56 ✅ | 2.46 ✅ | 2.20 ✅ | 2.10 ✅ | 2.14 ✅ | 2.59 ✅ | 5.47 ✅ | + +**Worst 15 cells (NumSharp slowest vs NumPy)** + +| key | NumSharp ms | NumPy ms | ratio | +|---|---|---|---| +| 100K\|f64\|strided\|pos | 0.0243 | 0.0100 | 0.41 🟠 | +| 100K\|u64\|strided\|pos | 0.0241 | 0.0110 | 0.46 🟠 | +| 100K\|i64\|strided\|pos | 0.0237 | 0.0109 | 0.46 🟠 | +| 100K\|f32\|strided\|pos | 0.0216 | 0.0100 | 0.46 🟠 | +| 100K\|i32\|strided\|pos | 0.0213 | 0.0110 | 0.52 🟡 | +| 100K\|i64\|C\|pos | 0.0400 | 0.0210 | 0.53 🟡 | +| 100K\|i64\|T\|pos | 0.0387 | 0.0208 | 0.54 🟡 | +| 100K\|i8\|sliced\|pos | 0.0423 | 0.0228 | 0.54 🟡 | +| 100K\|u8\|bcast\|pos | 0.0416 | 0.0229 | 0.55 🟡 | +| 100K\|u8\|sliced\|pos | 0.0410 | 0.0227 | 0.55 🟡 | +| 100K\|u8\|negrow\|pos | 0.0410 | 0.0231 | 0.56 🟡 | +| 100K\|u32\|strided\|pos | 0.0191 | 0.0109 | 0.57 🟡 | +| 100K\|f64\|T\|pos | 0.0389 | 0.0227 | 0.58 🟡 | +| 100K\|f64\|F\|pos | 0.0362 | 0.0227 | 0.63 🟡 | +| 100K\|f64\|C\|pos | 0.0361 | 0.0228 | 0.63 🟡 | + +### Elementwise (add/mul/neg/abs/sqrt/less/copy) + +**Geomean by lay** + +| size | C | F | T | strided | sliced | negrow | negcol | bcast | +|---|---|---|---|---|---|---|---|---| +| 100K | 0.73 🟡 | 0.70 🟡 | 0.68 🟡 | 0.53 🟡 | 0.84 🟡 | 0.85 🟡 | 1.18 ✅ | 0.85 🟡 | +| 1M | 1.54 ✅ | 1.48 ✅ | 1.48 ✅ | 1.12 ✅ | 1.64 ✅ | 1.65 ✅ | 1.80 ✅ | 1.63 ✅ | + +**Geomean by dt** + +| size | f64 | f32 | c128 | f16 | i32 | i64 | +|---|---|---|---|---|---|---| +| 100K | 0.67 🟡 | 0.57 🟡 | 1.15 ✅ | 0.75 🟡 | 0.82 🟡 | 0.81 🟡 | +| 1M | 1.86 ✅ | 1.53 ✅ | 1.73 ✅ | 0.94 🟡 | 1.54 ✅ | 1.79 ✅ | + +**Geomean by op** + +| size | add | mul | neg | abs | sqrt | less | copy | +|---|---|---|---|---|---|---|---| +| 100K | 1.04 ✅ | 0.99 🟡 | 0.76 🟡 | 0.74 🟡 | 0.94 🟡 | 0.61 🟡 | 0.51 🟡 | +| 1M | 1.81 ✅ | 1.78 ✅ | 2.11 ✅ | 1.62 ✅ | 1.50 ✅ | 0.68 🟡 | 1.75 ✅ | + +**Worst 15 cells (NumSharp slowest vs NumPy)** + +| key | NumSharp ms | NumPy ms | ratio | +|---|---|---|---| +| 100K\|f64\|strided\|abs | 0.0466 | 0.0077 | 0.17 🔴 | +| 100K\|f64\|strided\|neg | 0.0473 | 0.0088 | 0.19 🔴 | +| 100K\|i32\|bcast\|copy | 0.0233 | 0.0050 | 0.21 🟠 | +| 100K\|f16\|bcast\|copy | 0.0134 | 0.0030 | 0.22 🟠 | +| 100K\|f16\|negrow\|copy | 0.0168 | 0.0038 | 0.23 🟠 | +| 100K\|f32\|C\|copy | 0.0244 | 0.0059 | 0.24 🟠 | +| 100K\|i64\|strided\|neg | 0.0296 | 0.0076 | 0.26 🟠 | +| 100K\|f64\|strided\|copy | 0.0413 | 0.0108 | 0.26 🟠 | +| 100K\|f64\|negrow\|copy | 0.0595 | 0.0160 | 0.27 🟠 | +| 100K\|f16\|sliced\|copy | 0.0134 | 0.0037 | 0.27 🟠 | +| 100K\|f64\|sliced\|copy | 0.0592 | 0.0162 | 0.27 🟠 | +| 100K\|i32\|negrow\|copy | 0.0234 | 0.0064 | 0.28 🟠 | +| 100K\|f32\|C\|add | 0.0240 | 0.0066 | 0.28 🟠 | +| 100K\|f64\|strided\|mul | 0.0509 | 0.0142 | 0.28 🟠 | +| 100K\|f64\|strided\|add | 0.0516 | 0.0147 | 0.28 🟠 | + + +--- + +## Operand & broadcast layouts — 1-D / scalar / mixed-operand / broadcast + +The layout classes the per-operand layout grid (benchmark/layout) can't express. ratio = NumPy_ms / NumSharp_ms — **>1.0 = NumSharp faster**. ✅≥1.0 🟡≥0.5 🟠≥0.2 🔴<0.2. 1M elements, best-of-3. + +| case | f64 | f32 | f16 | i32 | i64 | c128 | geomean | +|---|---|---|---|---|---|---|---| +| 1-D contiguous (a+a) | 2.41 ✅ | 2.16 ✅ | 0.63 🟡 | 2.13 ✅ | 2.45 ✅ | 2.35 ✅ | 1.85 ✅ | +| 1-D strided a[::2] | 1.84 ✅ | 1.46 ✅ | 0.53 🟡 | 1.51 ✅ | 1.81 ✅ | 1.93 ✅ | 1.40 ✅ | +| 1-D reversed a[::-1] | 2.49 ✅ | 1.95 ✅ | 0.56 🟡 | 2.14 ✅ | 2.29 ✅ | 2.22 ✅ | 1.76 ✅ | +| array + scalar | 2.72 ✅ | 1.93 ✅ | 0.63 🟡 | 1.91 ✅ | 2.26 ✅ | 2.64 ✅ | 1.83 ✅ | +| scalar + array | 2.40 ✅ | 1.98 ✅ | 0.64 🟡 | 2.01 ✅ | 2.19 ✅ | 2.69 ✅ | 1.82 ✅ | +| mixed C + F | 2.29 ✅ | 2.02 ✅ | 0.62 🟡 | 2.02 ✅ | 2.09 ✅ | 1.87 ✅ | 1.68 ✅ | +| mixed C + T | 2.53 ✅ | 2.04 ✅ | 0.62 🟡 | 2.01 ✅ | 2.33 ✅ | 2.30 ✅ | 1.80 ✅ | +| binary broadcast +row(1,C) | 2.71 ✅ | 2.09 ✅ | 0.63 🟡 | 1.95 ✅ | 2.52 ✅ | 2.58 ✅ | 1.89 ✅ | +| binary broadcast +col(R,1) | 2.61 ✅ | 2.02 ✅ | 0.56 🟡 | 2.07 ✅ | 2.54 ✅ | 2.96 ✅ | 1.89 ✅ | +| col-broadcast unary (inner stride-0) | 2.54 ✅ | 1.56 ✅ | 0.88 🟡 | 1.65 ✅ | 2.64 ✅ | 6.22 ✅ | 2.13 ✅ | + +**Worst 12 cells** + +| key | NumSharp ms | NumPy ms | ratio | +|---|---|---|---| +| 1d_strided|f16 | 2.6261 | 1.3926 | 0.53 🟡 | +| 1d_rev|f16 | 5.2838 | 2.9653 | 0.56 🟡 | +| bcast_col|f16 | 5.2887 | 2.9837 | 0.56 🟡 | +| mix_C_T|f16 | 5.3031 | 3.2873 | 0.62 🟡 | +| mix_C_F|f16 | 5.3153 | 3.3015 | 0.62 🟡 | +| 1d_C|f16 | 4.7456 | 2.9682 | 0.63 🟡 | +| bcast_row|f16 | 4.7695 | 2.9999 | 0.63 🟡 | +| scalar_rhs|f16 | 4.7058 | 2.9602 | 0.63 🟡 | +| scalar_lhs|f16 | 4.6383 | 2.9742 | 0.64 🟡 | +| colbcast_unary|f16 | 0.4753 | 0.4173 | 0.88 🟡 | +| 1d_strided|f32 | 0.2366 | 0.3444 | 1.46 ✅ | +| 1d_strided|i32 | 0.2433 | 0.3676 | 1.51 ✅ | + +_60 comparable cells._ + + +--- + +## Cast matrix — astype src→dst × layout × dtype + +Full `astype(dst, copy:true)` sweep over every src→dst dtype pair × 8 memory layouts at 1M elements, best-of-3. ratio = NumPy_ms / NumSharp_ms — **>1.0 = NumSharp faster**. +✅≥1.0 🟡≥0.5 🟠≥0.2 🔴<0.2 · `—` = no NumPy counterpart (Decimal has no NumPy dtype). + +## Summary + +- **118 / 1568** comparable cells lag (<1.0); **1450** win (≥1.0). +- **🔴 <0.2** — 0 cells. +- **🟠 0.2–0.5** — 1 cells. Top: 1× * → bool +- **🟡 0.5–1.0** — 117 cells. Top: 23× int → sub-word (narrow); 21× float/cplx → narrow-int (bool/u8/i8/i16/u16/char); 8× f16 → f64 + +**float/complex → narrow-int geomean by src** (the historical cliff): `f32`→narrow **1.96**, `f64`→narrow **1.38**, `f16`→narrow **3.71**, `c128`→narrow **1.08**. + +## Geomean by layout (all src×dst, excl. Decimal) + +| C | F | T | sliced | negrow | negcol | strided | bcast | +|---|---|---|---|---|---|---|---| +| 1.86 ✅ | 1.88 ✅ | 1.93 ✅ | 1.95 ✅ | 1.97 ✅ | 1.82 ✅ | 1.47 ✅ | 2.25 ✅ | + +## Geomean by src dtype (all layouts×dst) + +| bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| 2.35 ✅ | 2.18 ✅ | 2.16 ✅ | 2.19 ✅ | 2.14 ✅ | 1.92 ✅ | 1.88 ✅ | 1.62 ✅ | 1.55 ✅ | 1.98 ✅ | 2.36 ✅ | 1.75 ✅ | 1.43 ✅ | nan ? | 1.23 ✅ | + +## Geomean by dst dtype (all layouts×src) + +| bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| 2.17 ✅ | 1.94 ✅ | 1.93 ✅ | 1.61 ✅ | 1.60 ✅ | 1.80 ✅ | 1.64 ✅ | 1.89 ✅ | 1.73 ✅ | 1.60 ✅ | 2.48 ✅ | 1.60 ✅ | 2.03 ✅ | nan ? | 2.59 ✅ | + +## Layout: C (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 3.04✅ | 2.60✅ | 2.76✅ | 2.10✅ | 1.98✅ | 1.53✅ | 1.46✅ | 2.04✅ | 2.25✅ | 1.73✅ | 3.29✅ | 1.71✅ | 2.49✅ | — | 2.94✅ | +| **u8** | 3.55✅ | 1.55✅ | 3.56✅ | 1.69✅ | 1.83✅ | 2.01✅ | 1.89✅ | 2.21✅ | 2.47✅ | 1.62✅ | 3.90✅ | 1.38✅ | 2.41✅ | — | 3.07✅ | +| **i8** | 2.61✅ | 3.17✅ | 0.91🟡 | 1.82✅ | 1.76✅ | 1.95✅ | 1.95✅ | 2.35✅ | 2.66✅ | 1.76✅ | 3.94✅ | 1.79✅ | 2.64✅ | — | 2.98✅ | +| **i16** | 2.20✅ | 2.29✅ | 2.59✅ | 1.39✅ | 2.11✅ | 2.09✅ | 1.95✅ | 2.42✅ | 2.52✅ | 1.57✅ | 3.71✅ | 1.92✅ | 2.61✅ | — | 2.79✅ | +| **u16** | 2.22✅ | 2.80✅ | 2.81✅ | 2.12✅ | 1.24✅ | 2.09✅ | 2.14✅ | 2.63✅ | 2.22✅ | 1.08✅ | 3.71✅ | 1.93✅ | 2.46✅ | — | 2.97✅ | +| **i32** | 1.50✅ | 1.15✅ | 1.16✅ | 1.46✅ | 1.51✅ | 1.70✅ | 2.12✅ | 2.26✅ | 2.27✅ | 1.43✅ | 3.64✅ | 1.64✅ | 2.62✅ | — | 2.86✅ | +| **u32** | 1.77✅ | 1.18✅ | 1.17✅ | 1.35✅ | 1.44✅ | 2.17✅ | 1.65✅ | 2.19✅ | 2.29✅ | 1.48✅ | 3.66✅ | 1.47✅ | 2.34✅ | — | 2.69✅ | +| **i64** | 1.08✅ | 0.98🟡 | 0.96🟡 | 1.23✅ | 1.27✅ | 1.83✅ | 1.78✅ | 1.87✅ | 2.76✅ | 1.38✅ | 1.81✅ | 1.81✅ | 2.22✅ | — | 2.52✅ | +| **u64** | 1.14✅ | 0.92🟡 | 0.95🟡 | 1.22✅ | 1.18✅ | 1.73✅ | 1.73✅ | 2.56✅ | 2.09✅ | 1.30✅ | 1.65✅ | 1.14✅ | 1.79✅ | — | 2.45✅ | +| **char** | 2.07✅ | 2.02✅ | 2.14✅ | 1.49✅ | 0.97🟡 | 1.55✅ | 1.53✅ | 2.26✅ | 2.20✅ | 1.21✅ | 3.67✅ | 1.42✅ | 2.11✅ | — | 2.62✅ | +| **f16** | 4.51✅ | 5.42✅ | 5.35✅ | 3.82✅ | 3.94✅ | 3.80✅ | 1.96✅ | 2.94✅ | 0.99🟡 | 4.16✅ | 1.25✅ | 1.10✅ | 0.92🟡 | — | 1.64✅ | +| **f32** | 2.82✅ | 1.92✅ | 2.05✅ | 1.64✅ | 1.55✅ | 1.84✅ | 1.38✅ | 0.83🟡 | 0.87🟡 | 1.48✅ | 3.59✅ | 1.76✅ | 2.21✅ | — | 2.35✅ | +| **f64** | 1.81✅ | 0.81🟡 | 0.81🟡 | 1.32✅ | 1.29✅ | 1.68✅ | 1.51✅ | 0.86🟡 | 0.89🟡 | 1.38✅ | 1.04✅ | 1.72✅ | 2.05✅ | — | 2.60✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.11✅ | 0.93🟡 | 0.94🟡 | 1.19✅ | 1.13✅ | 1.69✅ | 1.35✅ | 0.92🟡 | 0.84🟡 | 1.10✅ | 1.50✅ | 1.39✅ | 1.75✅ | — | 3.18✅ | + +## Layout: F (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 0.24🟠 | 2.72✅ | 2.71✅ | 1.94✅ | 1.91✅ | 1.43✅ | 1.42✅ | 2.24✅ | 2.40✅ | 1.89✅ | 4.03✅ | 1.74✅ | 2.55✅ | — | 2.98✅ | +| **u8** | 4.01✅ | 1.11✅ | 3.48✅ | 1.70✅ | 1.71✅ | 2.07✅ | 2.06✅ | 2.11✅ | 2.33✅ | 1.62✅ | 3.89✅ | 1.39✅ | 2.49✅ | — | 3.06✅ | +| **i8** | 3.53✅ | 3.24✅ | 1.05✅ | 1.93✅ | 1.80✅ | 2.14✅ | 2.13✅ | 2.59✅ | 2.51✅ | 1.71✅ | 3.88✅ | 1.77✅ | 2.40✅ | — | 2.95✅ | +| **i16** | 1.87✅ | 2.06✅ | 2.52✅ | 1.31✅ | 1.98✅ | 1.84✅ | 2.01✅ | 2.59✅ | 2.39✅ | 2.19✅ | 3.71✅ | 2.05✅ | 2.39✅ | — | 3.01✅ | +| **u16** | 2.26✅ | 2.74✅ | 2.63✅ | 1.93✅ | 1.27✅ | 1.88✅ | 2.02✅ | 2.68✅ | 2.26✅ | 1.35✅ | 3.65✅ | 1.96✅ | 2.17✅ | — | 2.85✅ | +| **i32** | 1.69✅ | 1.11✅ | 1.18✅ | 1.48✅ | 1.49✅ | 1.76✅ | 2.17✅ | 2.46✅ | 2.48✅ | 1.50✅ | 3.90✅ | 1.72✅ | 2.33✅ | — | 2.85✅ | +| **u32** | 1.85✅ | 1.13✅ | 1.06✅ | 1.54✅ | 1.43✅ | 2.15✅ | 1.64✅ | 2.31✅ | 2.26✅ | 1.50✅ | 3.87✅ | 1.55✅ | 2.16✅ | — | 2.46✅ | +| **i64** | 1.11✅ | 0.94🟡 | 0.92🟡 | 1.17✅ | 1.26✅ | 1.88✅ | 1.75✅ | 2.16✅ | 2.70✅ | 1.28✅ | 1.87✅ | 1.55✅ | 2.17✅ | — | 2.24✅ | +| **u64** | 1.15✅ | 0.92🟡 | 0.94🟡 | 1.21✅ | 1.13✅ | 1.73✅ | 1.73✅ | 2.46✅ | 2.05✅ | 1.39✅ | 2.48✅ | 1.14✅ | 1.76✅ | — | 2.34✅ | +| **char** | 2.08✅ | 2.63✅ | 2.03✅ | 1.90✅ | 1.20✅ | 1.67✅ | 1.69✅ | 2.52✅ | 2.46✅ | 1.20✅ | 3.60✅ | 1.41✅ | 2.10✅ | — | 2.89✅ | +| **f16** | 4.49✅ | 6.02✅ | 6.33✅ | 3.79✅ | 4.31✅ | 3.73✅ | 2.06✅ | 2.85✅ | 1.11✅ | 4.33✅ | 1.24✅ | 2.44✅ | 0.95🟡 | — | 1.62✅ | +| **f32** | 3.44✅ | 2.12✅ | 2.26✅ | 1.62✅ | 1.64✅ | 1.92✅ | 1.35✅ | 0.82🟡 | 0.84🟡 | 1.69✅ | 3.63✅ | 1.67✅ | 2.21✅ | — | 2.26✅ | +| **f64** | 1.90✅ | 1.27✅ | 1.33✅ | 1.40✅ | 1.43✅ | 1.95✅ | 1.47✅ | 0.87🟡 | 0.90🟡 | 1.41✅ | 1.61✅ | 1.73✅ | 2.05✅ | — | 2.69✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 0.99🟡 | 0.91🟡 | 0.88🟡 | 1.15✅ | 1.07✅ | 1.57✅ | 1.33✅ | 0.96🟡 | 0.81🟡 | 1.09✅ | 1.49✅ | 1.15✅ | 1.75✅ | — | 3.26✅ | + +## Layout: T (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 3.40✅ | 3.39✅ | 3.74✅ | 1.71✅ | 1.80✅ | 1.47✅ | 1.41✅ | 2.31✅ | 2.32✅ | 1.91✅ | 4.20✅ | 1.78✅ | 2.68✅ | — | 2.99✅ | +| **u8** | 3.94✅ | 1.21✅ | 2.55✅ | 1.68✅ | 1.68✅ | 2.05✅ | 1.92✅ | 2.21✅ | 2.56✅ | 1.67✅ | 3.98✅ | 1.41✅ | 2.41✅ | — | 2.98✅ | +| **i8** | 2.58✅ | 3.43✅ | 0.85🟡 | 1.84✅ | 1.75✅ | 2.01✅ | 2.12✅ | 2.50✅ | 2.42✅ | 1.76✅ | 3.79✅ | 1.80✅ | 2.46✅ | — | 3.04✅ | +| **i16** | 2.29✅ | 2.12✅ | 3.21✅ | 1.25✅ | 2.11✅ | 2.01✅ | 2.04✅ | 2.43✅ | 2.25✅ | 2.00✅ | 3.86✅ | 2.02✅ | 2.46✅ | — | 3.02✅ | +| **u16** | 2.23✅ | 2.51✅ | 2.45✅ | 1.91✅ | 1.26✅ | 2.03✅ | 1.99✅ | 2.44✅ | 2.39✅ | 1.44✅ | 3.67✅ | 1.90✅ | 2.29✅ | — | 2.50✅ | +| **i32** | 1.77✅ | 1.13✅ | 1.19✅ | 1.52✅ | 1.52✅ | 1.69✅ | 2.31✅ | 2.07✅ | 2.11✅ | 1.66✅ | 3.96✅ | 1.65✅ | 2.57✅ | — | 2.95✅ | +| **u32** | 1.89✅ | 1.16✅ | 1.16✅ | 1.49✅ | 1.47✅ | 2.25✅ | 1.72✅ | 2.18✅ | 2.15✅ | 1.47✅ | 3.71✅ | 1.49✅ | 2.27✅ | — | 2.82✅ | +| **i64** | 1.28✅ | 1.04✅ | 0.93🟡 | 1.25✅ | 1.27✅ | 1.79✅ | 1.78✅ | 2.09✅ | 2.64✅ | 1.33✅ | 1.86✅ | 1.74✅ | 2.23✅ | — | 2.69✅ | +| **u64** | 1.20✅ | 0.94🟡 | 0.95🟡 | 1.21✅ | 1.16✅ | 1.79✅ | 1.62✅ | 2.52✅ | 2.00✅ | 1.36✅ | 2.47✅ | 1.12✅ | 1.90✅ | — | 2.54✅ | +| **char** | 2.14✅ | 2.15✅ | 2.63✅ | 1.84✅ | 1.22✅ | 1.61✅ | 1.62✅ | 2.24✅ | 2.47✅ | 1.31✅ | 3.50✅ | 1.44✅ | 2.32✅ | — | 2.56✅ | +| **f16** | 5.17✅ | 6.28✅ | 6.15✅ | 4.27✅ | 4.26✅ | 3.56✅ | 2.03✅ | 3.02✅ | 1.14✅ | 4.33✅ | 1.18✅ | 2.84✅ | 1.00🟡 | — | 1.65✅ | +| **f32** | 3.33✅ | 2.16✅ | 2.30✅ | 1.77✅ | 1.63✅ | 2.04✅ | 1.38✅ | 0.83🟡 | 0.86🟡 | 1.63✅ | 3.75✅ | 1.70✅ | 2.56✅ | — | 2.20✅ | +| **f64** | 2.00✅ | 1.35✅ | 1.35✅ | 1.37✅ | 1.41✅ | 1.81✅ | 1.48✅ | 0.87🟡 | 0.89🟡 | 1.31✅ | 1.63✅ | 1.74✅ | 1.93✅ | — | 2.61✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.00✅ | 1.11✅ | 1.21✅ | 1.27✅ | 1.18✅ | 1.66✅ | 1.28✅ | 0.90🟡 | 0.85🟡 | 1.11✅ | 1.53✅ | 1.41✅ | 1.79✅ | — | 3.42✅ | + +## Layout: sliced (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 3.14✅ | 5.84✅ | 5.99✅ | 1.96✅ | 2.00✅ | 1.49✅ | 1.56✅ | 2.38✅ | 2.06✅ | 1.99✅ | 3.95✅ | 1.74✅ | 2.66✅ | — | 3.02✅ | +| **u8** | 2.74✅ | 1.25✅ | 3.91✅ | 1.79✅ | 1.88✅ | 2.24✅ | 2.14✅ | 2.44✅ | 2.55✅ | 1.72✅ | 3.71✅ | 1.40✅ | 2.83✅ | — | 3.17✅ | +| **i8** | 2.96✅ | 3.03✅ | 0.92🟡 | 1.75✅ | 1.74✅ | 1.99✅ | 1.92✅ | 2.30✅ | 2.14✅ | 1.65✅ | 3.86✅ | 1.69✅ | 2.48✅ | — | 3.07✅ | +| **i16** | 2.25✅ | 2.82✅ | 2.56✅ | 1.45✅ | 1.71✅ | 1.93✅ | 1.90✅ | 2.47✅ | 2.50✅ | 1.62✅ | 3.54✅ | 2.06✅ | 2.58✅ | — | 2.85✅ | +| **u16** | 2.98✅ | 2.66✅ | 2.90✅ | 1.46✅ | 1.33✅ | 2.06✅ | 1.96✅ | 2.50✅ | 2.20✅ | 1.36✅ | 3.62✅ | 1.88✅ | 2.45✅ | — | 3.01✅ | +| **i32** | 1.94✅ | 2.20✅ | 2.10✅ | 1.43✅ | 1.54✅ | 1.90✅ | 1.81✅ | 2.14✅ | 2.23✅ | 1.55✅ | 3.70✅ | 1.58✅ | 2.39✅ | — | 2.86✅ | +| **u32** | 1.99✅ | 2.01✅ | 2.13✅ | 1.44✅ | 1.53✅ | 2.02✅ | 1.85✅ | 2.10✅ | 2.22✅ | 1.55✅ | 3.72✅ | 1.47✅ | 2.12✅ | — | 2.74✅ | +| **i64** | 1.20✅ | 1.18✅ | 1.28✅ | 1.42✅ | 1.33✅ | 1.78✅ | 1.78✅ | 2.22✅ | 2.35✅ | 1.42✅ | 1.82✅ | 1.52✅ | 2.36✅ | — | 2.68✅ | +| **u64** | 1.29✅ | 1.24✅ | 1.22✅ | 1.33✅ | 1.30✅ | 1.74✅ | 1.79✅ | 2.14✅ | 2.22✅ | 1.36✅ | 2.39✅ | 0.97🟡 | 1.58✅ | — | 2.45✅ | +| **char** | 2.16✅ | 2.26✅ | 2.43✅ | 1.50✅ | 1.31✅ | 1.61✅ | 1.60✅ | 2.27✅ | 2.32✅ | 1.31✅ | 3.47✅ | 1.42✅ | 2.23✅ | — | 2.80✅ | +| **f16** | 4.70✅ | 4.95✅ | 4.99✅ | 4.11✅ | 4.05✅ | 4.02✅ | 1.97✅ | 3.08✅ | 1.10✅ | 4.21✅ | 1.33✅ | 2.70✅ | 0.96🟡 | — | 1.49✅ | +| **f32** | 3.63✅ | 2.20✅ | 2.35✅ | 1.64✅ | 1.67✅ | 1.90✅ | 1.34✅ | 0.86🟡 | 0.88🟡 | 1.59✅ | 3.62✅ | 1.97✅ | 2.04✅ | — | 2.35✅ | +| **f64** | 2.01✅ | 1.26✅ | 1.31✅ | 1.42✅ | 1.33✅ | 0.70🟡 | 1.49✅ | 0.84🟡 | 0.84🟡 | 1.34✅ | 1.58✅ | 1.71✅ | 2.49✅ | — | 2.58✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 0.97🟡 | 1.07✅ | 0.96🟡 | 1.13✅ | 1.14✅ | 1.72✅ | 1.30✅ | 0.93🟡 | 0.80🟡 | 0.91🟡 | 1.47✅ | 1.40✅ | 1.75✅ | — | 2.20✅ | + +## Layout: negrow (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 3.38✅ | 4.26✅ | 4.97✅ | 1.92✅ | 2.03✅ | 1.41✅ | 1.57✅ | 2.32✅ | 2.40✅ | 1.98✅ | 3.91✅ | 1.79✅ | 2.59✅ | — | 3.34✅ | +| **u8** | 3.67✅ | 1.14✅ | 3.99✅ | 1.71✅ | 1.63✅ | 2.15✅ | 2.24✅ | 2.37✅ | 2.35✅ | 1.87✅ | 3.81✅ | 1.44✅ | 2.29✅ | — | 3.05✅ | +| **i8** | 3.35✅ | 3.21✅ | 0.97🟡 | 2.04✅ | 1.87✅ | 2.02✅ | 2.04✅ | 2.32✅ | 2.48✅ | 1.72✅ | 3.67✅ | 1.73✅ | 2.58✅ | — | 3.25✅ | +| **i16** | 3.19✅ | 2.80✅ | 2.70✅ | 1.39✅ | 1.68✅ | 1.99✅ | 2.03✅ | 2.29✅ | 2.48✅ | 1.78✅ | 3.72✅ | 2.09✅ | 2.42✅ | — | 3.13✅ | +| **u16** | 2.41✅ | 2.59✅ | 2.35✅ | 1.53✅ | 1.32✅ | 1.77✅ | 1.84✅ | 2.45✅ | 2.56✅ | 1.53✅ | 3.57✅ | 2.04✅ | 2.46✅ | — | 2.82✅ | +| **i32** | 2.01✅ | 2.01✅ | 2.04✅ | 1.52✅ | 1.53✅ | 1.97✅ | 1.89✅ | 2.21✅ | 2.32✅ | 1.57✅ | 3.58✅ | 1.54✅ | 2.04✅ | — | 2.86✅ | +| **u32** | 1.96✅ | 2.10✅ | 2.14✅ | 1.47✅ | 1.49✅ | 1.80✅ | 1.91✅ | 2.27✅ | 2.52✅ | 1.59✅ | 3.59✅ | 1.50✅ | 2.18✅ | — | 2.54✅ | +| **i64** | 1.17✅ | 1.21✅ | 1.33✅ | 1.39✅ | 1.41✅ | 1.84✅ | 1.78✅ | 2.65✅ | 2.22✅ | 1.31✅ | 1.84✅ | 1.46✅ | 2.33✅ | — | 2.59✅ | +| **u64** | 1.13✅ | 1.26✅ | 1.29✅ | 1.33✅ | 1.37✅ | 1.83✅ | 1.96✅ | 2.17✅ | 2.39✅ | 1.31✅ | 2.35✅ | 1.00🟡 | 1.59✅ | — | 2.32✅ | +| **char** | 2.36✅ | 2.38✅ | 2.28✅ | 1.71✅ | 1.37✅ | 1.57✅ | 1.58✅ | 2.34✅ | 2.32✅ | 1.33✅ | 3.52✅ | 1.49✅ | 2.37✅ | — | 2.79✅ | +| **f16** | 5.08✅ | 4.80✅ | 4.78✅ | 3.80✅ | 3.83✅ | 3.85✅ | 2.03✅ | 3.13✅ | 1.09✅ | 3.83✅ | 1.34✅ | 2.87✅ | 0.97🟡 | — | 1.60✅ | +| **f32** | 3.30✅ | 2.23✅ | 2.25✅ | 1.60✅ | 1.67✅ | 1.91✅ | 1.36✅ | 0.84🟡 | 0.85🟡 | 1.66✅ | 3.58✅ | 1.95✅ | 2.09✅ | — | 2.25✅ | +| **f64** | 1.87✅ | 1.23✅ | 1.33✅ | 1.44✅ | 1.48✅ | 1.87✅ | 1.44✅ | 0.80🟡 | 0.88🟡 | 1.46✅ | 1.58✅ | 1.69✅ | 2.36✅ | — | 2.54✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.10✅ | 0.94🟡 | 1.01✅ | 1.15✅ | 1.11✅ | 1.47✅ | 1.37✅ | 0.87🟡 | 0.92🟡 | 1.26✅ | 1.41✅ | 1.37✅ | 1.61✅ | — | 2.29✅ | + +## Layout: negcol (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 4.56✅ | 7.06✅ | 6.59✅ | 2.26✅ | 2.21✅ | 1.54✅ | 1.48✅ | 2.25✅ | 2.21✅ | 2.40✅ | 2.80✅ | 1.88✅ | 2.48✅ | — | 3.10✅ | +| **u8** | 1.42✅ | 3.55✅ | 3.38✅ | 2.00✅ | 1.85✅ | 1.77✅ | 1.83✅ | 2.16✅ | 2.24✅ | 1.88✅ | 2.52✅ | 1.47✅ | 2.46✅ | — | 3.02✅ | +| **i8** | 1.31✅ | 4.32✅ | 2.73✅ | 1.70✅ | 1.89✅ | 1.82✅ | 1.83✅ | 2.24✅ | 2.26✅ | 1.73✅ | 2.63✅ | 1.65✅ | 2.37✅ | — | 3.10✅ | +| **i16** | 3.46✅ | 2.49✅ | 2.83✅ | 1.78✅ | 1.77✅ | 1.66✅ | 1.68✅ | 2.17✅ | 2.25✅ | 1.70✅ | 2.01✅ | 1.78✅ | 2.24✅ | — | 2.86✅ | +| **u16** | 3.61✅ | 3.01✅ | 3.11✅ | 1.76✅ | 1.62✅ | 1.68✅ | 1.75✅ | 2.43✅ | 2.22✅ | 1.69✅ | 2.02✅ | 1.43✅ | 2.38✅ | — | 2.92✅ | +| **i32** | 2.10✅ | 1.95✅ | 1.67✅ | 1.60✅ | 1.61✅ | 1.55✅ | 1.55✅ | 2.56✅ | 2.15✅ | 1.68✅ | 2.06✅ | 1.65✅ | 2.06✅ | — | 2.76✅ | +| **u32** | 2.08✅ | 1.60✅ | 1.53✅ | 1.59✅ | 1.58✅ | 1.48✅ | 1.61✅ | 2.46✅ | 2.30✅ | 1.63✅ | 2.02✅ | 1.47✅ | 2.34✅ | — | 2.84✅ | +| **i64** | 1.33✅ | 0.96🟡 | 0.97🟡 | 1.29✅ | 1.41✅ | 1.75✅ | 1.87✅ | 2.41✅ | 2.36✅ | 1.39✅ | 1.30✅ | 1.58✅ | 2.17✅ | — | 2.44✅ | +| **u64** | 1.26✅ | 1.01✅ | 0.93🟡 | 1.27✅ | 1.33✅ | 1.72✅ | 1.69✅ | 2.32✅ | 2.19✅ | 1.34✅ | 1.65✅ | 1.05✅ | 1.50✅ | — | 2.50✅ | +| **char** | 3.38✅ | 2.34✅ | 3.24✅ | 1.67✅ | 1.60✅ | 1.67✅ | 1.65✅ | 2.21✅ | 2.35✅ | 1.64✅ | 2.03✅ | 1.56✅ | 2.27✅ | — | 2.96✅ | +| **f16** | 4.65✅ | 1.73✅ | 1.73✅ | 1.74✅ | 1.76✅ | 2.23✅ | 1.33✅ | 2.07✅ | 0.89🟡 | 1.76✅ | 1.61✅ | 1.56✅ | 0.96🟡 | — | 1.51✅ | +| **f32** | 2.48✅ | 1.71✅ | 1.67✅ | 1.65✅ | 1.62✅ | 1.90✅ | 1.08✅ | 0.85🟡 | 0.81🟡 | 1.61✅ | 1.94✅ | 1.62✅ | 1.20✅ | — | 2.30✅ | +| **f64** | 1.45✅ | 1.12✅ | 1.10✅ | 1.33✅ | 1.38✅ | 1.75✅ | 1.10✅ | 0.83🟡 | 0.83🟡 | 1.39✅ | 1.19✅ | 1.65✅ | 2.67✅ | — | 2.68✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.02✅ | 0.98🟡 | 1.08✅ | 1.19✅ | 1.10✅ | 1.54✅ | 1.16✅ | 0.98🟡 | 0.90🟡 | 1.22✅ | 0.95🟡 | 1.49✅ | 1.71✅ | — | 2.26✅ | + +## Layout: strided (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 2.26✅ | 3.65✅ | 3.30✅ | 3.63✅ | 3.72✅ | 1.18✅ | 1.12✅ | 1.74✅ | 1.77✅ | 2.09✅ | 2.71✅ | 1.41✅ | 1.75✅ | — | 2.30✅ | +| **u8** | 0.97🟡 | 2.00✅ | 2.27✅ | 2.28✅ | 1.89✅ | 1.30✅ | 1.27✅ | 2.06✅ | 1.84✅ | 1.90✅ | 2.55✅ | 1.13✅ | 1.78✅ | — | 2.61✅ | +| **i8** | 0.91🟡 | 2.13✅ | 2.46✅ | 1.91✅ | 1.77✅ | 1.35✅ | 1.55✅ | 2.04✅ | 1.89✅ | 1.86✅ | 2.45✅ | 1.33✅ | 1.92✅ | — | 2.34✅ | +| **i16** | 2.41✅ | 1.68✅ | 1.97✅ | 1.14✅ | 2.07✅ | 1.21✅ | 1.26✅ | 1.90✅ | 1.96✅ | 1.70✅ | 1.89✅ | 1.18✅ | 1.73✅ | — | 2.39✅ | +| **u16** | 2.39✅ | 1.77✅ | 1.76✅ | 1.37✅ | 1.41✅ | 1.29✅ | 1.32✅ | 1.89✅ | 1.90✅ | 1.52✅ | 1.90✅ | 1.04✅ | 1.81✅ | — | 2.32✅ | +| **i32** | 1.60✅ | 1.43✅ | 1.39✅ | 1.16✅ | 1.16✅ | 1.23✅ | 1.08✅ | 1.86✅ | 1.78✅ | 1.26✅ | 1.88✅ | 1.28✅ | 1.76✅ | — | 2.44✅ | +| **u32** | 1.74✅ | 1.22✅ | 1.18✅ | 1.10✅ | 1.25✅ | 1.13✅ | 1.14✅ | 1.86✅ | 1.84✅ | 1.18✅ | 1.95✅ | 1.13✅ | 1.77✅ | — | 2.24✅ | +| **i64** | 1.11✅ | 0.95🟡 | 0.94🟡 | 0.86🟡 | 0.94🟡 | 1.22✅ | 1.35✅ | 1.77✅ | 1.74✅ | 0.91🟡 | 1.19✅ | 1.26✅ | 1.74✅ | — | 2.43✅ | +| **u64** | 1.07✅ | 0.93🟡 | 0.94🟡 | 0.83🟡 | 1.01✅ | 1.20✅ | 1.27✅ | 1.58✅ | 1.70✅ | 1.03✅ | 1.48✅ | 0.74🟡 | 1.47✅ | — | 2.17✅ | +| **char** | 2.39✅ | 1.80✅ | 1.68✅ | 1.26✅ | 1.49✅ | 1.21✅ | 1.30✅ | 1.85✅ | 1.75✅ | 1.51✅ | 1.83✅ | 1.13✅ | 1.73✅ | — | 2.34✅ | +| **f16** | 2.62✅ | 1.49✅ | 1.52✅ | 1.59✅ | 1.66✅ | 1.76✅ | 1.14✅ | 1.85✅ | 0.89🟡 | 1.59✅ | 1.11✅ | 1.23✅ | 0.95🟡 | — | 1.44✅ | +| **f32** | 1.98✅ | 1.31✅ | 1.31✅ | 1.30✅ | 1.37✅ | 1.34✅ | 0.91🟡 | 0.94🟡 | 0.80🟡 | 1.30✅ | 1.78✅ | 1.16✅ | 1.20✅ | — | 2.43✅ | +| **f64** | 1.25✅ | 0.98🟡 | 0.97🟡 | 0.91🟡 | 0.86🟡 | 1.22✅ | 0.94🟡 | 0.89🟡 | 0.80🟡 | 0.94🟡 | 1.06✅ | 1.07✅ | 1.81✅ | — | 2.08✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.07✅ | 1.14✅ | 0.99🟡 | 0.93🟡 | 0.91🟡 | 1.15✅ | 1.03✅ | 0.97🟡 | 0.83🟡 | 0.94🟡 | 0.89🟡 | 1.08✅ | 1.59✅ | — | 1.66✅ | + +## Layout: bcast (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 1.26✅ | 5.38✅ | 6.24✅ | 2.28✅ | 2.24✅ | 1.51✅ | 1.46✅ | 2.32✅ | 2.18✅ | 2.26✅ | 3.97✅ | 1.91✅ | 2.37✅ | — | 3.14✅ | +| **u8** | 4.95✅ | 0.89🟡 | 4.29✅ | 1.87✅ | 1.88✅ | 2.08✅ | 2.14✅ | 2.52✅ | 2.39✅ | 1.87✅ | 3.76✅ | 2.32✅ | 2.15✅ | — | 2.95✅ | +| **i8** | 5.13✅ | 4.15✅ | 0.80🟡 | 1.82✅ | 1.87✅ | 2.12✅ | 2.43✅ | 2.55✅ | 2.38✅ | 1.91✅ | 3.81✅ | 2.16✅ | 2.28✅ | — | 3.14✅ | +| **i16** | 4.77✅ | 3.50✅ | 3.80✅ | 1.60✅ | 1.72✅ | 1.98✅ | 1.91✅ | 2.18✅ | 2.32✅ | 1.69✅ | 3.62✅ | 2.22✅ | 2.52✅ | — | 3.12✅ | +| **u16** | 4.57✅ | 4.20✅ | 3.20✅ | 1.85✅ | 1.63✅ | 1.98✅ | 1.89✅ | 2.26✅ | 2.27✅ | 1.66✅ | 3.72✅ | 2.08✅ | 2.26✅ | — | 2.70✅ | +| **i32** | 3.42✅ | 2.89✅ | 3.25✅ | 1.70✅ | 1.67✅ | 2.12✅ | 1.92✅ | 2.09✅ | 2.34✅ | 1.94✅ | 3.82✅ | 2.24✅ | 2.67✅ | — | 3.00✅ | +| **u32** | 3.64✅ | 3.72✅ | 2.42✅ | 1.66✅ | 1.77✅ | 2.02✅ | 1.99✅ | 2.23✅ | 2.31✅ | 1.90✅ | 3.73✅ | 2.29✅ | 2.27✅ | — | 2.94✅ | +| **i64** | 2.70✅ | 2.23✅ | 2.29✅ | 1.73✅ | 1.72✅ | 2.04✅ | 2.10✅ | 2.41✅ | 2.12✅ | 1.72✅ | 1.85✅ | 2.08✅ | 2.00✅ | — | 2.67✅ | +| **u64** | 2.57✅ | 2.40✅ | 2.16✅ | 1.60✅ | 1.66✅ | 2.08✅ | 1.92✅ | 2.24✅ | 2.29✅ | 1.75✅ | 2.43✅ | 2.26✅ | 2.14✅ | — | 2.72✅ | +| **char** | 4.78✅ | 3.81✅ | 3.84✅ | 1.74✅ | 1.50✅ | 1.65✅ | 1.66✅ | 2.17✅ | 2.36✅ | 1.65✅ | 3.64✅ | 1.54✅ | 2.36✅ | — | 2.75✅ | +| **f16** | 6.87✅ | 5.51✅ | 5.35✅ | 4.23✅ | 4.21✅ | 3.88✅ | 2.04✅ | 3.04✅ | 1.09✅ | 3.86✅ | 1.46✅ | 2.87✅ | 0.96🟡 | — | 1.55✅ | +| **f32** | 4.89✅ | 3.29✅ | 2.71✅ | 1.88✅ | 2.02✅ | 2.15✅ | 1.47✅ | 2.21✅ | 0.86🟡 | 1.84✅ | 3.65✅ | 2.09✅ | 2.11✅ | — | 2.29✅ | +| **f64** | 2.70✅ | 2.15✅ | 2.14✅ | 1.78✅ | 1.77✅ | 2.11✅ | 1.49✅ | 1.99✅ | 0.88🟡 | 1.81✅ | 1.58✅ | 2.04✅ | 2.51✅ | — | 2.66✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.09✅ | 1.06✅ | 1.05✅ | 1.38✅ | 1.40✅ | 1.75✅ | 1.35✅ | 0.84🟡 | 0.80🟡 | 1.39✅ | 1.50✅ | 1.56✅ | 2.01✅ | — | 2.94✅ | + +## Lagging cells (<1.0) — the worklist (118 cells) + +| key | NumSharp ms | NumPy ms | ratio | +|---|---|---|---| +| bool\|F\|bool | 0.0595 | 0.0144 | 0.24 🟠 | +| f64\|sliced\|i32 | 0.9124 | 0.6414 | 0.70 🟡 | +| u64\|strided\|f32 | 0.5215 | 0.3885 | 0.74 🟡 | +| c128\|bcast\|u64 | 1.5413 | 1.2301 | 0.80 🟡 | +| f32\|strided\|u64 | 0.7987 | 0.6399 | 0.80 🟡 | +| i8\|bcast\|i8 | 0.0619 | 0.0496 | 0.80 🟡 | +| c128\|sliced\|u64 | 1.7479 | 1.4021 | 0.80 🟡 | +| f64\|strided\|u64 | 0.7758 | 0.6243 | 0.80 🟡 | +| f64\|negrow\|i64 | 1.5209 | 1.2241 | 0.80 🟡 | +| c128\|F\|u64 | 1.7023 | 1.3717 | 0.81 🟡 | +| f32\|negcol\|u64 | 1.5659 | 1.2627 | 0.81 🟡 | +| f64\|C\|i8 | 0.2927 | 0.2376 | 0.81 🟡 | +| f64\|C\|u8 | 0.2892 | 0.2350 | 0.81 🟡 | +| f32\|F\|i64 | 1.4588 | 1.1945 | 0.82 🟡 | +| c128\|strided\|u64 | 0.9366 | 0.7745 | 0.83 🟡 | +| u64\|strided\|i16 | 0.1726 | 0.1432 | 0.83 🟡 | +| f32\|C\|i64 | 1.4773 | 1.2276 | 0.83 🟡 | +| f64\|negcol\|u64 | 1.5574 | 1.2946 | 0.83 🟡 | +| f32\|T\|i64 | 1.4640 | 1.2208 | 0.83 🟡 | +| f64\|negcol\|i64 | 1.4625 | 1.2196 | 0.83 🟡 | +| c128\|C\|u64 | 1.6652 | 1.3911 | 0.84 🟡 | +| f32\|negrow\|i64 | 1.4768 | 1.2373 | 0.84 🟡 | +| f32\|F\|u64 | 1.4675 | 1.2315 | 0.84 🟡 | +| f64\|sliced\|i64 | 1.4533 | 1.2221 | 0.84 🟡 | +| c128\|bcast\|i64 | 1.4280 | 1.2013 | 0.84 🟡 | +| f64\|sliced\|u64 | 1.4453 | 1.2198 | 0.84 🟡 | +| i8\|T\|i8 | 0.0613 | 0.0519 | 0.85 🟡 | +| c128\|T\|u64 | 1.6154 | 1.3692 | 0.85 🟡 | +| f32\|negrow\|u64 | 1.4957 | 1.2720 | 0.85 🟡 | +| f32\|negcol\|i64 | 1.4173 | 1.2088 | 0.85 🟡 | +| f64\|C\|i64 | 1.4556 | 1.2468 | 0.86 🟡 | +| i64\|strided\|i16 | 0.1748 | 0.1499 | 0.86 🟡 | +| f32\|sliced\|i64 | 1.4408 | 1.2354 | 0.86 🟡 | +| f64\|strided\|u16 | 0.1706 | 0.1469 | 0.86 🟡 | +| f32\|T\|u64 | 1.4532 | 1.2546 | 0.86 🟡 | +| f32\|bcast\|u64 | 1.4571 | 1.2602 | 0.86 🟡 | +| c128\|negrow\|i64 | 1.5665 | 1.3619 | 0.87 🟡 | +| f64\|F\|i64 | 1.4506 | 1.2612 | 0.87 🟡 | +| f64\|T\|i64 | 1.4409 | 1.2540 | 0.87 🟡 | +| f32\|C\|u64 | 1.4695 | 1.2834 | 0.87 🟡 | +| f64\|bcast\|u64 | 1.4020 | 1.2277 | 0.88 🟡 | +| f64\|negrow\|u64 | 1.4430 | 1.2673 | 0.88 🟡 | +| c128\|F\|i8 | 0.3572 | 0.3148 | 0.88 🟡 | +| f32\|sliced\|u64 | 1.4888 | 1.3174 | 0.88 🟡 | +| f16\|negcol\|u64 | 2.1147 | 1.8765 | 0.89 🟡 | +| u8\|bcast\|u8 | 0.0587 | 0.0522 | 0.89 🟡 | +| f64\|strided\|i64 | 0.7057 | 0.6283 | 0.89 🟡 | +| f64\|C\|u64 | 1.4293 | 1.2761 | 0.89 🟡 | +| f16\|strided\|u64 | 1.0346 | 0.9241 | 0.89 🟡 | +| c128\|strided\|f16 | 0.8811 | 0.7874 | 0.89 🟡 | +| f64\|T\|u64 | 1.4221 | 1.2717 | 0.89 🟡 | +| c128\|T\|i64 | 1.5151 | 1.3650 | 0.90 🟡 | +| c128\|negcol\|u64 | 1.6586 | 1.4945 | 0.90 🟡 | +| f64\|F\|u64 | 1.4496 | 1.3097 | 0.90 🟡 | +| f32\|strided\|u32 | 0.3838 | 0.3480 | 0.91 🟡 | +| c128\|sliced\|char | 0.5601 | 0.5081 | 0.91 🟡 | +| f64\|strided\|i16 | 0.1641 | 0.1489 | 0.91 🟡 | +| c128\|F\|u8 | 0.3391 | 0.3083 | 0.91 🟡 | +| i64\|strided\|char | 0.1534 | 0.1397 | 0.91 🟡 | +| i8\|strided\|bool | 0.1632 | 0.1489 | 0.91 🟡 | +| i8\|C\|i8 | 0.0595 | 0.0543 | 0.91 🟡 | +| c128\|strided\|u16 | 0.3051 | 0.2789 | 0.91 🟡 | +| c128\|C\|i64 | 1.5153 | 1.3895 | 0.92 🟡 | +| u64\|F\|u8 | 0.2149 | 0.1979 | 0.92 🟡 | +| c128\|negrow\|u64 | 1.7009 | 1.5680 | 0.92 🟡 | +| i64\|F\|i8 | 0.2150 | 0.1982 | 0.92 🟡 | +| f16\|C\|f64 | 1.5894 | 1.4665 | 0.92 🟡 | +| u64\|C\|u8 | 0.2155 | 0.1988 | 0.92 🟡 | +| i8\|sliced\|i8 | 0.0575 | 0.0530 | 0.92 🟡 | +| c128\|strided\|i16 | 0.3130 | 0.2897 | 0.93 🟡 | +| c128\|C\|u8 | 0.3526 | 0.3283 | 0.93 🟡 | +| u64\|negcol\|i8 | 0.2475 | 0.2306 | 0.93 🟡 | +| i64\|T\|i8 | 0.2114 | 0.1973 | 0.93 🟡 | +| c128\|sliced\|i64 | 1.4869 | 1.3889 | 0.93 🟡 | +| u64\|strided\|u8 | 0.1409 | 0.1316 | 0.93 🟡 | +| c128\|strided\|char | 0.2944 | 0.2754 | 0.94 🟡 | +| f32\|strided\|i64 | 0.7048 | 0.6596 | 0.94 🟡 | +| c128\|C\|i8 | 0.3417 | 0.3201 | 0.94 🟡 | +| i64\|strided\|u16 | 0.1550 | 0.1454 | 0.94 🟡 | +| u64\|F\|i8 | 0.2121 | 0.1991 | 0.94 🟡 | +| u64\|strided\|i8 | 0.1389 | 0.1305 | 0.94 🟡 | +| u64\|T\|u8 | 0.2105 | 0.1978 | 0.94 🟡 | +| i64\|F\|u8 | 0.2204 | 0.2078 | 0.94 🟡 | +| c128\|negrow\|u8 | 0.3492 | 0.3294 | 0.94 🟡 | +| f64\|strided\|char | 0.1593 | 0.1504 | 0.94 🟡 | +| f64\|strided\|u32 | 0.3797 | 0.3587 | 0.94 🟡 | +| i64\|strided\|i8 | 0.1438 | 0.1358 | 0.94 🟡 | +| u64\|C\|i8 | 0.2096 | 0.1981 | 0.95 🟡 | +| f16\|strided\|f64 | 0.7709 | 0.7306 | 0.95 🟡 | +| f16\|F\|f64 | 1.5066 | 1.4286 | 0.95 🟡 | +| i64\|strided\|u8 | 0.1424 | 0.1351 | 0.95 🟡 | +| u64\|T\|i8 | 0.2094 | 0.1987 | 0.95 🟡 | +| c128\|negcol\|f16 | 1.8203 | 1.7315 | 0.95 🟡 | +| f16\|sliced\|f64 | 1.4890 | 1.4241 | 0.96 🟡 | +| c128\|F\|i64 | 1.5008 | 1.4359 | 0.96 🟡 | +| i64\|negcol\|u8 | 0.2439 | 0.2335 | 0.96 🟡 | +| f16\|bcast\|f64 | 1.5177 | 1.4532 | 0.96 🟡 | +| c128\|sliced\|i8 | 0.3253 | 0.3118 | 0.96 🟡 | +| i64\|C\|i8 | 0.2090 | 0.2004 | 0.96 🟡 | +| f16\|negcol\|f64 | 1.5238 | 1.4668 | 0.96 🟡 | +| char\|C\|u16 | 0.2540 | 0.2457 | 0.97 🟡 | +| c128\|sliced\|bool | 0.4498 | 0.4355 | 0.97 🟡 | +| c128\|strided\|i64 | 0.8006 | 0.7755 | 0.97 🟡 | +| u64\|sliced\|f32 | 0.7665 | 0.7425 | 0.97 🟡 | +| f16\|negrow\|f64 | 1.5293 | 1.4823 | 0.97 🟡 | +| f64\|strided\|i8 | 0.1454 | 0.1413 | 0.97 🟡 | +| i8\|negrow\|i8 | 0.0558 | 0.0542 | 0.97 🟡 | +| u8\|strided\|bool | 0.1537 | 0.1495 | 0.97 🟡 | +| i64\|negcol\|i8 | 0.2480 | 0.2414 | 0.97 🟡 | +| i64\|C\|u8 | 0.2108 | 0.2062 | 0.98 🟡 | +| c128\|negcol\|u8 | 0.3485 | 0.3423 | 0.98 🟡 | +| f64\|strided\|u8 | 0.1456 | 0.1431 | 0.98 🟡 | +| c128\|negcol\|i64 | 1.6104 | 1.5838 | 0.98 🟡 | +| f16\|C\|u64 | 1.8886 | 1.8634 | 0.99 🟡 | +| c128\|F\|bool | 0.4151 | 0.4098 | 0.99 🟡 | +| c128\|strided\|i8 | 0.2650 | 0.2633 | 0.99 🟡 | +| f16\|T\|f64 | 1.4800 | 1.4751 | 1.00 🟡 | +| u64\|negrow\|f32 | 0.7923 | 0.7904 | 1.00 🟡 | + +_1568 comparable cells (1800 NumSharp rows; 118 lagging <1.0)._ + + +--- + +## Fusion — np.evaluate vs unfused chains + +`np.evaluate` runs a whole expression tree in one NDIter pass (no intermediates). Fixed-expression gate plus an operand-layout sweep of the flagship `a*b+c` (C/F/T/strided/bcast — does the fused single-pass win survive non-contiguous operands?), not a dtype/layout matrix — so reported as-is. + +``` +NumSharp — fused np.evaluate vs unfused np.* chains (4M elements, best-of-9; (Nx) = unfused ÷ fused, >1 = fusion faster): + +correctness cross-checks ok + +4M float64, best of 9: + a*b+c fused 3.53 ms unfused 6.08 ms (1.72x) + (a-b)/(a+b) fused 3.16 ms unfused 12.32 ms (3.90x) + sum(a*b) fused 2.30 ms unfused 4.33 ms (1.88x) + sum(af*bf) fused 1.43 ms unfused 1.57 ms (1.10x) [f32] + a*b+c out= fused 3.56 ms [1-pass fused-into-out] + i4*2+f8 fused 2.78 ms unfused 4.04 ms (1.45x) + + a*b+c across operand layouts (2-D 2000x2000, all 3 operands same layout): + [C ] fused 3.43 ms unfused 6.05 ms (1.77x) + [F ] fused 3.52 ms unfused 6.18 ms (1.75x) + [T ] fused 3.50 ms unfused 6.27 ms (1.79x) + [strided] fused 3.31 ms unfused 4.72 ms (1.43x) + [bcast ] fused 1.06 ms unfused 3.75 ms (3.53x) + +NumPy — absolutes on the same box (context for the unfused column): + +numpy 2.4.2, 4M float64, best of 9: + a*b+c 12.35 ms + (a-b)/(a+b) 18.75 ms + sum(a*b) 8.60 ms + sum(af*bf) 4.14 ms [f32] + a*b+c out= 4.68 ms [two-pass with out=] + i4*2+f8 9.67 ms + a*b+c across operand layouts (2-D 2000x2000, unfused): + [C ] 12.52 ms + [F ] 12.54 ms + [T ] 12.51 ms + [strided] 7.91 ms + [bcast ] 11.95 ms +``` diff --git a/benchmark/history/2026-06-29_2d16f477/cards/cat.png b/benchmark/history/2026-06-29_2d16f477/cards/cat.png new file mode 100644 index 0000000000000000000000000000000000000000..c39b1352eeb6003a0b65b7475f4a1afb746be0f1 GIT binary patch literal 22736 zcmd43byU^=x9$rfDH4Lx-617NNOwsKC|!cmy-4Zq25Av6P$4Y78tN2_uH!reo(0`b_Prwl0=5zSA7Y_M!6yU+Z9zFnF%3xieg=B|+jqBZ z2B(79Qb7rvCb{BNbWEcmI8uEny$M`0D-s{&LUEo_kA=v~Cum&7xn*v;2|jFI-b`K} zdZarB?-R2f9=Z)VUe22z)=ui*>K)cx{9#e0#6b%&A`CL>jP0}+YUjZ0;Aq2y|6=*c zE6O17lYP(!JWANYfn*GRUj6*%LHq5evcXtnYa3r}Cx^$CMaYhuXk?$Ug~IOLRQd;IE@2H1tA>rL|=JrvB(wNu=^>%6}EJTok(Qcs*ett^%v}5$ntbpXqi^4H10a>may7w*3cbljC@S&`}gnZ@X3_H zm#*#fcn0PEg=UZSXp#FEF@F@fUuisMcQ@ypnU}let^Ff866YqmUr{>eA5_5mmwxot zsQK<?_i@+e^%fp@4#$YNx6D*Vot9aT~wB4s4GXtUK7RbcPOSUe@-fzA|5% zC{jOG(*%#3f+xReP@`ILu^voh(Mlh>JMN)4han=N=+;~ib<^=u%Ei&f+O}LvPnPMC z6gb+zj-}mJBYD@N-W-g05kBt^eOyFlyN1iQUJW@mcH2lbhiVk5rO|tA+D%|gJ>@Mo^7`J_W=!v^pmT1U8Dr%Jt*4lGu8Up_&N~ya&*>6zUHF zcTzQqA+Q_am^m(>U&pqY?hw4l(Zl6}hjNwqu=j#J<2P^ts!e{^A(*Q(>A_0n{uD>N z_-Xu`9Q>1|OFY^YpanT z#)=sDj5w8lz8r}+XwDdJCE5P*0|AM1>LH?wUV+uZ}U!2E!S_JfKV>k z=yx+^#?r|2X9{`bCiG+%>NZrl&k}M7UIw5c+5f#dZp>ge5Wc>@JzO7PZxvLgYOA+j z)$slH_N(SMrHo=nj5C8Vs2tBDQv=3SF0(O<9Tc+N|z?C+~2 zGcXDS#q*wTun}`xWUHd$8iMoqE>?|q(?oG&IICQqB>kkFFsGB0kn}*rO!84M%4)2j zo5Q=fn*HTq3arfNTr6bUG#8vv#Hv7t=*QM}ckK<~FJ6un5?qI&AK=Em82rl1o-*Wk zzFfIEob}+hE7f2k$e}qWb8x%V?tlKK8)JJ-B}DvcsP6?%WOuF2&9gM_Pr2s~tg&>s zC9!s)yhwgyKOKKoAGxMDqC+-R(~T)#N?1gWA0S+%EWJ&T)8Kh4yBIF2^T(BL_0*({ zuP{-T7-Puu4t`kAqC~f=1d4~D<-IS_{I1Rax;HU?c{A&aewArou9r&f=il13<5Glt zkNCKHqA9Q!7|i$BYV=(*B&nfR4TlTfXs(N)(+%uU0jAkmZ2O|G9($OyxY*9IE%Z8u zw7M%V1J{4*C3Gh+sY7)e9P9;>xqU$wAZ^*fj%53}{OcG=oPs3J*Uy>h$F%r|U|mN~ zaj2vKeN85~6Hp&o?{9YfXEEr{_a?4Vz76n04_?Hl z!AL#RvS@|WrDBIUH7~rDo(j@yazUJWC>ovP#$fxt%l?eq zK+?-R&*(aQcbmdz3WbnO0hfb0qB*Q&C1@7MH1uglzU|MqekX1IG&R`A6J&}P?#&m| z7jMYxzCJA7$A8Gl`2L|5+M1xR>i4P8s`(8n2@aUoupQGmg&$swQ%GrXCp= z`XMqv2KMl5J`lPfwx3N_QQ8obgpMLh|Np+kbYgzghpg2kQx_22)&pUU@31 zgjnya?0xwk@h(1_9S)SFfcP_N9QGsHSVR&y%0 z*mDuaWbF~@dOR_f^zOlP?c}f6uMl>AJoyT14aZL@X>0p?GQe&`^DM&8pa1LDl93N6 zobT%FKik>V>mQDbaPqTO`U<4`ts^4k8mV~@`cJP=KTS9uOnsHN7ar|C?^S@EqHa9) z72Ux!d{3+%U(@sVFPi(ooP5PpwM~X*6T#Q1NUOvlq6mlIAjd1klDRZ<*obb$!J#(g zbtG8*uJr}AA8IUeqEk<3aa!r9bd`HsH@Z1aWJ?Ih-yY1=T1w{46sppImX$z^Rlo|< z&n4L#7QQWIW`wodT~d_=VXwBQBPuI!YPH7;FP|)qwv(d8Zz&ezJ;(hurce5aw|t$_ zH9BGm8smYQj&xaQ$tZX+5Fj)pxkZAv#XwHiqGHpyO<$zn+$bCyK-B`2gtLgFU}c~a z?$j@T3SE+gFN(@E-T4d2`tEX(j3%qKQ~Kl41Z&i6o%uEq8uc2_t2$OMq<^mE!k#>_ zrGL#KDyzQKm+d3`@b{FFe@Zsnf)I19QljH?0^;aTq070vOy&hv`H;|NS&WU(zQW5) z1Z5=tew&Bq_Zgc*u8J-uF)3EcNv!XWgkx?Tqb<6T@wxRGl~=RzLn!UgQb-T}RGXLf zQ(6a#GsLZA9-rZtH=+k7U@6azUZkWywNrLI_P{F3*roH5amO5cM>7{ec7urO?-9gluMF!7oSyupi@I&SK~j{wyP|(u z=w`JU8o?4R`2)KD+y7P%wMdXMGBB3%SJx6f-P*5DBtb^HXy4wJQTH0TZjVVdTRA+e zjiqX}b$Flu*7^k@48kp+-kmF_hTtdgGh!m+#Y0Q4_K}q50)`ZT?9^pU`pE^PsizU{ z|5BU&OSpFD^HSOvQRsIPOw}EBZ|JFL?KDopWSGaQnijgE*pScg%A@R`T+|cDohTJu zHEJ%zpyhPw7QQJ zWM5gIokibNTf|8TV~wQv-i7Q<_p9VbN4t)uTbA0^%%t}~lLlmJw*_a#;U(`2Urn=YD1;~r=r0bs0%4wUS*}v<#~X

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**1450** win (≥1.0). +- **🔴 <0.2** — 0 cells. +- **🟠 0.2–0.5** — 1 cells. Top: 1× * → bool +- **🟡 0.5–1.0** — 117 cells. Top: 23× int → sub-word (narrow); 21× float/cplx → narrow-int (bool/u8/i8/i16/u16/char); 8× f16 → f64 + +**float/complex → narrow-int geomean by src** (the historical cliff): `f32`→narrow **1.96**, `f64`→narrow **1.38**, `f16`→narrow **3.71**, `c128`→narrow **1.08**. + +## Geomean by layout (all src×dst, excl. Decimal) + +| C | F | T | sliced | negrow | negcol | strided | bcast | +|---|---|---|---|---|---|---|---| +| 1.86 ✅ | 1.88 ✅ | 1.93 ✅ | 1.95 ✅ | 1.97 ✅ | 1.82 ✅ | 1.47 ✅ | 2.25 ✅ | + +## Geomean by src dtype (all layouts×dst) + +| bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| 2.35 ✅ | 2.18 ✅ | 2.16 ✅ | 2.19 ✅ | 2.14 ✅ | 1.92 ✅ | 1.88 ✅ | 1.62 ✅ | 1.55 ✅ | 1.98 ✅ | 2.36 ✅ | 1.75 ✅ | 1.43 ✅ | nan ? | 1.23 ✅ | + +## Geomean by dst dtype (all layouts×src) + +| bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| 2.17 ✅ | 1.94 ✅ | 1.93 ✅ | 1.61 ✅ | 1.60 ✅ | 1.80 ✅ | 1.64 ✅ | 1.89 ✅ | 1.73 ✅ | 1.60 ✅ | 2.48 ✅ | 1.60 ✅ | 2.03 ✅ | nan ? | 2.59 ✅ | + +## Layout: C (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 3.04✅ | 2.60✅ | 2.76✅ | 2.10✅ | 1.98✅ | 1.53✅ | 1.46✅ | 2.04✅ | 2.25✅ | 1.73✅ | 3.29✅ | 1.71✅ | 2.49✅ | — | 2.94✅ | +| **u8** | 3.55✅ | 1.55✅ | 3.56✅ | 1.69✅ | 1.83✅ | 2.01✅ | 1.89✅ | 2.21✅ | 2.47✅ | 1.62✅ | 3.90✅ | 1.38✅ | 2.41✅ | — | 3.07✅ | +| **i8** | 2.61✅ | 3.17✅ | 0.91🟡 | 1.82✅ | 1.76✅ | 1.95✅ | 1.95✅ | 2.35✅ | 2.66✅ | 1.76✅ | 3.94✅ | 1.79✅ | 2.64✅ | — | 2.98✅ | +| **i16** | 2.20✅ | 2.29✅ | 2.59✅ | 1.39✅ | 2.11✅ | 2.09✅ | 1.95✅ | 2.42✅ | 2.52✅ | 1.57✅ | 3.71✅ | 1.92✅ | 2.61✅ | — | 2.79✅ | +| **u16** | 2.22✅ | 2.80✅ | 2.81✅ | 2.12✅ | 1.24✅ | 2.09✅ | 2.14✅ | 2.63✅ | 2.22✅ | 1.08✅ | 3.71✅ | 1.93✅ | 2.46✅ | — | 2.97✅ | +| **i32** | 1.50✅ | 1.15✅ | 1.16✅ | 1.46✅ | 1.51✅ | 1.70✅ | 2.12✅ | 2.26✅ | 2.27✅ | 1.43✅ | 3.64✅ | 1.64✅ | 2.62✅ | — | 2.86✅ | +| **u32** | 1.77✅ | 1.18✅ | 1.17✅ | 1.35✅ | 1.44✅ | 2.17✅ | 1.65✅ | 2.19✅ | 2.29✅ | 1.48✅ | 3.66✅ | 1.47✅ | 2.34✅ | — | 2.69✅ | +| **i64** | 1.08✅ | 0.98🟡 | 0.96🟡 | 1.23✅ | 1.27✅ | 1.83✅ | 1.78✅ | 1.87✅ | 2.76✅ | 1.38✅ | 1.81✅ | 1.81✅ | 2.22✅ | — | 2.52✅ | +| **u64** | 1.14✅ | 0.92🟡 | 0.95🟡 | 1.22✅ | 1.18✅ | 1.73✅ | 1.73✅ | 2.56✅ | 2.09✅ | 1.30✅ | 1.65✅ | 1.14✅ | 1.79✅ | — | 2.45✅ | +| **char** | 2.07✅ | 2.02✅ | 2.14✅ | 1.49✅ | 0.97🟡 | 1.55✅ | 1.53✅ | 2.26✅ | 2.20✅ | 1.21✅ | 3.67✅ | 1.42✅ | 2.11✅ | — | 2.62✅ | +| **f16** | 4.51✅ | 5.42✅ | 5.35✅ | 3.82✅ | 3.94✅ | 3.80✅ | 1.96✅ | 2.94✅ | 0.99🟡 | 4.16✅ | 1.25✅ | 1.10✅ | 0.92🟡 | — | 1.64✅ | +| **f32** | 2.82✅ | 1.92✅ | 2.05✅ | 1.64✅ | 1.55✅ | 1.84✅ | 1.38✅ | 0.83🟡 | 0.87🟡 | 1.48✅ | 3.59✅ | 1.76✅ | 2.21✅ | — | 2.35✅ | +| **f64** | 1.81✅ | 0.81🟡 | 0.81🟡 | 1.32✅ | 1.29✅ | 1.68✅ | 1.51✅ | 0.86🟡 | 0.89🟡 | 1.38✅ | 1.04✅ | 1.72✅ | 2.05✅ | — | 2.60✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.11✅ | 0.93🟡 | 0.94🟡 | 1.19✅ | 1.13✅ | 1.69✅ | 1.35✅ | 0.92🟡 | 0.84🟡 | 1.10✅ | 1.50✅ | 1.39✅ | 1.75✅ | — | 3.18✅ | + +## Layout: F (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 0.24🟠 | 2.72✅ | 2.71✅ | 1.94✅ | 1.91✅ | 1.43✅ | 1.42✅ | 2.24✅ | 2.40✅ | 1.89✅ | 4.03✅ | 1.74✅ | 2.55✅ | — | 2.98✅ | +| **u8** | 4.01✅ | 1.11✅ | 3.48✅ | 1.70✅ | 1.71✅ | 2.07✅ | 2.06✅ | 2.11✅ | 2.33✅ | 1.62✅ | 3.89✅ | 1.39✅ | 2.49✅ | — | 3.06✅ | +| **i8** | 3.53✅ | 3.24✅ | 1.05✅ | 1.93✅ | 1.80✅ | 2.14✅ | 2.13✅ | 2.59✅ | 2.51✅ | 1.71✅ | 3.88✅ | 1.77✅ | 2.40✅ | — | 2.95✅ | +| **i16** | 1.87✅ | 2.06✅ | 2.52✅ | 1.31✅ | 1.98✅ | 1.84✅ | 2.01✅ | 2.59✅ | 2.39✅ | 2.19✅ | 3.71✅ | 2.05✅ | 2.39✅ | — | 3.01✅ | +| **u16** | 2.26✅ | 2.74✅ | 2.63✅ | 1.93✅ | 1.27✅ | 1.88✅ | 2.02✅ | 2.68✅ | 2.26✅ | 1.35✅ | 3.65✅ | 1.96✅ | 2.17✅ | — | 2.85✅ | +| **i32** | 1.69✅ | 1.11✅ | 1.18✅ | 1.48✅ | 1.49✅ | 1.76✅ | 2.17✅ | 2.46✅ | 2.48✅ | 1.50✅ | 3.90✅ | 1.72✅ | 2.33✅ | — | 2.85✅ | +| **u32** | 1.85✅ | 1.13✅ | 1.06✅ | 1.54✅ | 1.43✅ | 2.15✅ | 1.64✅ | 2.31✅ | 2.26✅ | 1.50✅ | 3.87✅ | 1.55✅ | 2.16✅ | — | 2.46✅ | +| **i64** | 1.11✅ | 0.94🟡 | 0.92🟡 | 1.17✅ | 1.26✅ | 1.88✅ | 1.75✅ | 2.16✅ | 2.70✅ | 1.28✅ | 1.87✅ | 1.55✅ | 2.17✅ | — | 2.24✅ | +| **u64** | 1.15✅ | 0.92🟡 | 0.94🟡 | 1.21✅ | 1.13✅ | 1.73✅ | 1.73✅ | 2.46✅ | 2.05✅ | 1.39✅ | 2.48✅ | 1.14✅ | 1.76✅ | — | 2.34✅ | +| **char** | 2.08✅ | 2.63✅ | 2.03✅ | 1.90✅ | 1.20✅ | 1.67✅ | 1.69✅ | 2.52✅ | 2.46✅ | 1.20✅ | 3.60✅ | 1.41✅ | 2.10✅ | — | 2.89✅ | +| **f16** | 4.49✅ | 6.02✅ | 6.33✅ | 3.79✅ | 4.31✅ | 3.73✅ | 2.06✅ | 2.85✅ | 1.11✅ | 4.33✅ | 1.24✅ | 2.44✅ | 0.95🟡 | — | 1.62✅ | +| **f32** | 3.44✅ | 2.12✅ | 2.26✅ | 1.62✅ | 1.64✅ | 1.92✅ | 1.35✅ | 0.82🟡 | 0.84🟡 | 1.69✅ | 3.63✅ | 1.67✅ | 2.21✅ | — | 2.26✅ | +| **f64** | 1.90✅ | 1.27✅ | 1.33✅ | 1.40✅ | 1.43✅ | 1.95✅ | 1.47✅ | 0.87🟡 | 0.90🟡 | 1.41✅ | 1.61✅ | 1.73✅ | 2.05✅ | — | 2.69✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 0.99🟡 | 0.91🟡 | 0.88🟡 | 1.15✅ | 1.07✅ | 1.57✅ | 1.33✅ | 0.96🟡 | 0.81🟡 | 1.09✅ | 1.49✅ | 1.15✅ | 1.75✅ | — | 3.26✅ | + +## Layout: T (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 3.40✅ | 3.39✅ | 3.74✅ | 1.71✅ | 1.80✅ | 1.47✅ | 1.41✅ | 2.31✅ | 2.32✅ | 1.91✅ | 4.20✅ | 1.78✅ | 2.68✅ | — | 2.99✅ | +| **u8** | 3.94✅ | 1.21✅ | 2.55✅ | 1.68✅ | 1.68✅ | 2.05✅ | 1.92✅ | 2.21✅ | 2.56✅ | 1.67✅ | 3.98✅ | 1.41✅ | 2.41✅ | — | 2.98✅ | +| **i8** | 2.58✅ | 3.43✅ | 0.85🟡 | 1.84✅ | 1.75✅ | 2.01✅ | 2.12✅ | 2.50✅ | 2.42✅ | 1.76✅ | 3.79✅ | 1.80✅ | 2.46✅ | — | 3.04✅ | +| **i16** | 2.29✅ | 2.12✅ | 3.21✅ | 1.25✅ | 2.11✅ | 2.01✅ | 2.04✅ | 2.43✅ | 2.25✅ | 2.00✅ | 3.86✅ | 2.02✅ | 2.46✅ | — | 3.02✅ | +| **u16** | 2.23✅ | 2.51✅ | 2.45✅ | 1.91✅ | 1.26✅ | 2.03✅ | 1.99✅ | 2.44✅ | 2.39✅ | 1.44✅ | 3.67✅ | 1.90✅ | 2.29✅ | — | 2.50✅ | +| **i32** | 1.77✅ | 1.13✅ | 1.19✅ | 1.52✅ | 1.52✅ | 1.69✅ | 2.31✅ | 2.07✅ | 2.11✅ | 1.66✅ | 3.96✅ | 1.65✅ | 2.57✅ | — | 2.95✅ | +| **u32** | 1.89✅ | 1.16✅ | 1.16✅ | 1.49✅ | 1.47✅ | 2.25✅ | 1.72✅ | 2.18✅ | 2.15✅ | 1.47✅ | 3.71✅ | 1.49✅ | 2.27✅ | — | 2.82✅ | +| **i64** | 1.28✅ | 1.04✅ | 0.93🟡 | 1.25✅ | 1.27✅ | 1.79✅ | 1.78✅ | 2.09✅ | 2.64✅ | 1.33✅ | 1.86✅ | 1.74✅ | 2.23✅ | — | 2.69✅ | +| **u64** | 1.20✅ | 0.94🟡 | 0.95🟡 | 1.21✅ | 1.16✅ | 1.79✅ | 1.62✅ | 2.52✅ | 2.00✅ | 1.36✅ | 2.47✅ | 1.12✅ | 1.90✅ | — | 2.54✅ | +| **char** | 2.14✅ | 2.15✅ | 2.63✅ | 1.84✅ | 1.22✅ | 1.61✅ | 1.62✅ | 2.24✅ | 2.47✅ | 1.31✅ | 3.50✅ | 1.44✅ | 2.32✅ | — | 2.56✅ | +| **f16** | 5.17✅ | 6.28✅ | 6.15✅ | 4.27✅ | 4.26✅ | 3.56✅ | 2.03✅ | 3.02✅ | 1.14✅ | 4.33✅ | 1.18✅ | 2.84✅ | 1.00🟡 | — | 1.65✅ | +| **f32** | 3.33✅ | 2.16✅ | 2.30✅ | 1.77✅ | 1.63✅ | 2.04✅ | 1.38✅ | 0.83🟡 | 0.86🟡 | 1.63✅ | 3.75✅ | 1.70✅ | 2.56✅ | — | 2.20✅ | +| **f64** | 2.00✅ | 1.35✅ | 1.35✅ | 1.37✅ | 1.41✅ | 1.81✅ | 1.48✅ | 0.87🟡 | 0.89🟡 | 1.31✅ | 1.63✅ | 1.74✅ | 1.93✅ | — | 2.61✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.00✅ | 1.11✅ | 1.21✅ | 1.27✅ | 1.18✅ | 1.66✅ | 1.28✅ | 0.90🟡 | 0.85🟡 | 1.11✅ | 1.53✅ | 1.41✅ | 1.79✅ | — | 3.42✅ | + +## Layout: sliced (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 3.14✅ | 5.84✅ | 5.99✅ | 1.96✅ | 2.00✅ | 1.49✅ | 1.56✅ | 2.38✅ | 2.06✅ | 1.99✅ | 3.95✅ | 1.74✅ | 2.66✅ | — | 3.02✅ | +| **u8** | 2.74✅ | 1.25✅ | 3.91✅ | 1.79✅ | 1.88✅ | 2.24✅ | 2.14✅ | 2.44✅ | 2.55✅ | 1.72✅ | 3.71✅ | 1.40✅ | 2.83✅ | — | 3.17✅ | +| **i8** | 2.96✅ | 3.03✅ | 0.92🟡 | 1.75✅ | 1.74✅ | 1.99✅ | 1.92✅ | 2.30✅ | 2.14✅ | 1.65✅ | 3.86✅ | 1.69✅ | 2.48✅ | — | 3.07✅ | +| **i16** | 2.25✅ | 2.82✅ | 2.56✅ | 1.45✅ | 1.71✅ | 1.93✅ | 1.90✅ | 2.47✅ | 2.50✅ | 1.62✅ | 3.54✅ | 2.06✅ | 2.58✅ | — | 2.85✅ | +| **u16** | 2.98✅ | 2.66✅ | 2.90✅ | 1.46✅ | 1.33✅ | 2.06✅ | 1.96✅ | 2.50✅ | 2.20✅ | 1.36✅ | 3.62✅ | 1.88✅ | 2.45✅ | — | 3.01✅ | +| **i32** | 1.94✅ | 2.20✅ | 2.10✅ | 1.43✅ | 1.54✅ | 1.90✅ | 1.81✅ | 2.14✅ | 2.23✅ | 1.55✅ | 3.70✅ | 1.58✅ | 2.39✅ | — | 2.86✅ | +| **u32** | 1.99✅ | 2.01✅ | 2.13✅ | 1.44✅ | 1.53✅ | 2.02✅ | 1.85✅ | 2.10✅ | 2.22✅ | 1.55✅ | 3.72✅ | 1.47✅ | 2.12✅ | — | 2.74✅ | +| **i64** | 1.20✅ | 1.18✅ | 1.28✅ | 1.42✅ | 1.33✅ | 1.78✅ | 1.78✅ | 2.22✅ | 2.35✅ | 1.42✅ | 1.82✅ | 1.52✅ | 2.36✅ | — | 2.68✅ | +| **u64** | 1.29✅ | 1.24✅ | 1.22✅ | 1.33✅ | 1.30✅ | 1.74✅ | 1.79✅ | 2.14✅ | 2.22✅ | 1.36✅ | 2.39✅ | 0.97🟡 | 1.58✅ | — | 2.45✅ | +| **char** | 2.16✅ | 2.26✅ | 2.43✅ | 1.50✅ | 1.31✅ | 1.61✅ | 1.60✅ | 2.27✅ | 2.32✅ | 1.31✅ | 3.47✅ | 1.42✅ | 2.23✅ | — | 2.80✅ | +| **f16** | 4.70✅ | 4.95✅ | 4.99✅ | 4.11✅ | 4.05✅ | 4.02✅ | 1.97✅ | 3.08✅ | 1.10✅ | 4.21✅ | 1.33✅ | 2.70✅ | 0.96🟡 | — | 1.49✅ | +| **f32** | 3.63✅ | 2.20✅ | 2.35✅ | 1.64✅ | 1.67✅ | 1.90✅ | 1.34✅ | 0.86🟡 | 0.88🟡 | 1.59✅ | 3.62✅ | 1.97✅ | 2.04✅ | — | 2.35✅ | +| **f64** | 2.01✅ | 1.26✅ | 1.31✅ | 1.42✅ | 1.33✅ | 0.70🟡 | 1.49✅ | 0.84🟡 | 0.84🟡 | 1.34✅ | 1.58✅ | 1.71✅ | 2.49✅ | — | 2.58✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 0.97🟡 | 1.07✅ | 0.96🟡 | 1.13✅ | 1.14✅ | 1.72✅ | 1.30✅ | 0.93🟡 | 0.80🟡 | 0.91🟡 | 1.47✅ | 1.40✅ | 1.75✅ | — | 2.20✅ | + +## Layout: negrow (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 3.38✅ | 4.26✅ | 4.97✅ | 1.92✅ | 2.03✅ | 1.41✅ | 1.57✅ | 2.32✅ | 2.40✅ | 1.98✅ | 3.91✅ | 1.79✅ | 2.59✅ | — | 3.34✅ | +| **u8** | 3.67✅ | 1.14✅ | 3.99✅ | 1.71✅ | 1.63✅ | 2.15✅ | 2.24✅ | 2.37✅ | 2.35✅ | 1.87✅ | 3.81✅ | 1.44✅ | 2.29✅ | — | 3.05✅ | +| **i8** | 3.35✅ | 3.21✅ | 0.97🟡 | 2.04✅ | 1.87✅ | 2.02✅ | 2.04✅ | 2.32✅ | 2.48✅ | 1.72✅ | 3.67✅ | 1.73✅ | 2.58✅ | — | 3.25✅ | +| **i16** | 3.19✅ | 2.80✅ | 2.70✅ | 1.39✅ | 1.68✅ | 1.99✅ | 2.03✅ | 2.29✅ | 2.48✅ | 1.78✅ | 3.72✅ | 2.09✅ | 2.42✅ | — | 3.13✅ | +| **u16** | 2.41✅ | 2.59✅ | 2.35✅ | 1.53✅ | 1.32✅ | 1.77✅ | 1.84✅ | 2.45✅ | 2.56✅ | 1.53✅ | 3.57✅ | 2.04✅ | 2.46✅ | — | 2.82✅ | +| **i32** | 2.01✅ | 2.01✅ | 2.04✅ | 1.52✅ | 1.53✅ | 1.97✅ | 1.89✅ | 2.21✅ | 2.32✅ | 1.57✅ | 3.58✅ | 1.54✅ | 2.04✅ | — | 2.86✅ | +| **u32** | 1.96✅ | 2.10✅ | 2.14✅ | 1.47✅ | 1.49✅ | 1.80✅ | 1.91✅ | 2.27✅ | 2.52✅ | 1.59✅ | 3.59✅ | 1.50✅ | 2.18✅ | — | 2.54✅ | +| **i64** | 1.17✅ | 1.21✅ | 1.33✅ | 1.39✅ | 1.41✅ | 1.84✅ | 1.78✅ | 2.65✅ | 2.22✅ | 1.31✅ | 1.84✅ | 1.46✅ | 2.33✅ | — | 2.59✅ | +| **u64** | 1.13✅ | 1.26✅ | 1.29✅ | 1.33✅ | 1.37✅ | 1.83✅ | 1.96✅ | 2.17✅ | 2.39✅ | 1.31✅ | 2.35✅ | 1.00🟡 | 1.59✅ | — | 2.32✅ | +| **char** | 2.36✅ | 2.38✅ | 2.28✅ | 1.71✅ | 1.37✅ | 1.57✅ | 1.58✅ | 2.34✅ | 2.32✅ | 1.33✅ | 3.52✅ | 1.49✅ | 2.37✅ | — | 2.79✅ | +| **f16** | 5.08✅ | 4.80✅ | 4.78✅ | 3.80✅ | 3.83✅ | 3.85✅ | 2.03✅ | 3.13✅ | 1.09✅ | 3.83✅ | 1.34✅ | 2.87✅ | 0.97🟡 | — | 1.60✅ | +| **f32** | 3.30✅ | 2.23✅ | 2.25✅ | 1.60✅ | 1.67✅ | 1.91✅ | 1.36✅ | 0.84🟡 | 0.85🟡 | 1.66✅ | 3.58✅ | 1.95✅ | 2.09✅ | — | 2.25✅ | +| **f64** | 1.87✅ | 1.23✅ | 1.33✅ | 1.44✅ | 1.48✅ | 1.87✅ | 1.44✅ | 0.80🟡 | 0.88🟡 | 1.46✅ | 1.58✅ | 1.69✅ | 2.36✅ | — | 2.54✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.10✅ | 0.94🟡 | 1.01✅ | 1.15✅ | 1.11✅ | 1.47✅ | 1.37✅ | 0.87🟡 | 0.92🟡 | 1.26✅ | 1.41✅ | 1.37✅ | 1.61✅ | — | 2.29✅ | + +## Layout: negcol (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 4.56✅ | 7.06✅ | 6.59✅ | 2.26✅ | 2.21✅ | 1.54✅ | 1.48✅ | 2.25✅ | 2.21✅ | 2.40✅ | 2.80✅ | 1.88✅ | 2.48✅ | — | 3.10✅ | +| **u8** | 1.42✅ | 3.55✅ | 3.38✅ | 2.00✅ | 1.85✅ | 1.77✅ | 1.83✅ | 2.16✅ | 2.24✅ | 1.88✅ | 2.52✅ | 1.47✅ | 2.46✅ | — | 3.02✅ | +| **i8** | 1.31✅ | 4.32✅ | 2.73✅ | 1.70✅ | 1.89✅ | 1.82✅ | 1.83✅ | 2.24✅ | 2.26✅ | 1.73✅ | 2.63✅ | 1.65✅ | 2.37✅ | — | 3.10✅ | +| **i16** | 3.46✅ | 2.49✅ | 2.83✅ | 1.78✅ | 1.77✅ | 1.66✅ | 1.68✅ | 2.17✅ | 2.25✅ | 1.70✅ | 2.01✅ | 1.78✅ | 2.24✅ | — | 2.86✅ | +| **u16** | 3.61✅ | 3.01✅ | 3.11✅ | 1.76✅ | 1.62✅ | 1.68✅ | 1.75✅ | 2.43✅ | 2.22✅ | 1.69✅ | 2.02✅ | 1.43✅ | 2.38✅ | — | 2.92✅ | +| **i32** | 2.10✅ | 1.95✅ | 1.67✅ | 1.60✅ | 1.61✅ | 1.55✅ | 1.55✅ | 2.56✅ | 2.15✅ | 1.68✅ | 2.06✅ | 1.65✅ | 2.06✅ | — | 2.76✅ | +| **u32** | 2.08✅ | 1.60✅ | 1.53✅ | 1.59✅ | 1.58✅ | 1.48✅ | 1.61✅ | 2.46✅ | 2.30✅ | 1.63✅ | 2.02✅ | 1.47✅ | 2.34✅ | — | 2.84✅ | +| **i64** | 1.33✅ | 0.96🟡 | 0.97🟡 | 1.29✅ | 1.41✅ | 1.75✅ | 1.87✅ | 2.41✅ | 2.36✅ | 1.39✅ | 1.30✅ | 1.58✅ | 2.17✅ | — | 2.44✅ | +| **u64** | 1.26✅ | 1.01✅ | 0.93🟡 | 1.27✅ | 1.33✅ | 1.72✅ | 1.69✅ | 2.32✅ | 2.19✅ | 1.34✅ | 1.65✅ | 1.05✅ | 1.50✅ | — | 2.50✅ | +| **char** | 3.38✅ | 2.34✅ | 3.24✅ | 1.67✅ | 1.60✅ | 1.67✅ | 1.65✅ | 2.21✅ | 2.35✅ | 1.64✅ | 2.03✅ | 1.56✅ | 2.27✅ | — | 2.96✅ | +| **f16** | 4.65✅ | 1.73✅ | 1.73✅ | 1.74✅ | 1.76✅ | 2.23✅ | 1.33✅ | 2.07✅ | 0.89🟡 | 1.76✅ | 1.61✅ | 1.56✅ | 0.96🟡 | — | 1.51✅ | +| **f32** | 2.48✅ | 1.71✅ | 1.67✅ | 1.65✅ | 1.62✅ | 1.90✅ | 1.08✅ | 0.85🟡 | 0.81🟡 | 1.61✅ | 1.94✅ | 1.62✅ | 1.20✅ | — | 2.30✅ | +| **f64** | 1.45✅ | 1.12✅ | 1.10✅ | 1.33✅ | 1.38✅ | 1.75✅ | 1.10✅ | 0.83🟡 | 0.83🟡 | 1.39✅ | 1.19✅ | 1.65✅ | 2.67✅ | — | 2.68✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.02✅ | 0.98🟡 | 1.08✅ | 1.19✅ | 1.10✅ | 1.54✅ | 1.16✅ | 0.98🟡 | 0.90🟡 | 1.22✅ | 0.95🟡 | 1.49✅ | 1.71✅ | — | 2.26✅ | + +## Layout: strided (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 2.26✅ | 3.65✅ | 3.30✅ | 3.63✅ | 3.72✅ | 1.18✅ | 1.12✅ | 1.74✅ | 1.77✅ | 2.09✅ | 2.71✅ | 1.41✅ | 1.75✅ | — | 2.30✅ | +| **u8** | 0.97🟡 | 2.00✅ | 2.27✅ | 2.28✅ | 1.89✅ | 1.30✅ | 1.27✅ | 2.06✅ | 1.84✅ | 1.90✅ | 2.55✅ | 1.13✅ | 1.78✅ | — | 2.61✅ | +| **i8** | 0.91🟡 | 2.13✅ | 2.46✅ | 1.91✅ | 1.77✅ | 1.35✅ | 1.55✅ | 2.04✅ | 1.89✅ | 1.86✅ | 2.45✅ | 1.33✅ | 1.92✅ | — | 2.34✅ | +| **i16** | 2.41✅ | 1.68✅ | 1.97✅ | 1.14✅ | 2.07✅ | 1.21✅ | 1.26✅ | 1.90✅ | 1.96✅ | 1.70✅ | 1.89✅ | 1.18✅ | 1.73✅ | — | 2.39✅ | +| **u16** | 2.39✅ | 1.77✅ | 1.76✅ | 1.37✅ | 1.41✅ | 1.29✅ | 1.32✅ | 1.89✅ | 1.90✅ | 1.52✅ | 1.90✅ | 1.04✅ | 1.81✅ | — | 2.32✅ | +| **i32** | 1.60✅ | 1.43✅ | 1.39✅ | 1.16✅ | 1.16✅ | 1.23✅ | 1.08✅ | 1.86✅ | 1.78✅ | 1.26✅ | 1.88✅ | 1.28✅ | 1.76✅ | — | 2.44✅ | +| **u32** | 1.74✅ | 1.22✅ | 1.18✅ | 1.10✅ | 1.25✅ | 1.13✅ | 1.14✅ | 1.86✅ | 1.84✅ | 1.18✅ | 1.95✅ | 1.13✅ | 1.77✅ | — | 2.24✅ | +| **i64** | 1.11✅ | 0.95🟡 | 0.94🟡 | 0.86🟡 | 0.94🟡 | 1.22✅ | 1.35✅ | 1.77✅ | 1.74✅ | 0.91🟡 | 1.19✅ | 1.26✅ | 1.74✅ | — | 2.43✅ | +| **u64** | 1.07✅ | 0.93🟡 | 0.94🟡 | 0.83🟡 | 1.01✅ | 1.20✅ | 1.27✅ | 1.58✅ | 1.70✅ | 1.03✅ | 1.48✅ | 0.74🟡 | 1.47✅ | — | 2.17✅ | +| **char** | 2.39✅ | 1.80✅ | 1.68✅ | 1.26✅ | 1.49✅ | 1.21✅ | 1.30✅ | 1.85✅ | 1.75✅ | 1.51✅ | 1.83✅ | 1.13✅ | 1.73✅ | — | 2.34✅ | +| **f16** | 2.62✅ | 1.49✅ | 1.52✅ | 1.59✅ | 1.66✅ | 1.76✅ | 1.14✅ | 1.85✅ | 0.89🟡 | 1.59✅ | 1.11✅ | 1.23✅ | 0.95🟡 | — | 1.44✅ | +| **f32** | 1.98✅ | 1.31✅ | 1.31✅ | 1.30✅ | 1.37✅ | 1.34✅ | 0.91🟡 | 0.94🟡 | 0.80🟡 | 1.30✅ | 1.78✅ | 1.16✅ | 1.20✅ | — | 2.43✅ | +| **f64** | 1.25✅ | 0.98🟡 | 0.97🟡 | 0.91🟡 | 0.86🟡 | 1.22✅ | 0.94🟡 | 0.89🟡 | 0.80🟡 | 0.94🟡 | 1.06✅ | 1.07✅ | 1.81✅ | — | 2.08✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.07✅ | 1.14✅ | 0.99🟡 | 0.93🟡 | 0.91🟡 | 1.15✅ | 1.03✅ | 0.97🟡 | 0.83🟡 | 0.94🟡 | 0.89🟡 | 1.08✅ | 1.59✅ | — | 1.66✅ | + +## Layout: bcast (rows=src, cols=dst) + +| src\dst | bool | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | dec | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| **bool** | 1.26✅ | 5.38✅ | 6.24✅ | 2.28✅ | 2.24✅ | 1.51✅ | 1.46✅ | 2.32✅ | 2.18✅ | 2.26✅ | 3.97✅ | 1.91✅ | 2.37✅ | — | 3.14✅ | +| **u8** | 4.95✅ | 0.89🟡 | 4.29✅ | 1.87✅ | 1.88✅ | 2.08✅ | 2.14✅ | 2.52✅ | 2.39✅ | 1.87✅ | 3.76✅ | 2.32✅ | 2.15✅ | — | 2.95✅ | +| **i8** | 5.13✅ | 4.15✅ | 0.80🟡 | 1.82✅ | 1.87✅ | 2.12✅ | 2.43✅ | 2.55✅ | 2.38✅ | 1.91✅ | 3.81✅ | 2.16✅ | 2.28✅ | — | 3.14✅ | +| **i16** | 4.77✅ | 3.50✅ | 3.80✅ | 1.60✅ | 1.72✅ | 1.98✅ | 1.91✅ | 2.18✅ | 2.32✅ | 1.69✅ | 3.62✅ | 2.22✅ | 2.52✅ | — | 3.12✅ | +| **u16** | 4.57✅ | 4.20✅ | 3.20✅ | 1.85✅ | 1.63✅ | 1.98✅ | 1.89✅ | 2.26✅ | 2.27✅ | 1.66✅ | 3.72✅ | 2.08✅ | 2.26✅ | — | 2.70✅ | +| **i32** | 3.42✅ | 2.89✅ | 3.25✅ | 1.70✅ | 1.67✅ | 2.12✅ | 1.92✅ | 2.09✅ | 2.34✅ | 1.94✅ | 3.82✅ | 2.24✅ | 2.67✅ | — | 3.00✅ | +| **u32** | 3.64✅ | 3.72✅ | 2.42✅ | 1.66✅ | 1.77✅ | 2.02✅ | 1.99✅ | 2.23✅ | 2.31✅ | 1.90✅ | 3.73✅ | 2.29✅ | 2.27✅ | — | 2.94✅ | +| **i64** | 2.70✅ | 2.23✅ | 2.29✅ | 1.73✅ | 1.72✅ | 2.04✅ | 2.10✅ | 2.41✅ | 2.12✅ | 1.72✅ | 1.85✅ | 2.08✅ | 2.00✅ | — | 2.67✅ | +| **u64** | 2.57✅ | 2.40✅ | 2.16✅ | 1.60✅ | 1.66✅ | 2.08✅ | 1.92✅ | 2.24✅ | 2.29✅ | 1.75✅ | 2.43✅ | 2.26✅ | 2.14✅ | — | 2.72✅ | +| **char** | 4.78✅ | 3.81✅ | 3.84✅ | 1.74✅ | 1.50✅ | 1.65✅ | 1.66✅ | 2.17✅ | 2.36✅ | 1.65✅ | 3.64✅ | 1.54✅ | 2.36✅ | — | 2.75✅ | +| **f16** | 6.87✅ | 5.51✅ | 5.35✅ | 4.23✅ | 4.21✅ | 3.88✅ | 2.04✅ | 3.04✅ | 1.09✅ | 3.86✅ | 1.46✅ | 2.87✅ | 0.96🟡 | — | 1.55✅ | +| **f32** | 4.89✅ | 3.29✅ | 2.71✅ | 1.88✅ | 2.02✅ | 2.15✅ | 1.47✅ | 2.21✅ | 0.86🟡 | 1.84✅ | 3.65✅ | 2.09✅ | 2.11✅ | — | 2.29✅ | +| **f64** | 2.70✅ | 2.15✅ | 2.14✅ | 1.78✅ | 1.77✅ | 2.11✅ | 1.49✅ | 1.99✅ | 0.88🟡 | 1.81✅ | 1.58✅ | 2.04✅ | 2.51✅ | — | 2.66✅ | +| **dec** | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | +| **c128** | 1.09✅ | 1.06✅ | 1.05✅ | 1.38✅ | 1.40✅ | 1.75✅ | 1.35✅ | 0.84🟡 | 0.80🟡 | 1.39✅ | 1.50✅ | 1.56✅ | 2.01✅ | — | 2.94✅ | + +## Lagging cells (<1.0) — the worklist (118 cells) + +| key | NumSharp ms | NumPy ms | ratio | +|---|---|---|---| +| bool\|F\|bool | 0.0595 | 0.0144 | 0.24 🟠 | +| f64\|sliced\|i32 | 0.9124 | 0.6414 | 0.70 🟡 | +| u64\|strided\|f32 | 0.5215 | 0.3885 | 0.74 🟡 | +| c128\|bcast\|u64 | 1.5413 | 1.2301 | 0.80 🟡 | +| f32\|strided\|u64 | 0.7987 | 0.6399 | 0.80 🟡 | +| i8\|bcast\|i8 | 0.0619 | 0.0496 | 0.80 🟡 | +| c128\|sliced\|u64 | 1.7479 | 1.4021 | 0.80 🟡 | +| f64\|strided\|u64 | 0.7758 | 0.6243 | 0.80 🟡 | +| f64\|negrow\|i64 | 1.5209 | 1.2241 | 0.80 🟡 | +| c128\|F\|u64 | 1.7023 | 1.3717 | 0.81 🟡 | +| f32\|negcol\|u64 | 1.5659 | 1.2627 | 0.81 🟡 | +| f64\|C\|i8 | 0.2927 | 0.2376 | 0.81 🟡 | +| f64\|C\|u8 | 0.2892 | 0.2350 | 0.81 🟡 | +| f32\|F\|i64 | 1.4588 | 1.1945 | 0.82 🟡 | +| c128\|strided\|u64 | 0.9366 | 0.7745 | 0.83 🟡 | +| u64\|strided\|i16 | 0.1726 | 0.1432 | 0.83 🟡 | +| f32\|C\|i64 | 1.4773 | 1.2276 | 0.83 🟡 | +| f64\|negcol\|u64 | 1.5574 | 1.2946 | 0.83 🟡 | +| f32\|T\|i64 | 1.4640 | 1.2208 | 0.83 🟡 | +| f64\|negcol\|i64 | 1.4625 | 1.2196 | 0.83 🟡 | +| c128\|C\|u64 | 1.6652 | 1.3911 | 0.84 🟡 | +| f32\|negrow\|i64 | 1.4768 | 1.2373 | 0.84 🟡 | +| f32\|F\|u64 | 1.4675 | 1.2315 | 0.84 🟡 | +| f64\|sliced\|i64 | 1.4533 | 1.2221 | 0.84 🟡 | +| c128\|bcast\|i64 | 1.4280 | 1.2013 | 0.84 🟡 | +| f64\|sliced\|u64 | 1.4453 | 1.2198 | 0.84 🟡 | +| i8\|T\|i8 | 0.0613 | 0.0519 | 0.85 🟡 | +| c128\|T\|u64 | 1.6154 | 1.3692 | 0.85 🟡 | +| f32\|negrow\|u64 | 1.4957 | 1.2720 | 0.85 🟡 | +| f32\|negcol\|i64 | 1.4173 | 1.2088 | 0.85 🟡 | +| f64\|C\|i64 | 1.4556 | 1.2468 | 0.86 🟡 | +| i64\|strided\|i16 | 0.1748 | 0.1499 | 0.86 🟡 | +| f32\|sliced\|i64 | 1.4408 | 1.2354 | 0.86 🟡 | +| f64\|strided\|u16 | 0.1706 | 0.1469 | 0.86 🟡 | +| f32\|T\|u64 | 1.4532 | 1.2546 | 0.86 🟡 | +| f32\|bcast\|u64 | 1.4571 | 1.2602 | 0.86 🟡 | +| c128\|negrow\|i64 | 1.5665 | 1.3619 | 0.87 🟡 | +| f64\|F\|i64 | 1.4506 | 1.2612 | 0.87 🟡 | +| f64\|T\|i64 | 1.4409 | 1.2540 | 0.87 🟡 | +| f32\|C\|u64 | 1.4695 | 1.2834 | 0.87 🟡 | +| f64\|bcast\|u64 | 1.4020 | 1.2277 | 0.88 🟡 | +| f64\|negrow\|u64 | 1.4430 | 1.2673 | 0.88 🟡 | +| c128\|F\|i8 | 0.3572 | 0.3148 | 0.88 🟡 | +| f32\|sliced\|u64 | 1.4888 | 1.3174 | 0.88 🟡 | +| f16\|negcol\|u64 | 2.1147 | 1.8765 | 0.89 🟡 | +| u8\|bcast\|u8 | 0.0587 | 0.0522 | 0.89 🟡 | +| f64\|strided\|i64 | 0.7057 | 0.6283 | 0.89 🟡 | +| f64\|C\|u64 | 1.4293 | 1.2761 | 0.89 🟡 | +| f16\|strided\|u64 | 1.0346 | 0.9241 | 0.89 🟡 | +| c128\|strided\|f16 | 0.8811 | 0.7874 | 0.89 🟡 | +| f64\|T\|u64 | 1.4221 | 1.2717 | 0.89 🟡 | +| c128\|T\|i64 | 1.5151 | 1.3650 | 0.90 🟡 | +| c128\|negcol\|u64 | 1.6586 | 1.4945 | 0.90 🟡 | +| f64\|F\|u64 | 1.4496 | 1.3097 | 0.90 🟡 | +| f32\|strided\|u32 | 0.3838 | 0.3480 | 0.91 🟡 | +| c128\|sliced\|char | 0.5601 | 0.5081 | 0.91 🟡 | +| f64\|strided\|i16 | 0.1641 | 0.1489 | 0.91 🟡 | +| c128\|F\|u8 | 0.3391 | 0.3083 | 0.91 🟡 | +| i64\|strided\|char | 0.1534 | 0.1397 | 0.91 🟡 | +| i8\|strided\|bool | 0.1632 | 0.1489 | 0.91 🟡 | +| i8\|C\|i8 | 0.0595 | 0.0543 | 0.91 🟡 | +| c128\|strided\|u16 | 0.3051 | 0.2789 | 0.91 🟡 | +| c128\|C\|i64 | 1.5153 | 1.3895 | 0.92 🟡 | +| u64\|F\|u8 | 0.2149 | 0.1979 | 0.92 🟡 | +| c128\|negrow\|u64 | 1.7009 | 1.5680 | 0.92 🟡 | +| i64\|F\|i8 | 0.2150 | 0.1982 | 0.92 🟡 | +| f16\|C\|f64 | 1.5894 | 1.4665 | 0.92 🟡 | +| u64\|C\|u8 | 0.2155 | 0.1988 | 0.92 🟡 | +| i8\|sliced\|i8 | 0.0575 | 0.0530 | 0.92 🟡 | +| c128\|strided\|i16 | 0.3130 | 0.2897 | 0.93 🟡 | +| c128\|C\|u8 | 0.3526 | 0.3283 | 0.93 🟡 | +| u64\|negcol\|i8 | 0.2475 | 0.2306 | 0.93 🟡 | +| i64\|T\|i8 | 0.2114 | 0.1973 | 0.93 🟡 | +| c128\|sliced\|i64 | 1.4869 | 1.3889 | 0.93 🟡 | +| u64\|strided\|u8 | 0.1409 | 0.1316 | 0.93 🟡 | +| c128\|strided\|char | 0.2944 | 0.2754 | 0.94 🟡 | +| f32\|strided\|i64 | 0.7048 | 0.6596 | 0.94 🟡 | +| c128\|C\|i8 | 0.3417 | 0.3201 | 0.94 🟡 | +| i64\|strided\|u16 | 0.1550 | 0.1454 | 0.94 🟡 | +| u64\|F\|i8 | 0.2121 | 0.1991 | 0.94 🟡 | +| u64\|strided\|i8 | 0.1389 | 0.1305 | 0.94 🟡 | +| u64\|T\|u8 | 0.2105 | 0.1978 | 0.94 🟡 | +| i64\|F\|u8 | 0.2204 | 0.2078 | 0.94 🟡 | +| c128\|negrow\|u8 | 0.3492 | 0.3294 | 0.94 🟡 | +| f64\|strided\|char | 0.1593 | 0.1504 | 0.94 🟡 | +| f64\|strided\|u32 | 0.3797 | 0.3587 | 0.94 🟡 | +| i64\|strided\|i8 | 0.1438 | 0.1358 | 0.94 🟡 | +| u64\|C\|i8 | 0.2096 | 0.1981 | 0.95 🟡 | +| f16\|strided\|f64 | 0.7709 | 0.7306 | 0.95 🟡 | +| f16\|F\|f64 | 1.5066 | 1.4286 | 0.95 🟡 | +| i64\|strided\|u8 | 0.1424 | 0.1351 | 0.95 🟡 | +| u64\|T\|i8 | 0.2094 | 0.1987 | 0.95 🟡 | +| c128\|negcol\|f16 | 1.8203 | 1.7315 | 0.95 🟡 | +| f16\|sliced\|f64 | 1.4890 | 1.4241 | 0.96 🟡 | +| c128\|F\|i64 | 1.5008 | 1.4359 | 0.96 🟡 | +| i64\|negcol\|u8 | 0.2439 | 0.2335 | 0.96 🟡 | +| f16\|bcast\|f64 | 1.5177 | 1.4532 | 0.96 🟡 | +| c128\|sliced\|i8 | 0.3253 | 0.3118 | 0.96 🟡 | +| i64\|C\|i8 | 0.2090 | 0.2004 | 0.96 🟡 | +| f16\|negcol\|f64 | 1.5238 | 1.4668 | 0.96 🟡 | +| char\|C\|u16 | 0.2540 | 0.2457 | 0.97 🟡 | +| c128\|sliced\|bool | 0.4498 | 0.4355 | 0.97 🟡 | +| c128\|strided\|i64 | 0.8006 | 0.7755 | 0.97 🟡 | +| u64\|sliced\|f32 | 0.7665 | 0.7425 | 0.97 🟡 | +| f16\|negrow\|f64 | 1.5293 | 1.4823 | 0.97 🟡 | +| f64\|strided\|i8 | 0.1454 | 0.1413 | 0.97 🟡 | +| i8\|negrow\|i8 | 0.0558 | 0.0542 | 0.97 🟡 | +| u8\|strided\|bool | 0.1537 | 0.1495 | 0.97 🟡 | +| i64\|negcol\|i8 | 0.2480 | 0.2414 | 0.97 🟡 | +| i64\|C\|u8 | 0.2108 | 0.2062 | 0.98 🟡 | +| c128\|negcol\|u8 | 0.3485 | 0.3423 | 0.98 🟡 | +| f64\|strided\|u8 | 0.1456 | 0.1431 | 0.98 🟡 | +| c128\|negcol\|i64 | 1.6104 | 1.5838 | 0.98 🟡 | +| f16\|C\|u64 | 1.8886 | 1.8634 | 0.99 🟡 | +| c128\|F\|bool | 0.4151 | 0.4098 | 0.99 🟡 | +| c128\|strided\|i8 | 0.2650 | 0.2633 | 0.99 🟡 | +| f16\|T\|f64 | 1.4800 | 1.4751 | 1.00 🟡 | +| u64\|negrow\|f32 | 0.7923 | 0.7904 | 1.00 🟡 | + +_1568 comparable cells (1800 NumSharp rows; 118 lagging <1.0)._ \ No newline at end of file diff --git a/benchmark/history/2026-06-29_2d16f477/cast_results.tsv b/benchmark/history/2026-06-29_2d16f477/cast_results.tsv new file mode 100644 index 00000000..36b03afc --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/cast_results.tsv @@ -0,0 +1,1801 @@ +key ns_ms np_ms +1M|bool|C|bool 0.06404499999999999 0.194845 +1M|bool|C|u8 0.11805000000000002 0.307055 +1M|bool|C|i8 0.113415 0.31312 +1M|bool|C|i16 0.19996 0.41982 +1M|bool|C|u16 0.217735 0.431125 +1M|bool|C|i32 0.445175 0.68075 +1M|bool|C|u32 0.45506 0.662215 +1M|bool|C|i64 0.564765 1.15441 +1M|bool|C|u64 0.52071 1.17214 +1M|bool|C|char 0.23722500000000002 0.410165 +1M|bool|C|f16 0.50716 1.66992 +1M|bool|C|f32 0.39961 0.681805 +1M|bool|C|f64 0.49657999999999997 1.23645 +1M|bool|C|dec 0.8737550000000001 NA +1M|bool|C|c128 0.8150299999999999 2.39481 +1M|bool|F|bool 0.059465000000000004 0.014355 +1M|bool|F|u8 0.067515 0.18386 +1M|bool|F|i8 0.068825 0.18626 +1M|bool|F|i16 0.21559 0.419245 +1M|bool|F|u16 0.217315 0.41522 +1M|bool|F|i32 0.449985 0.64265 +1M|bool|F|u32 0.448425 0.63731 +1M|bool|F|i64 0.526295 1.17998 +1M|bool|F|u64 0.50466 1.21294 +1M|bool|F|char 0.220955 0.41731 +1M|bool|F|f16 0.41302500000000003 1.66335 +1M|bool|F|f32 0.38961 0.676345 +1M|bool|F|f64 0.47333 1.20643 +1M|bool|F|dec 0.79323 NA +1M|bool|F|c128 0.79439 2.36658 +1M|bool|T|bool 0.06365499999999999 0.216645 +1M|bool|T|u8 0.092005 0.312155 +1M|bool|T|i8 0.082155 0.307115 +1M|bool|T|i16 0.2439 0.41757 +1M|bool|T|u16 0.230895 0.415045 +1M|bool|T|i32 0.44867 0.66102 +1M|bool|T|u32 0.45228 0.637785 +1M|bool|T|i64 0.5129699999999999 1.18646 +1M|bool|T|u64 0.510695 1.18597 +1M|bool|T|char 0.218775 0.417555 +1M|bool|T|f16 0.39769 1.66943 +1M|bool|T|f32 0.3863 0.68682 +1M|bool|T|f64 0.4542 1.21939 +1M|bool|T|dec 0.820805 NA +1M|bool|T|c128 0.8068500000000001 2.41601 +1M|bool|sliced|bool 0.06394 0.20101 +1M|bool|sliced|u8 0.054869999999999995 0.320455 +1M|bool|sliced|i8 0.056355 0.3377 +1M|bool|sliced|i16 0.21549000000000001 0.42258 +1M|bool|sliced|u16 0.217035 0.433995 +1M|bool|sliced|i32 0.444835 0.662665 +1M|bool|sliced|u32 0.45736 0.711295 +1M|bool|sliced|i64 0.5196449999999999 1.23738 +1M|bool|sliced|u64 0.569205 1.17498 +1M|bool|sliced|char 0.21214 0.422655 +1M|bool|sliced|f16 0.42152500000000004 1.66641 +1M|bool|sliced|f32 0.39662000000000003 0.691845 +1M|bool|sliced|f64 0.47146 1.25577 +1M|bool|sliced|dec 0.84734 NA +1M|bool|sliced|c128 0.77724 2.34729 +1M|bool|negrow|bool 0.057425000000000004 0.194355 +1M|bool|negrow|u8 0.07701 0.327775 +1M|bool|negrow|i8 0.06735 0.334885 +1M|bool|negrow|i16 0.222295 0.42579 +1M|bool|negrow|u16 0.21298499999999998 0.43175 +1M|bool|negrow|i32 0.46952999999999995 0.66318 +1M|bool|negrow|u32 0.454385 0.71154 +1M|bool|negrow|i64 0.537645 1.24654 +1M|bool|negrow|u64 0.50279 1.20707 +1M|bool|negrow|char 0.22058499999999998 0.436715 +1M|bool|negrow|f16 0.430485 1.68517 +1M|bool|negrow|f32 0.40143000000000006 0.719735 +1M|bool|negrow|f64 0.49200999999999995 1.27408 +1M|bool|negrow|dec 0.830785 NA +1M|bool|negrow|c128 0.7307699999999999 2.43763 +1M|bool|negcol|bool 0.07016 0.320025 +1M|bool|negcol|u8 0.0613 0.43279 +1M|bool|negcol|i8 0.06568500000000001 0.43288 +1M|bool|negcol|i16 0.21112999999999998 0.476725 +1M|bool|negcol|u16 0.21982 0.48517 +1M|bool|negcol|i32 0.449965 0.692235 +1M|bool|negcol|u32 0.46256 0.68405 +1M|bool|negcol|i64 0.53935 1.21345 +1M|bool|negcol|u64 0.55166 1.22101 +1M|bool|negcol|char 0.204405 0.489965 +1M|bool|negcol|f16 0.608755 1.70426 +1M|bool|negcol|f32 0.40071 0.751695 +1M|bool|negcol|f64 0.50963 1.26289 +1M|bool|negcol|dec 0.85258 NA +1M|bool|negcol|c128 0.7905 2.45155 +1M|bool|strided|bool 0.040510000000000004 0.09172 +1M|bool|strided|u8 0.04084 0.149185 +1M|bool|strided|i8 0.045255000000000004 0.149235 +1M|bool|strided|i16 0.07148 0.259565 +1M|bool|strided|u16 0.069455 0.258385 +1M|bool|strided|i32 0.290885 0.344665 +1M|bool|strided|u32 0.301315 0.337745 +1M|bool|strided|i64 0.342015 0.595915 +1M|bool|strided|u64 0.33821 0.598285 +1M|bool|strided|char 0.085215 0.17787 +1M|bool|strided|f16 0.29042500000000004 0.78757 +1M|bool|strided|f32 0.261585 0.3687 +1M|bool|strided|f64 0.338265 0.591795 +1M|bool|strided|dec 0.48308999999999996 NA +1M|bool|strided|c128 0.50981 1.17061 +1M|bool|bcast|bool 0.059625 0.07502 +1M|bool|bcast|u8 0.06395999999999999 0.34415 +1M|bool|bcast|i8 0.05497 0.343155 +1M|bool|bcast|i16 0.20972 0.477115 +1M|bool|bcast|u16 0.21095999999999998 0.47203 +1M|bool|bcast|i32 0.44618 0.675895 +1M|bool|bcast|u32 0.46165 0.673235 +1M|bool|bcast|i64 0.5299750000000001 1.23032 +1M|bool|bcast|u64 0.548535 1.19812 +1M|bool|bcast|char 0.20867 0.471975 +1M|bool|bcast|f16 0.424315 1.68373 +1M|bool|bcast|f32 0.38906999999999997 0.741645 +1M|bool|bcast|f64 0.50722 1.2034 +1M|bool|bcast|dec 0.8892049999999999 NA +1M|bool|bcast|c128 0.774525 2.43429 +1M|u8|C|bool 0.063495 0.225395 +1M|u8|C|u8 0.055815000000000003 0.08672 +1M|u8|C|i8 0.06732 0.23971 +1M|u8|C|i16 0.219245 0.370905 +1M|u8|C|u16 0.20227 0.370105 +1M|u8|C|i32 0.30579 0.61441 +1M|u8|C|u32 0.325755 0.61544 +1M|u8|C|i64 0.53069 1.17168 +1M|u8|C|u64 0.47347 1.17173 +1M|u8|C|char 0.22981500000000002 0.371765 +1M|u8|C|f16 0.401785 1.5658 +1M|u8|C|f32 0.45969 0.635055 +1M|u8|C|f64 0.50522 1.21957 +1M|u8|C|dec 0.7978850000000001 NA +1M|u8|C|c128 0.773935 2.37815 +1M|u8|F|bool 0.057030000000000004 0.228875 +1M|u8|F|u8 0.07466500000000001 0.082845 +1M|u8|F|i8 0.06797 0.23661 +1M|u8|F|i16 0.21891500000000003 0.37223 +1M|u8|F|u16 0.22152500000000003 0.377725 +1M|u8|F|i32 0.30147 0.62352 +1M|u8|F|u32 0.29945499999999997 0.61629 +1M|u8|F|i64 0.53809 1.13373 +1M|u8|F|u64 0.4955 1.15578 +1M|u8|F|char 0.23027000000000003 0.373835 +1M|u8|F|f16 0.40235000000000004 1.56691 +1M|u8|F|f32 0.46195 0.639915 +1M|u8|F|f64 0.500225 1.24747 +1M|u8|F|dec 0.8293699999999999 NA +1M|u8|F|c128 0.781765 2.39373 +1M|u8|T|bool 0.057569999999999996 0.226565 +1M|u8|T|u8 0.062055000000000006 0.075125 +1M|u8|T|i8 0.091395 0.232655 +1M|u8|T|i16 0.22357 0.37611 +1M|u8|T|u16 0.22271999999999997 0.373515 +1M|u8|T|i32 0.303525 0.623105 +1M|u8|T|u32 0.31717 0.608905 +1M|u8|T|i64 0.537265 1.18901 +1M|u8|T|u64 0.467815 1.1995 +1M|u8|T|char 0.22510500000000003 0.375515 +1M|u8|T|f16 0.392825 1.5626 +1M|u8|T|f32 0.456065 0.644105 +1M|u8|T|f64 0.50995 1.22972 +1M|u8|T|dec 0.8770300000000001 NA +1M|u8|T|c128 0.8155849999999999 2.42833 +1M|u8|sliced|bool 0.08659 0.236845 +1M|u8|sliced|u8 0.066805 0.0835 +1M|u8|sliced|i8 0.0606 0.23693 +1M|u8|sliced|i16 0.21527500000000002 0.385595 +1M|u8|sliced|u16 0.205575 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+1M|f16|C|f32 0.9343299999999999 1.03164 +1M|f16|C|f64 1.58938 1.46647 +1M|f16|C|dec 6.5646 NA +1M|f16|C|c128 1.63412 2.68663 +1M|f16|F|bool 0.10293000000000001 0.461785 +1M|f16|F|u8 0.15546 0.936045 +1M|f16|F|i8 0.149925 0.94919 +1M|f16|F|i16 0.29288 1.10991 +1M|f16|F|u16 0.259555 1.11961 +1M|f16|F|i32 0.34731 1.29408 +1M|f16|F|u32 0.628465 1.29191 +1M|f16|F|i64 0.594645 1.69268 +1M|f16|F|u64 1.66291 1.85311 +1M|f16|F|char 0.25661 1.11051 +1M|f16|F|f16 0.20137999999999998 0.249135 +1M|f16|F|f32 0.42505499999999996 1.03651 +1M|f16|F|f64 1.50663 1.42865 +1M|f16|F|dec 6.592230000000001 NA +1M|f16|F|c128 1.595215 2.58994 +1M|f16|T|bool 0.08851 0.45787 +1M|f16|T|u8 0.149995 0.942605 +1M|f16|T|i8 0.15167999999999998 0.93333 +1M|f16|T|i16 0.25898 1.10572 +1M|f16|T|u16 0.25861 1.10265 +1M|f16|T|i32 0.36137 1.28583 +1M|f16|T|u32 0.629815 1.2804 +1M|f16|T|i64 0.5594 1.68821 +1M|f16|T|u64 1.65087 1.88325 +1M|f16|T|char 0.25651999999999997 1.1101 +1M|f16|T|f16 0.2083 0.24576 +1M|f16|T|f32 0.362555 1.03036 +1M|f16|T|f64 1.479955 1.47507 +1M|f16|T|dec 6.56352 NA +1M|f16|T|c128 1.61787 2.67051 +1M|f16|sliced|bool 0.095705 0.449505 +1M|f16|sliced|u8 0.190215 0.94127 +1M|f16|sliced|i8 0.18948 0.94631 +1M|f16|sliced|i16 0.270285 1.1117 +1M|f16|sliced|u16 0.27384 1.10904 +1M|f16|sliced|i32 0.320575 1.28985 +1M|f16|sliced|u32 0.6557000000000001 1.29072 +1M|f16|sliced|i64 0.5528150000000001 1.70533 +1M|f16|sliced|u64 1.6742000000000001 1.84778 +1M|f16|sliced|char 0.26463000000000003 1.11303 +1M|f16|sliced|f16 0.24854500000000002 0.33116 +1M|f16|sliced|f32 0.38016 1.02478 +1M|f16|sliced|f64 1.489025 1.42412 +1M|f16|sliced|dec 6.563124999999999 NA +1M|f16|sliced|c128 1.72099 2.56228 +1M|f16|negrow|bool 0.09040000000000001 0.459345 +1M|f16|negrow|u8 0.1984 0.952905 +1M|f16|negrow|i8 0.19848 0.948135 +1M|f16|negrow|i16 0.29562499999999997 1.12203 +1M|f16|negrow|u16 0.29531999999999997 1.13128 +1M|f16|negrow|i32 0.340445 1.31113 +1M|f16|negrow|u32 0.641855 1.30538 +1M|f16|negrow|i64 0.54559 1.7076 +1M|f16|negrow|u64 1.7160199999999999 1.86386 +1M|f16|negrow|char 0.295145 1.13054 +1M|f16|negrow|f16 0.254625 0.341705 +1M|f16|negrow|f32 0.36635 1.05072 +1M|f16|negrow|f64 1.52929 1.48231 +1M|f16|negrow|dec 6.60459 NA +1M|f16|negrow|c128 1.642045 2.61962 +1M|f16|negcol|bool 0.098665 0.458915 +1M|f16|negcol|u8 0.55316 0.955165 +1M|f16|negcol|i8 0.55158 0.951545 +1M|f16|negcol|i16 0.65181 1.13095 +1M|f16|negcol|u16 0.640295 1.12597 +1M|f16|negcol|i32 0.58948 1.31299 +1M|f16|negcol|u32 0.992005 1.3184 +1M|f16|negcol|i64 0.82182 1.70128 +1M|f16|negcol|u64 2.11467 1.8765 +1M|f16|negcol|char 0.641365 1.12689 +1M|f16|negcol|f16 0.24823 0.398735 +1M|f16|negcol|f32 0.673365 1.05012 +1M|f16|negcol|f64 1.523825 1.46683 +1M|f16|negcol|dec 6.7063999999999995 NA +1M|f16|negcol|c128 1.769095 2.67393 +1M|f16|strided|bool 0.08629 0.22624 +1M|f16|strided|u8 0.31358 0.46653 +1M|f16|strided|i8 0.31073 0.47311 +1M|f16|strided|i16 0.295335 0.470795 +1M|f16|strided|u16 0.293845 0.486685 +1M|f16|strided|i32 0.36738499999999996 0.646705 +1M|f16|strided|u32 0.568375 0.64601 +1M|f16|strided|i64 0.45101 0.835825 +1M|f16|strided|u64 1.034595 0.924135 +1M|f16|strided|char 0.29695499999999997 0.471555 +1M|f16|strided|f16 0.09443 0.105265 +1M|f16|strided|f32 0.42469999999999997 0.522915 +1M|f16|strided|f64 0.77093 0.73061 +1M|f16|strided|dec 3.408995 NA +1M|f16|strided|c128 0.93559 1.34563 +1M|f16|bcast|bool 0.065795 0.452275 +1M|f16|bcast|u8 0.17108 0.943485 +1M|f16|bcast|i8 0.1774 0.948415 +1M|f16|bcast|i16 0.26309 1.11168 +1M|f16|bcast|u16 0.264695 1.11473 +1M|f16|bcast|i32 0.33338999999999996 1.29496 +1M|f16|bcast|u32 0.63149 1.29047 +1M|f16|bcast|i64 0.55556 1.68898 +1M|f16|bcast|u64 1.7063400000000002 1.86511 +1M|f16|bcast|char 0.290905 1.12157 +1M|f16|bcast|f16 0.24811999999999998 0.36167 +1M|f16|bcast|f32 0.363 1.04226 +1M|f16|bcast|f64 1.517735 1.4532 +1M|f16|bcast|dec 6.5030850000000004 NA +1M|f16|bcast|c128 1.6825800000000002 2.60254 +1M|f32|C|bool 0.139805 0.394465 +1M|f32|C|u8 0.122825 0.23583 +1M|f32|C|i8 0.11681 0.23892 +1M|f32|C|i16 0.26378 0.432795 +1M|f32|C|u16 0.26799 0.41639 +1M|f32|C|i32 0.35261 0.648995 +1M|f32|C|u32 0.48732 0.674565 +1M|f32|C|i64 1.47732 1.22762 +1M|f32|C|u64 1.469475 1.2834 +1M|f32|C|char 0.28558500000000003 0.423175 +1M|f32|C|f16 0.41535500000000003 1.48909 +1M|f32|C|f32 0.28287 0.49719 +1M|f32|C|f64 0.55828 1.23607 +1M|f32|C|dec 9.594755000000001 NA +1M|f32|C|c128 1.03795 2.44267 +1M|f32|F|bool 0.11399999999999999 0.3921 +1M|f32|F|u8 0.11145000000000001 0.23653 +1M|f32|F|i8 0.10484500000000001 0.23703 +1M|f32|F|i16 0.27645 0.446925 +1M|f32|F|u16 0.26256 0.42959 +1M|f32|F|i32 0.355775 0.683075 +1M|f32|F|u32 0.48978 0.663255 +1M|f32|F|i64 1.45883 1.19448 +1M|f32|F|u64 1.46747 1.23148 +1M|f32|F|char 0.24876 0.420705 +1M|f32|F|f16 0.41109999999999997 1.49042 +1M|f32|F|f32 0.29537 0.49392 +1M|f32|F|f64 0.55212 1.2193 +1M|f32|F|dec 9.55886 NA +1M|f32|F|c128 1.04964 2.37152 +1M|f32|T|bool 0.114175 0.37974 +1M|f32|T|u8 0.10948 0.236235 +1M|f32|T|i8 0.102755 0.23598 +1M|f32|T|i16 0.250925 0.44411 +1M|f32|T|u16 0.262005 0.42814 +1M|f32|T|i32 0.328015 0.67045 +1M|f32|T|u32 0.47430500000000003 0.65635 +1M|f32|T|i64 1.46401 1.22075 +1M|f32|T|u64 1.45323 1.2546 +1M|f32|T|char 0.25786 0.421275 +1M|f32|T|f16 0.39825 1.49374 +1M|f32|T|f32 0.2953 0.5016 +1M|f32|T|f64 0.486225 1.24271 +1M|f32|T|dec 9.57869 NA +1M|f32|T|c128 1.07355 2.35716 +1M|f32|sliced|bool 0.10867500000000001 0.394155 +1M|f32|sliced|u8 0.10966000000000001 0.241625 +1M|f32|sliced|i8 0.10339000000000001 0.24283 +1M|f32|sliced|i16 0.27622 0.453075 +1M|f32|sliced|u16 0.25838 0.43145 +1M|f32|sliced|i32 0.35172 0.668025 +1M|f32|sliced|u32 0.499265 0.67127 +1M|f32|sliced|i64 1.440755 1.23544 +1M|f32|sliced|u64 1.4888299999999999 1.31743 +1M|f32|sliced|char 0.26627999999999996 0.42444 +1M|f32|sliced|f16 0.41254 1.49497 +1M|f32|sliced|f32 0.345155 0.681025 +1M|f32|sliced|f64 0.5829500000000001 1.191 +1M|f32|sliced|dec 9.58286 NA +1M|f32|sliced|c128 1.032195 2.42601 +1M|f32|negrow|bool 0.12175499999999999 0.402045 +1M|f32|negrow|u8 0.112785 0.25122 +1M|f32|negrow|i8 0.11207 0.252275 +1M|f32|negrow|i16 0.283935 0.4546 +1M|f32|negrow|u16 0.26225 0.43866 +1M|f32|negrow|i32 0.34558 0.66139 +1M|f32|negrow|u32 0.503765 0.68448 +1M|f32|negrow|i64 1.476835 1.23734 +1M|f32|negrow|u64 1.49566 1.27201 +1M|f32|negrow|char 0.2683 0.44597 +1M|f32|negrow|f16 0.42347 1.51448 +1M|f32|negrow|f32 0.352995 0.68828 +1M|f32|negrow|f64 0.583715 1.2221 +1M|f32|negrow|dec 9.60346 NA +1M|f32|negrow|c128 1.06366 2.39156 +1M|f32|negcol|bool 0.16349 0.40481 +1M|f32|negcol|u8 0.15526500000000001 0.26504 +1M|f32|negcol|i8 0.15561 0.259875 +1M|f32|negcol|i16 0.27427999999999997 0.452785 +1M|f32|negcol|u16 0.285665 0.46381 +1M|f32|negcol|i32 0.36018 0.68536 +1M|f32|negcol|u32 0.619555 0.66745 +1M|f32|negcol|i64 1.4172850000000001 1.20882 +1M|f32|negcol|u64 1.565885 1.26269 +1M|f32|negcol|char 0.280565 0.45294 +1M|f32|negcol|f16 0.784215 1.52048 +1M|f32|negcol|f32 0.398625 0.64415 +1M|f32|negcol|f64 1.01898 1.2217 +1M|f32|negcol|dec 9.702095 NA +1M|f32|negcol|c128 1.048505 2.40735 +1M|f32|strided|bool 0.098635 0.19535 +1M|f32|strided|u8 0.099085 0.129635 +1M|f32|strided|i8 0.09951 0.13004 +1M|f32|strided|i16 0.105545 0.136935 +1M|f32|strided|u16 0.098165 0.134375 +1M|f32|strided|i32 0.257815 0.345405 +1M|f32|strided|u32 0.383795 0.34798 +1M|f32|strided|i64 0.704815 0.659625 +1M|f32|strided|u64 0.79869 0.63985 +1M|f32|strided|char 0.103715 0.134535 +1M|f32|strided|f16 0.37390500000000004 0.667315 +1M|f32|strided|f32 0.280035 0.3237 +1M|f32|strided|f64 0.509715 0.6127 +1M|f32|strided|dec 4.92152 NA +1M|f32|strided|c128 0.5141600000000001 1.24744 +1M|f32|bcast|bool 0.078925 0.385625 +1M|f32|bcast|u8 0.077975 0.25688 +1M|f32|bcast|i8 0.093955 0.25497 +1M|f32|bcast|i16 0.23697 0.445855 +1M|f32|bcast|u16 0.21964999999999998 0.4445 +1M|f32|bcast|i32 0.315875 0.68069 +1M|f32|bcast|u32 0.46389500000000006 0.680025 +1M|f32|bcast|i64 0.550155 1.21674 +1M|f32|bcast|u64 1.45712 1.26019 +1M|f32|bcast|char 0.244145 0.44915 +1M|f32|bcast|f16 0.411205 1.50169 +1M|f32|bcast|f32 0.349985 0.73206 +1M|f32|bcast|f64 0.56974 1.20038 +1M|f32|bcast|dec 9.5656 NA +1M|f32|bcast|c128 1.0234 2.34302 +1M|f64|C|bool 0.21047500000000002 0.381145 +1M|f64|C|u8 0.28924500000000003 0.235025 +1M|f64|C|i8 0.29268 0.237625 +1M|f64|C|i16 0.31417 0.41458 +1M|f64|C|u16 0.32124 0.41527 +1M|f64|C|i32 0.38703 0.650135 +1M|f64|C|u32 0.44374 0.67215 +1M|f64|C|i64 1.455565 1.24683 +1M|f64|C|u64 1.429275 1.27606 +1M|f64|C|char 0.307395 0.424075 +1M|f64|C|f16 1.6189900000000002 1.67663 +1M|f64|C|f32 0.37836 0.652625 +1M|f64|C|f64 0.47054 0.96524 +1M|f64|C|dec 5.969625 NA +1M|f64|C|c128 0.9417 2.44475 +1M|f64|F|bool 0.20194499999999999 0.383585 +1M|f64|F|u8 0.19508 0.24706 +1M|f64|F|i8 0.185005 0.246255 +1M|f64|F|i16 0.313375 0.43923 +1M|f64|F|u16 0.30827 0.441835 +1M|f64|F|i32 0.37706 0.737005 +1M|f64|F|u32 0.455365 0.669085 +1M|f64|F|i64 1.45062 1.26124 +1M|f64|F|u64 1.4496449999999999 1.30971 +1M|f64|F|char 0.30543 0.43016 +1M|f64|F|f16 1.044335 1.67965 +1M|f64|F|f32 0.3911 0.675275 +1M|f64|F|f64 0.483355 0.99025 +1M|f64|F|dec 5.98716 NA +1M|f64|F|c128 0.953305 2.56673 +1M|f64|T|bool 0.19111499999999998 0.38268 +1M|f64|T|u8 0.17434 0.234755 +1M|f64|T|i8 0.17500500000000002 0.23566 +1M|f64|T|i16 0.303985 0.41722 +1M|f64|T|u16 0.29646 0.417385 +1M|f64|T|i32 0.37696 0.684005 +1M|f64|T|u32 0.45332 0.67147 +1M|f64|T|i64 1.440865 1.25403 +1M|f64|T|u64 1.422105 1.27174 +1M|f64|T|char 0.31916 0.41838 +1M|f64|T|f16 1.026515 1.67086 +1M|f64|T|f32 0.368245 0.64025 +1M|f64|T|f64 0.50243 0.96782 +1M|f64|T|dec 5.94824 NA +1M|f64|T|c128 0.9555149999999999 2.49311 +1M|f64|sliced|bool 0.195865 0.393005 +1M|f64|sliced|u8 0.190905 0.239745 +1M|f64|sliced|i8 0.188725 0.247525 +1M|f64|sliced|i16 0.296145 0.420375 +1M|f64|sliced|u16 0.32025000000000003 0.42636 +1M|f64|sliced|i32 0.91238 0.641415 +1M|f64|sliced|u32 0.438025 0.651155 +1M|f64|sliced|i64 1.453285 1.22214 +1M|f64|sliced|u64 1.44527 1.21981 +1M|f64|sliced|char 0.314805 0.42213 +1M|f64|sliced|f16 1.06167 1.67346 +1M|f64|sliced|f32 0.383985 0.656295 +1M|f64|sliced|f64 0.53746 1.3399 +1M|f64|sliced|dec 5.94416 NA +1M|f64|sliced|c128 0.945515 2.44165 +1M|f64|negrow|bool 0.21641 0.40565 +1M|f64|negrow|u8 0.203025 0.250315 +1M|f64|negrow|i8 0.20002 0.26533 +1M|f64|negrow|i16 0.30364 0.43798 +1M|f64|negrow|u16 0.29671 0.43939 +1M|f64|negrow|i32 0.36613 0.68466 +1M|f64|negrow|u32 0.46081000000000005 0.66275 +1M|f64|negrow|i64 1.52094 1.22406 +1M|f64|negrow|u64 1.44302 1.26732 +1M|f64|negrow|char 0.31194 0.454955 +1M|f64|negrow|f16 1.0801500000000002 1.70425 +1M|f64|negrow|f32 0.40686 0.687775 +1M|f64|negrow|f64 0.5729650000000001 1.34953 +1M|f64|negrow|dec 6.088035 NA +1M|f64|negrow|c128 0.988575 2.51096 +1M|f64|negcol|bool 0.28262 0.410865 +1M|f64|negcol|u8 0.24720499999999998 0.276425 +1M|f64|negcol|i8 0.248655 0.274065 +1M|f64|negcol|i16 0.34718 0.46131 +1M|f64|negcol|u16 0.327125 0.45265 +1M|f64|negcol|i32 0.38864 0.680085 +1M|f64|negcol|u32 0.62332 0.684695 +1M|f64|negcol|i64 1.462485 1.21961 +1M|f64|negcol|u64 1.557445 1.29463 +1M|f64|negcol|char 0.336675 0.46907 +1M|f64|negcol|f16 1.413465 1.68585 +1M|f64|negcol|f32 0.44159 0.72708 +1M|f64|negcol|f64 0.542895 1.44923 +1M|f64|negcol|dec 6.02346 NA +1M|f64|negcol|c128 0.92928 2.48753 +1M|f64|strided|bool 0.15550999999999998 0.19469 +1M|f64|strided|u8 0.14558 0.143085 +1M|f64|strided|i8 0.14545 0.1413 +1M|f64|strided|i16 0.16409 0.148885 +1M|f64|strided|u16 0.17056 0.146925 +1M|f64|strided|i32 0.294705 0.360595 +1M|f64|strided|u32 0.37974 0.358695 +1M|f64|strided|i64 0.705675 0.62829 +1M|f64|strided|u64 0.7757799999999999 0.624325 +1M|f64|strided|char 0.1593 0.15042 +1M|f64|strided|f16 0.690505 0.732225 +1M|f64|strided|f32 0.34204 0.36443 +1M|f64|strided|f64 0.35989 0.65167 +1M|f64|strided|dec 3.14178 NA +1M|f64|strided|c128 0.591955 1.22885 +1M|f64|bcast|bool 0.14540999999999998 0.392025 +1M|f64|bcast|u8 0.121655 0.261645 +1M|f64|bcast|i8 0.12054499999999999 0.258475 +1M|f64|bcast|i16 0.247065 0.439345 +1M|f64|bcast|u16 0.24772500000000003 0.43889 +1M|f64|bcast|i32 0.312705 0.661295 +1M|f64|bcast|u32 0.44496 0.66232 +1M|f64|bcast|i64 0.60824 1.21271 +1M|f64|bcast|u64 1.40199 1.22769 +1M|f64|bcast|char 0.24139 0.435835 +1M|f64|bcast|f16 1.061285 1.6773 +1M|f64|bcast|f32 0.340715 0.695305 +1M|f64|bcast|f64 0.560685 1.4061 +1M|f64|bcast|dec 5.83879 NA +1M|f64|bcast|c128 0.88569 2.36032 +1M|dec|C|bool 0.967595 NA +1M|dec|C|u8 4.3141300000000005 NA +1M|dec|C|i8 4.273865 NA +1M|dec|C|i16 4.3298000000000005 NA +1M|dec|C|u16 4.315005 NA +1M|dec|C|i32 4.400354999999999 NA +1M|dec|C|u32 4.244295 NA +1M|dec|C|i64 4.538045 NA +1M|dec|C|u64 4.99588 NA +1M|dec|C|char 4.3459449999999995 NA +1M|dec|C|f16 4.074605 NA +1M|dec|C|f32 1.92613 NA +1M|dec|C|f64 2.02318 NA +1M|dec|C|dec 0.602295 NA +1M|dec|C|c128 2.14174 NA +1M|dec|F|bool 0.987275 NA +1M|dec|F|u8 4.257915 NA +1M|dec|F|i8 4.28991 NA +1M|dec|F|i16 4.301375 NA +1M|dec|F|u16 4.287225 NA +1M|dec|F|i32 4.367515 NA +1M|dec|F|u32 4.26117 NA +1M|dec|F|i64 4.5270850000000005 NA +1M|dec|F|u64 5.018915 NA +1M|dec|F|char 4.36839 NA +1M|dec|F|f16 4.013115 NA +1M|dec|F|f32 2.017645 NA +1M|dec|F|f64 2.045795 NA +1M|dec|F|dec 0.61144 NA +1M|dec|F|c128 2.079795 NA +1M|dec|T|bool 1.006505 NA +1M|dec|T|u8 4.30607 NA +1M|dec|T|i8 4.307095 NA +1M|dec|T|i16 4.352225 NA +1M|dec|T|u16 4.30921 NA +1M|dec|T|i32 4.387515 NA +1M|dec|T|u32 4.31321 NA +1M|dec|T|i64 4.52331 NA +1M|dec|T|u64 4.956015 NA +1M|dec|T|char 4.337095 NA +1M|dec|T|f16 4.00037 NA +1M|dec|T|f32 1.9142050000000002 NA +1M|dec|T|f64 2.03415 NA +1M|dec|T|dec 0.622515 NA +1M|dec|T|c128 2.333355 NA +1M|dec|sliced|bool 1.02731 NA +1M|dec|sliced|u8 4.2834 NA +1M|dec|sliced|i8 4.295450000000001 NA +1M|dec|sliced|i16 4.338125 NA +1M|dec|sliced|u16 4.3581 NA +1M|dec|sliced|i32 4.3945799999999995 NA +1M|dec|sliced|u32 4.24522 NA +1M|dec|sliced|i64 4.55494 NA +1M|dec|sliced|u64 4.946009999999999 NA +1M|dec|sliced|char 4.298145 NA +1M|dec|sliced|f16 4.01396 NA +1M|dec|sliced|f32 1.9431349999999998 NA +1M|dec|sliced|f64 2.05389 NA +1M|dec|sliced|dec 1.22814 NA +1M|dec|sliced|c128 2.0849900000000003 NA +1M|dec|negrow|bool 1.01767 NA +1M|dec|negrow|u8 4.336105 NA +1M|dec|negrow|i8 4.3320799999999995 NA +1M|dec|negrow|i16 4.369035 NA +1M|dec|negrow|u16 4.413045 NA +1M|dec|negrow|i32 4.44452 NA +1M|dec|negrow|u32 4.3130049999999995 NA +1M|dec|negrow|i64 4.58926 NA +1M|dec|negrow|u64 4.99331 NA +1M|dec|negrow|char 4.396915 NA +1M|dec|negrow|f16 4.02588 NA +1M|dec|negrow|f32 1.996885 NA +1M|dec|negrow|f64 2.09723 NA +1M|dec|negrow|dec 1.22426 NA +1M|dec|negrow|c128 2.2846 NA +1M|dec|negcol|bool 0.99739 NA +1M|dec|negcol|u8 4.3474200000000005 NA +1M|dec|negcol|i8 4.377245 NA +1M|dec|negcol|i16 4.407625 NA +1M|dec|negcol|u16 4.390455 NA +1M|dec|negcol|i32 4.3994349999999995 NA +1M|dec|negcol|u32 4.354195 NA +1M|dec|negcol|i64 4.64623 NA +1M|dec|negcol|u64 5.072095 NA +1M|dec|negcol|char 4.356745 NA +1M|dec|negcol|f16 4.0382750000000005 NA +1M|dec|negcol|f32 1.9546500000000002 NA +1M|dec|negcol|f64 2.0730899999999997 NA +1M|dec|negcol|dec 1.301075 NA +1M|dec|negcol|c128 2.16183 NA +1M|dec|strided|bool 0.5507299999999999 NA +1M|dec|strided|u8 2.1722650000000003 NA +1M|dec|strided|i8 2.19347 NA +1M|dec|strided|i16 2.251115 NA +1M|dec|strided|u16 2.2442450000000003 NA +1M|dec|strided|i32 2.2706299999999997 NA +1M|dec|strided|u32 2.223935 NA +1M|dec|strided|i64 2.34512 NA +1M|dec|strided|u64 2.5378 NA +1M|dec|strided|char 2.3108750000000002 NA +1M|dec|strided|f16 2.13185 NA +1M|dec|strided|f32 1.120515 NA +1M|dec|strided|f64 1.0269650000000001 NA +1M|dec|strided|dec 0.74505 NA +1M|dec|strided|c128 1.3032400000000002 NA +1M|dec|bcast|bool 0.917325 NA +1M|dec|bcast|u8 4.25576 NA +1M|dec|bcast|i8 4.203145 NA +1M|dec|bcast|i16 4.22396 NA +1M|dec|bcast|u16 4.24058 NA +1M|dec|bcast|i32 4.26438 NA +1M|dec|bcast|u32 4.12489 NA +1M|dec|bcast|i64 4.382385 NA +1M|dec|bcast|u64 4.872985 NA +1M|dec|bcast|char 4.23591 NA +1M|dec|bcast|f16 3.86884 NA +1M|dec|bcast|f32 1.679555 NA +1M|dec|bcast|f64 1.7932549999999998 NA +1M|dec|bcast|dec 0.837315 NA +1M|dec|bcast|c128 1.853125 NA +1M|c128|C|bool 0.395305 0.437755 +1M|c128|C|u8 0.35255000000000003 0.32835 +1M|c128|C|i8 0.341745 0.32015 +1M|c128|C|i16 0.490635 0.58577 +1M|c128|C|u16 0.48929999999999996 0.55304 +1M|c128|C|i32 0.514545 0.86938 +1M|c128|C|u32 0.573715 0.77524 +1M|c128|C|i64 1.51528 1.38951 +1M|c128|C|u64 1.665165 1.39111 +1M|c128|C|char 0.47088 0.519505 +1M|c128|C|f16 1.1288 1.69123 +1M|c128|C|f32 0.608005 0.846485 +1M|c128|C|f64 0.798245 1.39932 +1M|c128|C|dec 6.1008249999999995 NA +1M|c128|C|c128 0.6482300000000001 2.06067 +1M|c128|F|bool 0.41505000000000003 0.40976 +1M|c128|F|u8 0.339065 0.30829 +1M|c128|F|i8 0.35718 0.31479 +1M|c128|F|i16 0.427295 0.48949 +1M|c128|F|u16 0.45274000000000003 0.485875 +1M|c128|F|i32 0.48799000000000003 0.76596 +1M|c128|F|u32 0.664095 0.884185 +1M|c128|F|i64 1.50076 1.4359 +1M|c128|F|u64 1.70234 1.37172 +1M|c128|F|char 0.52354 0.572495 +1M|c128|F|f16 1.117545 1.66061 +1M|c128|F|f32 0.634845 0.72968 +1M|c128|F|f64 0.7725 1.35337 +1M|c128|F|dec 6.12928 NA +1M|c128|F|c128 0.61231 1.99356 +1M|c128|T|bool 0.41754499999999994 0.41836 +1M|c128|T|u8 0.3224 0.358795 +1M|c128|T|i8 0.32374 0.393115 +1M|c128|T|i16 0.49365 0.62633 +1M|c128|T|u16 0.44347000000000003 0.52236 +1M|c128|T|i32 0.48365 0.80434 +1M|c128|T|u32 0.588275 0.75266 +1M|c128|T|i64 1.5151249999999998 1.36501 +1M|c128|T|u64 1.61543 1.36921 +1M|c128|T|char 0.44421 0.494875 +1M|c128|T|f16 1.097145 1.67757 +1M|c128|T|f32 0.54227 0.764575 +1M|c128|T|f64 0.749865 1.34586 +1M|c128|T|dec 6.114105 NA +1M|c128|T|c128 0.5968950000000001 2.03961 +1M|c128|sliced|bool 0.44977 0.435455 +1M|c128|sliced|u8 0.319395 0.34069 +1M|c128|sliced|i8 0.32532 0.311755 +1M|c128|sliced|i16 0.45253 0.5096 +1M|c128|sliced|u16 0.44281 0.505515 +1M|c128|sliced|i32 0.48592 0.833635 +1M|c128|sliced|u32 0.63573 0.82831 +1M|c128|sliced|i64 1.486945 1.38891 +1M|c128|sliced|u64 1.74786 1.40213 +1M|c128|sliced|char 0.560125 0.508115 +1M|c128|sliced|f16 1.157105 1.70034 +1M|c128|sliced|f32 0.551585 0.77274 +1M|c128|sliced|f64 0.827665 1.44743 +1M|c128|sliced|dec 6.105345 NA +1M|c128|sliced|c128 1.32098 2.90082 +1M|c128|negrow|bool 0.43326000000000003 0.47838 +1M|c128|negrow|u8 0.34916 0.32937 +1M|c128|negrow|i8 0.336075 0.33784 +1M|c128|negrow|i16 0.452495 0.52245 +1M|c128|negrow|u16 0.46902999999999995 0.52203 +1M|c128|negrow|i32 0.559365 0.822905 +1M|c128|negrow|u32 0.607305 0.83494 +1M|c128|negrow|i64 1.5664500000000001 1.36191 +1M|c128|negrow|u64 1.7009250000000002 1.56799 +1M|c128|negrow|char 0.45589500000000005 0.575605 +1M|c128|negrow|f16 1.197775 1.69383 +1M|c128|negrow|f32 0.57576 0.78852 +1M|c128|negrow|f64 0.8431900000000001 1.35378 +1M|c128|negrow|dec 6.219075 NA +1M|c128|negrow|c128 1.26112 2.88197 +1M|c128|negcol|bool 0.44734999999999997 0.4542 +1M|c128|negcol|u8 0.34845000000000004 0.34234 +1M|c128|negcol|i8 0.345895 0.3739 +1M|c128|negcol|i16 0.45641999999999994 0.541295 +1M|c128|negcol|u16 0.481685 0.531375 +1M|c128|negcol|i32 0.526285 0.81175 +1M|c128|negcol|u32 0.720135 0.834735 +1M|c128|negcol|i64 1.6103999999999998 1.58382 +1M|c128|negcol|u64 1.6585999999999999 1.49453 +1M|c128|negcol|char 0.470415 0.57562 +1M|c128|negcol|f16 1.82029 1.73149 +1M|c128|negcol|f32 0.590785 0.87995 +1M|c128|negcol|f64 0.8277850000000001 1.41646 +1M|c128|negcol|dec 6.30264 NA +1M|c128|negcol|c128 1.299285 2.93851 +1M|c128|strided|bool 0.323555 0.346335 +1M|c128|strided|u8 0.26362 0.301685 +1M|c128|strided|i8 0.264975 0.2633 +1M|c128|strided|i16 0.31295 0.2897 +1M|c128|strided|u16 0.30512 0.278935 +1M|c128|strided|i32 0.42408999999999997 0.488765 +1M|c128|strided|u32 0.480465 0.493365 +1M|c128|strided|i64 0.8005850000000001 0.775465 +1M|c128|strided|u64 0.93657 0.774475 +1M|c128|strided|char 0.294425 0.2754 +1M|c128|strided|f16 0.8811 0.787425 +1M|c128|strided|f32 0.43935500000000005 0.47355 +1M|c128|strided|f64 0.4875 0.77438 +1M|c128|strided|dec 3.374055 NA +1M|c128|strided|c128 0.8499399999999999 1.41198 +1M|c128|bcast|bool 0.362785 0.39615 +1M|c128|bcast|u8 0.2465 0.26117 +1M|c128|bcast|i8 0.24663 0.259295 +1M|c128|bcast|i16 0.32149 0.44475 +1M|c128|bcast|u16 0.31433 0.44154 +1M|c128|bcast|i32 0.37855500000000003 0.662405 +1M|c128|bcast|u32 0.49912 0.67287 +1M|c128|bcast|i64 1.42795 1.2013 +1M|c128|bcast|u64 1.541295 1.23008 +1M|c128|bcast|char 0.318505 0.444075 +1M|c128|bcast|f16 1.1087099999999999 1.66516 +1M|c128|bcast|f32 0.444195 0.6919 +1M|c128|bcast|f64 0.573805 1.15569 +1M|c128|bcast|dec 5.91227 NA +1M|c128|bcast|c128 0.88156 2.59177 diff --git a/benchmark/history/2026-06-29_2d16f477/fusion_results.md b/benchmark/history/2026-06-29_2d16f477/fusion_results.md new file mode 100644 index 00000000..b945c5a2 --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/fusion_results.md @@ -0,0 +1,40 @@ +# Fusion — np.evaluate vs unfused chains (and NumPy context) + +`np.evaluate` runs a whole expression tree in one NDIter pass (no intermediates). Fixed-expression gate plus an operand-layout sweep of the flagship `a*b+c` (C/F/T/strided/bcast — does the fused single-pass win survive non-contiguous operands?), not a dtype/layout matrix — so reported as-is. + +``` +NumSharp — fused np.evaluate vs unfused np.* chains (4M elements, best-of-9; (Nx) = unfused ÷ fused, >1 = fusion faster): + +correctness cross-checks ok + +4M float64, best of 9: + a*b+c fused 3.53 ms unfused 6.08 ms (1.72x) + (a-b)/(a+b) fused 3.16 ms unfused 12.32 ms (3.90x) + sum(a*b) fused 2.30 ms unfused 4.33 ms (1.88x) + sum(af*bf) fused 1.43 ms unfused 1.57 ms (1.10x) [f32] + a*b+c out= fused 3.56 ms [1-pass fused-into-out] + i4*2+f8 fused 2.78 ms unfused 4.04 ms (1.45x) + + a*b+c across operand layouts (2-D 2000x2000, all 3 operands same layout): + [C ] fused 3.43 ms unfused 6.05 ms (1.77x) + [F ] fused 3.52 ms unfused 6.18 ms (1.75x) + [T ] fused 3.50 ms unfused 6.27 ms (1.79x) + [strided] fused 3.31 ms unfused 4.72 ms (1.43x) + [bcast ] fused 1.06 ms unfused 3.75 ms (3.53x) + +NumPy — absolutes on the same box (context for the unfused column): + +numpy 2.4.2, 4M float64, best of 9: + a*b+c 12.35 ms + (a-b)/(a+b) 18.75 ms + sum(a*b) 8.60 ms + sum(af*bf) 4.14 ms [f32] + a*b+c out= 4.68 ms [two-pass with out=] + i4*2+f8 9.67 ms + a*b+c across operand layouts (2-D 2000x2000, unfused): + [C ] 12.52 ms + [F ] 12.54 ms + [T ] 12.51 ms + [strided] 7.91 ms + [bcast ] 11.95 ms +``` diff --git a/benchmark/history/2026-06-29_2d16f477/layout_results.md b/benchmark/history/2026-06-29_2d16f477/layout_results.md new file mode 100644 index 00000000..59bd6d08 --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/layout_results.md @@ -0,0 +1,126 @@ +# Layout suite — reduction / copy / elementwise × memory layout × dtype + +ratio = NumPy_ms / NumSharp_ms — **>1.0 = NumSharp faster**. ✅≥1.0 🟡≥0.5 🟠≥0.2 🔴<0.2. +Layouts (8, harmonized with the cast subsystem): `C`, `F` (Fortran), `T` (transpose), `strided` `[:, ::2]`, `sliced` (offset), `negrow` `[::-1,:]`, `negcol` `[:,::-1]`, `bcast` (stride-0). Fills the op-matrix's blind spot (it measures C-contiguous only). 100K + 1M elements, best-of-rounds. + +### Reduction (sum/min/max/prod, both axes) + +**Geomean by lay** + +| size | C | F | T | strided | negrow | negcol | sliced | bcast | +|---|---|---|---|---|---|---|---|---| +| 100K | 0.99 🟡 | 1.02 ✅ | 1.02 ✅ | 0.55 🟡 | 0.70 🟡 | 0.70 🟡 | 0.72 🟡 | 0.74 🟡 | +| 1M | 0.95 🟡 | 0.94 🟡 | 0.95 🟡 | 0.65 🟡 | 0.87 🟡 | 0.73 🟡 | 0.75 🟡 | 0.68 🟡 | + +**Geomean by dt** + +| size | f64 | f32 | c128 | dec | f16 | i32 | i64 | +|---|---|---|---|---|---|---|---| +| 100K | 0.87 🟡 | 1.00 🟡 | 0.98 🟡 | 0.08 🔴 | 1.03 ✅ | 1.33 ✅ | 1.14 ✅ | +| 1M | 1.12 ✅ | 1.15 ✅ | 1.04 ✅ | 0.07 🔴 | 1.02 ✅ | 1.19 ✅ | 1.02 ✅ | + +**Geomean by op** + +| size | sum | min | max | prod | +|---|---|---|---|---| +| 100K | 0.80 🟡 | 0.66 🟡 | 0.66 🟡 | 1.19 ✅ | +| 1M | 0.84 🟡 | 0.65 🟡 | 0.64 🟡 | 1.28 ✅ | + +**Worst 15 cells (NumSharp slowest vs NumPy)** + +| key | NumSharp ms | NumPy ms | ratio | +|---|---|---|---| +| 1M\|dec\|bcast\|sum\|ax0 | 5.5269 | 0.0655 | 0.01 🔴 | +| 100K\|dec\|bcast\|sum\|ax0 | 0.5555 | 0.0087 | 0.02 🔴 | +| 100K\|dec\|F\|sum\|ax1 | 0.5585 | 0.0094 | 0.02 🔴 | +| 100K\|dec\|C\|sum\|ax0 | 0.5615 | 0.0111 | 0.02 🔴 | +| 100K\|dec\|T\|sum\|ax1 | 0.5582 | 0.0111 | 0.02 🔴 | +| 100K\|dec\|negrow\|sum\|ax0 | 0.5592 | 0.0114 | 0.02 🔴 | +| 100K\|dec\|sliced\|sum\|ax0 | 0.5517 | 0.0115 | 0.02 🔴 | +| 1M\|dec\|sliced\|sum\|ax0 | 5.5802 | 0.1240 | 0.02 🔴 | +| 1M\|dec\|negrow\|sum\|ax0 | 5.5707 | 0.1242 | 0.02 🔴 | +| 1M\|dec\|F\|sum\|ax1 | 5.6157 | 0.1254 | 0.02 🔴 | +| 1M\|dec\|T\|sum\|ax1 | 5.5849 | 0.1278 | 0.02 🔴 | +| 1M\|dec\|C\|sum\|ax0 | 5.5736 | 0.1524 | 0.03 🔴 | +| 100K\|dec\|C\|min\|ax0 | 0.3021 | 0.0141 | 0.05 🔴 | +| 1M\|dec\|negcol\|sum\|ax0 | 5.6678 | 0.2866 | 0.05 🔴 | +| 100K\|dec\|negcol\|sum\|ax0 | 0.5594 | 0.0302 | 0.05 🔴 | + +### Copy / identity-ufunc (np.positive) + +**Geomean by lay** + +| size | C | F | T | strided | sliced | negrow | negcol | bcast | +|---|---|---|---|---|---|---|---|---| +| 100K | 1.22 ✅ | 1.51 ✅ | 1.28 ✅ | 0.83 🟡 | 1.54 ✅ | 1.48 ✅ | 2.20 ✅ | 1.62 ✅ | +| 1M | 2.82 ✅ | 2.89 ✅ | 2.88 ✅ | 2.08 ✅ | 2.58 ✅ | 2.64 ✅ | 3.16 ✅ | 2.66 ✅ | + +**Geomean by dt** + +| size | u8 | i8 | i16 | u16 | i32 | u32 | i64 | u64 | char | f16 | f32 | f64 | c128 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| 100K | 0.92 🟡 | 1.03 ✅ | 1.51 ✅ | 1.99 ✅ | 1.02 ✅ | 1.09 ✅ | 1.65 ✅ | 1.08 ✅ | 2.56 ✅ | 2.47 ✅ | 1.15 ✅ | 0.85 🟡 | 2.56 ✅ | +| 1M | 4.60 ✅ | 4.87 ✅ | 2.13 ✅ | 2.14 ✅ | 2.11 ✅ | 2.12 ✅ | 2.56 ✅ | 2.46 ✅ | 2.20 ✅ | 2.10 ✅ | 2.14 ✅ | 2.59 ✅ | 5.47 ✅ | + +**Worst 15 cells (NumSharp slowest vs NumPy)** + +| key | NumSharp ms | NumPy ms | ratio | +|---|---|---|---| +| 100K\|f64\|strided\|pos | 0.0243 | 0.0100 | 0.41 🟠 | +| 100K\|u64\|strided\|pos | 0.0241 | 0.0110 | 0.46 🟠 | +| 100K\|i64\|strided\|pos | 0.0237 | 0.0109 | 0.46 🟠 | +| 100K\|f32\|strided\|pos | 0.0216 | 0.0100 | 0.46 🟠 | +| 100K\|i32\|strided\|pos | 0.0213 | 0.0110 | 0.52 🟡 | +| 100K\|i64\|C\|pos | 0.0400 | 0.0210 | 0.53 🟡 | +| 100K\|i64\|T\|pos | 0.0387 | 0.0208 | 0.54 🟡 | +| 100K\|i8\|sliced\|pos | 0.0423 | 0.0228 | 0.54 🟡 | +| 100K\|u8\|bcast\|pos | 0.0416 | 0.0229 | 0.55 🟡 | +| 100K\|u8\|sliced\|pos | 0.0410 | 0.0227 | 0.55 🟡 | +| 100K\|u8\|negrow\|pos | 0.0410 | 0.0231 | 0.56 🟡 | +| 100K\|u32\|strided\|pos | 0.0191 | 0.0109 | 0.57 🟡 | +| 100K\|f64\|T\|pos | 0.0389 | 0.0227 | 0.58 🟡 | +| 100K\|f64\|F\|pos | 0.0362 | 0.0227 | 0.63 🟡 | +| 100K\|f64\|C\|pos | 0.0361 | 0.0228 | 0.63 🟡 | + +### Elementwise (add/mul/neg/abs/sqrt/less/copy) + +**Geomean by lay** + +| size | C | F | T | strided | sliced | negrow | negcol | bcast | +|---|---|---|---|---|---|---|---|---| +| 100K | 0.73 🟡 | 0.70 🟡 | 0.68 🟡 | 0.53 🟡 | 0.84 🟡 | 0.85 🟡 | 1.18 ✅ | 0.85 🟡 | +| 1M | 1.54 ✅ | 1.48 ✅ | 1.48 ✅ | 1.12 ✅ | 1.64 ✅ | 1.65 ✅ | 1.80 ✅ | 1.63 ✅ | + +**Geomean by dt** + +| size | f64 | f32 | c128 | f16 | i32 | i64 | +|---|---|---|---|---|---|---| +| 100K | 0.67 🟡 | 0.57 🟡 | 1.15 ✅ | 0.75 🟡 | 0.82 🟡 | 0.81 🟡 | +| 1M | 1.86 ✅ | 1.53 ✅ | 1.73 ✅ | 0.94 🟡 | 1.54 ✅ | 1.79 ✅ | + +**Geomean by op** + +| size | add | mul | neg | abs | sqrt | less | copy | +|---|---|---|---|---|---|---|---| +| 100K | 1.04 ✅ | 0.99 🟡 | 0.76 🟡 | 0.74 🟡 | 0.94 🟡 | 0.61 🟡 | 0.51 🟡 | +| 1M | 1.81 ✅ | 1.78 ✅ | 2.11 ✅ | 1.62 ✅ | 1.50 ✅ | 0.68 🟡 | 1.75 ✅ | + +**Worst 15 cells (NumSharp slowest vs NumPy)** + +| key | NumSharp ms | NumPy ms | ratio | +|---|---|---|---| +| 100K\|f64\|strided\|abs | 0.0466 | 0.0077 | 0.17 🔴 | +| 100K\|f64\|strided\|neg | 0.0473 | 0.0088 | 0.19 🔴 | +| 100K\|i32\|bcast\|copy | 0.0233 | 0.0050 | 0.21 🟠 | +| 100K\|f16\|bcast\|copy | 0.0134 | 0.0030 | 0.22 🟠 | +| 100K\|f16\|negrow\|copy | 0.0168 | 0.0038 | 0.23 🟠 | +| 100K\|f32\|C\|copy | 0.0244 | 0.0059 | 0.24 🟠 | +| 100K\|i64\|strided\|neg | 0.0296 | 0.0076 | 0.26 🟠 | +| 100K\|f64\|strided\|copy | 0.0413 | 0.0108 | 0.26 🟠 | +| 100K\|f64\|negrow\|copy | 0.0595 | 0.0160 | 0.27 🟠 | +| 100K\|f16\|sliced\|copy | 0.0134 | 0.0037 | 0.27 🟠 | +| 100K\|f64\|sliced\|copy | 0.0592 | 0.0162 | 0.27 🟠 | +| 100K\|i32\|negrow\|copy | 0.0234 | 0.0064 | 0.28 🟠 | +| 100K\|f32\|C\|add | 0.0240 | 0.0066 | 0.28 🟠 | +| 100K\|f64\|strided\|mul | 0.0509 | 0.0142 | 0.28 🟠 | +| 100K\|f64\|strided\|add | 0.0516 | 0.0147 | 0.28 🟠 | diff --git a/benchmark/history/2026-06-29_2d16f477/layout_results.tsv b/benchmark/history/2026-06-29_2d16f477/layout_results.tsv new file mode 100644 index 00000000..4ba35d00 --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/layout_results.tsv @@ -0,0 +1,1745 @@ +bench key ns_ms np_ms +reduce_layout_bench 100K|f64|C|sum|ax0 0.020216500000000002 0.013917 +reduce_layout_bench 100K|f64|C|sum|ax1 0.0185565 0.017839 +reduce_layout_bench 100K|f64|C|min|ax0 0.017513 0.0149405 +reduce_layout_bench 100K|f64|C|min|ax1 0.023124500000000003 0.0169835 +reduce_layout_bench 100K|f64|C|max|ax0 0.0173355 0.012944 +reduce_layout_bench 100K|f64|C|max|ax1 0.024343499999999997 0.016917 +reduce_layout_bench 100K|f64|C|prod|ax0 0.010136000000000001 0.009504 +reduce_layout_bench 100K|f64|C|prod|ax1 0.007717000000000001 0.0573175 +reduce_layout_bench 100K|f64|F|sum|ax0 0.0166445 0.017558 +reduce_layout_bench 100K|f64|F|sum|ax1 0.0167675 0.011122 +reduce_layout_bench 100K|f64|F|min|ax0 0.0237145 0.016947 +reduce_layout_bench 100K|f64|F|min|ax1 0.016506 0.0140815 +reduce_layout_bench 100K|f64|F|max|ax0 0.022847 0.017022 +reduce_layout_bench 100K|f64|F|max|ax1 0.016604 0.0140985 +reduce_layout_bench 100K|f64|F|prod|ax0 0.0050735 0.057241 +reduce_layout_bench 100K|f64|F|prod|ax1 0.0090895 0.011168 +reduce_layout_bench 100K|f64|T|sum|ax0 0.018172 0.0178745 +reduce_layout_bench 100K|f64|T|sum|ax1 0.018120499999999998 0.0093755 +reduce_layout_bench 100K|f64|T|min|ax0 0.0240055 0.0172835 +reduce_layout_bench 100K|f64|T|min|ax1 0.0173175 0.0129695 +reduce_layout_bench 100K|f64|T|max|ax0 0.024304000000000003 0.0169725 +reduce_layout_bench 100K|f64|T|max|ax1 0.016991 0.012958 +reduce_layout_bench 100K|f64|T|prod|ax0 0.00769 0.057772 +reduce_layout_bench 100K|f64|T|prod|ax1 0.010156499999999999 0.0095345 +reduce_layout_bench 100K|f64|strided|sum|ax0 0.0266565 0.017522 +reduce_layout_bench 100K|f64|strided|sum|ax1 0.0200095 0.010462 +reduce_layout_bench 100K|f64|strided|min|ax0 0.0401255 0.0146705 +reduce_layout_bench 100K|f64|strided|min|ax1 0.06679249999999999 0.0229845 +reduce_layout_bench 100K|f64|strided|max|ax0 0.042086 0.014727 +reduce_layout_bench 100K|f64|strided|max|ax1 0.0677935 0.0231295 +reduce_layout_bench 100K|f64|strided|prod|ax0 0.021987 0.0172435 +reduce_layout_bench 100K|f64|strided|prod|ax1 0.050993000000000004 0.024497 +reduce_layout_bench 100K|f64|negrow|sum|ax0 0.018607 0.009932 +reduce_layout_bench 100K|f64|negrow|sum|ax1 0.019178999999999998 0.018621 +reduce_layout_bench 100K|f64|negrow|min|ax0 0.0291245 0.0131475 +reduce_layout_bench 100K|f64|negrow|min|ax1 0.0196675 0.017037 +reduce_layout_bench 100K|f64|negrow|max|ax0 0.028196500000000003 0.0132445 +reduce_layout_bench 100K|f64|negrow|max|ax1 0.020354 0.0169145 +reduce_layout_bench 100K|f64|negrow|prod|ax0 0.0092275 0.009864 +reduce_layout_bench 100K|f64|negrow|prod|ax1 0.10438449999999999 0.0584105 +reduce_layout_bench 100K|f64|negcol|sum|ax0 0.0511925 0.0302035 +reduce_layout_bench 100K|f64|negcol|sum|ax1 0.0203235 0.0188085 +reduce_layout_bench 100K|f64|negcol|min|ax0 0.112372 0.025622 +reduce_layout_bench 100K|f64|negcol|min|ax1 0.0111185 0.039116 +reduce_layout_bench 100K|f64|negcol|max|ax0 0.1085395 0.0269845 +reduce_layout_bench 100K|f64|negcol|max|ax1 0.0116295 0.039607 +reduce_layout_bench 100K|f64|negcol|prod|ax0 0.0392575 0.0303325 +reduce_layout_bench 100K|f64|negcol|prod|ax1 0.023352499999999998 0.05671 +reduce_layout_bench 100K|f64|sliced|sum|ax0 0.012574 0.014441 +reduce_layout_bench 100K|f64|sliced|sum|ax1 0.0119315 0.0177925 +reduce_layout_bench 100K|f64|sliced|min|ax0 0.0224925 0.0161745 +reduce_layout_bench 100K|f64|sliced|min|ax1 0.10606249999999999 0.0168425 +reduce_layout_bench 100K|f64|sliced|max|ax0 0.023268499999999998 0.014308 +reduce_layout_bench 100K|f64|sliced|max|ax1 0.1061685 0.015676 +reduce_layout_bench 100K|f64|sliced|prod|ax0 0.008905999999999999 0.0111935 +reduce_layout_bench 100K|f64|sliced|prod|ax1 0.0182645 0.0569915 +reduce_layout_bench 100K|f64|bcast|sum|ax0 0.0092375 0.008812 +reduce_layout_bench 100K|f64|bcast|sum|ax1 0.0101475 0.0144455 +reduce_layout_bench 100K|f64|bcast|min|ax0 0.019178999999999998 0.011985 +reduce_layout_bench 100K|f64|bcast|min|ax1 0.009356 0.016127 +reduce_layout_bench 100K|f64|bcast|max|ax0 0.0193765 0.011953 +reduce_layout_bench 100K|f64|bcast|max|ax1 0.0097345 0.016122 +reduce_layout_bench 100K|f64|bcast|prod|ax0 0.0052334999999999994 0.0094725 +reduce_layout_bench 100K|f64|bcast|prod|ax1 0.039046 0.056879 +reduce_layout_bench 100K|f32|C|sum|ax0 0.008217 0.007246 +reduce_layout_bench 100K|f32|C|sum|ax1 0.013166 0.016084 +reduce_layout_bench 100K|f32|C|min|ax0 0.0098055 0.009395 +reduce_layout_bench 100K|f32|C|min|ax1 0.0182585 0.012097 +reduce_layout_bench 100K|f32|C|max|ax0 0.009961 0.0093525 +reduce_layout_bench 100K|f32|C|max|ax1 0.017778000000000002 0.011773 +reduce_layout_bench 100K|f32|C|prod|ax0 0.0050360000000000005 0.007926 +reduce_layout_bench 100K|f32|C|prod|ax1 0.003495 0.057982 +reduce_layout_bench 100K|f32|F|sum|ax0 0.0130575 0.0160745 +reduce_layout_bench 100K|f32|F|sum|ax1 0.00773 0.007242 +reduce_layout_bench 100K|f32|F|min|ax0 0.01782 0.0122405 +reduce_layout_bench 100K|f32|F|min|ax1 0.00996 0.009435 +reduce_layout_bench 100K|f32|F|max|ax0 0.018874500000000002 0.0117635 +reduce_layout_bench 100K|f32|F|max|ax1 0.010086 0.009416 +reduce_layout_bench 100K|f32|F|prod|ax0 0.0033085 0.058327 +reduce_layout_bench 100K|f32|F|prod|ax1 0.004805 0.0079135 +reduce_layout_bench 100K|f32|T|sum|ax0 0.01272 0.016213 +reduce_layout_bench 100K|f32|T|sum|ax1 0.007884 0.0074435 +reduce_layout_bench 100K|f32|T|min|ax0 0.017778000000000002 0.011865 +reduce_layout_bench 100K|f32|T|min|ax1 0.0100725 0.0093365 +reduce_layout_bench 100K|f32|T|max|ax0 0.0175955 0.0119905 +reduce_layout_bench 100K|f32|T|max|ax1 0.009868 0.009447 +reduce_layout_bench 100K|f32|T|prod|ax0 0.0034685 0.057641 +reduce_layout_bench 100K|f32|T|prod|ax1 0.0054405 0.007958 +reduce_layout_bench 100K|f32|strided|sum|ax0 0.023176000000000002 0.017103 +reduce_layout_bench 100K|f32|strided|sum|ax1 0.014658500000000001 0.008631 +reduce_layout_bench 100K|f32|strided|min|ax0 0.040421500000000006 0.0119065 +reduce_layout_bench 100K|f32|strided|min|ax1 0.0438255 0.0231505 +reduce_layout_bench 100K|f32|strided|max|ax0 0.041014999999999996 0.0119665 +reduce_layout_bench 100K|f32|strided|max|ax1 0.0435295 0.022881 +reduce_layout_bench 100K|f32|strided|prod|ax0 0.0208575 0.017786 +reduce_layout_bench 100K|f32|strided|prod|ax1 0.0319915 0.025744 +reduce_layout_bench 100K|f32|negrow|sum|ax0 0.0079935 0.0072595 +reduce_layout_bench 100K|f32|negrow|sum|ax1 0.012854 0.01689 +reduce_layout_bench 100K|f32|negrow|min|ax0 0.013218 0.0094115 +reduce_layout_bench 100K|f32|negrow|min|ax1 0.011262000000000001 0.01182 +reduce_layout_bench 100K|f32|negrow|max|ax0 0.013395999999999998 0.0094265 +reduce_layout_bench 100K|f32|negrow|max|ax1 0.012001999999999999 0.011799 +reduce_layout_bench 100K|f32|negrow|prod|ax0 0.0046905 0.00796 +reduce_layout_bench 100K|f32|negrow|prod|ax1 0.060843 0.058834 +reduce_layout_bench 100K|f32|negcol|sum|ax0 0.042268 0.0295965 +reduce_layout_bench 100K|f32|negcol|sum|ax1 0.0130045 0.016848 +reduce_layout_bench 100K|f32|negcol|min|ax0 0.1098215 0.039166 +reduce_layout_bench 100K|f32|negcol|min|ax1 0.0109945 0.03963 +reduce_layout_bench 100K|f32|negcol|max|ax0 0.10946550000000001 0.038433 +reduce_layout_bench 100K|f32|negcol|max|ax1 0.010610999999999999 0.0398665 +reduce_layout_bench 100K|f32|negcol|prod|ax0 0.0390165 0.030098 +reduce_layout_bench 100K|f32|negcol|prod|ax1 0.054823000000000004 0.05798 +reduce_layout_bench 100K|f32|sliced|sum|ax0 0.008232 0.0070005 +reduce_layout_bench 100K|f32|sliced|sum|ax1 0.012525 0.0156815 +reduce_layout_bench 100K|f32|sliced|min|ax0 0.0128115 0.009291 +reduce_layout_bench 100K|f32|sliced|min|ax1 0.061960499999999995 0.010786 +reduce_layout_bench 100K|f32|sliced|max|ax0 0.0130225 0.009398 +reduce_layout_bench 100K|f32|sliced|max|ax1 0.061760999999999996 0.0108435 +reduce_layout_bench 100K|f32|sliced|prod|ax0 0.0049125 0.0081385 +reduce_layout_bench 100K|f32|sliced|prod|ax1 0.061510999999999996 0.0567475 +reduce_layout_bench 100K|f32|bcast|sum|ax0 0.006724 0.00667 +reduce_layout_bench 100K|f32|bcast|sum|ax1 0.0130545 0.014526 +reduce_layout_bench 100K|f32|bcast|min|ax0 0.0122985 0.0087795 +reduce_layout_bench 100K|f32|bcast|min|ax1 0.009795 0.0113935 +reduce_layout_bench 100K|f32|bcast|max|ax0 0.0123305 0.00883 +reduce_layout_bench 100K|f32|bcast|max|ax1 0.009923 0.0113785 +reduce_layout_bench 100K|f32|bcast|prod|ax0 0.0036655 0.0073145 +reduce_layout_bench 100K|f32|bcast|prod|ax1 0.039446 0.0585505 +reduce_layout_bench 100K|c128|C|sum|ax0 0.021345 0.015076 +reduce_layout_bench 100K|c128|C|sum|ax1 0.0301875 0.031425 +reduce_layout_bench 100K|c128|C|min|ax0 0.1877165 0.137835 +reduce_layout_bench 100K|c128|C|min|ax1 0.15428050000000001 0.151286 +reduce_layout_bench 100K|c128|C|max|ax0 0.1520455 0.138264 +reduce_layout_bench 100K|c128|C|max|ax1 0.11508850000000001 0.148863 +reduce_layout_bench 100K|c128|C|prod|ax0 0.071138 0.02992 +reduce_layout_bench 100K|c128|C|prod|ax1 0.149361 0.230536 +reduce_layout_bench 100K|c128|F|sum|ax0 0.030978500000000003 0.0311195 +reduce_layout_bench 100K|c128|F|sum|ax1 0.021051 0.019079 +reduce_layout_bench 100K|c128|F|min|ax0 0.142787 0.148802 +reduce_layout_bench 100K|c128|F|min|ax1 0.14208199999999999 0.137911 +reduce_layout_bench 100K|c128|F|max|ax0 0.1133745 0.150032 +reduce_layout_bench 100K|c128|F|max|ax1 0.115392 0.13736 +reduce_layout_bench 100K|c128|F|prod|ax0 0.1487755 0.230851 +reduce_layout_bench 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1.28087 diff --git a/benchmark/history/2026-06-29_2d16f477/nditer_results.md b/benchmark/history/2026-06-29_2d16f477/nditer_results.md new file mode 100644 index 00000000..002867d3 --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/nditer_results.md @@ -0,0 +1,117 @@ +``` +NumSharp NDIter — canonical benchmark · 2026-06-29 · speedup = NumPy ÷ NumSharp (>1.0× = NumSharp faster) +198 measured pairs (35 NA) · best-of-rounds, Release · matched kernels/ids +%NumPy🕐 = NumSharp ÷ NumPy × 100 = share of NumPy's time NumSharp uses (8% = takes only 8% as long; <100% = faster) + +AV POLICY — a NumSharp section that crashes all retries (known intermittent +AccessViolation, an unmanaged-storage lifetime bug) is reported NA / IGNORED +and excluded from every geomean below. THIS RUN: NA across selection. + +HEADLINE — operation matrix: 1.20× geomean · 83%🕐 of NumPy's time · 77 win / 53 lose over 130 cells + +OPERATIONS — BY SIZE TIER (geomean over all families) + slower ◄───────── 1.0 (parity) ─────────► faster +scalar ████████████▌ ...... 1.26× 79%🕐 ( 17 win / 9 lose) +1K ███████████▌ ....... 1.16× 86%🕐 ( 15 win / 11 lose) +100K ██████████▋ ........ 1.07× 94%🕐 ( 12 win / 14 lose) +1M ████████████▉ ...... 1.30× 77%🕐 ( 17 win / 9 lose) +10M ████████████▎ ...... 1.23× 82%🕐 ( 16 win / 10 lose) +ALL ███████████▉ ....... 1.20× 83%🕐 ( 77 win / 53 lose) + +OPERATIONS — BY CATEGORY (geomean over its families, all sizes) + slower ◄───────── 1.0 (parity) ─────────► faster +elementwise████████████▊ ...... 1.28× 78%🕐 ( 31 win / 9 lose) +reductions █████████████████▍ 1.74× 57%🕐 ( 29 win / 11 lose) +selection (no data) +copy/cast ███████▎ ........... 0.73× 138%🕐 ( 9 win / 16 lose) ◄ SLOWER +index-math ███████▋ ........... 0.77× 130%🕐 ( 4 win / 6 lose) ◄ SLOWER +dtypes ███████████▌ ....... 1.16× 86%🕐 ( 4 win / 11 lose) + +CATEGORY × TIER geomean +category scalar 1K 100K 1M 10M +elementwise 1.65× 1.54× 1.14× 1.10× 1.08× +reductions 2.68× 2.00× 1.51× 1.43× 1.38× +selection - - - - - +copy/cast 0.59× 0.53× 0.41× 1.40× 1.12× +index-math 0.34× 0.51× 0.99× 1.23× 1.26× +dtypes 0.70× 0.81× 1.87× 1.39× 1.44× + +PER-FAMILY × TIER (NumPy ÷ NumSharp; >1.0 = NumSharp faster) +family scalar 1K 100K 1M 10M geomean +-- elementwise + add 1.61× 1.43× 0.96× 1.00× 1.02× 1.18× + sqrt 1.67× 1.16× 1.01× 1.00× 1.01× 1.15× + copy 1.67× 2.35× 1.62× 1.39× 1.64× 1.71× + strided 1.65× 1.28× 0.93× 1.01× 0.99× 1.15× + bcast 1.68× 1.32× 0.89× 0.95× 0.95× 1.12× + reversed 1.65× 1.26× 0.93× 0.99× 0.95× 1.13× + castbuf 1.87× 2.20× 1.63× 1.36× 1.12× 1.59× + mixbuf 1.42× 1.72× 1.41× 1.20× 1.07× 1.35× +-- reductions + sum 1.92× 1.85× 2.58× 1.76× 1.60× 1.92× + sum ax0 1.71× 0.86× 1.10× 0.96× 0.96× 1.09× + sum ax1 1.81× 0.92× 1.52× 1.79× 1.58× 1.48× + sum dt= 1.89× 1.35× 0.48× 0.46× 0.54× 0.79× + amin 1.69× 1.62× 0.71× 0.71× 0.76× 1.01× + cumsum 1.35× 1.13× 1.07× 1.87× 1.65× 1.38× + any(F) 10.04× 8.39× 2.00× 1.23× 1.00× 2.90× + any(hit) 10.25× 8.49× 8.50× 7.88× 7.98× 8.58× +-- selection + where NA NA NA NA NA + a[mask] NA NA NA NA NA + a[mask]= NA NA NA NA NA + count_nz NA NA NA NA NA + argwhere NA NA NA NA NA + a[idx] NA NA NA NA NA + a[idx]= NA NA NA NA NA +-- copy/cast + flatten 0.41× 0.33× 0.17× 2.21× 1.13× 0.56× + astype 0.31× 0.53× 0.54× 1.94× 1.89× 0.80× + ravel.T 0.50× 0.58× 0.53× 2.22× 1.09× 0.82× + in-place 1.45× 0.77× 0.96× 1.03× 1.04× 1.03× + less->b 0.81× 0.52× 0.25× 0.55× 0.75× 0.53× +-- index-math + unravel 0.36× 0.50× 0.96× 1.00× 1.04× 0.71× + ravel_mi 0.32× 0.53× 1.01× 1.53× 1.54× 0.83× +-- dtypes + complex 0.71× 0.58× 0.97× 0.77× 0.93× 0.78× + float16 0.72× 0.64× 0.58× 0.56× 0.57× 0.61× + int8 0.66× 1.43× 11.53× 6.16× 5.66× 3.28× + +CONSTRUCTION — iterator build+dispose vs np.nditer (size-invariant, 1K) + slower ◄───────── 1.0 (parity) ─────────► faster +1op █████████▋ ......... 0.97× 103%🕐 ( 0 win / 1 lose) ◄ SLOWER +3op_exl ███████████████████▶ 2.44× 41%🕐 ( 1 win / 0 lose) +ufunc ███████████████████▶ 2.91× 34%🕐 ( 1 win / 0 lose) +bufcast ███████████████████▶ 2.56× 39%🕐 ( 1 win / 0 lose) +multiindex █████████████▉ ..... 1.40× 72%🕐 ( 1 win / 0 lose) +8op ███████████████████▶ 3.67× 27%🕐 ( 1 win / 0 lose) +4d █████████████████▊ 1.78× 56%🕐 ( 1 win / 0 lose) +8d ██████████████████▌ 1.85× 54%🕐 ( 1 win / 0 lose) +strided2d ███████████████████▌ 1.95× 51%🕐 ( 1 win / 0 lose) +geomean ███████████████████▶ 2.03× 49%🕐 ( 8 win / 1 lose) + +CHUNK-WIDTH dispatch — strided rows, 2M total, inner width w (NumPy = np.positive) + slower ◄───────── 1.0 (parity) ─────────► faster +w=4 ██████▉ ............ 0.69× 145%🕐 ( 0 win / 1 lose) ◄ SLOWER +w=16 █████████▊ ......... 0.98× 102%🕐 ( 0 win / 1 lose) ◄ PARITY +w=64 ██████████▏ ........ 1.02× 98%🕐 ( 1 win / 0 lose) ◄ PARITY +w=256 ██████████████▌ .... 1.45× 69%🕐 ( 1 win / 0 lose) +w=1024 █████████████ ...... 1.31× 76%🕐 ( 1 win / 0 lose) + +PATHOLOGY canaries — known taxes/losses to track (NumPy ÷ NumSharp) + bcast_reduce 522.42× (522.4× faster, faster) + allocate 1.06× (1.1× faster, faster) + overlap_copy 1.77× (1.8× faster, faster) + forder_out 1.17× (1.2× faster, faster) + zerodim 1.55× (1.6× faster, faster) + +DIVIDENDS — NumSharp-only machinery (NumPy baseline = closest it can do) + scalar 1K 100K 1M 10M note +fuse7 12.55× 3.86× 1.44× 1.67× 2.09× vs chained 6× add +reuse 6.06× 6.12× 1.07× 1.00× 1.00× vs rebuild each call +par8 - 0.67× 2.78× 3.95× 5.40× vs single-thread + +biggest NumSharp wins: i8@100K 11.53× · anyeh@1 10.25× · anyff@1 10.04× · anyeh@100K 8.50× · anyeh@1K 8.49× +most behind: flatten@100K 0.17× · lessbool@100K 0.25× · astype@1 0.31× · ravelmi@1 0.32× · flatten@1K 0.33× +``` diff --git a/benchmark/history/2026-06-29_2d16f477/nditer_results.tsv b/benchmark/history/2026-06-29_2d16f477/nditer_results.tsv new file mode 100644 index 00000000..5287796d --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/nditer_results.tsv @@ -0,0 +1,199 @@ +id ns_ms np_ms +add@1 0.00017091899999999998 0.00027511100051924586 +sqrt@1 0.000147608 0.0002462064987048507 +copy@1 0.00014795500000000002 0.00024708000011742115 +sadd@1 0.00016643999999999999 0.0002754010003991425 +bcast@1 0.000163277 0.0002747275005094707 +frev@1 0.0001479935 0.00024455050006508825 +castbuf@1 0.000306009 0.0005734744993969799 +mixbuf@1 0.000321592 0.0004560724995099008 +add@1K 0.00029071625000000003 0.00041510125156491996 +sqrt@1K 0.00071336625 0.0008306362491566688 +copy@1K 0.00020665499999999998 0.0004851775011047721 +sadd@1K 0.00048831875 0.0006255500018596649 +bcast@1K 0.00059921875 0.0007919212512206286 +frev@1K 0.00038528250000000004 0.0004862500005401671 +castbuf@1K 0.00048334374999999994 0.001063978747697547 +mixbuf@1K 0.00057059125 0.0009794600016903132 +add@100K 0.024144560000000002 0.02318923994898796 +sqrt@100K 0.055474760000000005 0.05583084002137184 +copy@100K 0.01193712 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"float64", + "n": 10000000, + "mean_ms": 7.449973989278078, + "stddev_ms": 0.26295293948002363, + "min_ms": 7.062999997287989, + "max_ms": 8.103900123387575, + "iterations": 50, + "ops_per_sec": 134.2286565616456, + "allocated_mb": 0.0 + } +] \ No newline at end of file diff --git a/benchmark/history/2026-06-29_2d16f477/operand_results.md b/benchmark/history/2026-06-29_2d16f477/operand_results.md new file mode 100644 index 00000000..c99072cf --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/operand_results.md @@ -0,0 +1,35 @@ +# Operand & broadcast layouts — 1-D / scalar / mixed-operand / broadcast (NumSharp vs NumPy 2.4.2) + +The layout classes the per-operand layout grid (benchmark/layout) can't express. ratio = NumPy_ms / NumSharp_ms — **>1.0 = NumSharp faster**. ✅≥1.0 🟡≥0.5 🟠≥0.2 🔴<0.2. 1M elements, best-of-3. + +| case | f64 | f32 | f16 | i32 | i64 | c128 | geomean | +|---|---|---|---|---|---|---|---| +| 1-D contiguous (a+a) | 2.41 ✅ | 2.16 ✅ | 0.63 🟡 | 2.13 ✅ | 2.45 ✅ | 2.35 ✅ | 1.85 ✅ | +| 1-D strided a[::2] | 1.84 ✅ | 1.46 ✅ | 0.53 🟡 | 1.51 ✅ | 1.81 ✅ | 1.93 ✅ | 1.40 ✅ | +| 1-D reversed a[::-1] | 2.49 ✅ | 1.95 ✅ | 0.56 🟡 | 2.14 ✅ | 2.29 ✅ | 2.22 ✅ | 1.76 ✅ | +| array + scalar | 2.72 ✅ | 1.93 ✅ | 0.63 🟡 | 1.91 ✅ | 2.26 ✅ | 2.64 ✅ | 1.83 ✅ | +| scalar + array | 2.40 ✅ | 1.98 ✅ | 0.64 🟡 | 2.01 ✅ | 2.19 ✅ | 2.69 ✅ | 1.82 ✅ | +| mixed C + F | 2.29 ✅ | 2.02 ✅ | 0.62 🟡 | 2.02 ✅ | 2.09 ✅ | 1.87 ✅ | 1.68 ✅ | +| mixed C + T | 2.53 ✅ | 2.04 ✅ | 0.62 🟡 | 2.01 ✅ | 2.33 ✅ | 2.30 ✅ | 1.80 ✅ | +| binary broadcast +row(1,C) | 2.71 ✅ | 2.09 ✅ | 0.63 🟡 | 1.95 ✅ | 2.52 ✅ | 2.58 ✅ | 1.89 ✅ | +| binary broadcast +col(R,1) | 2.61 ✅ | 2.02 ✅ | 0.56 🟡 | 2.07 ✅ | 2.54 ✅ | 2.96 ✅ | 1.89 ✅ | +| col-broadcast unary (inner stride-0) | 2.54 ✅ | 1.56 ✅ | 0.88 🟡 | 1.65 ✅ | 2.64 ✅ | 6.22 ✅ | 2.13 ✅ | + +**Worst 12 cells** + +| key | NumSharp ms | NumPy ms | ratio | +|---|---|---|---| +| 1d_strided|f16 | 2.6261 | 1.3926 | 0.53 🟡 | +| 1d_rev|f16 | 5.2838 | 2.9653 | 0.56 🟡 | +| bcast_col|f16 | 5.2887 | 2.9837 | 0.56 🟡 | +| mix_C_T|f16 | 5.3031 | 3.2873 | 0.62 🟡 | +| mix_C_F|f16 | 5.3153 | 3.3015 | 0.62 🟡 | +| 1d_C|f16 | 4.7456 | 2.9682 | 0.63 🟡 | +| bcast_row|f16 | 4.7695 | 2.9999 | 0.63 🟡 | +| scalar_rhs|f16 | 4.7058 | 2.9602 | 0.63 🟡 | +| scalar_lhs|f16 | 4.6383 | 2.9742 | 0.64 🟡 | +| colbcast_unary|f16 | 0.4753 | 0.4173 | 0.88 🟡 | +| 1d_strided|f32 | 0.2366 | 0.3444 | 1.46 ✅ | +| 1d_strided|i32 | 0.2433 | 0.3676 | 1.51 ✅ | + +_60 comparable cells._ diff --git a/benchmark/history/2026-06-29_2d16f477/operand_results.tsv b/benchmark/history/2026-06-29_2d16f477/operand_results.tsv new file mode 100644 index 00000000..5f4e50f8 --- /dev/null +++ b/benchmark/history/2026-06-29_2d16f477/operand_results.tsv @@ -0,0 +1,61 @@ +key ns_ms np_ms +1d_C|f64 0.5691166666666666 1.36976 +1d_strided|f64 0.3628766666666667 0.66686 +1d_rev|f64 0.5322366666666667 1.32495 +scalar_rhs|f64 0.4992766666666667 1.36048 +scalar_lhs|f64 0.5587166666666668 1.3394 +mix_C_F|f64 0.8186766666666667 1.8782 +mix_C_T|f64 0.70328 1.78032 +bcast_row|f64 0.52167 1.41286 +bcast_col|f64 0.5482766666666666 1.43244 +colbcast_unary|f64 0.5332166666666667 1.35224 +1d_C|f32 0.32621 0.703287 +1d_strided|f32 0.23663 0.344387 +1d_rev|f32 0.34344 0.66936 +scalar_rhs|f32 0.34775666666666666 0.670813 +scalar_lhs|f32 0.34694 0.68693 +mix_C_F|f32 0.50469 1.02077 +mix_C_T|f32 0.4891666666666667 0.999833 +bcast_row|f32 0.34182 0.714413 +bcast_col|f32 0.3460266666666667 0.699707 +colbcast_unary|f32 0.42733666666666664 0.666257 +1d_C|f16 4.7456466666666675 2.96824 +1d_strided|f16 2.6261066666666664 1.39256 +1d_rev|f16 5.283753333333333 2.96533 +scalar_rhs|f16 4.705803333333334 2.96017 +scalar_lhs|f16 4.638323333333333 2.97422 +mix_C_F|f16 5.315286666666666 3.30154 +mix_C_T|f16 5.303096666666666 3.28732 +bcast_row|f16 4.769466666666667 2.99994 +bcast_col|f16 5.288733333333334 2.9837 +colbcast_unary|f16 0.47533 0.41733 +1d_C|i32 0.33177333333333336 0.70638 +1d_strided|i32 0.24333333333333332 0.367587 +1d_rev|i32 0.33537 0.716103 +scalar_rhs|i32 0.34471999999999997 0.65918 +scalar_lhs|i32 0.33294333333333337 0.669623 +mix_C_F|i32 0.5219666666666667 1.05189 +mix_C_T|i32 0.5173633333333333 1.03758 +bcast_row|i32 0.36722333333333335 0.7161 +bcast_col|i32 0.34591999999999995 0.71619 +colbcast_unary|i32 0.4046333333333333 0.668973 +1d_C|i64 0.51168 1.2558 +1d_strided|i64 0.35958666666666667 0.65039 +1d_rev|i64 0.5528 1.26582 +scalar_rhs|i64 0.56057 1.26708 +scalar_lhs|i64 0.5831866666666666 1.27582 +mix_C_F|i64 0.8870866666666667 1.85743 +mix_C_T|i64 0.7374533333333333 1.71484 +bcast_row|i64 0.5335599999999999 1.34415 +bcast_col|i64 0.5419533333333334 1.37558 +colbcast_unary|i64 0.5066766666666667 1.3353 +1d_C|c128 1.1989666666666667 2.81701 +1d_strided|c128 0.7017766666666667 1.35608 +1d_rev|c128 1.2076566666666666 2.68437 +scalar_rhs|c128 1.0411299999999999 2.75122 +scalar_lhs|c128 1.03091 2.76844 +mix_C_F|c128 2.84676 5.31294 +mix_C_T|c128 1.7903333333333333 4.11381 +bcast_row|c128 1.13306 2.92596 +bcast_col|c128 1.14221 3.37681 +colbcast_unary|c128 0.7807066666666667 4.85264 diff --git a/benchmark/history/latest b/benchmark/history/latest index 5b47fd0b..69b0d2db 120000 --- a/benchmark/history/latest +++ b/benchmark/history/latest @@ -1 +1 @@ -2026-06-23_e3b7c268 \ No newline at end of file +2026-06-29_2d16f477 \ No newline at end of file From 68fec242220a29bd88be5b92136c501d26aa13b9 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Tue, 30 Jun 2026 07:36:42 +0300 Subject: [PATCH 04/24] fix(reductions,casts): normalize non-0/1 bool bytes everywhere bool is used numerically A boolean's numeric value is exactly 0 or 1 (NumPy: every nonzero counts as 1), but a bool buffer can legally hold non-0/1 bytes: np.frombuffer returns a zero-copy VIEW (like NumPy), and framework interop (Numpy.NET, P/Invoke, mmap, network) wraps foreign buffers. NumSharp aliased bool to uint8 storage in several SIMD/scalar fast paths and accumulated/reinterpreted the RAW byte (e.g. byte 255 contributed 255), diverging from NumPy across sum-family reductions and narrow casts. Four independent paths read the raw byte instead of normalizing nonzero->1: 1. Flat sum/prod/mean - DirectILKernelGenerator.cs::EmitConvertTo widened the raw byte for a Boolean source. Now normalizes (ldc.i4.0; cgt.un) before widening, mirroring the existing to==Boolean '!= 0'. Covers flat reductions and scans. 2. Axis sum/prod/mean - Reduction.Axis.Widening.cs aliased (Boolean,*) to the byte-widening SIMD kernel. Removed the 3 bool entries so bool falls through to the scalar reducer (CombineScalarsPromoted -> ConvertToInt64Bits/ConvertToDouble), which already normalizes via '!= 0'. 3. Flat var/std - Default.Reduction.Var.cs::VarMomentsRealDispatch read bool as byte. Added VarMomentsBool (reads byte != 0 ? 1.0 : 0.0). 4. Narrow casts astype(bool->i8/u8/i16/u16/f16) - the SIMD subword/widen/xToHalf cast kernels reinterpreted the raw byte (bool->wide already used the scalar normalizing path). TryGetCastKernel/TryGetStridedCastKernel now return null for a Boolean source, routing every bool cast to NDIterCasting.ConvertValue (normalizes via ReadAsInt64 'bool ? 1 : 0'). Also makes the dispatch match its own documented contract. Before/after (bytes [0,1,2,3,255,0,127,128] -> logical [F,T,T,T,T,F,T,T]): sum 516->6, mean 64.5->0.75, var 8033.75->0.1875, std 89.63->0.4330127 2D sum total 10->4, axis0 [4,6,0]->[2,2,0], axis1 [3,7]->[2,2] astype(u8) [0,1,2,3,255,..]->[0,1,1,1,1,0,1,1] Min/max/any/all/argmin/argmax over bool were already correct (result casts back to bool, so nonzero->True is preserved); proper 0/1 bool buffers are unaffected (normalization is idempotent). Verification (NumPy 2.4.2 as oracle): - New regression suite test/NumSharp.UnitTest/BoolNonBinaryReductionTests.cs: 19 tests (flat+axis sum/prod/mean/var/std, 5 narrow + 2 wide casts, 0/1 guard), green on net8.0 and net10.0. - Differential probes: 38/38 and 30/30 ops bit-equal to NumPy. - No regression: full suite 11166 pass / 0 fail; FuzzMatrix 69/69 corpora; 1290 reduction tests. Trade-off: bool->{i8,u8,i16,u16,f16} casts lose their SIMD subword fast path (now scalar). A perf-preserving alternative is a one-pass SIMD clamp min(byte,1) before the existing kernel; deferred. --- .../Math/Reduction/Default.Reduction.Var.cs | 26 +++- .../Direct/DirectILKernelGenerator.Cast.cs | 11 ++ ...KernelGenerator.Reduction.Axis.Widening.cs | 15 +- .../Kernels/Direct/DirectILKernelGenerator.cs | 13 ++ .../BoolNonBinaryReductionTests.cs | 129 ++++++++++++++++++ 5 files changed, 188 insertions(+), 6 deletions(-) create mode 100644 test/NumSharp.UnitTest/BoolNonBinaryReductionTests.cs diff --git a/src/NumSharp.Core/Backends/Default/Math/Reduction/Default.Reduction.Var.cs b/src/NumSharp.Core/Backends/Default/Math/Reduction/Default.Reduction.Var.cs index 555bd6c1..16accaa1 100644 --- a/src/NumSharp.Core/Backends/Default/Math/Reduction/Default.Reduction.Var.cs +++ b/src/NumSharp.Core/Backends/Default/Math/Reduction/Default.Reduction.Var.cs @@ -308,11 +308,35 @@ private static unsafe double VarMomentsReal(TIn* p, long[] dims, long[] str return sq / (n - ddof); } + ///

+ /// Two-pass variance over a strided bool buffer. A boolean's numeric value is 0 or 1, never + /// its raw storage byte, so each element is normalized via '!= 0' before accumulation — + /// matching NumPy, where var/std of a bool array operate on the {0,1} values even when the + /// underlying buffer (np.frombuffer view / interop) holds non-0/1 bytes. + /// + private static unsafe double VarMomentsBool(byte* p, long[] dims, long[] strides, int ndim, bool contig, long n, int ddof) + { + double sum = 0; + for (long i = 0; i < n; i++) + sum += p[contig ? i : FlatStrideOffset(i, dims, strides, ndim)] != 0 ? 1.0 : 0.0; + double mean = sum / n; + double sq = 0; + for (long i = 0; i < n; i++) + { + double v = (p[contig ? i : FlatStrideOffset(i, dims, strides, ndim)] != 0 ? 1.0 : 0.0) - mean; + sq += v * v; + } + return sq / (n - ddof); + } + /// Dispatch the real-typed strided two-pass on the input dtype (bool→byte, char→ushort). private static unsafe double VarMomentsRealDispatch(NPTypeCode tc, byte* basePtr, long[] dims, long[] strides, int ndim, bool contig, long n, int ddof) => tc switch { - NPTypeCode.Boolean => VarMomentsReal((byte*)basePtr, dims, strides, ndim, contig, n, ddof), + // bool is NOT byte here: its numeric value is 0/1, not the raw storage byte. A bool + // buffer may hold non-0/1 bytes (np.frombuffer view / interop); normalize via '!= 0' + // so var/std count True as 1 (NumPy parity). See VarMomentsBool. + NPTypeCode.Boolean => VarMomentsBool((byte*)basePtr, dims, strides, ndim, contig, n, ddof), NPTypeCode.Byte => VarMomentsReal((byte*)basePtr, dims, strides, ndim, contig, n, ddof), NPTypeCode.SByte => VarMomentsReal((sbyte*)basePtr, dims, strides, ndim, contig, n, ddof), NPTypeCode.Int16 => VarMomentsReal((short*)basePtr, dims, strides, ndim, contig, n, ddof), diff --git a/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Cast.cs b/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Cast.cs index 25b98ec9..7047611f 100644 --- a/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Cast.cs +++ b/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Cast.cs @@ -98,6 +98,13 @@ public unsafe delegate void StridedCastKernel( public static CastKernel TryGetCastKernel(NPTypeCode srcType, NPTypeCode dstType) { if (!Enabled) return null; + // Boolean SOURCE: a bool's numeric value is exactly 0 or 1, never its raw storage byte. The + // SIMD subword/widen/xToHalf paths below reinterpret the byte, which corrupts bool buffers + // holding non-0/1 bytes (np.frombuffer is a zero-copy VIEW; interop wraps foreign buffers) — + // e.g. bool[byte=255]->uint8 must be 1, not 255. Route every bool cast to the scalar + // NDIterCasting.ConvertValue path, which normalizes via '!= 0' (ReadAsInt64). bool->bool + // same-type copies never reach here (TryCopySameType handles them first). + if (srcType == NPTypeCode.Boolean) return null; // NumPy-faithful cvtt fast path for the common float->int32 downcast (beats NumPy). var floatToInt32 = TryGetFloatToInt32Kernel(srcType, dstType); if (floatToInt32 != null) return floatToInt32; @@ -163,6 +170,10 @@ public static CastKernel TryGetCastKernel(NPTypeCode srcType, NPTypeCode dstType public static StridedCastKernel TryGetStridedCastKernel(NPTypeCode srcType, NPTypeCode dstType) { if (!Enabled) return null; + // Boolean SOURCE: normalize via the scalar ConvertValue path (see TryGetCastKernel). The + // subword/widen strided kernels below reinterpret the raw byte and would corrupt non-0/1 + // bool buffers. bool->bool same-type strided copy is handled by TryCopySameType first. + if (srcType == NPTypeCode.Boolean) return null; // Same-type 1B/2B strided copy (the sub-word strided/negcol cliff): SIMD deinterleave // (ss==2) / reverse (ss==-1) / per-row memcpy (ss==1). Gated to src==dst & size {1,2}; // cross-type and 4/8/16-byte same-type fall through to the existing fast paths. diff --git a/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Reduction.Axis.Widening.cs b/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Reduction.Axis.Widening.cs index d9f11518..3d44f3f9 100644 --- a/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Reduction.Axis.Widening.cs +++ b/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Reduction.Axis.Widening.cs @@ -85,8 +85,16 @@ internal static unsafe AxisReductionKernel TryGetAxisReductionWideningKernel( if (!Avx2.IsSupported) return null; // Pair table — single source of truth. Alias routes reinterpret - // bit-identical pointers (char==ushort, bool==byte storage, and - // Int64 accumulators for zero-extended unsigned inputs). + // bit-identical pointers (char==ushort, and Int64 accumulators for + // zero-extended unsigned inputs). + // + // NOTE: Boolean is deliberately ABSENT. bool==byte storage is bit-identical + // only over {0,1}, but a bool buffer may legally hold non-0/1 bytes (np.frombuffer + // is a zero-copy view / interop wraps a foreign buffer), and sum/prod/mean must + // count each True as exactly 1 — not its raw byte. Bool therefore falls through to + // the scalar reducer (CombineScalarsPromoted -> ConvertToInt64Bits/ConvertToDouble), + // which normalizes via '!= 0'. (Min/Max over bool stay same-type and are handled + // by the byte-reinterpret SIMD path in the caller, where {0,1} ordering is exact.) return (key.InputType, key.AccumulatorType) switch { // ---- integer -> 64-bit integer accumulator ---- @@ -98,8 +106,6 @@ internal static unsafe AxisReductionKernel TryGetAxisReductionWideningKernel( (NPTypeCode.SByte, NPTypeCode.Int64) => MakeWideningKernel(op), (NPTypeCode.Byte, NPTypeCode.UInt64) => MakeWideningKernel(op), (NPTypeCode.Byte, NPTypeCode.Int64) => MakeWideningKernel(op), - (NPTypeCode.Boolean, NPTypeCode.Int64) => MakeWideningKernel(op), - (NPTypeCode.Boolean, NPTypeCode.UInt64) => MakeWideningKernel(op), (NPTypeCode.Int32, NPTypeCode.Int64) => MakeWideningKernel(op), (NPTypeCode.UInt32, NPTypeCode.UInt64) => MakeWideningKernel(op), (NPTypeCode.UInt32, NPTypeCode.Int64) => MakeWideningKernel(op), @@ -113,7 +119,6 @@ internal static unsafe AxisReductionKernel TryGetAxisReductionWideningKernel( (NPTypeCode.Char, NPTypeCode.Double) => MakeWideningKernel(op), (NPTypeCode.SByte, NPTypeCode.Double) => MakeWideningKernel(op), (NPTypeCode.Byte, NPTypeCode.Double) => MakeWideningKernel(op), - (NPTypeCode.Boolean, NPTypeCode.Double) => MakeWideningKernel(op), _ => null }; diff --git a/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.cs b/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.cs index c08a87c7..9c1b9209 100644 --- a/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.cs +++ b/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.cs @@ -883,6 +883,19 @@ internal static void EmitConvertTo(ILGenerator il, NPTypeCode from, NPTypeCode t if (from == to) return; // No conversion needed + // A Boolean's numeric value is exactly 0 or 1 — never its raw storage byte. A bool buffer + // can legally hold non-0/1 bytes (np.frombuffer is a zero-copy VIEW, like NumPy; interop + // wraps a foreign buffer), so normalize nonzero->1 BEFORE widening to any numeric type. + // Without this, sum/mean/var/std over such a buffer accumulate the raw bytes (e.g. byte 255 + // contributes 255) instead of counting True. Mirrors the to==Boolean '!= 0' below, applied + // to the source side. Idempotent for proper 0/1 bools (0->0, 1->1). + if (from == NPTypeCode.Boolean) // to != Boolean here (from == to already returned) + { + il.Emit(OpCodes.Ldc_I4_0); + il.Emit(OpCodes.Cgt_Un); // value = (value != 0) ? 1 : 0 + from = NPTypeCode.Byte; // continue the conversion from a clean 0/1 byte + } + // Special case: decimal conversions require method calls if (from == NPTypeCode.Decimal || to == NPTypeCode.Decimal) { diff --git a/test/NumSharp.UnitTest/BoolNonBinaryReductionTests.cs b/test/NumSharp.UnitTest/BoolNonBinaryReductionTests.cs new file mode 100644 index 00000000..eceb143f --- /dev/null +++ b/test/NumSharp.UnitTest/BoolNonBinaryReductionTests.cs @@ -0,0 +1,129 @@ +using Microsoft.VisualStudio.TestTools.UnitTesting; +using System; +using System.Linq; +using AwesomeAssertions; +using NumSharp; + +namespace NumSharp.UnitTest +{ + /// + /// Regression tests for boolean reductions over buffers holding NON-0/1 bytes. + /// + /// A boolean's numeric value is exactly 0 or 1 (NumPy: every nonzero counts as 1), but a bool + /// buffer can legally contain non-0/1 bytes — + /// returns a zero-copy VIEW (like NumPy), and framework interop wraps foreign buffers. NumSharp + /// used to sum the RAW storage bytes (e.g. byte 255 contributed 255) in three distinct paths: + /// 1. flat sum/prod/mean — IL EmitConvertTo widened the raw byte + /// (DirectILKernelGenerator.cs) + /// 2. axis sum/prod/mean — bool was aliased to byte in the widening-SIMD pair table + /// (DirectILKernelGenerator.Reduction.Axis.Widening.cs) + /// 3. flat var/std — VarMomentsRealDispatch read bool as byte + /// (Default.Reduction.Var.cs) + /// 4. narrow casts astype(bool->i8/u8/i16/u16/f16) — the SIMD subword/widen/xToHalf cast + /// kernels reinterpreted the raw byte (DirectILKernelGenerator.Cast.cs; fixed by routing + /// every bool-source cast to the scalar normalizing ConvertValue path). + /// The fix normalizes nonzero->1 at each conversion point (idempotent for proper 0/1 bools). + /// + /// All expected values were produced by NumPy 2.4.2: + /// + /// >>> b = np.frombuffer(bytes([0,1,2,3,255,0,127,128]), dtype=bool) # [F,T,T,T,T,F,T,T] + /// >>> b.sum(), b.mean(), b.var(), b.std() # 6, 0.75, 0.1875, 0.4330127018922193 + /// >>> b2 = np.frombuffer(bytes([1,2,0,3,4,0]), dtype=bool).reshape(2,3) # [[T,T,F],[T,T,F]] + /// >>> b2.sum(), b2.sum(0).tolist(), b2.sum(1).tolist() # 4, [2,2,0], [2,2] + /// >>> b2.prod(0).tolist(), [round(x,6) for x in b2.var(1)] # [1,1,0], [0.222222, 0.222222] + /// + /// + [TestClass] + public class BoolNonBinaryReductionTests : TestClass + { + // bytes [0,1,2,3,255,0,127,128] -> logical [F,T,T,T,T,F,T,T] (6 True) + private static NDArray B1D() => np.frombuffer(new byte[] { 0, 1, 2, 3, 255, 0, 127, 128 }, NPTypeCode.Boolean); + + // bytes [1,2,0,3,4,0] -> [[T,T,F],[T,T,F]] + private static NDArray B2D() => np.frombuffer(new byte[] { 1, 2, 0, 3, 4, 0 }, NPTypeCode.Boolean).reshape(2, 3); + + // ---- flat reductions ---- + + [TestMethod] + public void Sum1D_CountsTrue() + => np.sum(B1D()).GetInt64(0).Should().Be(6L); + + [TestMethod] + public void Mean1D() + => np.mean(B1D()).GetDouble(0).Should().BeApproximately(0.75, 1e-12); + + [TestMethod] + public void Var1D() + => np.var(B1D()).GetDouble(0).Should().BeApproximately(0.1875, 1e-12); + + [TestMethod] + public void Std1D() + => np.std(B1D()).GetDouble(0).Should().BeApproximately(0.4330127018922193, 1e-12); + + [TestMethod] + public void Prod1D_AllTrue_IsOne() + => np.prod(np.frombuffer(new byte[] { 1, 2, 3 }, NPTypeCode.Boolean)).GetInt64(0).Should().Be(1L); + + // ---- axis reductions ---- + + [TestMethod] + public void Sum2D_Total() + => np.sum(B2D()).GetInt64(0).Should().Be(4L); + + [TestMethod] + public void Sum2D_Axis0() + => np.sum(B2D(), 0).ToArray().Should().Equal(new long[] { 2, 2, 0 }); + + [TestMethod] + public void Sum2D_Axis1() + => np.sum(B2D(), 1).ToArray().Should().Equal(new long[] { 2, 2 }); + + [TestMethod] + public void Prod2D_Axis0() + => np.prod(B2D(), 0).ToArray().Should().Equal(new long[] { 1, 1, 0 }); + + [TestMethod] + public void Var2D_Axis1() + { + var v = np.var(B2D(), 1).ToArray(); + v.Should().HaveCount(2); + v[0].Should().BeApproximately(2.0 / 9.0, 1e-12); // var([1,1,0]) = 2/9 + v[1].Should().BeApproximately(2.0 / 9.0, 1e-12); + } + + [TestMethod] + public void Std2D_Axis1() + { + var s = np.std(B2D(), 1).ToArray(); + s.Should().HaveCount(2); + s[0].Should().BeApproximately(Math.Sqrt(2.0 / 9.0), 1e-12); + s[1].Should().BeApproximately(Math.Sqrt(2.0 / 9.0), 1e-12); + } + + // ---- casts: astype(bool -> T) must normalize non-0/1 bytes to 0/1 for EVERY target ---- + // (narrow targets i8/u8/i16/u16/f16 used to reinterpret the raw byte). Norm is the + // logical value of B1D(): [0,1,1,1,1,0,1,1]. + private static readonly long[] Norm = { 0, 1, 1, 1, 1, 0, 1, 1 }; + + [TestMethod] public void Astype_ToInt8() + => B1D().astype(NPTypeCode.SByte).astype(NPTypeCode.Int64).ToArray().Should().Equal(Norm); + [TestMethod] public void Astype_ToUInt8() + => B1D().astype(NPTypeCode.Byte).astype(NPTypeCode.Int64).ToArray().Should().Equal(Norm); + [TestMethod] public void Astype_ToInt16() + => B1D().astype(NPTypeCode.Int16).astype(NPTypeCode.Int64).ToArray().Should().Equal(Norm); + [TestMethod] public void Astype_ToUInt16() + => B1D().astype(NPTypeCode.UInt16).astype(NPTypeCode.Int64).ToArray().Should().Equal(Norm); + [TestMethod] public void Astype_ToHalf() + => B1D().astype(NPTypeCode.Half).astype(NPTypeCode.Double).ToArray().Should().Equal(new double[] { 0, 1, 1, 1, 1, 0, 1, 1 }); + [TestMethod] public void Astype_ToInt32_Wide() + => B1D().astype(NPTypeCode.Int32).astype(NPTypeCode.Int64).ToArray().Should().Equal(Norm); + [TestMethod] public void Astype_ToSingle_Wide() + => B1D().astype(NPTypeCode.Single).astype(NPTypeCode.Double).ToArray().Should().Equal(new double[] { 0, 1, 1, 1, 1, 0, 1, 1 }); + + // ---- regression guard: canonical 0/1 buffers must keep working ---- + + [TestMethod] + public void Sum_CanonicalBytes_Unchanged() + => np.sum(np.frombuffer(new byte[] { 0, 1, 1, 0, 1 }, NPTypeCode.Boolean)).GetInt64(0).Should().Be(3L); + } +} From 22589ad6bbb680d1d6ca3c5c6f00259b481c134f Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Tue, 30 Jun 2026 08:13:50 +0300 Subject: [PATCH 05/24] fix(dtype): make Char promote/compute as the uint16 masquerade (6 Char bugs + collected) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Char is NumSharp's representation of a 16-bit unsigned integer (System.Char / UTF-16 code unit) with no direct NumPy dtype — its only sound analogue is uint16, so Char MUST promote and compute bit-identically to uint16 everywhere. A differential-fuzz Char gate (generate every op as uint16 in NumPy 2.4.2, relabel uint16->char, assert bit-identical) surfaced six places where it did not. All expected values below were probed against NumPy 2.4.2 as uint16. Bug 1 — promote(Char, *) ranked Char in group 0 / priority 0 (with uint8/Byte), and the static arr_arr / arr_scalar promotion tables carried wrong Char rows (e.g. (char,uint8)->uint8). char[321] + uint8[65] returned dtype Byte / value 130 (386 truncated to a byte). Fixed every Char entry to mirror uint16's rank: arr_arr: (char,uint8)->char, (char,int8/int16)->int32, (char,float16)->float32 and the symmetric (uint8/int8/int16/float16, char) entries. arr_scalar: char ARRAY wins over every integer/bool scalar (-> char); (char,float16) scalar -> float32. char + uint8 now yields a 2-byte-unsigned result (value 386), not a truncated byte. Bug 1b — the same mis-rank computed comparisons at the truncated width: greater(char 321, uint8 65) returned False and equal returned True (both operands collapsed to 65). Fixed by the same table change (comparisons resolve their compare-width through result_type) -> greater True, equal False. Bug 2 — reciprocal(char) returned Double because IsInteger() deliberately excludes Char, so it fell to the float loop. NumPy reciprocal(uint16) takes the integer loop (1//x, 0 for |x|>=2). Admit Char to the integer branch and added a Char case to ReciprocalInteger (preserves Char, narrow-type 1/0 -> 0 sentinel). Bug 3 — power(char, float32) returned Double. Root cause was a power-promotion override `lhsGroup<=2 && rhsGroup==3 -> float64` that fired for EVERY int-base ** float-exp. That is a NumPy-1.x rule NEP50 removed: result_type already computes power promotion (uint16**f32->f32, int32**f32->f64, weak-int**f_arr->f_arr). Removed the override. Also fixes the broader pre-existing bug where {bool,int8,int16,uint8,uint16} ** float32 wrongly upcast to float64. Bug 4 — power crashed on a char scalar exponent: the scalar-exponent fast path read it via Convert.ToDouble(char), whose IConvertible.ToDouble throws InvalidCastException. Read the char code point directly (mirrors the existing Half special-case). power(uint16, 2) now returns uint16 [9,16]. Bug 5 — (Boolean, Char) was missing from the arr_scalar table, so a bool array op a char SCALAR threw KeyNotFoundException '(Boolean, Char)'. Not bitwise-specific — any binary op hit it (add too). Added (bool,char)->char. Bug 6 — invert(char) with N >= SIMD width threw NotSupportedException: the BitwiseNot SIMD path emitted Vector, which the BCL does not have (CanUseSimd(Char) is already false for the same reason). Excluded Char from the BitwiseNot SIMD eligibility gate; the scalar path computes ~x bit-exactly. Collected along the way: - AsNumpyDtypeName(Char) reported "uint8" (Char is 2 bytes) -> now "uint16"; graduated the [OpenBugs] T1_33 audit test that documented it. - The power-override removal also corrects the non-Char int**float32 promotions. - bool-array + char-scalar failed for ALL binary ops, not just bitwise. Verification: - Char≡uint16 differential in NumSharp (uint16 is NumPy-validated): 496 binary/unary op × dtype × order combos, 11 memory layouts (strided/reversed/transposed/broadcast/2D/axis), 36 astype combos — 0 diverged. - New CharUInt16MasqueradeTests.cs (26 tests) pins all six bugs with NumPy-probed values plus arr_arr/arr_scalar table differentials and memory-layout coverage. - Updated find_common_type Case23 (char+int16: Int16 -> Int32, matching uint16). - Full suite green: 11224 passed / 0 failed (net10.0, CI filter); FuzzMatrix NumPy-oracle gate green; net8.0 + net10.0 compile clean. --- .../Backends/Default/Math/Default.Power.cs | 5 + .../Default/Math/Default.Reciprocal.cs | 13 +- .../Default/Math/DefaultEngine.BinaryOp.cs | 38 +-- .../Direct/DirectILKernelGenerator.Unary.cs | 9 +- src/NumSharp.Core/Backends/NPTypeCode.cs | 4 +- .../Logic/np.find_common_type.cs | 33 +- .../AuditV2/AuditV2_CastingRandomUtilities.cs | 5 +- .../Casting/CharUInt16MasqueradeTests.cs | 287 ++++++++++++++++++ .../Logic/np.find_common_type.Test.cs | 5 +- 9 files changed, 350 insertions(+), 49 deletions(-) create mode 100644 test/NumSharp.UnitTest/Casting/CharUInt16MasqueradeTests.cs diff --git a/src/NumSharp.Core/Backends/Default/Math/Default.Power.cs b/src/NumSharp.Core/Backends/Default/Math/Default.Power.cs index deffd5af..c8fcf814 100644 --- a/src/NumSharp.Core/Backends/Default/Math/Default.Power.cs +++ b/src/NumSharp.Core/Backends/Default/Math/Default.Power.cs @@ -159,6 +159,11 @@ private static double ReadScalarAsDouble(NDArray nd) // (the scalar-exponent fast paths support Half — see ScalarEqualsExact). Cast it directly. if (v is Half h) return (double)h; + // char's IConvertible.ToDouble() throws InvalidCastException (bug 4: power with a + // char scalar exponent crashed here). Char is a 16-bit unsigned integer — read its + // code point directly. + if (v is char c) + return c; return Convert.ToDouble(v, System.Globalization.CultureInfo.InvariantCulture); } diff --git a/src/NumSharp.Core/Backends/Default/Math/Default.Reciprocal.cs b/src/NumSharp.Core/Backends/Default/Math/Default.Reciprocal.cs index e5298057..bc7e1e11 100644 --- a/src/NumSharp.Core/Backends/Default/Math/Default.Reciprocal.cs +++ b/src/NumSharp.Core/Backends/Default/Math/Default.Reciprocal.cs @@ -30,7 +30,10 @@ public override NDArray Reciprocal(NDArray nd, NPTypeCode? typeCode = null, NDAr if (loopType == NPTypeCode.Boolean) loopType = NPTypeCode.SByte; - if (loopType.IsInteger()) + // Char is a 16-bit unsigned integer (NumSharp's uint16 masquerade): NumPy + // reciprocal(uint16) takes the integer loop (1//x, 0 for |x|>=2), NOT a float + // loop. IsInteger() deliberately excludes Char, so admit it explicitly here. + if (loopType.IsInteger() || loopType == NPTypeCode.Char) { // Integer loop: C-truncating 1/x with the probed per-type 1/0 sentinel. // The hand loop is the compute path; a provided out/where gets a masked @@ -97,6 +100,14 @@ private static unsafe NDArray ReciprocalInteger(NDArray nd) for (long i = 0; i < n; i++) { var x = src[contig ? i : FlatStrideOffset(i, dims, strides, ndim)]; dst[i] = x == 0 ? (ushort)0 : (ushort)(1 / x); } break; } + case NPTypeCode.Char: + { + // Char == 16-bit unsigned: same narrow-type 1/0 -> 0 sentinel as UInt16. + var src = (char*)basePtr; + var dst = (char*)result.Address; + for (long i = 0; i < n; i++) { var x = src[contig ? i : FlatStrideOffset(i, dims, strides, ndim)]; dst[i] = x == 0 ? (char)0 : (char)(1 / x); } + break; + } case NPTypeCode.Int32: { var src = (int*)basePtr; diff --git a/src/NumSharp.Core/Backends/Default/Math/DefaultEngine.BinaryOp.cs b/src/NumSharp.Core/Backends/Default/Math/DefaultEngine.BinaryOp.cs index 22790dfe..752338ae 100644 --- a/src/NumSharp.Core/Backends/Default/Math/DefaultEngine.BinaryOp.cs +++ b/src/NumSharp.Core/Backends/Default/Math/DefaultEngine.BinaryOp.cs @@ -64,35 +64,17 @@ internal unsafe NDArray ExecuteBinaryOp(NDArray lhs, NDArray rhs, BinaryOp op, resultType = NPTypeCode.Double; } - // NumPy Power promotion (NEP50). Most cases are already handled by - // _FindCommonType (which applies weak/strict scalar rules correctly): - // - i32_arr ** i32_arr → int32 - // - i32_arr ** i64_arr → int64 - // - f32_arr ** f64_arr → float64 + // NumPy Power promotion (NEP50) is exactly result_type(base, exp) — which + // _FindCommonType already computes, including the int-base/float-exp cases: + // - i32_arr ** i32_arr → int32 - f32_arr ** f64_arr → float64 + // - i32_arr ** f32_arr → float64 - i16_arr ** f32_arr → float32 + // - u16_arr ** f32_arr → float32 (uint16 fits in float32, probed 2.4.2) // - f32_arr ** i32_arr → float64 (NEP50 strict) - // - f32_arr ** Python int (0-D weak) → float32 (NEP50 weak) - // - i32_arr ** f32_scalar (cross-array) → float64 (handled below) - // - // The one rule _FindCommonType doesn't cover is `int_scalar ** float_arr`: - // a 0-D int scalar is treated as "weak", which preserves the float's dtype, - // but NumPy's int-base + float-exp rule promotes unconditionally to float64. - // (Group 0=Byte/Char, 1=signed int, 2=unsigned int, 3=float, 4=decimal) - // - // Known limitation: explicit 0-D integer arrays (`np.array(2, int32)`) - // are indistinguishable from C# `int 2` after `np.asanyarray`. NumPy would - // strict-promote `f32_arr ** np.int32(2)` to float64; NumSharp preserves - // float32 for both `f32_arr ** 2` (correct) and `f32_arr ** np.array(2, int32)` - // (misaligned with NumPy but rare in idiomatic C# code). - if (op == BinaryOp.Power) - { - var lhsGroup = lhsType.GetGroup(); - var rhsGroup = rhsType.GetGroup(); - - if (lhsGroup <= 2 && rhsGroup == 3) - { - resultType = NPTypeCode.Double; - } - } + // - 2 (weak int) ** f32_arr → float32 - 2 ** f64_arr → float64 + // A prior override forced `lhsGroup<=2 && rhsGroup==3 → float64` for EVERY + // int-base ** float-exp, which wrongly upcast {bool,int8,int16,uint8,uint16, + // char} ** float32 to float64 (NumPy keeps float32) — a NumPy-1.x rule NEP50 + // removed. Removed: result_type (above) is the single source of truth. // ufunc dtype= (NumPy loop-signature override): the loop runs IN the // requested dtype — inputs are cast (same_kind-validated, NumPy's diff --git a/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Unary.cs b/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Unary.cs index 8f1dc3bd..11bfb30c 100644 --- a/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Unary.cs +++ b/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Unary.cs @@ -198,12 +198,17 @@ internal static bool CanUseUnarySimd(UnaryKernelKey key) if (!key.IsSameType) return false; - // BitwiseNot works for integer types only (and bool) + // BitwiseNot works for integer types only (and bool). Char is excluded: + // there is no Vector in the BCL (CanUseSimd(Char) is false for the same + // reason), so a Char invert with N>=vector-width crashed at kernel-compile time + // ("Specified type is not supported"). The scalar path handles Char bit-exactly + // (~x as 16-bit unsigned). Bug 6. if (key.Op == UnaryOp.BitwiseNot) { return key.InputType != NPTypeCode.Single && key.InputType != NPTypeCode.Double && - key.InputType != NPTypeCode.Decimal; + key.InputType != NPTypeCode.Decimal && + key.InputType != NPTypeCode.Char; } // LogicalNot is boolean-only, no SIMD (uses scalar comparison) diff --git a/src/NumSharp.Core/Backends/NPTypeCode.cs b/src/NumSharp.Core/Backends/NPTypeCode.cs index 25fb7d3d..e2ec86fd 100644 --- a/src/NumSharp.Core/Backends/NPTypeCode.cs +++ b/src/NumSharp.Core/Backends/NPTypeCode.cs @@ -558,7 +558,9 @@ internal static string AsNumpyDtypeName(this NPTypeCode typeCode) case NPTypeCode.Boolean: return "bool"; case NPTypeCode.Char: - return "uint8"; + // Char is a 16-bit unsigned integer (System.Char / UTF-16 code unit). + // NumSharp's closest NumPy analogue is uint16 — NOT uint8 (which is Byte). + return "uint16"; case NPTypeCode.SByte: return "int8"; case NPTypeCode.Byte: diff --git a/src/NumSharp.Core/Logic/np.find_common_type.cs b/src/NumSharp.Core/Logic/np.find_common_type.cs index cc086842..ca7cdd87 100644 --- a/src/NumSharp.Core/Logic/np.find_common_type.cs +++ b/src/NumSharp.Core/Logic/np.find_common_type.cs @@ -185,7 +185,7 @@ static np() typemap_arr_arr.Add((np.uint8, np.float64), np.float64); typemap_arr_arr.Add((np.uint8, np.complex128), np.complex128); typemap_arr_arr.Add((np.uint8, np.@decimal), np.@decimal); - typemap_arr_arr.Add((np.uint8, np.@char), np.uint8); + typemap_arr_arr.Add((np.uint8, np.@char), np.@char); // Char ranks as uint16: uint8+uint16 -> uint16 (label char) typemap_arr_arr.Add((np.uint8, np.int8), np.int16); typemap_arr_arr.Add((np.uint8, np.float16), np.float16); @@ -204,12 +204,12 @@ static np() typemap_arr_arr.Add((np.int8, np.float64), np.float64); typemap_arr_arr.Add((np.int8, np.complex128), np.complex128); typemap_arr_arr.Add((np.int8, np.@decimal), np.@decimal); - typemap_arr_arr.Add((np.int8, np.@char), np.int8); + typemap_arr_arr.Add((np.int8, np.@char), np.int32); // Char ranks as uint16: int8+uint16 -> int32 typemap_arr_arr.Add((np.@char, np.@char), np.@char); typemap_arr_arr.Add((np.@char, np.@bool), np.@char); - typemap_arr_arr.Add((np.@char, np.uint8), np.uint8); - typemap_arr_arr.Add((np.@char, np.int16), np.int16); + typemap_arr_arr.Add((np.@char, np.uint8), np.@char); // Char ranks as uint16: uint16+uint8 -> uint16 (label char) + typemap_arr_arr.Add((np.@char, np.int16), np.int32); // Char ranks as uint16: uint16+int16 -> int32 typemap_arr_arr.Add((np.@char, np.uint16), np.uint16); typemap_arr_arr.Add((np.@char, np.int32), np.int32); typemap_arr_arr.Add((np.@char, np.uint32), np.uint32); @@ -219,8 +219,8 @@ static np() typemap_arr_arr.Add((np.@char, np.float64), np.float64); typemap_arr_arr.Add((np.@char, np.complex128), np.complex128); typemap_arr_arr.Add((np.@char, np.@decimal), np.@decimal); - typemap_arr_arr.Add((np.@char, np.int8), np.int8); - typemap_arr_arr.Add((np.@char, np.float16), np.float16); + typemap_arr_arr.Add((np.@char, np.int8), np.int32); // Char ranks as uint16: uint16+int8 -> int32 + typemap_arr_arr.Add((np.@char, np.float16), np.float32); // Char ranks as uint16: uint16+float16 -> float32 typemap_arr_arr.Add((np.int16, np.@bool), np.int16); typemap_arr_arr.Add((np.int16, np.uint8), np.int16); @@ -234,7 +234,7 @@ static np() typemap_arr_arr.Add((np.int16, np.float64), np.float64); typemap_arr_arr.Add((np.int16, np.complex128), np.complex128); typemap_arr_arr.Add((np.int16, np.@decimal), np.@decimal); - typemap_arr_arr.Add((np.int16, np.@char), np.int16); + typemap_arr_arr.Add((np.int16, np.@char), np.int32); // Char ranks as uint16: int16+uint16 -> int32 typemap_arr_arr.Add((np.int16, np.int8), np.int16); typemap_arr_arr.Add((np.int16, np.float16), np.float32); @@ -349,7 +349,7 @@ static np() typemap_arr_arr.Add((np.float16, np.float64), np.float64); typemap_arr_arr.Add((np.float16, np.complex128), np.complex128); typemap_arr_arr.Add((np.float16, np.@decimal), np.@decimal); - typemap_arr_arr.Add((np.float16, np.@char), np.float16); + typemap_arr_arr.Add((np.float16, np.@char), np.float32); // Char ranks as uint16: float16+uint16 -> float32 typemap_arr_arr.Add((np.float64, np.@bool), np.float64); typemap_arr_arr.Add((np.float64, np.uint8), np.float64); @@ -451,6 +451,7 @@ static np() var typemap_arr_scalar = new Dictionary<(Type, Type), Type>(); typemap_arr_scalar.Add((np.@bool, np.@bool), np.@bool); typemap_arr_scalar.Add((np.@bool, np.uint8), np.uint8); + typemap_arr_scalar.Add((np.@bool, np.@char), np.@char); // bug 5: bool array + char scalar -> char (was missing -> KeyNotFoundException) typemap_arr_scalar.Add((np.@bool, np.int16), np.int16); typemap_arr_scalar.Add((np.@bool, np.uint16), np.uint16); typemap_arr_scalar.Add((np.@bool, np.int32), np.int32); @@ -495,20 +496,22 @@ static np() typemap_arr_scalar.Add((np.int8, np.complex128), np.complex128); typemap_arr_scalar.Add((np.int8, np.@decimal), np.int8); + // Char array behaves as a uint16 array: NEP50 array-wins over every + // integer/bool scalar (-> char), float/complex scalar promotes by kind. typemap_arr_scalar.Add((np.@char, np.@char), np.@char); typemap_arr_scalar.Add((np.@char, np.@bool), np.@char); typemap_arr_scalar.Add((np.@char, np.uint8), np.@char); - typemap_arr_scalar.Add((np.@char, np.int16), np.int16); - typemap_arr_scalar.Add((np.@char, np.uint16), np.uint16); - typemap_arr_scalar.Add((np.@char, np.int32), np.int32); - typemap_arr_scalar.Add((np.@char, np.uint32), np.uint32); - typemap_arr_scalar.Add((np.@char, np.int64), np.int64); - typemap_arr_scalar.Add((np.@char, np.uint64), np.uint64); + typemap_arr_scalar.Add((np.@char, np.int16), np.@char); + typemap_arr_scalar.Add((np.@char, np.uint16), np.@char); + typemap_arr_scalar.Add((np.@char, np.int32), np.@char); + typemap_arr_scalar.Add((np.@char, np.uint32), np.@char); + typemap_arr_scalar.Add((np.@char, np.int64), np.@char); + typemap_arr_scalar.Add((np.@char, np.uint64), np.@char); typemap_arr_scalar.Add((np.@char, np.float32), np.float32); typemap_arr_scalar.Add((np.@char, np.float64), np.float64); typemap_arr_scalar.Add((np.@char, np.complex128), np.complex128); typemap_arr_scalar.Add((np.@char, np.int8), np.@char); - typemap_arr_scalar.Add((np.@char, np.float16), np.float16); + typemap_arr_scalar.Add((np.@char, np.float16), np.float32); // uint16 array + float16 scalar -> float32 typemap_arr_scalar.Add((np.int16, np.@bool), np.int16); typemap_arr_scalar.Add((np.int16, np.uint8), np.int16); diff --git a/test/NumSharp.UnitTest/AuditV2/AuditV2_CastingRandomUtilities.cs b/test/NumSharp.UnitTest/AuditV2/AuditV2_CastingRandomUtilities.cs index 6aedc010..866a4f74 100644 --- a/test/NumSharp.UnitTest/AuditV2/AuditV2_CastingRandomUtilities.cs +++ b/test/NumSharp.UnitTest/AuditV2/AuditV2_CastingRandomUtilities.cs @@ -155,8 +155,9 @@ public void T1_32_Modf_TupleElementNameTypo() // Char (System.Char, 2-byte UTF-16 unit) is mapped to "uint8" — the same // string Byte returns. Any consumer doing dtype-name interop will see // an 8-bit announcement for what is actually a 16-bit storage. + // FIXED: Char now maps to "uint16" (its 16-bit-unsigned masquerade). // ----------------------------------------------------------------------- - [TestMethod, OpenBugs(IssueUrl = "audit-v2-T1.33")] + [TestMethod] public void T1_33_AsNumpyDtypeName_Char_MisreportsSize() { var mi = typeof(NPTypeCodeExtensions).GetMethod( @@ -174,6 +175,8 @@ public void T1_33_AsNumpyDtypeName_Char_MisreportsSize() "Char (2 bytes) and Byte (1 byte) must not share the same numpy dtype name"); charName.Should().NotBe("uint8", "Char is 2 bytes; closest NumPy analogue is 'uint16' or 'U1' (unicode), never 'uint8'"); + charName.Should().Be("uint16", + "Char is NumSharp's 16-bit-unsigned masquerade -> uint16"); } // ----------------------------------------------------------------------- diff --git a/test/NumSharp.UnitTest/Casting/CharUInt16MasqueradeTests.cs b/test/NumSharp.UnitTest/Casting/CharUInt16MasqueradeTests.cs new file mode 100644 index 00000000..5625f1d1 --- /dev/null +++ b/test/NumSharp.UnitTest/Casting/CharUInt16MasqueradeTests.cs @@ -0,0 +1,287 @@ +using System; +using System.Linq; +using AwesomeAssertions; +using Microsoft.VisualStudio.TestTools.UnitTesting; +using NumSharp; +using NumSharp.Backends; + +namespace NumSharp.UnitTest.Casting +{ + /// + /// Char is NumSharp's representation of a 16-bit unsigned integer (System.Char / + /// UTF-16 code unit). It has NO direct NumPy dtype; its closest analogue is uint16, + /// so Char MUST promote and compute bit-identically to uint16 everywhere. + /// + /// These tests pin the six Char≡uint16 bugs that the differential-fuzz Char gate + /// surfaced (all expected values probed against NumPy 2.4.2 as uint16), plus the + /// broader bugs found alongside them. The 14×op×order sweep at the bottom proves the + /// masquerade holds against the already-NumPy-validated uint16 path. + /// + [TestClass] + public class CharUInt16MasqueradeTests : TestClass + { + // ---- helpers --------------------------------------------------------- + private static NDArray Chars(params int[] xs) => np.array(xs.Select(v => (char)v).ToArray()); + private static NDArray U16(params int[] xs) => np.array(xs.Select(v => (ushort)v).ToArray()); + + /// Flatten + read any integer/char result as long[] (Convert.ToInt64(char) throws, so unbox char by hand). + private static long[] Ints(NDArray r) + { + r = r.flatten(); + var o = new long[r.size]; + for (int i = 0; i < r.size; i++) + { + var v = r.GetValue(i); + o[i] = v is char c ? c : Convert.ToInt64(v); + } + return o; + } + + private static double[] Doubles(NDArray r) + { + r = r.flatten(); + var o = new double[r.size]; + for (int i = 0; i < r.size; i++) + { + var v = r.GetValue(i); + o[i] = v is char c ? c : Convert.ToDouble(v); + } + return o; + } + + /// Char and UInt16 are the same dtype for the masquerade. + private static NPTypeCode Norm(NPTypeCode t) => t == NPTypeCode.Char ? NPTypeCode.UInt16 : t; + + // ===================================================================== + // Bug 1 — promote(Char, *) ranked Char below uint8 and truncated the high byte. + // char[321] + uint8[65] gave dtype Byte / value 130 (386 mod 256). + // NumPy: np.array([321],uint16) + np.array([65],uint8) -> uint16, 386. + // ===================================================================== + [TestMethod] + public void Bug1_CharPlusByte_PromotesToUInt16Width_NoTruncation() + { + var r = Chars(321) + np.array(new byte[] { 65 }); + Norm(r.typecode).Should().Be(NPTypeCode.UInt16, "char + uint8 promotes like uint16 + uint8 -> uint16"); + Ints(r).Should().BeEquivalentTo(new long[] { 386 }, "386 must NOT truncate to a byte (130)"); + } + + [TestMethod] + public void Bug1_CharPlusByte_BitIdenticalToUInt16PlusByte() + { + var rc = Chars(321) + np.array(new byte[] { 65 }); + var ru = U16(321) + np.array(new byte[] { 65 }); + Norm(rc.typecode).Should().Be(Norm(ru.typecode)); + Ints(rc).Should().BeEquivalentTo(Ints(ru)); + } + + // ===================================================================== + // Bug 1b — the same mis-rank corrupted comparisons (compared at the truncated width). + // greater(char 321, uint8 65) gave False; equal gave True (321 & 65 both -> 65). + // NumPy: greater -> True, equal -> False, less -> False. + // ===================================================================== + [TestMethod] + public void Bug1b_Comparisons_UseFullUInt16Width() + { + var a = Chars(321); + var b = np.array(new byte[] { 65 }); + np.greater(a, b).GetBoolean(0).Should().BeTrue("321 > 65"); + np.equal(a, b).GetBoolean(0).Should().BeFalse("321 != 65 (no high-byte truncation)"); + np.less(a, b).GetBoolean(0).Should().BeFalse("321 is not < 65"); + } + + // ===================================================================== + // Bug 2 — reciprocal(char) returned Double (Char not seen as integer). + // NumPy: reciprocal(uint16) -> uint16 with C-truncating 1//x (0 for |x|>=2, 1 for 1). + // ===================================================================== + [TestMethod] + public void Bug2_Reciprocal_Char_TakesIntegerLoop() + { + var r = np.reciprocal(Chars(2, 3, 5)); + Norm(r.typecode).Should().Be(NPTypeCode.UInt16, "reciprocal(uint16) stays integer, not float"); + Ints(r).Should().BeEquivalentTo(new long[] { 0, 0, 0 }); + + var r1 = np.reciprocal(Chars(1)); + Norm(r1.typecode).Should().Be(NPTypeCode.UInt16); + Ints(r1).Should().BeEquivalentTo(new long[] { 1 }); + } + + // ===================================================================== + // Bug 3 — power(char, float32) returned Double (Char mis-ranked above float32). + // NumPy: power(uint16, float32) -> float32. The override that caused this also + // wrongly upcast EVERY {bool,int8,int16,uint8,uint16} ** float32 to float64. + // ===================================================================== + [TestMethod] + public void Bug3_Power_CharBase_Float32Exp_IsFloat32() + { + var r = np.power(Chars(3), np.array(new float[] { 2f })); + r.typecode.Should().Be(NPTypeCode.Single, "power(uint16, float32) -> float32, not float64"); + Doubles(r).Should().BeEquivalentTo(new double[] { 9.0 }); + } + + [DataTestMethod] + // probed NumPy 2.4.2: small ints fit in float32, int32+ need float64 + [DataRow(NPTypeCode.SByte, NPTypeCode.Single)] + [DataRow(NPTypeCode.Int16, NPTypeCode.Single)] + [DataRow(NPTypeCode.Byte, NPTypeCode.Single)] + [DataRow(NPTypeCode.UInt16, NPTypeCode.Single)] + [DataRow(NPTypeCode.Int32, NPTypeCode.Double)] + [DataRow(NPTypeCode.UInt32, NPTypeCode.Double)] + [DataRow(NPTypeCode.Int64, NPTypeCode.Double)] + public void Bug3b_Power_IntBase_Float32Exp_FollowsResultType(NPTypeCode baseType, NPTypeCode expected) + { + // Collected bug: the removed power override forced float64 for ALL int-base ** float-exp. + var baseArr = np.array(new int[] { 2 }).astype(baseType); + var r = np.power(baseArr, np.array(new float[] { 3f })); + r.typecode.Should().Be(expected); + Doubles(r).Should().BeEquivalentTo(new double[] { 8.0 }); + } + + // ===================================================================== + // Bug 4 — power crashed on a char scalar exponent (Convert.ToDouble(char) throws + // InvalidCastException in the scalar-exponent fast path). + // NumPy: power(uint16, 2) -> uint16 [9, 16]. + // ===================================================================== + [TestMethod] + public void Bug4_Power_CharScalarExponent_DoesNotThrow() + { + var baseArr = Chars(3, 4); + + var rChar = np.power(baseArr, NDArray.Scalar((char)2)); // char scalar (the crash) + var rInt = np.power(baseArr, 2); // int scalar + var rArr = np.power(baseArr, Chars(2, 2)); // char array exponent + + foreach (var r in new[] { rChar, rInt, rArr }) + { + Norm(r.typecode).Should().Be(NPTypeCode.UInt16); + Ints(r).Should().BeEquivalentTo(new long[] { 9, 16 }); + } + } + + // ===================================================================== + // Bug 5 — (Boolean, Char) was missing from the arr_scalar promotion table, so a + // bool array op a char SCALAR threw KeyNotFoundException '(Boolean, Char)'. + // Not bitwise-specific — any binary op hit it. NumPy(bool, uint16): and->[1,0], + // or->[5,7], xor->[4,7], add->[6,5]. + // ===================================================================== + [TestMethod] + public void Bug5_BoolArray_CharScalar_DoesNotThrow() + { + var b = np.array(new bool[] { true, false }); + var s = NDArray.Scalar((char)5); + + Ints(np.bitwise_and(b, s)).Should().BeEquivalentTo(new long[] { 1, 0 }); + Ints(np.bitwise_or(b, s)).Should().BeEquivalentTo(new long[] { 5, 5 }); + Ints(np.bitwise_xor(b, s)).Should().BeEquivalentTo(new long[] { 4, 5 }); + Ints(np.add(b, s)).Should().BeEquivalentTo(new long[] { 6, 5 }); + + Norm(np.bitwise_and(b, s).typecode).Should().Be(NPTypeCode.UInt16); + } + + [TestMethod] + public void Bug5_BoolArray_CharArray_BitwiseMatchesUInt16() + { + var b = np.array(new bool[] { true, false }); + Ints(np.bitwise_and(b, Chars(5, 7))).Should().BeEquivalentTo(new long[] { 1, 0 }); + Ints(np.bitwise_or(b, Chars(5, 7))).Should().BeEquivalentTo(new long[] { 5, 7 }); + Ints(np.bitwise_xor(b, Chars(5, 7))).Should().BeEquivalentTo(new long[] { 4, 7 }); + } + + // ===================================================================== + // Bug 6 — invert(char) with N >= SIMD width threw NotSupportedException: the + // BitwiseNot SIMD path emitted Vector, which does not exist. N<16 (scalar + // path) worked. NumPy: invert(uint16) -> uint16 ones-complement at every size. + // ===================================================================== + [DataTestMethod] + [DataRow(1)] + [DataRow(3)] + [DataRow(15)] // last scalar-path size on V256 + [DataRow(16)] // first SIMD-width size (the crash) + [DataRow(17)] + [DataRow(64)] // multiple vectors + tail + public void Bug6_Invert_Char_AllSizes(int n) + { + var vals = Enumerable.Range(0, n).Select(i => i * 7 % 65536).ToArray(); + var r = np.invert(Chars(vals)); + Norm(r.typecode).Should().Be(NPTypeCode.UInt16); + var expected = vals.Select(v => (long)(ushort)~(ushort)v).ToArray(); + Ints(r).Should().BeEquivalentTo(expected, "invert is ~x as 16-bit unsigned"); + } + + [TestMethod] + public void Bug6_Invert_Char_MatchesUInt16_AcrossSimdBoundary() + { + var vals = Enumerable.Range(0, 40).Select(i => i * 1000 % 65536).ToArray(); + Ints(np.invert(Chars(vals))).Should().BeEquivalentTo(Ints(np.invert(U16(vals)))); + } + + // ===================================================================== + // Collected: AsNumpyDtypeName(Char) reported "uint8" (Char is 2 bytes -> uint16). + // ===================================================================== + [TestMethod] + public void Collected_AsNumpyDtypeName_Char_IsUInt16() + { + NPTypeCode.Char.AsNumpyDtypeName().Should().Be("uint16"); + NPTypeCode.Char.AsNumpyDtypeName().Should().NotBe(NPTypeCode.Byte.AsNumpyDtypeName()); + } + + // ===================================================================== + // Differential: Char promotes identically to uint16 in BOTH promotion tables. + // uint16 is already validated against NumPy, so this proves the masquerade. + // ===================================================================== + private static readonly NPTypeCode[] AllTypes = + { + NPTypeCode.Boolean, NPTypeCode.Byte, NPTypeCode.SByte, NPTypeCode.Int16, NPTypeCode.UInt16, + NPTypeCode.Int32, NPTypeCode.UInt32, NPTypeCode.Int64, NPTypeCode.UInt64, NPTypeCode.Char, + NPTypeCode.Half, NPTypeCode.Single, NPTypeCode.Double, NPTypeCode.Decimal, NPTypeCode.Complex + }; + + [TestMethod] + public void Differential_ArrArrPromotion_CharEqualsUInt16() + { + foreach (var x in AllTypes) + { + Norm(np._FindCommonArrayType(NPTypeCode.Char, x)) + .Should().Be(Norm(np._FindCommonArrayType(NPTypeCode.UInt16, x)), $"char⊗{x} == uint16⊗{x}"); + Norm(np._FindCommonArrayType(x, NPTypeCode.Char)) + .Should().Be(Norm(np._FindCommonArrayType(x, NPTypeCode.UInt16)), $"{x}⊗char == {x}⊗uint16"); + } + } + + [TestMethod] + public void Differential_ArrScalarPromotion_CharEqualsUInt16() + { + foreach (var x in AllTypes) + { + // char ARRAY vs scalar X == uint16 ARRAY vs scalar X + Norm(np._FindCommonArrayScalarType(NPTypeCode.Char, x)) + .Should().Be(Norm(np._FindCommonArrayScalarType(NPTypeCode.UInt16, x)), $"char-array ⊗ {x}-scalar"); + // X ARRAY vs char SCALAR == X ARRAY vs uint16 SCALAR (this is where (bool,char) was missing) + Norm(np._FindCommonArrayScalarType(x, NPTypeCode.Char)) + .Should().Be(Norm(np._FindCommonArrayScalarType(x, NPTypeCode.UInt16)), $"{x}-array ⊗ char-scalar"); + } + } + + // ===================================================================== + // Memory-layout (DOD): strided / reversed / broadcast / axis char ops match uint16. + // ===================================================================== + [TestMethod] + public void Layout_StridedReversedBroadcast_CharMatchesUInt16() + { + int[] d = { 10, 20, 30, 40, 50, 60 }; + + Ints(np.invert(Chars(d)["::2"])).Should().BeEquivalentTo(Ints(np.invert(U16(d)["::2"]))); + Ints(np.reciprocal(Chars(d)["::-1"])).Should().BeEquivalentTo(Ints(np.reciprocal(U16(d)["::-1"]))); + Ints((Chars(d)["1::2"] * np.array(new byte[] { 2, 2, 2 }))) + .Should().BeEquivalentTo(Ints((U16(d)["1::2"] * np.array(new byte[] { 2, 2, 2 })))); + + // broadcast (2,1) + (1,3) + var cb = Chars(1, 2).reshape(2, 1) + Chars(10, 20, 30).reshape(1, 3); + var ub = U16(1, 2).reshape(2, 1) + U16(10, 20, 30).reshape(1, 3); + Ints(cb).Should().BeEquivalentTo(Ints(ub)); + + // axis reductions on a 2-D char view + Ints(np.sum(Chars(d).reshape(2, 3), 0)).Should().BeEquivalentTo(Ints(np.sum(U16(d).reshape(2, 3), 0))); + Ints(np.max(Chars(d).reshape(2, 3), 1)).Should().BeEquivalentTo(Ints(np.max(U16(d).reshape(2, 3), 1))); + } + } +} diff --git a/test/NumSharp.UnitTest/Logic/np.find_common_type.Test.cs b/test/NumSharp.UnitTest/Logic/np.find_common_type.Test.cs index b36a0267..c11bed3f 100644 --- a/test/NumSharp.UnitTest/Logic/np.find_common_type.Test.cs +++ b/test/NumSharp.UnitTest/Logic/np.find_common_type.Test.cs @@ -178,8 +178,11 @@ public void Case22() [TestMethod] public void Case23() { + // Char is NumSharp's 16-bit-unsigned (uint16) masquerade: char + int16 promotes + // exactly like uint16 + int16 -> int32 (int16 cannot hold the uint16 range). + // Probed NumPy 2.4.2: np.result_type(np.uint16, np.int16) == int32. var r = np.find_common_type(new[] {np.@char, np.int16}, new NPTypeCode[0]); - r.Should().Be(NPTypeCode.Int16); + r.Should().Be(NPTypeCode.Int32); } /// From 9c8873f3ce65bc4542b68201cba858282b1bc798 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Tue, 30 Jun 2026 09:28:49 +0300 Subject: [PATCH 06/24] test(openbugs): reproduce NEP50 out-of-range python-scalar gap (collected during Char work) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Collected while making Char promote as the uint16 masquerade — the Char≡uint16 differential vs NumPy 2.4.2 surfaced it, but it is NOT Char-specific: it affects every integer dtype. NumPy 2.x (NEP50) folds a Python int operand into the array's dtype but FIRST range-checks it, raising OverflowError before any element-wise work when the value is not representable: np.array([1,2],uint16) + 70000 -> OverflowError (70000 > 65535) np.array([1,2],uint16) * -1 -> OverflowError (-1 < 0, unsigned) np.array([1],int8) + 200 -> OverflowError (200 > 127) np.power(np.array([2,3],uint16),-1) -> OverflowError (-1 out of uint16 range) In-range scalars whose RESULT overflows wrap fine (uint16[1,2]-5 -> [65532,65533]). NumSharp promotes a C# scalar purely TYPE-based (the arr_scalar table), so it silently coerces/wraps the value instead of range-checking it: uint16[1,2] + 70000 -> [4465,4466] uint16[1,2] * -1 -> [65535,65534] int8[1] + 200 -> wraps power(uint16[2,3],-1) -> [0,43691] The power case is doubly wrong: no throw AND a nonsense modular inverse (43691 == 3^-1 mod 2^16). Inconsistency proving this is a gap, not a design choice: NumSharp's OWN fused path already enforces it — np.evaluate throws the exact OverflowException (NDExpr.Typing.cs "Python integer {value} out of bounds for {dtype}"). Only the operator / ufunc path skips the check. Adds 5 [OpenBugs] reproductions (asserting the correct OverflowError; failing today, excluded from CI via TestCategory!=OpenBugs). Remove [OpenBugs] when the operator/ ufunc weak-scalar path range-checks the value like np.evaluate already does. --- .../OpenBugs.ScalarOverflow.cs | 102 ++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 test/NumSharp.UnitTest/OpenBugs.ScalarOverflow.cs diff --git a/test/NumSharp.UnitTest/OpenBugs.ScalarOverflow.cs b/test/NumSharp.UnitTest/OpenBugs.ScalarOverflow.cs new file mode 100644 index 00000000..8855a447 --- /dev/null +++ b/test/NumSharp.UnitTest/OpenBugs.ScalarOverflow.cs @@ -0,0 +1,102 @@ +using System; +using System.Linq; +using AwesomeAssertions; +using Microsoft.VisualStudio.TestTools.UnitTesting; +using NumSharp; + +namespace NumSharp.UnitTest +{ + // ===================================================================== + // OPEN BUG — NEP50 out-of-range python-int scalar is NOT range-checked + // ===================================================================== + // + // Collected while making Char promote as the uint16 masquerade (the Char≡uint16 + // differential surfaced it; it is NOT Char-specific — it affects every integer + // dtype). NumPy 2.x (NEP50) treats a Python int operand as "weak" and folds it + // into the array's dtype, but FIRST checks it is representable in that dtype: + // an out-of-range value raises OverflowError BEFORE any element-wise work. + // + // >>> np.array([1,2], np.uint16) + 70000 # 70000 > 65535 + // OverflowError: Python integer 70000 out of bounds for uint16 + // >>> np.array([1,2], np.uint16) * -1 # -1 < 0 (unsigned) + // OverflowError: Python integer -1 out of bounds for uint16 + // >>> np.array([1], np.int8) + 200 # 200 > 127 + // OverflowError: Python integer 200 out of bounds for int8 + // >>> np.power(np.array([2,3], np.uint16), -1) + // OverflowError: Python integer -1 out of bounds for uint16 + // + // In-range scalars whose RESULT overflows are fine and wrap (probed 2.4.2): + // >>> np.array([1,2], np.uint16) - 5 # 5 fits; result wraps + // array([65532, 65533], dtype=uint16) + // >>> np.array([1], np.int8) + 100 # 100 fits + // array([101], dtype=int8) + // + // NumSharp's promotion of a C# scalar is purely TYPE-based (the arr_scalar + // table), so it silently coerces/wraps the value instead of range-checking it: + // uint16[1,2] + 70000 -> [4465,4466] (70000 & 0xFFFF == 4464, +1/+2) + // uint16[1,2] * -1 -> [65535,65534] + // int8[1] + 200 -> wraps, no throw + // power(uint16[2,3],-1)-> [0, 43691] (43691 is the modular inverse of 3!) + // + // Inconsistency proving this is a gap, not a deliberate design choice: NumSharp's + // OWN fused path ALREADY enforces the rule — np.evaluate throws the exact + // OverflowException (NDExpr.Typing.cs: "Python integer {value} out of bounds for + // {dtype}"). Only the operator / ufunc path skips the check. + // + // EXPECTED (when fixed): each op below throws OverflowException (NumPy's + // OverflowError), matching np.evaluate. Remove [OpenBugs] when fixed. + // ===================================================================== + public partial class OpenBugs + { + private static NDArray CharArr(params int[] xs) => np.array(xs.Select(v => (char)v).ToArray()); + + [TestMethod, OpenBugs(IssueUrl = "nep50-scalar-out-of-range")] + public void ScalarOverflow_UInt16_PlusTooLargeInt_ShouldRaise() + { + // NumPy: uint16 array + 70000 -> OverflowError (70000 > 65535). + Action act = () => { var _ = np.array(new ushort[] { 1, 2 }) + 70000; }; + act.Should().Throw( + "a Python int outside the uint16 range must raise, not wrap to [4465,4466]"); + } + + [TestMethod, OpenBugs(IssueUrl = "nep50-scalar-out-of-range")] + public void ScalarOverflow_Char_PlusTooLargeInt_ShouldRaise() + { + // Char is the uint16 masquerade -> same rule as uint16. + Action act = () => { var _ = CharArr(1, 2) + 70000; }; + act.Should().Throw(); + } + + [TestMethod, OpenBugs(IssueUrl = "nep50-scalar-out-of-range")] + public void ScalarOverflow_Unsigned_TimesNegativeOne_ShouldRaise() + { + // NumPy: uint16 array * -1 -> OverflowError (-1 < 0 for unsigned). + Action actU16 = () => { var _ = np.array(new ushort[] { 1, 2 }) * -1; }; + actU16.Should().Throw("-1 is out of range for an unsigned dtype"); + + Action actByte = () => { var _ = np.array(new byte[] { 1, 2 }) + (-1); }; + actByte.Should().Throw("-1 is out of range for uint8"); + } + + [TestMethod, OpenBugs(IssueUrl = "nep50-scalar-out-of-range")] + public void ScalarOverflow_Signed_OutOfRangeInt_ShouldRaise() + { + // NumPy: int8 array + 200 -> OverflowError (200 > 127); int16 + 40000 -> OverflowError. + Action actI8 = () => { var _ = np.array(new sbyte[] { 1 }) + 200; }; + actI8.Should().Throw("200 is out of range for int8"); + + Action actI16 = () => { var _ = np.array(new short[] { 1 }) + 40000; }; + actI16.Should().Throw("40000 is out of range for int16"); + } + + [TestMethod, OpenBugs(IssueUrl = "nep50-scalar-out-of-range")] + public void ScalarOverflow_Power_UnsignedBase_NegativeIntExponent_ShouldRaise() + { + // NumPy: power(uint16, -1) -> OverflowError (-1 out of uint16 range), checked + // BEFORE the negative-exponent rule. NumSharp instead computes a modular + // inverse ([0, 43691]) — doubly wrong (no throw AND a nonsense value). + Action act = () => { var _ = np.power(CharArr(2, 3), -1); }; + act.Should().Throw(); + } + } +} From 7a975d3ed551a50edaca087eff552449df553677 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Tue, 30 Jun 2026 09:47:27 +0300 Subject: [PATCH 07/24] test(fuzz): expand differential-fuzz grids to all 15 dtypes & all ops (Char woven, Decimal oracle) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Extend the NumPy differential-fuzz corpora to cover the full NumSharp dtype matrix across every op, instead of the 13 NumPy-representable dtypes on a curated subset of ops. The two NumPy-orphan dtypes (Char, Decimal) are now first-class grid members, and the per-mode dtype axes are widened toward ALL_DTYPES. Net corpus growth: the op corpus went 35.5K -> 53.3K cases (+16.3K, ~46%) plus 234 new Decimal cases; committed corpus ~68K. The full FuzzMatrix gate is green (72/72), CI-style. WHAT CHANGED 1. Char WOVEN into every tier (was excluded — no NumPy char dtype). - gen_oracle.char_tier(mode): generates each Char op through the uint16 NumPy proxy and relabels uint16->char (bytes intact), appending the cases into the SAME tier file as their native kin (binary_arith/unary/bitwise/comparison/reduce/scan/stat/ manip/sort/tail/astype_full). The existing per-tier [FuzzMatrix] tests now assert NumSharp's Char === uint16. 3,726 Char cases woven, all green. - Replaces the earlier bolt-on `char` mode + char.jsonl + FuzzCorpusTests.Char. 2. Native dtype subsets widened to ALL_DTYPES where the op is type-general: SORT/STAT/SCAN/MANIP/TAIL/PARAM/CNZ/NAN_REDUCE/LOGIC/ALIAS/COPYTO (CLIP keeps every ordered dtype; TAIL/ALIAS exclude bool — gen subtracts, NumPy bans bool `-`). 3. Decimal differential coverage via an INDEPENDENT C# oracle (no NumPy analog): - test/oracle/gen_decimal_oracle.cs computes every expected value with naive scalar System.Decimal arithmetic (NOT NumSharp kernels) and emits the harness JSONL schema -> decimal_{unary,binary,reduce,scan}.jsonl (234 cases: 4 unary, 8 binary arith + 6 comparison, 5 reductions, 2 scans, over 13 single + 7 pair layouts). - BitDiff tokenizes Decimal by canonical VALUE (scale-insensitive: 1.0m === 1.00m), since there is no NumPy decimal scale to match. ALL GREEN — no decimal kernel bug. HARNESS - FuzzCorpus.DtypeToTC: map "char" and "decimal". - OpRegistry.ApplyArgsort: wire Boolean/Decimal/Complex (were harness gaps, now green). - FuzzCorpusTests: 4 Decimal* tiers; Char-woven note. BUGS FOUND (carved out of the green corpus, reproduced under [OpenBugs], CI-excluded; remove the carve + test when fixed): OpenBugs.Char.cs (OpenBugsCharTests): - promote(Char,Byte) -> Byte: Char ranks below uint8 in the promotion table, so Char x uint8 truncates the Char's high byte. Corrupts arithmetic (char[321]+uint8[65] -> Byte 130, not uint16 386) AND comparisons (greater(321,65) -> False, equal -> True). - reciprocal(char) -> Double (should be uint16/char integer reciprocal). - power(char, float32) -> Double (should be float32; Char mis-ranked above float32). - power(char, ...) scalar path -> InvalidCastException (kernel calls Convert.To*(char)). - bitwise_*(bool, char) -> KeyNotFoundException '(Boolean, Char)' (unregistered kernel). - invert(char) N>=16 -> NotSupportedException (Vector256 path omits Char; N<=15 scalar path works). OpenBugs.DtypeCoverage.cs (OpenBugsDtypeCoverageTests): - clip(bool) on non-contiguous (strided/transposed/F-contig) -> NotSupportedException (contiguous path handles bool; the general clip kernel omits it). NON-BUGS (classified, documented carves — NOT OpenBugs): - complex self-multiply ULP: NumSharp matches NumPy's SCALAR z*z exactly; NumPy's own array ufunc disagrees by ~1 ULP on a catastrophic-cancellation _cbase value. Carved complex from ALIAS (ill-conditioned input, not a bug). - argsort/ "not supported" was an OpRegistry wiring gap, now fixed -> green. Docs: Fuzz/README.md ledger + .claude/CLAUDE.md updated (corpus counts, char_tier, gen_decimal_oracle.cs, value-aware decimal compare, [OpenBugs] dispositions). --- .claude/CLAUDE.md | 15 +- test/NumSharp.UnitTest/Fuzz/BitDiff.cs | 12 + test/NumSharp.UnitTest/Fuzz/FuzzCorpus.cs | 9 +- .../NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs | 31 + test/NumSharp.UnitTest/Fuzz/OpRegistry.cs | 4 + test/NumSharp.UnitTest/Fuzz/README.md | 37 +- .../Fuzz/corpus/aliasing.jsonl | 128 +- .../Fuzz/corpus/astype_full.jsonl | 676 ++ .../Fuzz/corpus/binary_arith.jsonl | 252 + .../Fuzz/corpus/binary_divmod_power.jsonl | 126 + .../Fuzz/corpus/bitwise.jsonl | 83 + .../Fuzz/corpus/comparison.jsonl | 270 + .../Fuzz/corpus/copyto.jsonl | 168 +- .../Fuzz/corpus/decimal_binary.jsonl | 98 + .../Fuzz/corpus/decimal_reduce.jsonl | 60 + .../Fuzz/corpus/decimal_scan.jsonl | 24 + .../Fuzz/corpus/decimal_unary.jsonl | 52 + .../NumSharp.UnitTest/Fuzz/corpus/logic.jsonl | 1482 ++- .../NumSharp.UnitTest/Fuzz/corpus/manip.jsonl | 6780 ++++++++++--- .../Fuzz/corpus/nanreduce.jsonl | 8384 +++++++++++++---- .../Fuzz/corpus/params.jsonl | 1496 ++- .../Fuzz/corpus/reduce.jsonl | 622 ++ test/NumSharp.UnitTest/Fuzz/corpus/scan.jsonl | 1484 +-- test/NumSharp.UnitTest/Fuzz/corpus/sort.jsonl | 119 +- test/NumSharp.UnitTest/Fuzz/corpus/stat.jsonl | 6548 ++++++++----- test/NumSharp.UnitTest/Fuzz/corpus/tail.jsonl | 3240 +++++-- .../NumSharp.UnitTest/Fuzz/corpus/unary.jsonl | 364 + .../Fuzz/corpus/unary_extra.jsonl | 364 + test/NumSharp.UnitTest/OpenBugs.Char.cs | 184 + .../OpenBugs.DtypeCoverage.cs | 53 + test/oracle/gen_decimal_oracle.cs | 317 + test/oracle/gen_oracle.py | 144 +- 32 files changed, 25449 insertions(+), 8177 deletions(-) create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_binary.jsonl create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_reduce.jsonl create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_scan.jsonl create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_unary.jsonl create mode 100644 test/NumSharp.UnitTest/OpenBugs.Char.cs create mode 100644 test/NumSharp.UnitTest/OpenBugs.DtypeCoverage.cs create mode 100644 test/oracle/gen_decimal_oracle.cs diff --git a/.claude/CLAUDE.md b/.claude/CLAUDE.md index 4fc7672c..754a84de 100644 --- a/.claude/CLAUDE.md +++ b/.claude/CLAUDE.md @@ -553,24 +553,29 @@ Proves every NDIter-backed op is **bit-identical** to NumPy 2.4.2 across the inp ``` test/oracle/ corpus generators (NumPy 2.4.2 — run by hand / nightly soak) layout_catalog.py memory-layout builders (the "44 variations": 26 single + 9 pair + 5 where) - gen_oracle.py deterministic op matrices (astype/binary/unary/reduce/where/… — ~90 ops) + gen_oracle.py deterministic op matrices (astype/binary/unary/reduce/where/… — ~90 ops); + per-mode dtype axes widened to ALL_DTYPES; Char woven into every tier + via the uint16 proxy (char_tier, relabelled uint16->char) + gen_decimal_oracle.cs INDEPENDENT C# oracle for Decimal (no NumPy analog): naive scalar + System.Decimal math -> decimal_{unary,binary,reduce,scan}.jsonl gen_index_oracle.py getter/setter index oracle (token index over 15 base recipes) fuzz_random.py seeded random fuzzer (imports the other two) test/NumSharp.UnitTest/Fuzz/ C# replay harness (no Python) FuzzCorpus.cs rebuilds EXACT NDArray views from (dtype,shape,strides,offset,bytes) OpRegistry.cs op-name → NumSharp call (pairs 1:1 with gen_oracle.py) - BitDiff.cs / Shrinker.cs bit-exact compare (NaN tokenized) / shrink a failure to 1 element + BitDiff.cs / Shrinker.cs bit-exact compare (NaN tokenized; Decimal by canonical VALUE so 1.0m≡1.00m) / shrink a failure to 1 element MisalignedRegistry.cs the documented, excused divergences (intended differences and excused cases) FuzzCorpusTests.cs one [FuzzMatrix] test per op-corpus file (checks dtype + shape + bytes + error parity) IndexOracleTests.cs index get/set differential gate (curated + dtype + seeded-random tiers) MetamorphicTests.cs oracle-free invariants (round-trips / involutions / identities — no NumPy) HarnessSelfTests.cs proves the harness has teeth (BitDiff detects value/NaN/-0 diffs; non-vacuous) - corpus/*.jsonl committed corpus (~51K cases / 28 tiers), copied to test output via the csproj glob + corpus/*.jsonl committed corpus (~68K cases / 32 tiers; op corpus ~53K incl. 3.7K Char woven + 234 Decimal), copied to test output via the csproj glob ``` - **Generators live in `test/oracle/`** and write the corpus into `test/NumSharp.UnitTest/Fuzz/corpus/` (path resolved relative to `test/oracle/`). CI replays the committed corpus, never the generators. -- **Three `FuzzMatrix` gates**: `FuzzCorpusTests` (the op corpus — ~40K cases / 25 tiers, checking dtype + shape + bytes + error parity), `IndexOracleTests` (the index oracle — `index_curated` 2,265 + `index_dtype` 104 + `index_random` 10,000; the advanced-indexing parity gate), and `MetamorphicTests` (12 NumPy-free invariants). A failing op case auto-shrinks to a 1-element repro. -- **Regenerate** (deterministic; needs `numpy==2.4.2`): `python test/oracle/gen_oracle.py ` (modes: `smoke astype_full binary divmod_power comparison unary reduce where place matmul bitwise unary_extra nanreduce scan stat logic modf manip sort tail params aliasing copyto errors`) + `python test/oracle/gen_index_oracle.py` (the `index_*` tiers) + `python test/oracle/fuzz_random.py 1234 2000 random_smoke.jsonl`, then `dotnet build` (copies the corpus to test output). +- **Three `FuzzMatrix` gates**: `FuzzCorpusTests` (the op corpus — ~53K cases across the tiers, checking dtype + shape + bytes + error parity; Char woven into every tier, 4 `Decimal*` tiers), `IndexOracleTests` (the index oracle — `index_curated` 2,265 + `index_dtype` 104 + `index_random` 10,000; the advanced-indexing parity gate), and `MetamorphicTests` (12 NumPy-free invariants). A failing op case auto-shrinks to a 1-element repro. +- **Dtype coverage**: per-mode dtype axes widened toward `ALL_DTYPES`. **Char** (no NumPy dtype) is woven into every tier via the uint16 proxy (`gen_oracle.char_tier`, relabelled uint16→char). **Decimal** (no NumPy analog) rides an independent C# oracle (`gen_decimal_oracle.cs`, naive scalar `System.Decimal`). Verified Char/clip-bool bugs are carved from the green corpus and reproduced under `[OpenBugs]` (`OpenBugs.Char.cs`, `OpenBugs.DtypeCoverage.cs`) — NOT excused in `MisalignedRegistry`. +- **Regenerate** (deterministic; needs `numpy==2.4.2`): `python test/oracle/gen_oracle.py ` (modes: `smoke astype_full binary divmod_power comparison unary reduce where place matmul bitwise unary_extra nanreduce scan stat logic modf manip sort tail params aliasing copyto errors`) + `python test/oracle/gen_index_oracle.py` (the `index_*` tiers) + `python test/oracle/fuzz_random.py 1234 2000 random_smoke.jsonl` + `dotnet run test/oracle/gen_decimal_oracle.cs` (the `decimal_*` tiers), then `dotnet build` (copies the corpus to test output). - **Run the gate**: `dotnet test --filter "TestCategory=FuzzMatrix"`. Each case is bit-exact (pass), a documented difference in `MisalignedRegistry` (excused, never silent), or a failure (red). Full divergence ledger: `test/NumSharp.UnitTest/Fuzz/README.md`. ## CI Pipeline diff --git a/test/NumSharp.UnitTest/Fuzz/BitDiff.cs b/test/NumSharp.UnitTest/Fuzz/BitDiff.cs index 9c75c819..3573af70 100644 --- a/test/NumSharp.UnitTest/Fuzz/BitDiff.cs +++ b/test/NumSharp.UnitTest/Fuzz/BitDiff.cs @@ -1,5 +1,7 @@ using System; using System.Collections.Generic; +using System.Globalization; +using System.Runtime.InteropServices; using System.Text; using NumSharp; @@ -65,6 +67,16 @@ private static string Token(byte[] b, int off, NPTypeCode tc) string m = double.IsNaN(im) ? "NaN" : Hex(b, off + 8, 8); return r + ":" + m; } + case NPTypeCode.Decimal: + { + // Decimal has no NumPy analog, so SCALE is not part of the contract — 1.0m and + // 1.00m are the same VALUE with different bytes. Tokenize by canonical value + // (trailing zeros stripped) so the gate flags wrong VALUES, not benign scale. + decimal d = MemoryMarshal.Read(b.AsSpan(off, 16)); + string s = d.ToString(CultureInfo.InvariantCulture); + if (s.IndexOf('.') >= 0) s = s.TrimEnd('0').TrimEnd('.'); + return s == "-0" ? "0" : s; + } default: return Hex(b, off, tc.SizeOf()); } diff --git a/test/NumSharp.UnitTest/Fuzz/FuzzCorpus.cs b/test/NumSharp.UnitTest/Fuzz/FuzzCorpus.cs index 7c07bdc6..9154cd88 100644 --- a/test/NumSharp.UnitTest/Fuzz/FuzzCorpus.cs +++ b/test/NumSharp.UnitTest/Fuzz/FuzzCorpus.cs @@ -74,7 +74,10 @@ public static List Load(string fileName) return list; } - // -- dtype token <-> NPTypeCode (13 NumPy-representable types; Char/Decimal have no NumPy analog) -- + // -- dtype token <-> NPTypeCode. 13 NumPy-representable types + "char": NumSharp's Char + // is bit-identical to uint16, so the oracle emits Char cases as uint16-proxy bytes + // relabelled "uint16"->"char" (gen_oracle._relabel_dtype). Decimal still has no NumPy + // analog (it rides a separate C#-decimal oracle, not this NumPy differential corpus). public static NPTypeCode DtypeToTC(string name) => name switch { "bool" => NPTypeCode.Boolean, @@ -86,10 +89,14 @@ public static List Load(string fileName) "uint32" => NPTypeCode.UInt32, "int64" => NPTypeCode.Int64, "uint64" => NPTypeCode.UInt64, + "char" => NPTypeCode.Char, "float16" => NPTypeCode.Half, "float32" => NPTypeCode.Single, "float64" => NPTypeCode.Double, "complex128" => NPTypeCode.Complex, + // Decimal: no NumPy analog — its cases come from the independent C# oracle + // (gen_decimal_oracle.cs), not the NumPy corpus. SliceFromBytes already handles it. + "decimal" => NPTypeCode.Decimal, _ => throw new NotSupportedException($"dtype '{name}' has no NumSharp NPTypeCode mapping") }; diff --git a/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs b/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs index 71bcb1ea..2118f94a 100644 --- a/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs +++ b/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs @@ -58,6 +58,14 @@ public class FuzzCorpusTests [TestCategory("FuzzMatrix")] public void Bitwise() => RunCorpus("bitwise.jsonl"); + // Char dtype (NumSharp-only, bit-identical to uint16) is WOVEN into every tier above: + // each Char op is generated through the uint16 NumPy proxy and relabelled uint16->char + // (gen_oracle.char_tier), appended into its native tier file (binary_arith/unary/bitwise/ + // comparison/reduce/scan/stat/manip/sort/tail/astype_full). So the existing per-tier gates + // assert NumSharp's Char ≡ uint16. The verified Char bugs (promote(Char,Byte)->Byte, + // reciprocal/power/invert) are carved out of the green corpus and reproduced in + // OpenBugs.Char.cs (class OpenBugsCharTests) under [OpenBugs]. + // W3 unary stragglers: exp2/expm1/log2/log10/log1p/sinh/cosh/tanh/arcsin/arccos/arctan/ // deg2rad/rad2deg/positive across all 13 dtypes and all 25 single-array layouts. [TestMethod] @@ -164,6 +172,29 @@ public void FuzzRegression() [TestCategory("FuzzMatrix")] public void Binary_DivModPower() => RunCorpus("binary_divmod_power.jsonl"); + // Decimal — the ONE NumSharp numeric dtype with no NumPy analog (System.Decimal: 16-byte, + // base-10). NumPy cannot be the oracle, so these corpora are generated by an INDEPENDENT C# + // oracle (test/oracle/gen_decimal_oracle.cs) that computes every expected value with naive + // scalar System.Decimal arithmetic — NO NumSharp kernels. The replay runs the same operand + // through NumSharp's decimal KERNELS and value-compares (BitDiff tokenizes decimal by + // canonical value, so benign scale 1.0m≡1.00m doesn't false-fail). A divergence is a real + // decimal kernel bug (strided/broadcast/reduction/scan iteration or accumulation). + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalUnary() => RunCorpus("decimal_unary.jsonl"); + + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalBinary() => RunCorpus("decimal_binary.jsonl"); + + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalReduce() => RunCorpus("decimal_reduce.jsonl"); + + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalScan() => RunCorpus("decimal_scan.jsonl"); + private static void RunCorpus(string file) { var cases = FuzzCorpus.Load(file); diff --git a/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs b/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs index ac6d091d..20660a72 100644 --- a/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs +++ b/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs @@ -196,9 +196,13 @@ public static NDArray Apply(string op, IReadOnlyDictionary NPTypeCode.UInt32 => np.argsort(a, axis), NPTypeCode.Int64 => np.argsort(a, axis), NPTypeCode.UInt64 => np.argsort(a, axis), + NPTypeCode.Char => np.argsort(a, axis), NPTypeCode.Single => np.argsort(a, axis), NPTypeCode.Double => np.argsort(a, axis), NPTypeCode.Half => np.argsort(a, axis), + NPTypeCode.Boolean => np.argsort(a, axis), + NPTypeCode.Decimal => np.argsort(a, axis), + NPTypeCode.Complex => np.argsort(a, axis), _ => throw new NotSupportedException($"argsort<{a.typecode}> not wired in OpRegistry") }; diff --git a/test/NumSharp.UnitTest/Fuzz/README.md b/test/NumSharp.UnitTest/Fuzz/README.md index f111211a..eacefb2c 100644 --- a/test/NumSharp.UnitTest/Fuzz/README.md +++ b/test/NumSharp.UnitTest/Fuzz/README.md @@ -12,11 +12,16 @@ NumPy is the oracle. Python (`test/oracle/`) generates a **committed, bytes-exac ``` test/oracle/ corpus generators (NumPy 2.4.2) layout_catalog.py the layout builders (single-array, pairwise, where-triple) - gen_oracle.py deterministic matrices (astype/binary/comparison/unary/reduce/where/place) + gen_oracle.py deterministic matrices (astype/binary/comparison/unary/reduce/where/place); + per-mode dtype axes widened to ALL_DTYPES; Char WOVEN into every tier + via the uint16 proxy (char_tier) — relabelled uint16->char, bytes intact + gen_decimal_oracle.cs INDEPENDENT C# oracle for Decimal (no NumPy analog): naive scalar + System.Decimal math -> decimal_{unary,binary,reduce,scan}.jsonl fuzz_random.py seeded random fuzzer (NumSharp-producible layouts) test/NumSharp.UnitTest/Fuzz/ FuzzCorpus.cs reconstructs EXACT NDArray views from (dtype,shape,strides,offset,bytes) - BitDiff.cs bit-exact compare; NaN tokenized; ULP helpers (for documented near-misses) + BitDiff.cs bit-exact compare; NaN tokenized; Decimal tokenized by canonical VALUE + (scale-insensitive: 1.0m == 1.00m); ULP helpers (documented near-misses) OpRegistry.cs op-name -> NumSharp call MisalignedRegistry.cs the explicit, documented set of intended/known divergences Shrinker.cs minimizes a failing element-wise case to a 1-element repro @@ -40,8 +45,13 @@ python test/oracle/gen_oracle.py where # np.where(cond,x,y) python test/oracle/gen_oracle.py place # np.place(arr,mask,vals) python test/oracle/gen_oracle.py matmul # T8 linalg: matmul/dot/outer (gufunc shapes, C/F layouts) python test/oracle/fuzz_random.py 1234 2000 random_smoke.jsonl +dotnet run test/oracle/gen_decimal_oracle.cs # Decimal tiers (independent C# System.Decimal oracle) ``` +Char rides every NumPy mode automatically (`char_tier` appends uint16-proxy cases relabelled to +`char`); there is no separate `char` mode. Decimal is the one dtype with no NumPy analog, so it has +its own C# generator (the last line above) rather than a `gen_oracle.py` mode. + Then `dotnet build` (copies the corpus to output) and run: ```bash @@ -72,5 +82,24 @@ for fix (remove the classifier branch + corpus tag when fixed). | bool arithmetic (`True+True -> 2`), size-1 result collapse | known bug | #12 | | ops vs raw NumPy stride/offset representation (offset!=0, junk size-1 strides) | unreachable via API | #11 | -**Scope:** `Char` and `Decimal` have no NumPy analog and are excluded from the differential corpus -(covered by the `Converts`-oracle cast tests). +### Char & Decimal dtype coverage (the two NumPy-orphan dtypes) + +Both are now exercised across the grids (previously excluded). Verified bugs are NOT excused in +`MisalignedRegistry` — each is carved out of the green corpus and reproduced under `[OpenBugs]` +(remove the carve + test when fixed): + +| dtype | op / combo | NumSharp | NumPy/value truth | Repro | +|-------|-----------|----------|-------------------|-------| +| Char | `promote(Char, Byte)` → Byte (truncates high byte) — corrupts arithmetic AND comparisons | Byte / wrong bool | uint16 (Char≡uint16) | `OpenBugsCharTests.Char_Add_Byte_*`, `Char_Compare_Byte_*` | +| Char | `reciprocal(char)` | Double | uint16 | `Char_Reciprocal_ReturnsDouble` | +| Char | `power(char, float32)` | Double | float32 | `Char_Power_Single_ReturnsDouble` | +| Char | `power(char, …)` scalar path | InvalidCastException (`Convert.To*(char)`) | computes | `Char_Power_ScalarChar_Crashes` | +| Char | `bitwise_*(bool, char)` | KeyNotFoundException `(Boolean,Char)` | uint16 | `Char_BitwiseAnd_Bool_KeyNotFound` | +| Char | `invert(char)` N≥16 (SIMD path) | NotSupportedException | uint16 | `Char_Invert_LargeN_NotSupported` | +| bool | `clip(bool)` non-contiguous (strided/transposed/F) | NotSupportedException | bool (contiguous works) | `OpenBugsDtypeCoverageTests.Clip_Bool_*` | + +`Decimal` rides an **independent C# oracle** (`gen_decimal_oracle.cs`, naive scalar `System.Decimal`) +across unary/binary/reduce/scan × 13 single + 7 pair layouts — **all green** (no decimal kernel bug +found). Two non-bugs were classified and carved (not OpenBugs): the complex self-multiply ULP +(NumSharp matches NumPy *scalar*; NumPy's own array ufunc disagrees on a catastrophic-cancellation +input) and `argsort/` "not supported" (a harness `OpRegistry` gap, now wired → green). diff --git a/test/NumSharp.UnitTest/Fuzz/corpus/aliasing.jsonl b/test/NumSharp.UnitTest/Fuzz/corpus/aliasing.jsonl index 2f62b8c1..eb9dcefb 100644 --- a/test/NumSharp.UnitTest/Fuzz/corpus/aliasing.jsonl +++ b/test/NumSharp.UnitTest/Fuzz/corpus/aliasing.jsonl @@ -1,40 +1,88 @@ 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b/test/NumSharp.UnitTest/Fuzz/corpus/unary.jsonl @@ -4912,3 +4912,367 @@ 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NumSharp documents Char as a 2-byte + /// UNSIGNED value, bit-identical to uint16 (see CLAUDE.md "Supported Types"), so the + /// CORRECT behavior of every Char op is exactly NumPy's uint16 behavior — verified against + /// NumPy 2.4.2 on 2026-06-30 and quoted per test. + /// + /// These combinations are CARVED OUT of the green corpus (they would otherwise red the + /// woven tiers) and reproduced here under [OpenBugs]. Each test asserts the CORRECT + /// (uint16-equivalent) result, so it FAILS while the bug exists and PASSES when fixed — + /// at which point re-add the carved pair/op in gen_oracle.char_tier and delete the test. + /// + /// Root causes (six distinct bugs): + /// 1. promote(Char, Byte) -> Byte — Char ranks BELOW uint8 in the promotion table, so + /// Char×uint8 truncates the Char's high byte. Corrupts arithmetic AND comparisons. + /// 2. reciprocal(Char) -> Double — Char not recognised as an integer dtype. + /// 3. power(Char, Single) -> Double — Char mis-ranked ABOVE float32 (inconsistent with #1). + /// 4. power(Char, …) scalar path crashes — kernel routes through Convert.To*(char), which + /// throws InvalidCastException for System.Char. + /// 5. bitwise_*(bool, Char) -> KeyNotFoundException — (Boolean, Char) mixed-type kernel + /// pair is unregistered. + /// 6. invert(Char) with N>=16 -> NotSupportedException — the SIMD Vector256<ushort> + /// bitwise-not path omits Char (N<=15 scalar path works; size-dependent). + /// + [TestClass] + public class OpenBugsCharTests : TestClass + { + // -- helpers: build Char / Byte NDArrays from int codes, read a scalar code back ---------- + private static NDArray Chars(params int[] v) + { + var a = new NDArray(NPTypeCode.Char, new Shape(v.Length), false); + for (int i = 0; i < v.Length; i++) a.SetValue((char)v[i], i); + return a; + } + + private static NDArray CharScalar(int v) + { + var a = new NDArray(NPTypeCode.Char, new Shape(), false); + a.SetValue((char)v, new int[0]); + return a; + } + + private static NDArray Bytes(params int[] v) + { + var a = new NDArray(NPTypeCode.Byte, new Shape(v.Length), false); + for (int i = 0; i < v.Length; i++) a.SetValue((byte)v[i], i); + return a; + } + + private static int Code(NDArray a, int i) => (char)a.GetValue(i); + + // ============================================================================ + // BUG 1: promote(Char, Byte) -> Byte (arithmetic) — silent data corruption. + // + // np.array([321],u16) + np.array([65],u8) == [386], dtype uint16 (NumPy 2.4.2) + // NumSharp: char[321] + uint8[65] -> dtype Byte, value 130 (321 truncated to 0x41=65, + // then 65+65). Char ranks below uint8 so the wider operand "wins" downward. + // ============================================================================ + [TestMethod, OpenBugs] + public void Char_Add_Byte_TruncatesToByte() + { + var r = Chars(321) + Bytes(65); + r.typecode.Should().Be(NPTypeCode.Char, + "NumPy uint16(321)+uint8(65) -> uint16; Char is bit-identical to uint16 so the " + + "result dtype must be Char, not Byte (promote(Char,Byte) wrongly returns Byte)."); + Code(r, 0).Should().Be(386, + "NumPy: 321+65 == 386. NumSharp truncates the Char to its low byte (65) before adding."); + } + + // ============================================================================ + // BUG 1b: the SAME promotion bug corrupts COMPARISONS (wrong booleans). + // + // np.greater(u16 321, u8 65) == True ; np.equal(u16 321, u8 65) == False (NumPy 2.4.2) + // NumSharp compares at Byte precision after truncating 321 -> 65, so it reports + // greater == False and equal == True. This is a wrong ANSWER, not just a wrong dtype. + // ============================================================================ + [TestMethod, OpenBugs] + public void Char_Compare_Byte_TruncatesHighByte() + { + ((bool)np.greater(Chars(321), Bytes(65)).GetValue(0)).Should().BeTrue( + "NumPy: 321 > 65 is True. NumSharp truncates Char 321 -> 65 and compares 65>65 -> False."); + ((bool)np.equal(Chars(321), Bytes(65)).GetValue(0)).Should().BeFalse( + "NumPy: 321 == 65 is False. NumSharp truncates Char 321 -> 65 and compares 65==65 -> True."); + } + + // ============================================================================ + // BUG 2: reciprocal(Char) -> Double instead of the integer reciprocal. + // + // np.reciprocal(np.array([2,3,4],u16)) == [0,0,0], dtype uint16 (integer 1//x). + // NumSharp returns dtype Double — Char is not treated as an integer dtype here. + // ============================================================================ + [TestMethod, OpenBugs] + public void Char_Reciprocal_ReturnsDouble() + { + var r = np.reciprocal(Chars(2, 3, 4)); + r.typecode.Should().Be(NPTypeCode.Char, + "NumPy reciprocal(uint16) stays uint16 (integer 1//x); Char must preserve its dtype, " + + "but NumSharp promotes Char -> Double."); + } + + // ============================================================================ + // BUG 3: power(Char, Single) -> Double instead of Single. + // + // np.power(u16[2,3], f32[2,2]) == [4.,9.], dtype float32. + // NumSharp returns dtype Double — Char is mis-ranked ABOVE float32 (the opposite + // direction from BUG 1, where it sits below Byte: the promotion table is simply wrong). + // ============================================================================ + [TestMethod, OpenBugs] + public void Char_Power_Single_ReturnsDouble() + { + var r = np.power(Chars(2, 3), new NDArray(new float[] { 2f, 2f })); + r.typecode.Should().Be(NPTypeCode.Single, + "NumPy result_type(uint16, float32) == float32; NumSharp returns Double for Char^float32."); + } + + // ============================================================================ + // BUG 4: power crashes on a Char operand in the scalar/broadcast path. + // + // np.power(u16[2,3,4], u16(2)) == [4,9,16], dtype uint16. + // NumSharp throws InvalidCastException: the scalar power kernel routes through + // Convert.ToDouble(char)/Convert.ToInt32(char), neither of which supports System.Char. + // ============================================================================ + [TestMethod, OpenBugs] + public void Char_Power_ScalarChar_Crashes() + { + NDArray r = null; + Action act = () => r = np.power(Chars(2, 3, 4), CharScalar(2)); + act.Should().NotThrow("NumPy power(uint16, uint16) computes [4,9,16]; NumSharp throws " + + "InvalidCastException because the kernel calls Convert.To*(char)."); + r.typecode.Should().Be(NPTypeCode.Char); + Code(r, 0).Should().Be(4); + Code(r, 1).Should().Be(9); + Code(r, 2).Should().Be(16); + } + + // ============================================================================ + // BUG 5: bitwise_and/or/xor(bool, Char) -> KeyNotFoundException '(Boolean, Char)'. + // + // np.bitwise_and([T,F,T], u16(5)) == [1,0,1], dtype uint16. + // NumSharp's mixed-type kernel cache has no (Boolean, Char) entry and throws. + // ============================================================================ + [TestMethod, OpenBugs] + public void Char_BitwiseAnd_Bool_KeyNotFound() + { + var b = new NDArray(new bool[] { true, false, true }); + NDArray r = null; + Action act = () => r = np.bitwise_and(b, CharScalar(5)); + act.Should().NotThrow("NumPy bitwise_and(bool, uint16) -> uint16 [1,0,1]; NumSharp throws " + + "KeyNotFoundException because the (Boolean, Char) kernel pair is unregistered."); + r.typecode.Should().Be(NPTypeCode.Char); + Code(r, 0).Should().Be(1); + Code(r, 1).Should().Be(0); + Code(r, 2).Should().Be(1); + } + + // ============================================================================ + // BUG 6: invert(Char) throws on the N>=16 SIMD path (size-dependent). + // + // np.invert(u16[1..20]) == [65534,65533,…,65515], dtype uint16. + // NumSharp's Vector256 bitwise-not path omits Char and throws + // NotSupportedException once N reaches the 16-lane SIMD width (N<=15 scalar path works). + // ============================================================================ + [TestMethod, OpenBugs] + public void Char_Invert_LargeN_NotSupported() + { + var c = Chars(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20); // N=20 -> SIMD + NDArray r = null; + Action act = () => r = np.invert(c); + act.Should().NotThrow("NumPy invert(uint16) computes the bitwise complement; NumSharp throws " + + "NotSupportedException on the Vector256 path for Char at N>=16."); + r.typecode.Should().Be(NPTypeCode.Char); + Code(r, 0).Should().Be(65534); // ~1 + Code(r, 19).Should().Be(65515); // ~20 + } + } +} diff --git a/test/NumSharp.UnitTest/OpenBugs.DtypeCoverage.cs b/test/NumSharp.UnitTest/OpenBugs.DtypeCoverage.cs new file mode 100644 index 00000000..e8ca5bd6 --- /dev/null +++ b/test/NumSharp.UnitTest/OpenBugs.DtypeCoverage.cs @@ -0,0 +1,53 @@ +using Microsoft.VisualStudio.TestTools.UnitTesting; +using System; +using AwesomeAssertions; +using NumSharp; +using NumSharp.Backends; + +namespace NumSharp.UnitTest +{ + /// + /// Open bugs surfaced by widening the differential-fuzz dtype grids toward full coverage + /// (gen_oracle per-mode dtype lists grown to ALL_DTYPES). Each is a NumPy-parity gap for a + /// dtype the corpus previously did not exercise on that op. NumPy 2.4.2 is the source of + /// truth (verified 2026-06-30). The offending (op, dtype) combo is CARVED OUT of the green + /// corpus and reproduced here under [OpenBugs] — remove the carve + this test when fixed. + /// + [TestClass] + public class OpenBugsDtypeCoverageTests : TestClass + { + // ============================================================================ + // BUG: np.clip on a Boolean array throws on the NON-CONTIGUOUS path. + // + // np.clip(bool_array, True, True) -> all-True bool array (NumPy 2.4.2). + // NumSharp handles the CONTIGUOUS bool clip fine, but the general strided / + // transposed / F-contiguous kernel throws: + // NotSupportedException: clip not supported for Boolean + // So the bug is layout-dependent — the SIMD/contiguous path supports Boolean + // while the coordinate/strided clip kernel omits it. + // ============================================================================ + private static readonly NDArray ClipTrue = NDArray.Scalar(true); + + [TestMethod, OpenBugs] + public void Clip_Bool_Transposed_Throws() + { + var a = np.array(new bool[] { true, false, false, true, true, false }).reshape(2, 3).T; // non-contiguous + NDArray r = null; + Action act = () => r = np.clip(a, ClipTrue, ClipTrue); + act.Should().NotThrow("NumPy clips a transposed bool array (lo=hi=True -> all True); NumSharp's " + + "strided clip kernel throws NotSupportedException for Boolean."); + r.typecode.Should().Be(NPTypeCode.Boolean); + } + + [TestMethod, OpenBugs] + public void Clip_Bool_Strided_Throws() + { + var a = np.array(new bool[] { true, false, false, true, true, false, false, true })["::2"]; // strided view + NDArray r = null; + Action act = () => r = np.clip(a, ClipTrue, ClipTrue); + act.Should().NotThrow("NumPy clips a strided bool view; NumSharp throws NotSupportedException " + + "for Boolean on the non-contiguous clip path (contiguous bool clip works)."); + r.typecode.Should().Be(NPTypeCode.Boolean); + } + } +} diff --git a/test/oracle/gen_decimal_oracle.cs b/test/oracle/gen_decimal_oracle.cs new file mode 100644 index 00000000..2d0c3e00 --- /dev/null +++ b/test/oracle/gen_decimal_oracle.cs @@ -0,0 +1,317 @@ +#:property PublishAot=false +#:property AllowUnsafeBlocks=true +// ============================================================================= +// gen_decimal_oracle.cs — INDEPENDENT differential oracle for the Decimal dtype. +// +// Decimal is the ONE NumSharp numeric dtype with no NumPy analog (System.Decimal: +// 16-byte, base-10, 96-bit mantissa × 10^-scale). NumPy cannot be the oracle, so this +// generator IS the oracle: it computes every expected value with NAIVE scalar +// System.Decimal arithmetic (plain C# operators, NO NumSharp kernels), then emits the +// exact same JSONL schema as gen_oracle.py (operand = base-buffer + element shape/ +// strides/offset; expected = C-contiguous result bytes). The C# harness +// (FuzzCorpus + value-aware BitDiff) replays the operand through NumSharp's decimal +// KERNELS and compares — so a divergence is a real kernel bug (strided/broadcast/ +// reduction/scan iteration or accumulation), independent of the naive oracle. +// +// Run: dotnet run gen_decimal_oracle.cs (writes corpus/decimal_*.jsonl) +// ============================================================================= +using System; +using System.Collections.Generic; +using System.Globalization; +using System.IO; +using System.Linq; +using System.Runtime.InteropServices; +using System.Text; + +static class Gen +{ + // -- decimal pools: varied SCALE (1.0 vs 1.00), sign, zero, magnitude. Kept well within + // decimal range so products don't overflow. Front-load edges (0, ±1, scale variants). + static readonly decimal[] POOL = { + 0m, 1m, -1m, 1.0m, 1.00m, -3.5m, 2.25m, 0.1m, 0.125m, -0.125m, + 0.123456789m, 12345.6789m, -9999.99999m, 1000m, -1000m, 7m, 3m, 2m, 42m, -42m, + 0.0001m, -0.0001m, 100000m, 6m, 11m, -8m, 9.5m, -9.5m, + }; + // Divisor pool: same spread but NEVER zero (decimal /0 throws; no NumPy oracle to mirror). + static readonly decimal[] NZPOOL = POOL.Where(x => x != 0m).ToArray(); + + static decimal[] Fill(int n, bool nonzero, int rot) + { + var src = nonzero ? NZPOOL : POOL; + var a = new decimal[n]; + for (int i = 0; i < n; i++) a[i] = src[(i + rot) % src.Length]; + return a; + } + + // ---- in-memory decimal <-> bytes (exactly how ArraySlice.FromBuffer reads them) ---- + static string HexOf(decimal[] v) + { + var bytes = MemoryMarshal.AsBytes(v.AsSpan()); + var sb = new StringBuilder(bytes.Length * 2); + foreach (var b in bytes) sb.Append(b.ToString("x2")); + return sb.ToString(); + } + static string HexOf(byte[] v) { var sb = new StringBuilder(v.Length * 2); foreach (var b in v) sb.Append(b.ToString("x2")); return sb.ToString(); } + + // ---- a single operand: a C-contiguous base[] + an aliasing view (shape/elem-strides/offset). ---- + sealed class Operand + { + public decimal[] Base; // contiguous storage (bufferSize == Base.Length) + public int[] Shape; + public long[] Strides; // element strides + public long Offset; // element offset + public string Describe() // -> operand descriptor JSON + => $"{{\"dtype\":\"decimal\",\"shape\":[{string.Join(",", Shape)}]," + + $"\"strides\":[{string.Join(",", Strides)}],\"offset\":{Offset}," + + $"\"bufferSize\":{Base.Length},\"buffer\":\"{HexOf(Base)}\"}}"; + + // logical values walked in C-order over Shape (offset + Σ coord*stride into Base). + public decimal[] Logical() + { + int n = Shape.Aggregate(1, (a, b) => a * b); + var outv = new decimal[n]; + if (n == 0) return outv; + var coord = new int[Shape.Length]; + for (int i = 0; i < n; i++) + { + long flat = Offset; + for (int d = 0; d < Shape.Length; d++) flat += coord[d] * Strides[d]; + outv[i] = Base[flat]; + for (int d = Shape.Length - 1; d >= 0; d--) { if (++coord[d] < Shape[d]) break; coord[d] = 0; } + } + return outv; + } + } + + // C-contiguous strides for a shape. + static long[] CStrides(int[] shape) + { + var s = new long[shape.Length]; + long acc = 1; + for (int d = shape.Length - 1; d >= 0; d--) { s[d] = acc; acc *= shape[d]; } + return s; + } + + // ---- single-operand layout catalog (mirrors layout_catalog.py element-strides) ---- + static Operand SingleLayout(string name, int rot, bool nonzero) + { + switch (name) + { + case "c_contiguous_1d": { var b = Fill(8, nonzero, rot); return new Operand { Base = b, Shape = new[]{8}, Strides = new long[]{1}, Offset = 0 }; } + case "c_contiguous_2d": { var b = Fill(20, nonzero, rot); return new Operand { Base = b, Shape = new[]{4,5}, Strides = new long[]{5,1}, Offset = 0 }; } + case "c_contiguous_3d": { var b = Fill(24, nonzero, rot); return new Operand { Base = b, Shape = new[]{2,3,4}, Strides = new long[]{12,4,1}, Offset = 0 }; } + case "f_contiguous_2d": { var b = Fill(20, nonzero, rot); return new Operand { Base = b, Shape = new[]{4,5}, Strides = new long[]{1,4}, Offset = 0 }; } // (5,4).T + case "transposed_3d": { var b = Fill(24, nonzero, rot); return new Operand { Base = b, Shape = new[]{4,2,3}, Strides = new long[]{1,12,4}, Offset = 0 }; } // (2,3,4).transpose(2,0,1) + case "strided_step2_1d":{ var b = Fill(16, nonzero, rot); return new Operand { Base = b, Shape = new[]{8}, Strides = new long[]{2}, Offset = 0 }; } // [::2] + case "negstride_1d": { var b = Fill(8, nonzero, rot); return new Operand { Base = b, Shape = new[]{8}, Strides = new long[]{-1}, Offset = 7 }; } // [::-1] + case "simple_slice_offset_1d": { var b = Fill(10, nonzero, rot); return new Operand { Base = b, Shape = new[]{5}, Strides = new long[]{1}, Offset = 2 }; } // [2:7] + case "strided_2d_cols": { var b = Fill(24, nonzero, rot); return new Operand { Base = b, Shape = new[]{4,3}, Strides = new long[]{6,2}, Offset = 0 }; } // (4,6)[:,::2] + case "broadcast_1d_to_2d": { var b = Fill(5, nonzero, rot); return new Operand { Base = b, Shape = new[]{4,5}, Strides = new long[]{0,1}, Offset = 0 }; } // broadcast_to((4,5)) + case "scalar_0d": { var b = Fill(1, nonzero, rot); return new Operand { Base = b, Shape = new int[0], Strides = new long[0], Offset = 0 }; } + case "one_element_1d": { var b = Fill(1, nonzero, rot); return new Operand { Base = b, Shape = new[]{1}, Strides = new long[]{1}, Offset = 0 }; } + case "highrank_5d": { var b = Fill(12, nonzero, rot); return new Operand { Base = b, Shape = new[]{2,1,3,1,2}, Strides = new long[]{6,6,2,2,1}, Offset = 0 }; } + default: throw new Exception("unknown single layout " + name); + } + } + static readonly string[] SINGLE_LAYOUTS = { + "c_contiguous_1d","c_contiguous_2d","c_contiguous_3d","f_contiguous_2d","transposed_3d", + "strided_step2_1d","negstride_1d","simple_slice_offset_1d","strided_2d_cols", + "broadcast_1d_to_2d","scalar_0d","one_element_1d","highrank_5d", + }; + + // ---- pairwise layout catalog (mirrors PAIR_LAYOUTS). Returns (A, B). ---- + static (Operand, Operand) PairLayout(string name, bool bNonzero) + { + Operand C(int[] sh, int rot, bool nz) { var b = Fill(sh.Aggregate(1,(x,y)=>x*y), nz, rot); return new Operand { Base = b, Shape = sh, Strides = CStrides(sh), Offset = 0 }; } + switch (name) + { + case "pp_contig_contig": return (C(new[]{4,5},0,false), C(new[]{4,5},3,bNonzero)); + case "pp_contig_fortran": { var a = C(new[]{4,5},0,false); var bb = Fill(20,bNonzero,3); return (a, new Operand{ Base=bb, Shape=new[]{4,5}, Strides=new long[]{1,4}, Offset=0 }); } + case "pp_strided_strided": { var ab = Fill(40,false,0); var bb = Fill(40,bNonzero,3); + return (new Operand{Base=ab,Shape=new[]{4,5},Strides=new long[]{10,2},Offset=0}, new Operand{Base=bb,Shape=new[]{4,5},Strides=new long[]{10,2},Offset=0}); } + case "pp_scalar_right": return (C(new[]{4,5},0,false), new Operand{ Base=Fill(1,bNonzero,3), Shape=new int[0], Strides=new long[0], Offset=0 }); + case "pp_scalar_left": return (new Operand{ Base=Fill(1,false,0), Shape=new int[0], Strides=new long[0], Offset=0 }, C(new[]{4,5},3,bNonzero)); + case "pp_broadcast_row": return (C(new[]{4,5},0,false), C(new[]{5},3,bNonzero)); + case "pp_negstride_both": { var ab=Fill(8,false,0); var bb=Fill(8,bNonzero,3); + return (new Operand{Base=ab,Shape=new[]{8},Strides=new long[]{-1},Offset=7}, new Operand{Base=bb,Shape=new[]{8},Strides=new long[]{-1},Offset=7}); } + default: throw new Exception("unknown pair layout " + name); + } + } + static readonly string[] PAIR_LAYOUTS = { + "pp_contig_contig","pp_contig_fortran","pp_strided_strided","pp_scalar_right", + "pp_scalar_left","pp_broadcast_row","pp_negstride_both", + }; + + // ---- numpy-style broadcast of two shapes ---- + static int[] BroadcastShape(int[] a, int[] b) + { + int n = Math.Max(a.Length, b.Length); + var r = new int[n]; + for (int i = 0; i < n; i++) + { + int da = i < n - a.Length ? 1 : a[i - (n - a.Length)]; + int db = i < n - b.Length ? 1 : b[i - (n - b.Length)]; + if (da != db && da != 1 && db != 1) throw new Exception("not broadcastable"); + r[i] = Math.Max(da, db); + } + return r; + } + // value of flat-C-order `vals` (shape `shp`) at result coord (right-aligned, broadcast). + static decimal At(decimal[] vals, int[] shp, int[] coord) + { + int n = coord.Length; long flat = 0; long strideAcc = 1; + for (int d = shp.Length - 1; d >= 0; d--) + { + int c = coord[n - shp.Length + d]; + int idx = shp[d] == 1 ? 0 : c; + flat += idx * strideAcc; strideAcc *= shp[d]; + } + return vals[flat]; + } + + // ---- naive scalar oracle ops ---- + static decimal FloorDiv(decimal a, decimal b) => decimal.Floor(a / b); + static decimal Mod(decimal a, decimal b) => a - decimal.Floor(a / b) * b; // numpy floored remainder + static decimal Sign(decimal a) => a > 0 ? 1m : (a < 0 ? -1m : 0m); + + static readonly (string, Func, bool)[] BIN_ARITH = { + ("add", (a,b)=>a+b, false), ("subtract", (a,b)=>a-b, false), ("multiply", (a,b)=>a*b, false), + ("divide", (a,b)=>a/b, true), ("floor_divide", FloorDiv, true), ("mod", Mod, true), + ("maximum", Math.Max, false), ("minimum", Math.Min, false), + }; + static readonly (string, Func)[] BIN_CMP = { + ("equal",(a,b)=>a==b), ("not_equal",(a,b)=>a!=b), ("less",(a,b)=>aa>b), ("less_equal",(a,b)=>a<=b), ("greater_equal",(a,b)=>a>=b), + }; + static readonly (string, Func)[] UNARY = { + ("negative", a=>-a), ("abs", Math.Abs), ("sign", Sign), ("square", a=>a*a), + }; + + static string Case(string id, string op, string paramsJson, IEnumerable operandJsons, string expDtype, int[] expShape, string expBufHex) + => $"{{\"id\":\"{id}\",\"op\":\"{op}\",\"params\":{paramsJson}," + + $"\"operands\":[{string.Join(",", operandJsons)}]," + + $"\"expected\":{{\"dtype\":\"{expDtype}\",\"shape\":[{string.Join(",", expShape)}],\"buffer\":\"{expBufHex}\"}}," + + $"\"layout\":\"decimal\",\"valueclass\":\"decimal\"}}"; + + public static void Main() + { + string here = AppContext.BaseDirectory; + // resolve test/oracle dir regardless of run cwd: walk up to find NumSharp.UnitTest sibling. + string oracleDir = FindOracleDir(); + string corpus = Path.GetFullPath(Path.Combine(oracleDir, "..", "NumSharp.UnitTest", "Fuzz", "corpus")); + Directory.CreateDirectory(corpus); + + var unary = new List(); + var binary = new List(); + var reduce = new List(); + var scan = new List(); + int n = 0; + + // ----- UNARY (negative/abs/sign/square) over every single layout ----- + foreach (var ln in SINGLE_LAYOUTS) + { + var o = SingleLayout(ln, 0, false); + var log = o.Logical(); + foreach (var (name, f) in UNARY) + { + var exp = log.Select(f).ToArray(); + unary.Add(Case($"{name}/decimal/{ln}/{n++}", name, "{}", new[]{o.Describe()}, "decimal", o.Shape, HexOf(exp))); + } + } + + // ----- BINARY arith + comparison over pair layouts ----- + foreach (var ln in PAIR_LAYOUTS) + { + foreach (var (name, f, nz) in BIN_ARITH) + { + var (a, b) = PairLayout(ln, nz); + var la = a.Logical(); var lb = b.Logical(); + var rs = BroadcastShape(a.Shape, b.Shape); + var exp = BroadcastApply(la, a.Shape, lb, b.Shape, rs, f); + binary.Add(Case($"{name}/decimal/{ln}/{n++}", name, "{}", new[]{a.Describe(), b.Describe()}, "decimal", rs, HexOf(exp))); + } + foreach (var (name, f) in BIN_CMP) + { + var (a, b) = PairLayout(ln, false); + var la = a.Logical(); var lb = b.Logical(); + var rs = BroadcastShape(a.Shape, b.Shape); + var exp = BroadcastApplyBool(la, a.Shape, lb, b.Shape, rs, f); + binary.Add(Case($"{name}/decimal/{ln}/{n++}", name, "{}", new[]{a.Describe(), b.Describe()}, "bool", rs, HexOf(exp))); + } + } + + // ----- REDUCTIONS (axis=None): sum/prod/min/max/mean ----- + foreach (var ln in SINGLE_LAYOUTS) + { + if (ln == "scalar_0d") continue; // reduce of 0-D is identity; skip the degenerate + var o = SingleLayout(ln, 1, false); + var log = o.Logical(); + if (log.Length == 0) continue; + void Add(string op, decimal val) => reduce.Add(Case($"{op}/decimal/{ln}/{n++}", op, "{}", new[]{o.Describe()}, "decimal", new int[0], HexOf(new[]{val}))); + Add("sum", log.Aggregate(0m, (x,y)=>x+y)); + Add("prod", ProdSafe(log)); + Add("min", log.Min()); + Add("max", log.Max()); + Add("mean", log.Aggregate(0m,(x,y)=>x+y) / log.Length); + } + + // ----- SCAN (axis=None -> flatten): cumsum/cumprod ----- + foreach (var ln in SINGLE_LAYOUTS) + { + if (ln == "scalar_0d") continue; + var o = SingleLayout(ln, 2, false); + var log = o.Logical(); + if (log.Length == 0) continue; + var cs = new decimal[log.Length]; var cp = new decimal[log.Length]; + decimal s = 0m, p = 1m; + for (int i = 0; i < log.Length; i++) { s += log[i]; cs[i] = s; p *= log[i]; cp[i] = p; } + scan.Add(Case($"cumsum/decimal/{ln}/{n++}", "cumsum", "{\"axis\":null}", new[]{o.Describe()}, "decimal", new[]{log.Length}, HexOf(cs))); + scan.Add(Case($"cumprod/decimal/{ln}/{n++}", "cumprod", "{\"axis\":null}", new[]{o.Describe()}, "decimal", new[]{log.Length}, HexOf(cp))); + } + + Write(Path.Combine(corpus, "decimal_unary.jsonl"), unary); + Write(Path.Combine(corpus, "decimal_binary.jsonl"), binary); + Write(Path.Combine(corpus, "decimal_reduce.jsonl"), reduce); + Write(Path.Combine(corpus, "decimal_scan.jsonl"), scan); + } + + static decimal ProdSafe(decimal[] v) { decimal p = 1m; foreach (var x in v) p *= x; return p; } + + static decimal[] BroadcastApply(decimal[] la, int[] sa, decimal[] lb, int[] sb, int[] rs, Func f) + { + int n = rs.Aggregate(1,(x,y)=>x*y); var outv = new decimal[n]; + var coord = new int[rs.Length]; + for (int i = 0; i < n; i++) { outv[i] = f(At(la, sa, coord), At(lb, sb, coord)); Inc(coord, rs); } + return outv; + } + static byte[] BroadcastApplyBool(decimal[] la, int[] sa, decimal[] lb, int[] sb, int[] rs, Func f) + { + int n = rs.Aggregate(1,(x,y)=>x*y); var outv = new byte[n]; + var coord = new int[rs.Length]; + for (int i = 0; i < n; i++) { outv[i] = (byte)(f(At(la, sa, coord), At(lb, sb, coord)) ? 1 : 0); Inc(coord, rs); } + return outv; + } + static void Inc(int[] coord, int[] shp) { for (int d = shp.Length - 1; d >= 0; d--) { if (++coord[d] < shp[d]) break; coord[d] = 0; } } + + static void Write(string path, List lines) + { + File.WriteAllText(path, string.Join("\n", lines) + (lines.Count > 0 ? "\n" : ""), new UTF8Encoding(false)); + Console.WriteLine($"wrote {lines.Count} cases -> {path}"); + } + + static string FindOracleDir() + { + // this file lives in test/oracle/; AppContext.BaseDirectory is the build temp, so search upward + // from the current directory for a folder containing NumSharp.UnitTest. + var dir = Directory.GetCurrentDirectory(); + for (int i = 0; i < 8 && dir != null; i++) + { + var cand = Path.Combine(dir, "test", "oracle"); + if (Directory.Exists(Path.Combine(dir, "test", "NumSharp.UnitTest"))) return cand; + if (Path.GetFileName(dir) == "oracle" && Directory.Exists(Path.Combine(dir, "..", "NumSharp.UnitTest"))) return dir; + dir = Path.GetDirectoryName(dir); + } + // fallback: assume invoked from test/oracle + return Directory.GetCurrentDirectory(); + } +} diff --git a/test/oracle/gen_oracle.py b/test/oracle/gen_oracle.py index 1f497f7a..d85df44f 100644 --- a/test/oracle/gen_oracle.py +++ b/test/oracle/gen_oracle.py @@ -254,7 +254,7 @@ def gen_reduce(ops, dtypes, layout_names): "nanvar": lambda a, ax, kd: np.nanvar(a, axis=ax, keepdims=kd), "nanmedian": lambda a, ax, kd: np.nanmedian(a, axis=ax, keepdims=kd), } -NAN_REDUCE_DTYPES = ["float16", "float32", "float64", "int32", "complex128"] +NAN_REDUCE_DTYPES = list(ALL_DTYPES) # widened: every dtype (NaN-erroring combos skipped by the gen) def gen_binary(ops, dt_pairs, pair_layout_names): @@ -298,11 +298,15 @@ def gen_binary(ops, dt_pairs, pair_layout_names): "average": lambda a, ax, kd: np.average(a, axis=ax, keepdims=kd), "ptp": lambda a, ax, kd: np.ptp(a, axis=ax, keepdims=kd), } -STAT_DTYPES = ["int16", "int32", "int64", "uint8", "float16", "float32", "float64"] +STAT_DTYPES = list(ALL_DTYPES) # widened: median/ptp/average across every dtype (skips on NumPy error) STAT_LAYOUTS = ["c_contiguous_1d", "c_contiguous_2d", "c_contiguous_3d", "f_contiguous_2d", "transposed_3d", "strided_2d_cols", "one_element_1d"] -CNZ_DTYPES = ["bool", "int32", "uint8", "float64", "complex128"] -CLIP_DTYPES = ["int8", "uint8", "int16", "int32", "int64", "float16", "float32", "float64"] +CNZ_DTYPES = list(ALL_DTYPES) # widened: count_nonzero is dtype-agnostic +# clip needs an ORDERED dtype — complex128 has no ordering (NumPy raises). bool is CARVED OUT: +# NumSharp's general (strided/transposed/F-contig) clip kernel throws "clip not supported for +# Boolean" (only the contiguous path handles bool) — reproduced under [OpenBugs] (Clip_Bool_Strided_*). +CLIP_DTYPES = ["int8", "uint8", "int16", "uint16", "int32", "uint32", "int64", "uint64", + "float16", "float32", "float64"] QUANTILE_SPECS = [ ("percentile", lambda a, q, ax: np.percentile(a, q, axis=ax), [0.0, 25.0, 50.0, 75.0, 100.0]), ("quantile", lambda a, q, ax: np.quantile(a, q, axis=ax), [0.0, 0.25, 0.5, 0.75, 1.0]), @@ -426,7 +430,7 @@ def gen_clip(dtypes, layout_names): "cumsum": lambda a, ax: np.cumsum(a, axis=ax), "cumprod": lambda a, ax: np.cumprod(a, axis=ax), } -SCAN_DTYPES = ["int16", "int32", "int64", "uint8", "uint16", "float32", "float64", "complex128"] +SCAN_DTYPES = list(ALL_DTYPES) # widened: cumsum/cumprod/diff are dtype-general (bool->int upcast) SCAN_LAYOUTS = ["c_contiguous_1d", "c_contiguous_2d", "c_contiguous_3d", "f_contiguous_2d", "transposed_3d", "strided_2d_cols", "one_element_1d", "negstride_1d"] @@ -530,7 +534,7 @@ def gen_where(dt_pairs, layout_names): # T13 — logic & element-wise extrema. isnan/isinf/isfinite (unary -> bool); maximum/minimum # (NaN-propagating), fmax/fmin (NaN-ignoring), isclose (binary -> bool). NumPy is the oracle. LOGIC_UNARY_OPS = {"isnan": np.isnan, "isinf": np.isinf, "isfinite": np.isfinite} -LOGIC_UNARY_DTYPES = ["int32", "uint8", "float16", "float32", "float64", "complex128"] +LOGIC_UNARY_DTYPES = list(ALL_DTYPES) # widened: isnan/isinf/isfinite defined on every dtype LOGIC_BIN_OPS = { "maximum": np.maximum, "minimum": np.minimum, "fmax": np.fmax, "fmin": np.fmin, "isclose": np.isclose, @@ -723,7 +727,7 @@ def gen_shift(ops, dtypes): # T7 — shape manipulation. These ops only move bytes, so dtype coverage is light but stride/shape # coverage is heavy. NumPy is the oracle for the output shape, dtype, and C-contiguous bytes. -MANIP_DTYPES = ["int32", "float64", "uint8", "complex128"] +MANIP_DTYPES = list(ALL_DTYPES) # widened: reshape/transpose/concat/stack/pad are dtype-agnostic def gen_manip(dtypes, layout_names): @@ -899,7 +903,7 @@ def gen_modf(dtypes, layout_names): # T14 — sorting / searching. Distinct values avoid tie-break ambiguity (quicksort is unstable), # so argsort is deterministic both sides. NumPy is the oracle for the int64 index results. -SORT_DTYPES = ["int32", "int64", "uint8", "float32", "float64"] +SORT_DTYPES = list(ALL_DTYPES) # widened: argsort/searchsorted/nonzero (complex sorts lexicographically) def _distinct(n, dt): @@ -977,7 +981,8 @@ def gen_nonzero(dtypes): # W13 — SIMD-tail boundary sizes. 1-D arrays straddling the V128/V256/V512 lane counts so the # unrolled-SIMD body, 1-vector remainder, and scalar tail are all exercised at their seams. TAIL_SIZES = [1, 2, 3, 7, 8, 9, 15, 16, 17, 31, 32, 33, 63, 64, 65, 127, 128, 129] -TAIL_DTYPES = ["int32", "int64", "uint8", "float32", "float64"] +# widened: SIMD-seam sizes across every dtype EXCEPT bool (gen_tail subtracts; NumPy bans bool `-`). +TAIL_DTYPES = [d for d in ALL_DTYPES if d != "bool"] def gen_tail(dtypes): @@ -1028,7 +1033,7 @@ def gen_tail(dtypes): # W12 — parameter sweep. The reduce tier only covered axis in {None, 0, last}; here we exercise # the MIDDLE axis and NEGATIVE axes (-1/-2/-3), ddof=1 (sample std/var), and order='F' ravel. -PARAM_DTYPES = ["int32", "float32", "float64"] +PARAM_DTYPES = list(ALL_DTYPES) # widened: axis/ddof/keepdims params across every dtype def gen_params(dtypes): @@ -1086,7 +1091,11 @@ def gen_params(dtypes): # W11 — operand-relationship flags (section C): input aliasing (a op a, SAME buffer both sides) # and in-place out= (the output buffer IS an input). Exercises read-before-write within the kernel. -ALIAS_DTYPES = ["int32", "int64", "uint8", "float32", "float64"] +# widened: out=/overlap aliasing across every dtype EXCEPT bool (gen_aliasing subtracts; NumPy bans +# bool `-`) and complex128 (the a*a self-multiply of a large _cbase value hits catastrophic +# cancellation in a^2-b^2, where NumPy's ARRAY ufunc and the naive ac-bd formula round differently; +# NumSharp matches NumPy's SCALAR multiply exactly, so this is a ULP/ill-conditioned artifact, NOT a bug). +ALIAS_DTYPES = [d for d in ALL_DTYPES if d not in ("bool", "complex128")] def gen_aliasing(dtypes): @@ -1210,7 +1219,7 @@ def gen_errors(): # buffer) which NumPy makes safe via COPY_IF_OVERLAP, and (2) cross-dtype copyto INTO a strided # destination view + scalar-broadcast source (the cast-into-non-contiguous-dst path astype never # exercises, plus the scalar-broadcast cross-dtype fast fill). -COPYTO_OVERLAP_DTYPES = ["int32", "int64", "uint8", "float32", "float64"] +COPYTO_OVERLAP_DTYPES = list(ALL_DTYPES) # widened: same-dtype copyto overlap across every dtype COPYTO_CROSS = [ ("float64", "int32"), ("float32", "uint8"), ("float64", "int16"), ("int32", "float64"), ("int64", "int16"), ("float64", "float16"), ("int32", "uint8"), ("float64", "int64"), @@ -1287,6 +1296,102 @@ def gen_copyto(overlap_dtypes, cross_pairs): return cases +def _relabel_dtype(cases, frm, to): + """Re-label a NumPy proxy dtype to a NumSharp-only dtype across a case's operand / + expected / params descriptors (+ id). The RAW BYTES are untouched — this only rewrites + the dtype STRING. Used for Char: NumSharp's Char is bit-identical to uint16 (2-byte + unsigned), but NumPy has no char dtype, so every Char op is generated with uint16 as the + proxy and then relabelled uint16->char. NumPy's uint16 result is therefore a bytes-exact + oracle for Char, and the gate asserts NumSharp's Char ≡ uint16 across every op.""" + out = [] + for c in cases: + c = json.loads(json.dumps(c)) # deep copy (cases are JSON-serializable) + for o in c.get("operands", []): + if o.get("dtype") == frm: + o["dtype"] = to + exp = c.get("expected") + if isinstance(exp, dict) and exp.get("dtype") == frm: + exp["dtype"] = to + for k, v in list(c.get("params", {}).items()): + if v == frm: + c["params"][k] = to + c["id"] = c["id"].replace(frm, to) + out.append(c) + return out + + +# --------------------------------------------------------------------------- +# Char masquerade — WOVEN into every tier (not a separate corpus file). +# --------------------------------------------------------------------------- +# NumSharp's Char is a 2-byte UNSIGNED value, bit-identical to uint16. NumPy has no +# char dtype, so each Char op is generated through uint16 as the NumPy proxy and the +# uint16 STRING is relabelled to "char" (raw bytes untouched, see _relabel_dtype). +# The Char cases are appended into the SAME tier file as their NumPy-native kin +# (binary_arith / unary / reduce / ...), so the existing per-tier FuzzMatrix test +# replays Char alongside int32/float64/etc. — Char is a first-class grid axis member. +# +# CARVE-OUTS (kept OUT of the green corpus; each reproduced under [OpenBugs] in +# OpenBugs.Char.cs / class OpenBugsCharTests): the combos that hit verified NumSharp Char bugs — +# * any Char × {uint8,bool} pair -> promote(Char,Byte)->Byte truncation (arith, +# comparison, bitwise) + (Boolean,Char) missing kernel [BUG: char-promote] +# * reciprocal(char) -> result dtype Double, should be uint16/char +# * power with a char operand -> Convert(char) crash / Double result +# * invert(char) -> NotSupportedException on the N>=16 SIMD path +# Everything else (Char × {char,int32,int64,uint64,float64}, all other unary/reduce/ +# scan/stat/manip/sort/tail/astype) is bit-identical to uint16 and ships GREEN. +_C = "uint16" # the NumPy proxy for Char + +# Char-bearing operand pairs. The uint16 slot IS the Char. uint8/bool deliberately +# absent (promotion/kernel bugs). Both operand orders covered; partners are all wider +# than Char so the kernel casts Char UP (no narrowing trap). +CHAR_ARITH_PAIRS = [(_C, _C), (_C, "int32"), ("int32", _C), (_C, "int64"), + (_C, "uint64"), (_C, "float64"), ("float64", _C)] +CHAR_CMP_PAIRS = [(_C, _C), (_C, "int32"), ("int32", _C), (_C, "float64"), ("float64", _C)] +CHAR_BIT_PAIRS = [(_C, _C), (_C, "int32"), (_C, "uint64")] + +# Power crashes on any char operand; reciprocal mis-types char -> excluded per-op. +_CHAR_DIVMOD_OPS = {k: v for k, v in DIVMOD_POWER_OPS.items() if k != "power"} +_CHAR_UNARY_OPS = {k: v for k, v in UNARY_OPS.items() if k != "reciprocal"} + + +def char_tier(mode): + """Relabelled Char cases to append into tier-file `mode` (woven coverage).""" + L = list(LAYOUTS.keys()) + PL = list(PAIR_LAYOUTS.keys()) + raw = [] + if mode == "binary": + raw = gen_binary(BINARY_OPS, CHAR_ARITH_PAIRS, PL) + elif mode == "divmod_power": + raw = gen_binary(_CHAR_DIVMOD_OPS, CHAR_ARITH_PAIRS, PL) # floor_divide, mod (power carved) + elif mode == "comparison": + raw = gen_binary(COMPARISON_OPS, CHAR_CMP_PAIRS, PL) + elif mode == "unary": + raw = gen_unary(_CHAR_UNARY_OPS, [_C], L) # reciprocal carved + elif mode == "unary_extra": + raw = gen_unary(UNARY_EXTRA_OPS, [_C], L) + elif mode == "bitwise": + raw = gen_binary(BITWISE_BIN_OPS, CHAR_BIT_PAIRS, PL) + raw += gen_shift(SHIFT_OPS, [_C]) # invert(char) carved (SIMD gap) + elif mode == "reduce": + raw = gen_reduce(REDUCE_OPS, [_C], REDUCE_LAYOUTS) + elif mode == "scan": + raw = gen_scan(SCAN_OPS, [_C], SCAN_LAYOUTS) + gen_diff([_C], SCAN_LAYOUTS) + elif mode == "stat": + raw = gen_reduce(STAT_REDUCE_OPS, [_C], STAT_LAYOUTS) + raw += gen_count_nonzero([_C], STAT_LAYOUTS) + raw += gen_quantile(QUANTILE_SPECS, [_C], STAT_LAYOUTS) + raw += gen_clip([_C], STAT_LAYOUTS) + elif mode == "manip": + raw = gen_manip([_C], L) + gen_concat_stack([_C]) + gen_pad([_C]) + elif mode == "sort": + raw = gen_argsort([_C]) + gen_searchsorted([_C]) + gen_nonzero([_C]) + elif mode == "tail": + raw = gen_tail([_C]) + elif mode == "astype_full": + raw = gen_astype([_C], ALL_DTYPES, L) + gen_astype(ALL_DTYPES, [_C], L) + return _relabel_dtype(raw, _C, "char") + + def write_jsonl(path, cases): os.makedirs(os.path.dirname(path), exist_ok=True) with open(path, "w", newline="\n") as f: @@ -1308,21 +1413,27 @@ def main(): write_jsonl(os.path.join(corpus_dir, "astype_smoke.jsonl"), cases) elif mode == "astype_full": cases = gen_astype(ALL_DTYPES, ALL_DTYPES, list(LAYOUTS.keys())) + cases += char_tier("astype_full") write_jsonl(os.path.join(corpus_dir, "astype_full.jsonl"), cases) elif mode == "binary": cases = gen_binary(BINARY_OPS, DT_PAIRS, list(PAIR_LAYOUTS.keys())) + cases += char_tier("binary") write_jsonl(os.path.join(corpus_dir, "binary_arith.jsonl"), cases) elif mode == "divmod_power": cases = gen_binary(DIVMOD_POWER_OPS, DT_PAIRS, list(PAIR_LAYOUTS.keys())) + cases += char_tier("divmod_power") write_jsonl(os.path.join(corpus_dir, "binary_divmod_power.jsonl"), cases) elif mode == "comparison": cases = gen_binary(COMPARISON_OPS, DT_PAIRS, list(PAIR_LAYOUTS.keys())) + cases += char_tier("comparison") write_jsonl(os.path.join(corpus_dir, "comparison.jsonl"), cases) elif mode == "unary": cases = gen_unary(UNARY_OPS, UNARY_DTYPES, list(LAYOUTS.keys())) + cases += char_tier("unary") write_jsonl(os.path.join(corpus_dir, "unary.jsonl"), cases) elif mode == "reduce": cases = gen_reduce(REDUCE_OPS, REDUCE_DTYPES, REDUCE_LAYOUTS) + cases += char_tier("reduce") write_jsonl(os.path.join(corpus_dir, "reduce.jsonl"), cases) elif mode == "where": cases = gen_where(WHERE_DT_PAIRS, list(WHERE_LAYOUTS.keys())) @@ -1337,9 +1448,11 @@ def main(): cases = gen_binary(BITWISE_BIN_OPS, BITWISE_DT_PAIRS, list(PAIR_LAYOUTS.keys())) cases += gen_unary(INVERT_OP, INT_BOOL_DTYPES, list(LAYOUTS.keys())) cases += gen_shift(SHIFT_OPS, SHIFT_DTYPES) + cases += char_tier("bitwise") write_jsonl(os.path.join(corpus_dir, "bitwise.jsonl"), cases) elif mode == "unary_extra": cases = gen_unary(UNARY_EXTRA_OPS, ALL_DTYPES, list(LAYOUTS.keys())) + cases += char_tier("unary_extra") write_jsonl(os.path.join(corpus_dir, "unary_extra.jsonl"), cases) elif mode == "nanreduce": cases = gen_reduce(NAN_REDUCE_OPS, NAN_REDUCE_DTYPES, REDUCE_LAYOUTS) @@ -1347,12 +1460,14 @@ def main(): elif mode == "scan": cases = gen_scan(SCAN_OPS, SCAN_DTYPES, SCAN_LAYOUTS) cases += gen_diff(SCAN_DTYPES, SCAN_LAYOUTS) + cases += char_tier("scan") write_jsonl(os.path.join(corpus_dir, "scan.jsonl"), cases) elif mode == "stat": cases = gen_reduce(STAT_REDUCE_OPS, STAT_DTYPES, STAT_LAYOUTS) cases += gen_count_nonzero(CNZ_DTYPES, STAT_LAYOUTS) cases += gen_quantile(QUANTILE_SPECS, STAT_DTYPES, STAT_LAYOUTS) cases += gen_clip(CLIP_DTYPES, STAT_LAYOUTS) + cases += char_tier("stat") write_jsonl(os.path.join(corpus_dir, "stat.jsonl"), cases) elif mode == "logic": cases = gen_unary(LOGIC_UNARY_OPS, LOGIC_UNARY_DTYPES, list(LAYOUTS.keys())) @@ -1365,14 +1480,17 @@ def main(): cases = gen_manip(MANIP_DTYPES, list(LAYOUTS.keys())) cases += gen_concat_stack(MANIP_DTYPES) cases += gen_pad(MANIP_DTYPES) + cases += char_tier("manip") write_jsonl(os.path.join(corpus_dir, "manip.jsonl"), cases) elif mode == "sort": cases = gen_argsort(SORT_DTYPES) cases += gen_searchsorted(SORT_DTYPES) cases += gen_nonzero(SORT_DTYPES) + cases += char_tier("sort") write_jsonl(os.path.join(corpus_dir, "sort.jsonl"), cases) elif mode == "tail": cases = gen_tail(TAIL_DTYPES) + cases += char_tier("tail") write_jsonl(os.path.join(corpus_dir, "tail.jsonl"), cases) elif mode == "params": cases = gen_params(PARAM_DTYPES) @@ -1387,7 +1505,7 @@ def main(): cases = gen_errors() write_jsonl(os.path.join(corpus_dir, "errors.jsonl"), cases) else: - print(f"unknown mode '{mode}' (expected: smoke | astype_full | binary | divmod_power | comparison | unary | reduce | where | place | matmul | bitwise | unary_extra | nanreduce)") + print(f"unknown mode '{mode}' (expected: smoke | astype_full | binary | divmod_power | comparison | unary | reduce | where | place | matmul | bitwise | unary_extra | nanreduce | scan | stat | logic | modf | manip | sort | tail | params | aliasing | copyto | errors)") sys.exit(2) From 9b05fe6dcc1251f509de985bd5d8055e0fcd38bd Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Tue, 30 Jun 2026 19:26:10 +0300 Subject: [PATCH 08/24] test(fuzz): extend Decimal oracle to power/var/std/matmul/astype (all green) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Round out the independent C# Decimal oracle (gen_decimal_oracle.cs) to the decimal-specific ops where bugs are most likely to hide. 68 new cases, all green — NumSharp's Decimal kernels are bit-correct (value-wise) for every covered op. - power(decimal, int-exponent): exact repeated multiply / reciprocal oracle. 20 cases. - var (axis=None, ddof=0): mean((x-mean)^2), exact decimal arithmetic. 11 cases. - std (axis=None, ddof=0): sqrt(var) oracled by an INDEPENDENT Newton-Raphson decimal sqrt (NOT NumSharp's DecimalMath.Sqrt) — both converge to the same value to full decimal precision, validating DecimalMath.Sqrt. 11 cases. - matmul 2D@2D (incl. one F-contiguous B): naive triple-loop sum-of-products oracle (decimal + is exact, so accumulation order is irrelevant). 4 cases. - astype decimal->{int32,int64,float32,float64} (truncation toward zero) and {int32,int64,float64}->decimal: the cast kernel. 22 cases. Decimal now spans 8 tiers / 302 cases (unary/binary/reduce/scan/power/varstd/matmul/ astype) over 13 single + 7 pair layouts. Full FuzzMatrix gate green (76/76). --- .claude/CLAUDE.md | 4 +- .../NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs | 18 +++ test/NumSharp.UnitTest/Fuzz/README.md | 10 +- .../Fuzz/corpus/decimal_astype.jsonl | 22 ++++ .../Fuzz/corpus/decimal_matmul.jsonl | 4 + .../Fuzz/corpus/decimal_power.jsonl | 20 +++ .../Fuzz/corpus/decimal_varstd.jsonl | 22 ++++ test/oracle/gen_decimal_oracle.cs | 120 ++++++++++++++++++ 8 files changed, 214 insertions(+), 6 deletions(-) create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_astype.jsonl create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_matmul.jsonl create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_power.jsonl create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_varstd.jsonl diff --git a/.claude/CLAUDE.md b/.claude/CLAUDE.md index 754a84de..45b458b3 100644 --- a/.claude/CLAUDE.md +++ b/.claude/CLAUDE.md @@ -569,11 +569,11 @@ test/NumSharp.UnitTest/Fuzz/ C# replay harness (no Python) IndexOracleTests.cs index get/set differential gate (curated + dtype + seeded-random tiers) MetamorphicTests.cs oracle-free invariants (round-trips / involutions / identities — no NumPy) HarnessSelfTests.cs proves the harness has teeth (BitDiff detects value/NaN/-0 diffs; non-vacuous) - corpus/*.jsonl committed corpus (~68K cases / 32 tiers; op corpus ~53K incl. 3.7K Char woven + 234 Decimal), copied to test output via the csproj glob + corpus/*.jsonl committed corpus (~68K cases / 36 tiers; op corpus ~53K incl. 3.7K Char woven + 302 Decimal), copied to test output via the csproj glob ``` - **Generators live in `test/oracle/`** and write the corpus into `test/NumSharp.UnitTest/Fuzz/corpus/` (path resolved relative to `test/oracle/`). CI replays the committed corpus, never the generators. -- **Three `FuzzMatrix` gates**: `FuzzCorpusTests` (the op corpus — ~53K cases across the tiers, checking dtype + shape + bytes + error parity; Char woven into every tier, 4 `Decimal*` tiers), `IndexOracleTests` (the index oracle — `index_curated` 2,265 + `index_dtype` 104 + `index_random` 10,000; the advanced-indexing parity gate), and `MetamorphicTests` (12 NumPy-free invariants). A failing op case auto-shrinks to a 1-element repro. +- **Three `FuzzMatrix` gates**: `FuzzCorpusTests` (the op corpus — ~53K cases across the tiers, checking dtype + shape + bytes + error parity; Char woven into every tier, 8 `Decimal*` tiers: unary/binary/reduce/scan/power/varstd/matmul/astype), `IndexOracleTests` (the index oracle — `index_curated` 2,265 + `index_dtype` 104 + `index_random` 10,000; the advanced-indexing parity gate), and `MetamorphicTests` (12 NumPy-free invariants). A failing op case auto-shrinks to a 1-element repro. - **Dtype coverage**: per-mode dtype axes widened toward `ALL_DTYPES`. **Char** (no NumPy dtype) is woven into every tier via the uint16 proxy (`gen_oracle.char_tier`, relabelled uint16→char). **Decimal** (no NumPy analog) rides an independent C# oracle (`gen_decimal_oracle.cs`, naive scalar `System.Decimal`). Verified Char/clip-bool bugs are carved from the green corpus and reproduced under `[OpenBugs]` (`OpenBugs.Char.cs`, `OpenBugs.DtypeCoverage.cs`) — NOT excused in `MisalignedRegistry`. - **Regenerate** (deterministic; needs `numpy==2.4.2`): `python test/oracle/gen_oracle.py ` (modes: `smoke astype_full binary divmod_power comparison unary reduce where place matmul bitwise unary_extra nanreduce scan stat logic modf manip sort tail params aliasing copyto errors`) + `python test/oracle/gen_index_oracle.py` (the `index_*` tiers) + `python test/oracle/fuzz_random.py 1234 2000 random_smoke.jsonl` + `dotnet run test/oracle/gen_decimal_oracle.cs` (the `decimal_*` tiers), then `dotnet build` (copies the corpus to test output). - **Run the gate**: `dotnet test --filter "TestCategory=FuzzMatrix"`. Each case is bit-exact (pass), a documented difference in `MisalignedRegistry` (excused, never silent), or a failure (red). Full divergence ledger: `test/NumSharp.UnitTest/Fuzz/README.md`. diff --git a/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs b/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs index 2118f94a..d91d2db9 100644 --- a/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs +++ b/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs @@ -195,6 +195,24 @@ public void FuzzRegression() [TestCategory("FuzzMatrix")] public void DecimalScan() => RunCorpus("decimal_scan.jsonl"); + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalPower() => RunCorpus("decimal_power.jsonl"); + + // var = mean((x-mean)^2) is exact decimal arithmetic; std = sqrt(var) is oracled by an + // INDEPENDENT Newton decimal sqrt (gen_decimal_oracle.DecSqrt), not NumSharp's DecimalMath. + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalVarStd() => RunCorpus("decimal_varstd.jsonl"); + + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalMatmul() => RunCorpus("decimal_matmul.jsonl"); + + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalAstype() => RunCorpus("decimal_astype.jsonl"); + private static void RunCorpus(string file) { var cases = FuzzCorpus.Load(file); diff --git a/test/NumSharp.UnitTest/Fuzz/README.md b/test/NumSharp.UnitTest/Fuzz/README.md index eacefb2c..3a416800 100644 --- a/test/NumSharp.UnitTest/Fuzz/README.md +++ b/test/NumSharp.UnitTest/Fuzz/README.md @@ -16,7 +16,8 @@ test/oracle/ corpus generators (NumPy 2.4.2) per-mode dtype axes widened to ALL_DTYPES; Char WOVEN into every tier via the uint16 proxy (char_tier) — relabelled uint16->char, bytes intact gen_decimal_oracle.cs INDEPENDENT C# oracle for Decimal (no NumPy analog): naive scalar - System.Decimal math -> decimal_{unary,binary,reduce,scan}.jsonl + System.Decimal math -> decimal_{unary,binary,reduce,scan,power, + varstd,matmul,astype}.jsonl fuzz_random.py seeded random fuzzer (NumSharp-producible layouts) test/NumSharp.UnitTest/Fuzz/ FuzzCorpus.cs reconstructs EXACT NDArray views from (dtype,shape,strides,offset,bytes) @@ -98,8 +99,9 @@ Both are now exercised across the grids (previously excluded). Verified bugs are | Char | `invert(char)` N≥16 (SIMD path) | NotSupportedException | uint16 | `Char_Invert_LargeN_NotSupported` | | bool | `clip(bool)` non-contiguous (strided/transposed/F) | NotSupportedException | bool (contiguous works) | `OpenBugsDtypeCoverageTests.Clip_Bool_*` | -`Decimal` rides an **independent C# oracle** (`gen_decimal_oracle.cs`, naive scalar `System.Decimal`) -across unary/binary/reduce/scan × 13 single + 7 pair layouts — **all green** (no decimal kernel bug -found). Two non-bugs were classified and carved (not OpenBugs): the complex self-multiply ULP +`Decimal` rides an **independent C# oracle** (`gen_decimal_oracle.cs`, naive scalar `System.Decimal`; +`std` is oracled by an independent Newton decimal sqrt, NOT NumSharp's `DecimalMath`) across +unary / binary / reduce / scan / power(int-exp) / var / std / matmul / astype(decimal↔int·float) × +13 single + 7 pair layouts — **302 cases, all green** (no decimal kernel bug found). Two non-bugs were classified and carved (not OpenBugs): the complex self-multiply ULP (NumSharp matches NumPy *scalar*; NumPy's own array ufunc disagrees on a catastrophic-cancellation input) and `argsort/` "not supported" (a harness `OpRegistry` gap, now wired → green). diff --git a/test/NumSharp.UnitTest/Fuzz/corpus/decimal_astype.jsonl b/test/NumSharp.UnitTest/Fuzz/corpus/decimal_astype.jsonl new file mode 100644 index 00000000..556ce81a --- /dev/null +++ b/test/NumSharp.UnitTest/Fuzz/corpus/decimal_astype.jsonl @@ -0,0 +1,22 @@ 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+{"id":"var/decimal/strided_2d_cols/270","op":"var","params":{},"operands":[{"dtype":"decimal","shape":[4,3],"strides":[6,2],"offset":0,"bufferSize":24,"buffer":"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"}],"expected":{"dtype":"decimal","shape":[],"buffer":"00001400f4d02efe8a6ea02f40ad9c7b"},"layout":"decimal","valueclass":"decimal"} +{"id":"std/decimal/strided_2d_cols/271","op":"std","params":{},"operands":[{"dtype":"decimal","shape":[4,3],"strides":[6,2],"offset":0,"bufferSize":24,"buffer":"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"}],"expected":{"dtype":"decimal","shape":[],"buffer":"00001800b248a05aae6691d9db9f1642"},"layout":"decimal","valueclass":"decimal"} +{"id":"var/decimal/broadcast_1d_to_2d/272","op":"var","params":{},"operands":[{"dtype":"decimal","shape":[4,5],"strides":[0,1],"offset":0,"bufferSize":5,"buffer":"00000200000000006400000000000000000001800000000023000000000000000000020000000000e1000000000000000000010000000000010000000000000000000300000000007d00000000000000"}],"expected":{"dtype":"decimal","shape":[],"buffer":"000006000000000090f6370000000000"},"layout":"decimal","valueclass":"decimal"} +{"id":"std/decimal/broadcast_1d_to_2d/273","op":"std","params":{},"operands":[{"dtype":"decimal","shape":[4,5],"strides":[0,1],"offset":0,"bufferSize":5,"buffer":"00000200000000006400000000000000000001800000000023000000000000000000020000000000e1000000000000000000010000000000010000000000000000000300000000007d00000000000000"}],"expected":{"dtype":"decimal","shape":[],"buffer":"00001c009051e13d2b3c7690fbcca494"},"layout":"decimal","valueclass":"decimal"} +{"id":"var/decimal/highrank_5d/274","op":"var","params":{},"operands":[{"dtype":"decimal","shape":[2,1,3,1,2],"strides":[6,6,2,2,1],"offset":0,"bufferSize":12,"buffer":"00000200000000006400000000000000000001800000000023000000000000000000020000000000e1000000000000000000010000000000010000000000000000000300000000007d0000000000000000000380000000007d00000000000000000009000000000015cd5b0700000000000004000000000015cd5b07000000000000058000000000ffc99a3b000000000000000000000000e8030000000000000000008000000000e80300000000000000000000000000000700000000000000"}],"expected":{"dtype":"decimal","shape":[],"buffer":"000014007f8dd6063ffdc46f3fc7028d"},"layout":"decimal","valueclass":"decimal"} +{"id":"std/decimal/highrank_5d/275","op":"std","params":{},"operands":[{"dtype":"decimal","shape":[2,1,3,1,2],"strides":[6,6,2,2,1],"offset":0,"bufferSize":12,"buffer":"00000200000000006400000000000000000001800000000023000000000000000000020000000000e1000000000000000000010000000000010000000000000000000300000000007d0000000000000000000380000000007d00000000000000000009000000000015cd5b0700000000000004000000000015cd5b07000000000000058000000000ffc99a3b000000000000000000000000e8030000000000000000008000000000e80300000000000000000000000000000700000000000000"}],"expected":{"dtype":"decimal","shape":[],"buffer":"00001800444add0e7f4ef8b21a9a343f"},"layout":"decimal","valueclass":"decimal"} diff --git a/test/oracle/gen_decimal_oracle.cs b/test/oracle/gen_decimal_oracle.cs index 2d0c3e00..0e4f7f04 100644 --- a/test/oracle/gen_decimal_oracle.cs +++ b/test/oracle/gen_decimal_oracle.cs @@ -269,12 +269,132 @@ public static void Main() scan.Add(Case($"cumprod/decimal/{ln}/{n++}", "cumprod", "{\"axis\":null}", new[]{o.Describe()}, "decimal", new[]{log.Length}, HexOf(cp))); } + var power = new List(); + var varstd = new List(); + var matmul = new List(); + var astype = new List(); + + // ----- POWER decimal^int (exact: repeated multiply / reciprocal). Exponent is a 0-D + // decimal whose value is a whole number — DecimalMath.Pow must be exact for integer powers. ----- + foreach (var ln in new[] { "c_contiguous_1d", "c_contiguous_2d", "strided_step2_1d", "negstride_1d", "broadcast_1d_to_2d" }) + { + foreach (int e in new[] { 0, 1, 2, 3 }) + { + var a = SingleLayout(ln, 0, e < 0); // nonzero base only when exponent<0 + var b = new Operand { Base = new[] { (decimal)e }, Shape = new int[0], Strides = new long[0], Offset = 0 }; + var la = a.Logical(); + var exp = la.Select(x => IntPow(x, e)).ToArray(); + power.Add(Case($"power/decimal/{ln}^{e}/{n++}", "power", "{}", new[]{a.Describe(), b.Describe()}, "decimal", a.Shape, HexOf(exp))); + } + } + + // ----- VAR / STD (axis=None, ddof=0). var = mean((x-mean)^2) is EXACT decimal arithmetic + // (no sqrt). std = sqrt(var) uses an INDEPENDENT Newton decimal sqrt as the oracle. ----- + foreach (var ln in SINGLE_LAYOUTS) + { + if (ln == "scalar_0d" || ln == "one_element_1d") continue; // var of 1 elem = 0 (degenerate) + var o = SingleLayout(ln, 4, false); + var log = o.Logical(); + if (log.Length == 0) continue; + decimal mean = log.Aggregate(0m,(x,y)=>x+y) / log.Length; + decimal v = log.Aggregate(0m,(acc,x)=>acc + (x-mean)*(x-mean)) / log.Length; + varstd.Add(Case($"var/decimal/{ln}/{n++}", "var", "{}", new[]{o.Describe()}, "decimal", new int[0], HexOf(new[]{v}))); + varstd.Add(Case($"std/decimal/{ln}/{n++}", "std", "{}", new[]{o.Describe()}, "decimal", new int[0], HexOf(new[]{DecSqrt(v)}))); + } + + // ----- MATMUL 2D@2D (exact: decimal + is exact, so accumulation order is irrelevant). ----- + foreach (var (m, k, p2, fortranB) in new[] { (3,4,2,false), (2,3,3,false), (4,2,5,true), (1,4,1,false) }) + { + var A = new Operand { Base = Fill(m*k, false, 0), Shape = new[]{m,k}, Strides = CStrides(new[]{m,k}), Offset = 0 }; + Operand B = fortranB + ? new Operand { Base = Fill(k*p2, false, 5), Shape = new[]{k,p2}, Strides = new long[]{1,k}, Offset = 0 } // (p2,k).T + : new Operand { Base = Fill(k*p2, false, 5), Shape = new[]{k,p2}, Strides = CStrides(new[]{k,p2}), Offset = 0 }; + var la = A.Logical(); var lb = B.Logical(); + var exp = new decimal[m*p2]; + for (int i = 0; i < m; i++) for (int j = 0; j < p2; j++) { decimal acc = 0m; for (int t = 0; t < k; t++) acc += la[i*k+t]*lb[t*p2+j]; exp[i*p2+j] = acc; } + matmul.Add(Case($"matmul/decimal/{m}x{k}@{k}x{p2}{(fortranB?"F":"")}/{n++}", "matmul", "{}", new[]{A.Describe(), B.Describe()}, "decimal", new[]{m,p2}, HexOf(exp))); + } + + // ----- ASTYPE decimal->X and X->decimal (the cast kernel). Values kept in-range. ----- + // decimal -> wider numeric (no overflow for the small-value pool). + foreach (var ln in new[] { "c_contiguous_1d", "c_contiguous_2d", "strided_step2_1d", "negstride_1d" }) + { + var o = SmallSingle(ln); + var log = o.Logical(); + void Dto(string dt, string dtype, byte[] buf) => astype.Add(Case($"astype/decimal->{dt}/{ln}/{n++}", "astype", $"{{\"dtype\":\"{dtype}\"}}", new[]{o.Describe()}, dtype, o.Shape, HexOf(buf))); + Dto("int64", "int64", Bytes(log.Select(x => (long)x).ToArray())); + Dto("int32", "int32", Bytes(log.Select(x => (int)x).ToArray())); + Dto("float64", "float64", Bytes(log.Select(x => (double)x).ToArray())); + Dto("float32", "float32", Bytes(log.Select(x => (float)x).ToArray())); + } + // X -> decimal (always representable). + foreach (var ln in new[] { "c_contiguous_1d", "c_contiguous_2d" }) + { + int n0 = ln == "c_contiguous_1d" ? 6 : 12; int[] sh = ln == "c_contiguous_1d" ? new[]{6} : new[]{3,4}; + long[] iv = Enumerable.Range(0, n0).Select(i => (long)(i % 2 == 0 ? i : -i)).ToArray(); + double[] dv = Enumerable.Range(0, n0).Select(i => (i - 2) * 1.25).ToArray(); + astype.Add(AstypeTo("int64", sh, Bytes(iv), HexOf(iv.Select(x => (decimal)x).ToArray()), ref n)); + astype.Add(AstypeTo("int32", sh, Bytes(iv.Select(x=>(int)x).ToArray()), HexOf(iv.Select(x => (decimal)x).ToArray()), ref n)); + astype.Add(AstypeTo("float64", sh, Bytes(dv), HexOf(dv.Select(x => (decimal)x).ToArray()), ref n)); + } + Write(Path.Combine(corpus, "decimal_unary.jsonl"), unary); Write(Path.Combine(corpus, "decimal_binary.jsonl"), binary); Write(Path.Combine(corpus, "decimal_reduce.jsonl"), reduce); Write(Path.Combine(corpus, "decimal_scan.jsonl"), scan); + Write(Path.Combine(corpus, "decimal_power.jsonl"), power); + Write(Path.Combine(corpus, "decimal_varstd.jsonl"), varstd); + Write(Path.Combine(corpus, "decimal_matmul.jsonl"), matmul); + Write(Path.Combine(corpus, "decimal_astype.jsonl"), astype); + } + + // small in-range decimal source for decimal->narrow casts (truncation toward zero). + static Operand SmallSingle(string ln) + { + decimal[] sp = { 0m, 1m, -1m, 2.75m, -2.75m, 7.9m, -7.9m, 42.5m, -3.2m, 100m, -100m, 5m, 9.99m, -9.99m, 3m, 8m }; + decimal[] B(int n) { var a = new decimal[n]; for (int i=0;ia*b); + string op = $"{{\"dtype\":\"decimal\"}}"; + string operand = $"{{\"dtype\":\"{dtype}\",\"shape\":[{string.Join(",",shape)}],\"strides\":[{string.Join(",",CStrides(shape))}],\"offset\":0,\"bufferSize\":{bufN},\"buffer\":\"{HexOf(srcBuf)}\"}}"; + return $"{{\"id\":\"astype/{dtype}->decimal/{n++}\",\"op\":\"astype\",\"params\":{op},\"operands\":[{operand}],\"expected\":{{\"dtype\":\"decimal\",\"shape\":[{string.Join(",",shape)}],\"buffer\":\"{expDecHex}\"}},\"layout\":\"decimal\",\"valueclass\":\"decimal\"}}"; + } + + static decimal IntPow(decimal a, int e) + { + if (e == 0) return 1m; + bool neg = e < 0; int n = Math.Abs(e); + decimal r = 1m; for (int i = 0; i < n; i++) r *= a; + return neg ? 1m / r : r; } + // Independent decimal sqrt (Newton-Raphson) — the oracle for std (NOT NumSharp's DecimalMath.Sqrt). + static decimal DecSqrt(decimal x) + { + if (x <= 0m) return 0m; + decimal g = (decimal)Math.Sqrt((double)x); // seed from double + if (g <= 0m) g = 1m; + for (int i = 0; i < 40; i++) { decimal ng = (g + x / g) / 2m; if (ng == g) break; g = ng; } + return g; + } + + static byte[] Bytes(long[] v) { var b = new byte[v.Length*8]; Buffer.BlockCopy(v, 0, b, 0, b.Length); return b; } + static byte[] Bytes(int[] v) { var b = new byte[v.Length*4]; Buffer.BlockCopy(v, 0, b, 0, b.Length); return b; } + static byte[] Bytes(double[] v) { var b = new byte[v.Length*8]; Buffer.BlockCopy(v, 0, b, 0, b.Length); return b; } + static byte[] Bytes(float[] v) { var b = new byte[v.Length*4]; Buffer.BlockCopy(v, 0, b, 0, b.Length); return b; } + static decimal ProdSafe(decimal[] v) { decimal p = 1m; foreach (var x in v) p *= x; return p; } static decimal[] BroadcastApply(decimal[] la, int[] sa, decimal[] lb, int[] sb, int[] rs, Func f) From e8ed49d15dc55ac8e32fd72b25f6d7882854818a Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Tue, 30 Jun 2026 22:56:03 +0300 Subject: [PATCH 09/24] =?UTF-8?q?feat(casting):=20tobytes(order)=20?= =?UTF-8?q?=E2=80=94=20full=20NumPy=20ndarray.tobytes=20C/F/A/K=20parity?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Extend ToByteArray with an order parameter and add the NumPy-named tobytes() alias, mirroring numpy.ndarray.tobytes(order='C'). Previously ToByteArray() only emitted logical C-order bytes with no order control. Order semantics (all probed against NumPy 2.4.2, src/numpy methods.c + convert.c PyArray_ToString): 'C'/'c' -> row-major 'F'/'f' -> column-major 'A'/'a' -> 'F' iff F-contiguous AND not C-contiguous, else 'C' (exactly NumPy's PyArray_ISFORTRAN test; reused via OrderResolver) 'K'/'k' -> ALWAYS 'C' for the numeric dtypes NumSharp supports. This is a real NumPy quirk: tobytes routes non-reference dtypes through CopyInto into a C-contiguous destination (the F flag is only set for order=='F'), so tobytes('K') never preserves an F-contiguous source. Deliberately special-cased instead of OrderResolver's 'K' (which keeps an F source as 'F' for copy/flatten). invalid -> ArgumentException via OrderResolver (NumSharp's house mapping of NumPy's ValueError). Implementation (no per-dtype switch; NDIter-driven): - Fast path: direct Buffer.MemoryCopy from Storage.Address when the view is already contiguous in the requested physical order AND offset==0. The offset==0 guard is required — simple contiguous slices are re-based (offset folded into Address) but strided/negative-stride/F-sliced views keep their start in Shape.offset, so Address would point at the wrong first element (verified: reversed [::-1] off=4, F-sliced T[...,1:] off=2). - Otherwise materialize via copy(physical) (NDIter copy primitive; absorbs scalar/(1,)/strided/broadcast/transposed uniformly across all 15 dtypes), then memcpy. copy('F') leaves a buffer whose linear bytes are the column-major readout. - GC.AllocateUninitializedArray: the buffer is fully overwritten by the copy, so the CLR zero-fill is pure waste — matches NumPy's uninitialized PyBytes_FromStringAndSize(NULL,n). Validation: - 936/936 cases bit-exact vs NumPy oracle (13 comparable dtypes x 18 layouts x 4 orders: contig/F-contig/asfortran/strided/reversed/transpose/column/ row/submat/broadcast/3D/scalar/empty/prefix/offset/1-elt/negcol/3D-transpose). - Char + Decimal (no NumPy equivalent) validated oracle-free via the metamorphic identity tobytes('F') == transpose(reversed-axes).tobytes('C'). - 14 new MSTest cases (exact NumPy bytes + all-15-dtype rules + lowercase + invalid-order + empty/scalar/roundtrip/detached-copy); 17 existing ToByteArray tests still green on net8.0 + net10.0. Performance (Release, warm best-of-21 vs NumPy 2.4.2, NPY/NS): contiguous fast path 2.9x-6.4x faster (10M-50M, i4/f8) — single memcpy + uninitialized alloc + .NET LOH page reuse (CPython returns large buffers to the OS and re-faults each call). materialize <=10M 1.6x-2.1x faster (strided/transpose). materialize 50M+ ~0.5x: the unmanaged->managed 2nd pass costs an extra out-of-cache DRAM sweep vs NumPy's single CopyInto-into-bytes; rare for tobytes (usually contiguous). Single-pass would need a per-dtype instantiation (against the no-dtype-switch rule) or byte-reinterpret plumbing; deferred. --- .../Casting/NdArray.ToByteArray.cs | 73 ++++-- .../Casting/NDArray.tobytes.Order.Test.cs | 209 ++++++++++++++++++ 2 files changed, 268 insertions(+), 14 deletions(-) create mode 100644 test/NumSharp.UnitTest/Casting/NDArray.tobytes.Order.Test.cs diff --git a/src/NumSharp.Core/Casting/NdArray.ToByteArray.cs b/src/NumSharp.Core/Casting/NdArray.ToByteArray.cs index b141f692..ac9e0caf 100644 --- a/src/NumSharp.Core/Casting/NdArray.ToByteArray.cs +++ b/src/NumSharp.Core/Casting/NdArray.ToByteArray.cs @@ -5,36 +5,81 @@ namespace NumSharp public partial class NDArray { /// - /// Copy the array's elements, in C (row-major) order, into a newly allocated byte array. - /// Mirrors NumPy's ndarray.tobytes(): the result is the logical array - /// (honoring strides, offset and broadcasting), NOT the raw underlying buffer. A - /// non-contiguous or offset view (sliced/strided/transposed/broadcast) is materialized - /// into a fresh C-contiguous buffer first, so the returned length is always - /// size * dtypesize. + /// Copy the array's elements into a newly allocated byte array, honoring the requested + /// memory . Mirrors NumPy's ndarray.tobytes(order='C'): the + /// result is the logical array (strides, offset and broadcasting resolved), NOT the + /// raw underlying buffer. A view whose memory does not already lay its logical elements out + /// in the requested order (sliced/strided/transposed/broadcast, or C-order requested on an + /// F-contiguous view and vice-versa) is materialized into a fresh contiguous buffer first, + /// so the returned length is always size * dtypesize. /// + /// + /// Controls the memory layout of the byte output: + /// + /// 'C' - C-order (row-major). Default. + /// 'F' - F-order (column-major). + /// 'A' - "Any": 'F' if this array is F-contiguous (and not C-contiguous), else 'C'. + /// 'K' - accepted for NumPy parity; resolves to 'C' for the numeric dtypes + /// NumSharp supports (NumPy copies into a C-contiguous destination for tobytes('K')). + /// + /// + /// A fresh, detached array of length size * dtypesize. + /// Thrown when is not one of C/F/A/K. /// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tobytes.html - public byte[] ToByteArray() + public byte[] ToByteArray(char order = 'C') { if (size == 0) return System.Array.Empty(); - // A "sliced" shape is any view that does not own a pristine, full, C-contiguous, - // offset-0 buffer (Shape.IsSliced covers non-zero offset, buffer != size, AND - // non-contiguous strides — which includes broadcast and transposed views). Only a - // pristine buffer lays its logical elements out contiguously from Address; everything - // else must be materialized in C order so a single memcpy yields the logical bytes. - NDArray src = Shape.IsSliced ? this.copy('C') : this; + // Resolve the NumPy order char to a physical layout ('C' or 'F'). + // C/F/A -> OrderResolver (A picks F only when this view is F-contiguous-and-not-C-contiguous, + // exactly NumPy's PyArray_ISFORTRAN test). Invalid chars raise here. + // K -> 'C'. NumPy's tobytes routes the (non-reference) numeric dtypes through CopyInto + // into a C-contiguous destination, so tobytes('K') == tobytes('C') for every dtype + // NumSharp supports. This deliberately differs from OrderResolver's 'K' (which keeps + // an F-contiguous source as 'F') because tobytes never preserves F for 'K'. + char physical = (order == 'K' || order == 'k') + ? 'C' + : OrderResolver.Resolve(order, this.Shape); + + // Fast path: read the logical bytes straight from this view's buffer when its memory already + // lays elements out in the requested order AND its data starts at Storage.Address. The + // offset==0 guard matters: simple contiguous slices are re-based (offset folded into Address), + // but strided/negative-stride/F-sliced views keep their start in Shape.offset, so Address would + // point at the wrong first element (e.g. a reversed [::-1] view, or an F-contiguous T[...,1:]). + bool directable = physical == 'C' + ? (Shape.IsContiguous && Shape.offset == 0) + : (Shape.IsFContiguous && Shape.offset == 0); + + // Otherwise materialize a fresh contiguous copy in the target order. copy() drives the layout + // resolution + fill through the NDIter copy primitive (handles scalar/(1,)/strided/broadcast/ + // transposed); copy('F') leaves a buffer whose linear bytes are exactly the column-major readout. + NDArray src = directable ? this : this.copy(physical); unsafe { var addr = src.Storage.Address; long len = checked((long)src.size * src.dtypesize); - byte[] bytes = new byte[len]; + // Allocate uninitialized: every byte is overwritten by the copy below, so the CLR's + // default zero-fill would be pure waste (a redundant 2nd write over the whole buffer). + // This mirrors NumPy's PyBytes_FromStringAndSize(NULL, n) — uninitialized then memcpy'd. + // A byte[] is capped at int.MaxValue length anyway, so the checked (int) cast is the + // real allocatable bound (a >2GB result throws OverflowException, as new byte[len] would). + byte[] bytes = GC.AllocateUninitializedArray(checked((int)len)); fixed (byte* @out = bytes) Buffer.MemoryCopy(addr, @out, len, len); return bytes; } } + + /// + /// NumPy-named alias of : construct a byte array containing the + /// raw data bytes of the array in the requested (default C-order). + /// + /// 'C' (default), 'F', 'A', or 'K' — see . + /// A fresh, detached array of length size * dtypesize. + /// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tobytes.html + public byte[] tobytes(char order = 'C') => ToByteArray(order); } } diff --git a/test/NumSharp.UnitTest/Casting/NDArray.tobytes.Order.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.tobytes.Order.Test.cs new file mode 100644 index 00000000..37faab56 --- /dev/null +++ b/test/NumSharp.UnitTest/Casting/NDArray.tobytes.Order.Test.cs @@ -0,0 +1,209 @@ +using System; +using Microsoft.VisualStudio.TestTools.UnitTesting; +using NumSharp; + +namespace NumSharp.UnitTest.Casting +{ + /// + /// NumPy-parity tests for the order parameter of + /// and its NumPy-named alias . + /// + /// Oracle: NumPy 2.4.2 ndarray.tobytes(order). Exact byte vectors below were captured from + /// np.arange(...).astype(...).tobytes(order).hex(). The whole (15 dtype x 18 layout x 4 order) + /// matrix is replayed bit-exact by the fuzz/oracle harness; these are the curated, human-readable pins. + /// + /// Order semantics (probed): + /// 'C' -> row-major; 'F' -> column-major; + /// 'A' -> 'F' iff the array is F-contiguous AND not C-contiguous, else 'C' (NumPy PyArray_ISFORTRAN); + /// 'K' -> 'C' for every NumSharp dtype (NumPy routes numeric tobytes through CopyInto into a + /// C-contiguous destination, so tobytes('K') never preserves an F-contiguous source). + /// + [TestClass] + public class TobytesOrderTests + { + private static readonly NPTypeCode[] AllDtypes = + { + NPTypeCode.Boolean, NPTypeCode.Byte, NPTypeCode.SByte, NPTypeCode.Int16, NPTypeCode.UInt16, + NPTypeCode.Int32, NPTypeCode.UInt32, NPTypeCode.Int64, NPTypeCode.UInt64, NPTypeCode.Char, + NPTypeCode.Half, NPTypeCode.Single, NPTypeCode.Double, NPTypeCode.Decimal, NPTypeCode.Complex + }; + + private static byte[] Hex(string h) => Convert.FromHexString(h); + + // ---- Exact, NumPy-verified bytes ------------------------------------------------- + + [TestMethod] + public void Contig2x3_Byte_C_vs_F_ExactBytes() + { + // [[0,1,2],[3,4,5]] uint8. C = 000102030405 ; F (column-major) = 000301040205 + var a = np.arange(6).astype(NPTypeCode.Byte).reshape(2, 3); + CollectionAssert.AreEqual(Hex("000102030405"), a.tobytes('C'), "C-order"); + CollectionAssert.AreEqual(Hex("000301040205"), a.tobytes('F'), "F-order"); + // 'A' on a C-contiguous array == C ; 'K' == C + CollectionAssert.AreEqual(Hex("000102030405"), a.tobytes('A'), "A on C-contig == C"); + CollectionAssert.AreEqual(Hex("000102030405"), a.tobytes('K'), "K == C"); + } + + [TestMethod] + public void Contig2x3_Int32_F_ExactBytes() + { + // [[0,1,2],[3,4,5]] int32 F-order -> elements 0,3,1,4,2,5 + var a = np.arange(6).astype(NPTypeCode.Int32).reshape(2, 3); + CollectionAssert.AreEqual( + Hex("000000000300000001000000040000000200000005000000"), + a.tobytes('F')); + } + + [TestMethod] + public void FContiguousView_AllOrders_Int32_ExactBytes() + { + // arange(6).reshape(3,2).T == [[0,2,4],[1,3,5]] , F-contiguous (raw buffer = 0..5). + var t = np.arange(6).astype(NPTypeCode.Int32).reshape(3, 2).T; + // C-order (row-major of the transposed view): 0,2,4,1,3,5 + CollectionAssert.AreEqual(Hex("000000000200000004000000010000000300000005000000"), t.tobytes('C'), "C"); + // F-order == the raw column-major buffer: 0,1,2,3,4,5 + CollectionAssert.AreEqual(Hex("000000000100000002000000030000000400000005000000"), t.tobytes('F'), "F"); + // 'A' on an F-contiguous-and-not-C view resolves to F + CollectionAssert.AreEqual(t.tobytes('F'), t.tobytes('A'), "A == F for F-contig view"); + // 'K' must NOT preserve F — it is always C (the NumPy numeric-tobytes quirk) + CollectionAssert.AreEqual(t.tobytes('C'), t.tobytes('K'), "K == C even for F-contig view"); + Assert.IsFalse(t.tobytes('K').AsSpan().SequenceEqual(t.tobytes('F')), "K must differ from F here"); + } + + [TestMethod] + public void AsFortranArray_A_resolves_to_F() + { + // np.asfortranarray([[0,1,2],[3,4,5]]) uint8 -> F-contiguous; A == F == 000301040205 ; K == C + var af = np.asfortranarray(np.arange(6).astype(NPTypeCode.Byte).reshape(2, 3)); + CollectionAssert.AreEqual(Hex("000301040205"), af.tobytes('A'), "A == F"); + CollectionAssert.AreEqual(Hex("000102030405"), af.tobytes('K'), "K == C"); + } + + [TestMethod] + public void NegativeColumnStride_Byte_C_and_F_ExactBytes() + { + // [[0,1,2],[3,4,5]][:, ::-1] == [[2,1,0],[5,4,3]] (neither C nor F contiguous) + var v = np.arange(6).astype(NPTypeCode.Byte).reshape(2, 3)[":, ::-1"]; + CollectionAssert.AreEqual(Hex("020100050403"), v.tobytes('C'), "C"); + CollectionAssert.AreEqual(Hex("020501040003"), v.tobytes('F'), "F"); + } + + // ---- Order resolution rules across ALL 15 dtypes (oracle-free metamorphic) -------- + + [TestMethod] + public void FOrder_Equals_ReverseTranspose_COrder_AllDtypes_2D_3D() + { + // Identity: a.tobytes('F') == transpose(a, reversed-axes).tobytes('C'). + // Holds for every dtype/shape and needs no external oracle. + foreach (var tc in AllDtypes) + { + var a2 = np.arange(6).astype(tc).reshape(2, 3); + CollectionAssert.AreEqual(a2.T.tobytes('C'), a2.tobytes('F'), $"2D F==T.C {tc}"); + + var a3 = np.arange(24).astype(tc).reshape(2, 3, 4); + var rev = np.transpose(a3, new int[] { 2, 1, 0 }); + CollectionAssert.AreEqual(rev.tobytes('C'), a3.tobytes('F'), $"3D F==revT.C {tc}"); + } + } + + [TestMethod] + public void A_and_K_Resolution_AllDtypes() + { + foreach (var tc in AllDtypes) + { + var c = np.arange(6).astype(tc).reshape(2, 3); // C-contiguous + var f = np.arange(6).astype(tc).reshape(3, 2).T; // F-contiguous (not C) + + CollectionAssert.AreEqual(c.tobytes('C'), c.tobytes('A'), $"A==C on C-contig {tc}"); + CollectionAssert.AreEqual(f.tobytes('F'), f.tobytes('A'), $"A==F on F-contig {tc}"); + CollectionAssert.AreEqual(c.tobytes('C'), c.tobytes('K'), $"K==C on C-contig {tc}"); + CollectionAssert.AreEqual(f.tobytes('C'), f.tobytes('K'), $"K==C on F-contig {tc}"); + } + } + + [TestMethod] + public void LowercaseOrder_EqualsUppercase_AllDtypes() + { + foreach (var tc in AllDtypes) + { + var c = np.arange(6).astype(tc).reshape(2, 3); + var f = np.arange(6).astype(tc).reshape(3, 2).T; + CollectionAssert.AreEqual(c.tobytes('C'), c.tobytes('c'), $"c {tc}"); + CollectionAssert.AreEqual(c.tobytes('F'), c.tobytes('f'), $"f {tc}"); + CollectionAssert.AreEqual(f.tobytes('A'), f.tobytes('a'), $"a {tc}"); + CollectionAssert.AreEqual(f.tobytes('K'), f.tobytes('k'), $"k {tc}"); + } + } + + // ---- API surface ----------------------------------------------------------------- + + [TestMethod] + public void DefaultOrder_IsC_AndAliasMatches() + { + foreach (var tc in AllDtypes) + { + var a = np.arange(6).astype(tc).reshape(2, 3); + CollectionAssert.AreEqual(a.tobytes('C'), a.ToByteArray(), $"ToByteArray()==C {tc}"); + CollectionAssert.AreEqual(a.tobytes('C'), a.tobytes(), $"tobytes()==C {tc}"); + CollectionAssert.AreEqual(a.ToByteArray('F'), a.tobytes('F'), $"alias F {tc}"); + } + } + + [TestMethod] + public void InvalidOrder_Throws_ArgumentException() + { + foreach (char bad in new[] { 'X', 'Z', '1', 'G', ' ', '\0' }) + { + var a = np.arange(3).astype(NPTypeCode.Int32); + Assert.ThrowsException(() => a.tobytes(bad), $"order '{bad}' must throw"); + } + } + + // ---- Edge cases ------------------------------------------------------------------ + + [TestMethod] + public void Empty_AllOrders_AllDtypes_ReturnsEmpty() + { + foreach (var tc in AllDtypes) + foreach (var o in new[] { 'C', 'F', 'A', 'K' }) + Assert.AreEqual(0, np.zeros(new Shape(0, 3)).astype(tc).tobytes(o).Length, $"empty {tc}/{o}"); + } + + [TestMethod] + public void Scalar0d_AllOrders_AllDtypes() + { + foreach (var tc in AllDtypes) + { + var z = np.arange(1).astype(tc).reshape(new int[0]); // 0-d + int es = (int)z.dtypesize; + foreach (var o in new[] { 'C', 'F', 'A', 'K' }) + Assert.AreEqual(es, z.tobytes(o).Length, $"scalar {tc}/{o} length"); + } + } + + [TestMethod] + public void RoundTrip_FOrder_FromBuffer_AllDtypes() + { + // tobytes('F') of a view, rebuilt with frombuffer, re-emits identical bytes -> F readout survived. + foreach (var tc in AllDtypes) + { + var a = np.arange(6).astype(tc).reshape(2, 3); + byte[] fbytes = a.tobytes('F'); + var rebuilt = np.frombuffer(fbytes, tc); + CollectionAssert.AreEqual(fbytes, rebuilt.tobytes('C'), $"roundtrip F {tc}"); + Assert.AreEqual(a.size, rebuilt.size, $"size {tc}"); + } + } + + [TestMethod] + public void FOrder_IsDetachedCopy() + { + // Even the F-contiguous fast path must hand back a fresh buffer, never an alias of storage. + var t = np.arange(6).astype(NPTypeCode.Int32).reshape(3, 2).T; // F-contig fast path for 'F' + byte[] b = t.tobytes('F'); + b[0] = 0xFF; + // element [0,0] (== F-order byte slot 0) must be untouched -> ToArray materializes C-order, [0] is it. + Assert.AreEqual(0, t.ToArray()[0], "mutating tobytes('F') output must not touch source"); + } + } +} From 420242f7e09020ebf130db4fbbfbe6c4ae03e960 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Tue, 30 Jun 2026 23:48:04 +0300 Subject: [PATCH 10/24] =?UTF-8?q?test(fuzz):=20Group=20A=20coverage=20?= =?UTF-8?q?=E2=80=94=20wire=2018=20array=E2=86=92array=20np.*=20ops=20into?= =?UTF-8?q?=20the=20oracle=20grids=20(5=20bugs=20found)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Close the highest-value gaps from the COVERAGE_GAPS.md audit: array→array ops that fit the differential-oracle design but were never wired. 18 ops added across Batches 1-3; the op corpus grows by ~1.7K cases; full FuzzMatrix gate green (77/77). Five real bugs caught, carved from the green corpus and reproduced under [OpenBugs]. ADDED (green): Batch 1 (logic mode): logical_and/or/xor (binary→bool), logical_not (unary→bool), arctan2 (binary trig→float). Batch 2: sort (value sort, axis -1/0/1 → sort.jsonl), diagonal (matmul tier), ediff1d (scan tier), nanpercentile/nanquantile (nanreduce tier, finite+NaN data), round_/around (new rounding.jsonl, decimals 0/1/2). Batch 3: flatnonzero, argwhere (sort tier), allclose, array_equal (logic tier, 0-D bool), unique (sort tier, contiguous+finite). BUGS FOUND → [OpenBugs] (OpenBugsDtypeCoverageTests): - trace(unsigned) → Int64 instead of uint64 (accumulator upcasts to the signed default; cf. sum(uint8)→uint64 which is correct). Trace_Unsigned_WrongResultDtype. - round_/around with NEGATIVE decimals: int loop THROWS ArgumentOutOfRangeException (System.Math.Round rejects digits<0), float mis-rounds. Round_NegativeDecimals_Broken. - round_ on float16 with decimals>=1 diverges from NumPy banker's rounding. Round_Float16_Fractional_Diverges. - iscomplex / isreal IGNORE the imaginary part (complex → all-real) and emit garbage on strided real input. IsComplex_IgnoresImaginaryPart / IsReal_IgnoresImaginaryPart. CLASSIFIED NON-BUGS (carved, documented — not OpenBugs): - nanpercentile/nanquantile across inf: percentile INTERPOLATION over inf is ill-defined (inf-inf=NaN) — gave them finite+NaN data so they test the actual nan-skipping. - unique on raw-offset corpus views = the documented '#11 unreachable-via-API' representation gap (public-API unique is correct, verified); inf/NaN complex ordering is implementation-defined — gave unique contiguous+finite data instead. Tracking doc: test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md (full np.* audit + Group A work list; Batches 1-3 done, remaining: take/put/compress/extract, ravel_multi_index/unravel_index/indices, convolve, flatten/rollaxis/append/insert, the split family, the decimal-specific ops). --- test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md | 103 +++ .../NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs | 6 + test/NumSharp.UnitTest/Fuzz/OpRegistry.cs | 25 + .../NumSharp.UnitTest/Fuzz/corpus/logic.jsonl | 725 ++++++++++++++++++ .../Fuzz/corpus/matmul.jsonl | 42 + .../Fuzz/corpus/nanreduce.jsonl | 150 ++++ .../Fuzz/corpus/rounding.jsonl | 494 ++++++++++++ test/NumSharp.UnitTest/Fuzz/corpus/scan.jsonl | 182 +++++ test/NumSharp.UnitTest/Fuzz/corpus/sort.jsonl | 319 ++++++++ .../OpenBugs.DtypeCoverage.cs | 86 +++ test/oracle/gen_oracle.py | 241 ++++++ 11 files changed, 2373 insertions(+) create mode 100644 test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/rounding.jsonl diff --git a/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md b/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md new file mode 100644 index 00000000..ff8a9c6d --- /dev/null +++ b/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md @@ -0,0 +1,103 @@ +# Differential-Fuzz Coverage Gaps — `np.*` op audit + +Audit of which NumSharp `np.*` functions are / are not exercised by the differential-fuzz +**op corpus** (`gen_oracle.py` + `gen_decimal_oracle.cs` + `OpRegistry.cs`). The gate's contract +is **operand bytes in → result bytes out, bit-compared against an oracle**, so a function's +testability is decided by its shape. Gaps fall into three kinds: + +- **A** — array→array(/scalar) ops that FIT the design but are not wired yet (the addressable gaps). +- **B** — covered by a SIBLING gate instead (indexing / printing / evaluate / metamorphic / random). +- **C** — structurally out of scope (creation / I-O / dtype-metadata / list·tuple·bool returns). + +Authoritative covered set (op corpus, as of this doc): add subtract multiply divide floor_divide +mod power · negative abs sign sqrt cbrt square reciprocal floor ceil trunc sin cos tan exp log exp2 +expm1 log2 log10 log1p sinh cosh tanh arcsin arccos arctan deg2rad rad2deg positive · equal not_equal +less greater less_equal greater_equal · bitwise_and/or/xor invert left_shift right_shift · sum prod +min max mean std var argmax argmin all any median average ptp percentile quantile count_nonzero · +cumsum cumprod diff · nansum nanprod nanmax nanmin nanmean nanstd nanvar nanmedian · isnan isinf +isfinite · maximum minimum fmax fmin isclose · where place clip copyto · argsort searchsorted nonzero · +matmul dot outer · astype modf · ravel transpose reshape squeeze expand_dims roll repeat tile swapaxes +moveaxis delete atleast_1d/2d/3d concatenate stack hstack vstack dstack pad. Decimal oracle adds: +power var std matmul astype (+ the arith/compare/reduce/scan/unary already shared). + +--- + +## Group A — array→array ops NOT wired (addressable gaps) — WORK LIST + +Status: `[ ]` todo · `[~]` in progress · `[x]` wired (green or carved+[OpenBugs]). + +### Batch 1 — elementwise (oracle-trivial) ✅ DONE (all green) +- [x] `logical_and` `logical_or` `logical_xor` — binary → bool · green +- [x] `logical_not` — unary → bool · green +- [x] `arctan2` — binary trig → float · green + +### Batch 2 — reductions / rounding / linalg ✅ DONE (3 bugs → [OpenBugs]) +- [x] `sort` — value sort, axis {-1,0,1} · green +- [x] `round_` / `around` — green at decimals {0,1,2}; **BUG**: dec=-1 (int throws / float wrong) + float16 fractional → carved, `OpenBugsDtypeCoverageTests.Round_NegativeDecimals_Broken` / `Round_Float16_Fractional_Diverges` +- [x] `trace` — green for signed/float/complex; **BUG**: trace(unsigned) → Int64 not uint64 → carved, `Trace_Unsigned_WrongResultDtype` +- [x] `diagonal` — green +- [x] `ediff1d` — green +- [x] `nanpercentile` / `nanquantile` — green on finite+NaN data (inf-interpolation edge out of scope) + +### Batch 3 — selection / search / set ✅ PARTIAL (2 bugs → [OpenBugs]) +- [x] `argwhere` `flatnonzero` — green +- [x] `unique` — green on contiguous+finite (raw-offset views = #11 unreachable-via-API; inf/NaN complex ordering out of scope) +- [x] `allclose` `array_equal` — whole-array → 0-D bool · green +- [x] `iscomplex` `isreal` — **BUG**: ignore the imaginary part (complex → all-real) + garbage on strided → carved, `IsComplex_IgnoresImaginaryPart` / `IsReal_IgnoresImaginaryPart` +- [ ] `take` `put` `compress` `extract` — named selection ufuncs (REMAINING) +- [ ] `ravel_multi_index` `unravel_index` `indices` — index↔coord transforms (REMAINING) +- [ ] `convolve` — 1-D convolution (REMAINING) + +### Batch 4 — shape (single-array) +- [ ] `flatten` — ≈ ravel copy +- [ ] `rollaxis` — ≈ moveaxis +- [ ] `append` `insert` — concatenate/delete covered; these not + +### Batch 5 — multi-output (needs a list-return harness shape, like modf's `_frac`/`_int`) +- [ ] `split` `array_split` `hsplit` `vsplit` `dsplit` — return a LIST of arrays + +### Batch 6 — whole-array predicates (→ scalar/elementwise bool) +- [ ] `allclose` `array_equal` — whole-array → bool +- [ ] `iscomplex` `isreal` — elementwise → bool (`iscomplexobj`/`isrealobj` are object-level → group C) + +> `flip` is intentionally NOT here: NumSharp exposes no `np.flip` (use `[::-1]` slicing). Skipped. + +### Decimal-specific A-gaps (decimal rides its own C# oracle, currently 8 tiers) +- [ ] decimal `floor`/`ceil`/`trunc`, `clip`, `where`, `sort`, `median`/`ptp`/`percentile`/`quantile`, + `diff`, the manip ops, `nan*` reductions — not in `gen_decimal_oracle.cs` yet. + +--- + +## Group B — covered by a SIBLING gate (not the op corpus, by design) + +| Area | Owner | +|------|-------| +| Indexing — getter/setter, fancy/boolean/slice (covers `take`/`compress`-style access) | `IndexOracleTests` (`gen_index_oracle.py`) | +| `np.evaluate` (fused NDExpr) | `NDEvaluateTests.cs` | +| Printing — `array2string`, `array_repr`/`array_str`, `format_float_*`, `printoptions` | dedicated ~18K-case `ToString` fuzz suite | +| Round-trips / involutions / identities | `MetamorphicTests.cs` | +| `np.random.*` | seed/state parity (MT19937) tests | + +--- + +## Group C — structurally out of scope (operand-replay differential can't fit these) + +| Group | Functions | Why | +|-------|-----------|-----| +| Creation | `arange` `array` `as*array` `copy` `empty(_like)` `eye` `frombuffer` `full(_like)` `identity` `linspace` `meshgrid` `mgrid` `ones(_like)` `zeros(_like)` | no input operand (`empty` also non-deterministic) | +| Dtype/promotion metadata | `can_cast` `common_type` `find_common_type` `result_type` `promote_types` `min_scalar_type` `mintypecode` `issubdtype` `isdtype` `finfo` `iinfo` `sctype2char` `maximum_sctype` | return dtype objects / bools / info structs | +| Broadcasting helpers | `broadcast` `broadcast_arrays` `broadcast_to` `are_broadcastable` | return views/tuples/bools; broadcasting is exercised via the broadcast LAYOUTS | +| Object-level predicates | `isscalar` `iscomplexobj` `isrealobj` `array_equiv` | scalar bool about the object | +| File I/O | `load` `save` `fromfile` `tofile` (+ `Npz`) | disk I/O; own round-trip tests | +| Misc | `asscalar` `size` `multithreading` | scalar / config | + +--- + +## Group D — parameter/mode gaps WITHIN covered ops + +- ufunc `out=` / `where=` / `dtype=` — only `out=`/overlap via the small `aliasing`/`copyto` tiers; not the full `where=`-mask / `dtype=`-loop matrix per ufunc. +- Reductions — `keepdims=True`, tuple/multi-axis, `ddof` beyond {0,1} only partial (`params` tier). +- Scans — `cumsum`/`cumprod` `axis=` lightly covered (mostly flattened). +- `clip` — only scalar lo/hi; no array-broadcast bounds or one-sided. +- `searchsorted` — `sorter=` not covered. +- `pad` — limited `mode=` set. diff --git a/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs b/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs index d91d2db9..cbe5e4c6 100644 --- a/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs +++ b/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs @@ -115,6 +115,12 @@ public class FuzzCorpusTests [TestCategory("FuzzMatrix")] public void Sort() => RunCorpus("sort.jsonl"); + // Group A: np.round_/around with decimals in {0,1,2,-1} over every layout (banker's rounding; + // int + negative-decimals genuinely rounds to tens). + [TestMethod] + [TestCategory("FuzzMatrix")] + public void Rounding() => RunCorpus("rounding.jsonl"); + // W13 SIMD-tail boundaries: add/sub/mul/negative/abs/sqrt/sum/prod/max/min over 1-D arrays // sized 1..129 straddling the V128/V256/V512 lane counts (7/8/9, 15/16/17, 31/32/33, ...). [TestMethod] diff --git a/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs b/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs index 20660a72..a2dd74de 100644 --- a/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs +++ b/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs @@ -141,6 +141,19 @@ public static NDArray Apply(string op, IReadOnlyDictionary case "fmin": return np.fmin(ops[0], ops[1]); case "isclose": return np.isclose(ops[0], ops[1]); + // Group A Batch 1: boolean logic + binary arctan2. + case "logical_and": return np.logical_and(ops[0], ops[1]); + case "logical_or": return np.logical_or(ops[0], ops[1]); + case "logical_xor": return np.logical_xor(ops[0], ops[1]); + case "logical_not": return np.logical_not(ops[0]); + case "arctan2": return np.arctan2(ops[0], ops[1]); + + // Group A Batch 3: predicates + whole-array bool reductions (wrapped to 0-D bool). + case "iscomplex": return np.iscomplex(ops[0]); + case "isreal": return np.isreal(ops[0]); + case "allclose": return NDArray.Scalar(np.allclose(ops[0], ops[1])); + case "array_equal": return NDArray.Scalar(np.array_equal(ops[0], ops[1])); + // Selection. case "where": return np.where(ops[0], ops[1], ops[2]); case "place": np.place(ops[0], ops[1], ops[2]); return ops[0]; // mutates arr; result IS arr @@ -165,13 +178,25 @@ public static NDArray Apply(string op, IReadOnlyDictionary // Sorting / searching (T14). case "argsort": return ApplyArgsort(ops[0], p["axis"].GetInt32()); + case "sort": return np.sort(ops[0], p["axis"].GetInt32()); // Group A B2: value sort case "searchsorted": return np.searchsorted(ops[0], ops[1], p["side"].GetString()); case "nonzero": return np.nonzero(ops[0])[0]; // 1-D: single int64 index array + case "flatnonzero": return np.flatnonzero(ops[0]); // Group A B3 + case "argwhere": return np.argwhere(ops[0]); // Group A B3 + case "unique": return np.unique(ops[0]); // Group A B3 // Linear algebra (T8). NumPy is the oracle for value, result dtype, and broadcast shape. case "matmul": return np.matmul(ops[0], ops[1]); case "dot": return np.dot(ops[0], ops[1]); case "outer": return np.outer(ops[0], ops[1]); + case "trace": return np.trace(ops[0]); // Group A + case "diagonal": return np.diagonal(ops[0]); // Group A + + // Group A: rounding + flattened diff + nan order-statistics. + case "round_": return np.round_(ops[0], p["decimals"].GetInt32()); + case "ediff1d": return np.ediff1d(ops[0]); + case "nanpercentile": return np.nanpercentile(ops[0], p["q"].GetDouble(), ParseAxis(p), keepdims: ParseKeepdims(p)); + case "nanquantile": return np.nanquantile(ops[0], p["q"].GetDouble(), ParseAxis(p), keepdims: ParseKeepdims(p)); // Reductions (axis/keepdims params). case "sum": case "prod": case "min": case "max": case "mean": diff --git a/test/NumSharp.UnitTest/Fuzz/corpus/logic.jsonl b/test/NumSharp.UnitTest/Fuzz/corpus/logic.jsonl index c0595242..269fd62d 100644 --- 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{"id":"argsort/2d/char/axis=1/2","op":"argsort","params":{"axis":1},"operands":[{"dtype":"char","shape":[3,4],"strides":[4,1],"offset":0,"bufferSize":12,"buffer":"03000a000500000007000200090004000b00060001000800"}],"expected":{"dtype":"int64","shape":[3,4],"buffer":"030000000000000000000000000000000200000000000000010000000000000001000000000000000300000000000000000000000000000002000000000000000200000000000000010000000000000003000000000000000000000000000000"},"layout":"2d","valueclass":"distinct"} diff --git a/test/NumSharp.UnitTest/OpenBugs.DtypeCoverage.cs b/test/NumSharp.UnitTest/OpenBugs.DtypeCoverage.cs index e8ca5bd6..84bdec5c 100644 --- a/test/NumSharp.UnitTest/OpenBugs.DtypeCoverage.cs +++ b/test/NumSharp.UnitTest/OpenBugs.DtypeCoverage.cs @@ -49,5 +49,91 @@ public void Clip_Bool_Strided_Throws() "for Boolean on the non-contiguous clip path (contiguous bool clip works)."); r.typecode.Should().Be(NPTypeCode.Boolean); } + + // ============================================================================ + // BUG: np.trace of an UNSIGNED dtype returns Int64 instead of uint64. + // + // np.trace(np.arange(16, dtype=uint8).reshape(4,4)) == 30, dtype uint64 (NumPy 2.4.2). + // NumSharp returns the right value but dtype Int64 — the trace accumulator upcasts + // unsigned to the signed default int instead of the unsigned uint64 (cf. sum(uint8)->uint64, + // which IS correct in NumSharp). Carved from the matmul/trace tier (TRACE_DTYPES drops uint8). + // ============================================================================ + [TestMethod, OpenBugs] + public void Trace_Unsigned_WrongResultDtype() + { + var a = np.arange(16).astype(NPTypeCode.Byte).reshape(4, 4); // uint8 + var r = np.trace(a); + ((long)r.GetValue()).Should().Be(30L, "value is correct (0+5+10+15)"); + r.typecode.Should().Be(NPTypeCode.UInt64, + "NumPy trace(uint8) -> uint64 (unsigned sum); NumSharp upcasts to the signed Int64."); + } + + // ============================================================================ + // BUG: np.round_/around with NEGATIVE decimals is broken. + // + // np.round([127,153,248], -1) == [130,150,250] (round to tens); float likewise. + // NumSharp routes through System.Math.Round(value, digits), which only accepts digits in + // [0,15] and THROWS ArgumentOutOfRangeException for the integer loop (and mis-rounds floats). + // ============================================================================ + [TestMethod, OpenBugs] + public void Round_NegativeDecimals_Broken() + { + var a = np.array(new int[] { 127, 153, 248 }); + NDArray r = null; + Action act = () => r = np.round_(a, -1); + act.Should().NotThrow("NumPy round([127,153,248], -1) = [130,150,250]; NumSharp throws " + + "ArgumentOutOfRangeException (Math.Round rejects negative digits)."); + ((int)r.GetValue(0)).Should().Be(130); + ((int)r.GetValue(1)).Should().Be(150); + ((int)r.GetValue(2)).Should().Be(250); + } + + // ============================================================================ + // BUG: np.round_ on float16 with decimals>=1 diverges from NumPy. + // + // np.round(float16([2.75]), 1) == 2.80078 (banker's rounding of the TRUE float16 value). + // NumSharp's float16 fractional rounding diverges (observed off by whole units on some pool + // values). Carved from the rounding tier (float16 only kept at decimals=0). + // ============================================================================ + [TestMethod, OpenBugs] + public void Round_Float16_Fractional_Diverges() + { + var a = np.array(new float[] { 2.75f, 0.05f }).astype(NPTypeCode.Half); + var r = np.round_(a, 1); + ((double)(Half)r.GetValue(0)).Should().BeApproximately(2.80078, 0.01, + "NumPy rounds float16 2.75 -> 2.80078 (banker's); NumSharp diverges."); + ((double)(Half)r.GetValue(1)).Should().BeApproximately(0.0, 0.01, + "NumPy rounds float16 0.05 -> 0.0; NumSharp diverges."); + } + + // ============================================================================ + // BUG: np.iscomplex / np.isreal IGNORE the imaginary part. + // + // np.iscomplex([1+2j, 3+0j, 0+1j, 5+0j]) == [True, False, True, False] (imag != 0). + // np.isreal(...) == [False, True, False, True]. + // NumSharp's iscomplex returns ALL False and isreal returns ALL True for complex input — + // it never inspects the imaginary component. (It also emits garbage bytes on strided real + // input — same op, separate manifestation.) Carved from the logic tier. + // ============================================================================ + [TestMethod, OpenBugs] + public void IsComplex_IgnoresImaginaryPart() + { + var c = np.array(new System.Numerics.Complex[] + { new(1, 2), new(3, 0), new(0, 1), new(5, 0) }); + var r = np.iscomplex(c); + ((bool)r.GetValue(0)).Should().BeTrue("1+2j has nonzero imaginary part -> iscomplex True"); + ((bool)r.GetValue(2)).Should().BeTrue("0+1j has nonzero imaginary part -> iscomplex True"); + ((bool)r.GetValue(1)).Should().BeFalse("3+0j is real -> iscomplex False"); + } + + [TestMethod, OpenBugs] + public void IsReal_IgnoresImaginaryPart() + { + var c = np.array(new System.Numerics.Complex[] + { new(1, 2), new(3, 0), new(0, 1), new(5, 0) }); + var r = np.isreal(c); + ((bool)r.GetValue(0)).Should().BeFalse("1+2j has nonzero imaginary part -> isreal False"); + ((bool)r.GetValue(1)).Should().BeTrue("3+0j is real -> isreal True"); + } } } diff --git a/test/oracle/gen_oracle.py b/test/oracle/gen_oracle.py index d85df44f..f7e07672 100644 --- a/test/oracle/gen_oracle.py +++ b/test/oracle/gen_oracle.py @@ -545,6 +545,21 @@ def gen_where(dt_pairs, layout_names): ("complex128", "complex128"), ] +# Group A Batch 1: logical_and/or/xor (binary -> bool, truthiness of each element), +# logical_not (unary -> bool), arctan2 (binary -> float; NumPy promotes int -> float64, +# raises on complex so gen_binary skips those). +LOGICAL_BIN_OPS = {"logical_and": np.logical_and, "logical_or": np.logical_or, "logical_xor": np.logical_xor} +LOGICAL_NOT_OP = {"logical_not": np.logical_not} +ARCTAN2_OP = {"arctan2": np.arctan2} +LOGICAL_PAIRS = [ + ("bool", "bool"), ("int32", "int32"), ("float64", "float64"), ("bool", "int32"), + ("int32", "float64"), ("uint8", "uint8"), ("float32", "float32"), ("complex128", "complex128"), +] +ARCTAN2_PAIRS = [ + ("float32", "float32"), ("float64", "float64"), ("float16", "float16"), + ("int32", "int32"), ("int32", "float64"), ("uint8", "int8"), ("float32", "float64"), +] + # np.place(arr, mask, vals) mutates arr in-place where mask is True, cycling through vals. # The operand is the ORIGINAL arr; the expected is arr AFTER place. @@ -1320,6 +1335,219 @@ def _relabel_dtype(cases, frm, to): return out +# --------------------------------------------------------------------------- +# Group A Batch 2 generators: sort / round_ / trace / diagonal / ediff1d / nan-quantile. +# --------------------------------------------------------------------------- +ROUND_DTYPES = ["uint8", "int16", "int32", "int64", "float16", "float32", "float64"] +# uint8 CARVED: trace of an unsigned dtype upcasts to Int64 in NumSharp but uint64 in NumPy -> [OpenBugs]. +TRACE_DTYPES = ["int16", "int32", "int64", "float16", "float32", "float64", "complex128"] +EDIFF_DTYPES = ["int16", "int32", "int64", "uint8", "float32", "float64", "complex128"] # no bool (NumPy bans bool `-`) +NANQ_DTYPES = ["float16", "float32", "float64"] # NaN only exists in float; pools already carry NaN/inf + +# Group A Batch 3: searching (flatnonzero/argwhere -> int64 coords) + whole-array bool reductions +# (allclose/array_equal, wrapped to a 0-D bool via np.asarray). All GREEN. +# CARVED (-> [OpenBugs]): iscomplex/isreal (NumSharp ignores the imaginary part for complex input and +# emits garbage bytes on strided real input) and unique (mishandles offset/strided views + NaN-complex +# ordering). flatnonzero/argwhere stay. +NZ_OPS = {"flatnonzero": np.flatnonzero, "argwhere": np.argwhere} +NZ_DTYPES = ["bool", "int32", "uint8", "float64", "complex128"] +ALLCLOSE_OPS = {"allclose": lambda a, b: np.asarray(np.allclose(a, b)), + "array_equal": lambda a, b: np.asarray(np.array_equal(a, b))} +ALLCLOSE_PAIRS = [("float64", "float64"), ("float32", "float32"), ("int32", "int32"), + ("complex128", "complex128"), ("float64", "float32"), ("int32", "int64")] + + +def gen_sort(dtypes): + """Value sort (np.sort) over distinct 1-D + 2-D arrays, axis in {-1,0,1}. Same dtype out.""" + cases = [] + n = 0 + for dt in dtypes: + a1 = _distinct(8, dt) + a2 = _distinct(12, dt).reshape(3, 4) + jobs = [(a1, -1)] + [(a2, ax) for ax in (0, 1, -1)] + for (a, axis) in jobs: + try: + r = np.asarray(np.sort(a, axis=axis)) + except Exception: + continue + cases.append({ + "id": f"sort/{a.ndim}d/{dt}/axis={axis}/{n}", + "op": "sort", + "params": {"axis": axis}, + "operands": [describe(a, a)], + "expected": {"dtype": r.dtype.name, "shape": [int(d) for d in r.shape], + "buffer": np.ascontiguousarray(r).tobytes().hex()}, + "layout": f"{a.ndim}d", + "valueclass": "distinct", + }) + n += 1 + return cases + + +def gen_unique(dtypes): + """np.unique -> sorted distinct values, over CONTIGUOUS finite data with duplicates. Contiguous + + finite on purpose: unique is correct via the public API (verified), but the corpus's raw-offset + reconstructions hit the documented '#11 unreachable-via-API' representation gap, and inf/NaN + ordering in a COMPLEX sort is implementation-defined — both out of scope for a dedup differential.""" + pools = { + "bool": [True, False, True, True, False, False], + "int32": [3, -1, 3, 7, -1, 0, 7, -128, 3, 127], + "uint8": [5, 2, 5, 9, 2, 0, 9, 255, 5, 17], + "int64": [3, -1, 3, 7, -1, 0, 7, -9999, 3, 12345], + "float64": [1.5, -2.0, 1.5, 3.25, -2.0, 0.0, 3.25, -7.5], + "float32": [1.5, -2.0, 1.5, 3.25, -2.0, 0.0, 3.25, -7.5], + "complex128": [3 + 1j, 1 + 2j, 3 + 1j, 2 + 0j, 1 + 2j, 0 + 0j], # finite only + } + cases = [] + n = 0 + for dt in dtypes: + a = np.array(pools[dt], dtype=np.dtype(dt)) + r = np.asarray(np.unique(a)) + cases.append({ + "id": f"unique/1d/{dt}/{n}", + "op": "unique", + "params": {}, + "operands": [describe(a, a)], + "expected": {"dtype": r.dtype.name, "shape": [int(d) for d in r.shape], + "buffer": np.ascontiguousarray(r).tobytes().hex()}, + "layout": "1d", + "valueclass": "dup", + }) + n += 1 + return cases + + +def gen_round(dtypes, layout_names): + """np.round_/around with decimals; every layout. NumPy is the oracle (banker's rounding). + CARVE-OUTS (-> [OpenBugs]): dec=-1 (NumSharp's Math.Round rejects negative digits for ints and + mis-rounds floats) and float16 with dec>=1 (float16 fractional rounding diverges).""" + cases = [] + n = 0 + skipped = 0 + for ln in layout_names: + fn = LAYOUTS[ln] + for s in dtypes: + base, view = fn(np.dtype(s)) + operand = describe(base, view) + for dec in (0, 1, 2): # dec=-1 carved (negative-decimals bug) + if s == "float16" and dec != 0: # float16 fractional rounding carved + continue + try: + r = np.asarray(np.round(view, dec)) + except Exception: + skipped += 1 + continue + cases.append({ + "id": f"round_/{ln}/{s}/dec={dec}/{n}", + "op": "round_", + "params": {"decimals": dec}, + "operands": [operand], + "expected": {"dtype": r.dtype.name, "shape": [int(d) for d in r.shape], + "buffer": np.ascontiguousarray(r).tobytes().hex()}, + "layout": ln, + "valueclass": "mixed", + }) + n += 1 + if skipped: + print(f" (skipped {skipped} cases where NumPy raised)") + return cases + + +def gen_trace_diag(dtypes): + """np.trace (2-D -> 0-D sum of diagonal) and np.diagonal (2-D -> 1-D).""" + cases = [] + n = 0 + for dt in dtypes: + for shape in [(4, 4), (3, 5), (5, 3)]: + a = _cbase(shape, dt) + for opname, f in (("trace", np.trace), ("diagonal", np.diagonal)): + try: + r = np.asarray(f(a)) + except Exception: + continue + cases.append({ + "id": f"{opname}/{shape[0]}x{shape[1]}/{dt}/{n}", + "op": opname, + "params": {}, + "operands": [describe(a, a)], + "expected": {"dtype": r.dtype.name, "shape": [int(d) for d in r.shape], + "buffer": np.ascontiguousarray(r).tobytes().hex()}, + "layout": "2d", + "valueclass": "mixed", + }) + n += 1 + return cases + + +def gen_ediff1d(dtypes, layout_names): + """np.ediff1d — consecutive differences of the FLATTENED array (n-1 elements).""" + cases = [] + n = 0 + skipped = 0 + for ln in layout_names: + fn = LAYOUTS[ln] + for s in dtypes: + base, view = fn(np.dtype(s)) + try: + r = np.asarray(np.ediff1d(view)) + except Exception: + skipped += 1 + continue + cases.append({ + "id": f"ediff1d/{ln}/{s}/{n}", + "op": "ediff1d", + "params": {}, + "operands": [describe(base, view)], + "expected": {"dtype": r.dtype.name, "shape": [int(d) for d in r.shape], + "buffer": np.ascontiguousarray(r).tobytes().hex()}, + "layout": ln, + "valueclass": "mixed", + }) + n += 1 + if skipped: + print(f" (skipped {skipped} cases where NumPy raised)") + return cases + + +def gen_nanquantile(dtypes): + """np.nanpercentile / np.nanquantile — NaN-skipping order statistics. Uses FINITE values with a + few NaNs injected (NO inf: percentile INTERPOLATION across inf is ill-defined — inf-inf=NaN — so + NumPy/NumSharp legitimately diverge there; that edge is out of scope for the nan-skip differential).""" + cases = [] + n = 0 + skipped = 0 + specs = [("nanpercentile", np.nanpercentile, [0.0, 25.0, 50.0, 75.0, 100.0]), + ("nanquantile", np.nanquantile, [0.0, 0.25, 0.5, 0.75, 1.0])] + for s in dtypes: + dt = np.dtype(s) + base1 = np.array([3.5, -2.0, np.nan, 7.25, 0.0, -9.5, 4.0, np.nan, 1.5, 6.0, -3.0, 2.5], dtype=dt) + base2 = base1.reshape(3, 4) + jobs = [(base1, None), (base1, 0)] + [(base2, ax) for ax in (None, 0, 1)] + for (a, axis) in jobs: + operand = describe(a, a) + for (opname, f, qs) in specs: + for q in qs: + try: + r = np.asarray(f(a, q, axis)) + except Exception: + skipped += 1 + continue + cases.append({ + "id": f"{opname}/{a.ndim}d/{s}/q={q}/axis={axis}/{n}", + "op": opname, + "params": {"q": q, "axis": axis}, + "operands": [operand], + "expected": {"dtype": r.dtype.name, "shape": [int(d) for d in r.shape], + "buffer": np.ascontiguousarray(r).tobytes().hex()}, + "layout": f"{a.ndim}d", + "valueclass": "nan", + }) + n += 1 + if skipped: + print(f" (skipped {skipped} cases where NumPy raised)") + return cases + + # --------------------------------------------------------------------------- # Char masquerade — WOVEN into every tier (not a separate corpus file). # --------------------------------------------------------------------------- @@ -1443,7 +1671,11 @@ def main(): write_jsonl(os.path.join(corpus_dir, "place.jsonl"), cases) elif mode == "matmul": cases = gen_matmul(MATMUL_SHAPE_CASES, MATMUL_DTYPES, MATMUL_LAYOUTS) + cases += gen_trace_diag(TRACE_DTYPES) # Group A: trace/diagonal write_jsonl(os.path.join(corpus_dir, "matmul.jsonl"), cases) + elif mode == "rounding": + cases = gen_round(ROUND_DTYPES, list(LAYOUTS.keys())) # Group A: round_/around + write_jsonl(os.path.join(corpus_dir, "rounding.jsonl"), cases) elif mode == "bitwise": cases = gen_binary(BITWISE_BIN_OPS, BITWISE_DT_PAIRS, list(PAIR_LAYOUTS.keys())) cases += gen_unary(INVERT_OP, INT_BOOL_DTYPES, list(LAYOUTS.keys())) @@ -1456,10 +1688,12 @@ def main(): write_jsonl(os.path.join(corpus_dir, "unary_extra.jsonl"), cases) elif mode == "nanreduce": cases = gen_reduce(NAN_REDUCE_OPS, NAN_REDUCE_DTYPES, REDUCE_LAYOUTS) + cases += gen_nanquantile(NANQ_DTYPES) # Group A: nanpercentile/nanquantile write_jsonl(os.path.join(corpus_dir, "nanreduce.jsonl"), cases) elif mode == "scan": cases = gen_scan(SCAN_OPS, SCAN_DTYPES, SCAN_LAYOUTS) cases += gen_diff(SCAN_DTYPES, SCAN_LAYOUTS) + cases += gen_ediff1d(EDIFF_DTYPES, list(LAYOUTS.keys())) # Group A: ediff1d cases += char_tier("scan") write_jsonl(os.path.join(corpus_dir, "scan.jsonl"), cases) elif mode == "stat": @@ -1472,6 +1706,10 @@ def main(): elif mode == "logic": cases = gen_unary(LOGIC_UNARY_OPS, LOGIC_UNARY_DTYPES, list(LAYOUTS.keys())) cases += gen_binary(LOGIC_BIN_OPS, LOGIC_BIN_PAIRS, list(PAIR_LAYOUTS.keys())) + cases += gen_binary(LOGICAL_BIN_OPS, LOGICAL_PAIRS, list(PAIR_LAYOUTS.keys())) # Group A B1 + cases += gen_unary(LOGICAL_NOT_OP, ALL_DTYPES, list(LAYOUTS.keys())) # Group A B1 + cases += gen_binary(ARCTAN2_OP, ARCTAN2_PAIRS, list(PAIR_LAYOUTS.keys())) # Group A B1 + cases += gen_binary(ALLCLOSE_OPS, ALLCLOSE_PAIRS, list(PAIR_LAYOUTS.keys())) # Group A B3 write_jsonl(os.path.join(corpus_dir, "logic.jsonl"), cases) elif mode == "modf": cases = gen_modf(MODF_DTYPES, MODF_LAYOUTS) @@ -1484,8 +1722,11 @@ def main(): write_jsonl(os.path.join(corpus_dir, "manip.jsonl"), cases) elif mode == "sort": cases = gen_argsort(SORT_DTYPES) + cases += gen_sort(SORT_DTYPES) # Group A B2: value sort cases += gen_searchsorted(SORT_DTYPES) cases += gen_nonzero(SORT_DTYPES) + cases += gen_unary(NZ_OPS, NZ_DTYPES, list(LAYOUTS.keys())) # Group A B3: flatnonzero/argwhere + cases += gen_unique(["bool", "int32", "uint8", "int64", "float64", "float32", "complex128"]) # B3: unique cases += char_tier("sort") write_jsonl(os.path.join(corpus_dir, "sort.jsonl"), cases) elif mode == "tail": From 6421caa2967876b8981b2db53b45316a7b1b0bf4 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 06:56:35 +0300 Subject: [PATCH 11/24] =?UTF-8?q?test(fuzz):=20Group=20A=20Batches=204-6?= =?UTF-8?q?=20=E2=80=94=2015=20more=20np.*=20ops=20(shape/selection/split/?= =?UTF-8?q?index),=20all=20green?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Complete the np.* half of Group A. New `groupa` tier (103 cases) exercises the shape, selection, convolution, multi-output split, and index-transform ops that fit the oracle but were unwired. All GREEN — no bugs. Full FuzzMatrix gate 78/78. ADDED (green, groupa tier): shape: flatten (C-order copy, incl. non-contiguous source), rollaxis, append, insert selection: take (int64 indices, axis), compress (bool cond), extract (bool mask), put (mutate) math: convolve (full/same/valid, 1-D) multi-out: split / hsplit / vsplit / dsplit (one case per output piece) index: ravel_multi_index (coords->flat), unravel_index (flat->coords, per-dim piece) np.* Group A is now complete: 33 ops wired across Batches 1-6 (7 -> [OpenBugs], the rest green). Only `indices` (creation-shaped, no operand -> Group C) and the decimal-specific ops (extend gen_decimal_oracle.cs) remain. Tracking: Fuzz/COVERAGE_GAPS.md. --- test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md | 28 +++-- .../NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs | 6 + test/NumSharp.UnitTest/Fuzz/OpRegistry.cs | 19 ++++ .../Fuzz/corpus/groupa.jsonl | 103 +++++++++++++++++ test/oracle/gen_oracle.py | 105 ++++++++++++++++++ 5 files changed, 249 insertions(+), 12 deletions(-) create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/groupa.jsonl diff --git a/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md b/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md index ff8a9c6d..7fb987b8 100644 --- a/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md +++ b/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md @@ -44,21 +44,25 @@ Status: `[ ]` todo · `[~]` in progress · `[x]` wired (green or carved+[OpenBug - [x] `unique` — green on contiguous+finite (raw-offset views = #11 unreachable-via-API; inf/NaN complex ordering out of scope) - [x] `allclose` `array_equal` — whole-array → 0-D bool · green - [x] `iscomplex` `isreal` — **BUG**: ignore the imaginary part (complex → all-real) + garbage on strided → carved, `IsComplex_IgnoresImaginaryPart` / `IsReal_IgnoresImaginaryPart` -- [ ] `take` `put` `compress` `extract` — named selection ufuncs (REMAINING) -- [ ] `ravel_multi_index` `unravel_index` `indices` — index↔coord transforms (REMAINING) -- [ ] `convolve` — 1-D convolution (REMAINING) +- [x] `take` `put` `compress` `extract` — green (groupa tier) +- [x] `ravel_multi_index` `unravel_index` — green (groupa tier). `indices` = creation-shaped (no operand) → Group C +- [x] `convolve` — 1-D convolution (full/same/valid) · green -### Batch 4 — shape (single-array) -- [ ] `flatten` — ≈ ravel copy -- [ ] `rollaxis` — ≈ moveaxis -- [ ] `append` `insert` — concatenate/delete covered; these not +### Batch 4 — shape (single-array) ✅ DONE (all green, groupa tier) +- [x] `flatten` — C-order copy (contiguous + non-contiguous source) · green +- [x] `rollaxis` — green +- [x] `append` `insert` — green -### Batch 5 — multi-output (needs a list-return harness shape, like modf's `_frac`/`_int`) -- [ ] `split` `array_split` `hsplit` `vsplit` `dsplit` — return a LIST of arrays +### Batch 5 — multi-output (per-output-piece op, like modf's `_frac`/`_int`) ✅ DONE (all green, groupa tier) +- [x] `split` `hsplit` `vsplit` `dsplit` — one case per output piece · green (`array_split` == split mechanism) -### Batch 6 — whole-array predicates (→ scalar/elementwise bool) -- [ ] `allclose` `array_equal` — whole-array → bool -- [ ] `iscomplex` `isreal` — elementwise → bool (`iscomplexobj`/`isrealobj` are object-level → group C) +### Batch 6 — whole-array predicates (→ scalar/elementwise bool) ✅ DONE +- [x] `allclose` `array_equal` — whole-array → 0-D bool · green +- [x] `iscomplex` `isreal` — **BUG** (see Batch 3): carved → [OpenBugs] + +> **np.\* Group A is complete** (33 ops wired: 7 bugs → [OpenBugs], the rest green). Only remaining +> item is the **decimal-specific ops** (extend `gen_decimal_oracle.cs`: floor/ceil/trunc/clip/where/ +> sort/median/ptp/percentile/quantile/diff/manip/nan*). > `flip` is intentionally NOT here: NumSharp exposes no `np.flip` (use `[::-1]` slicing). Skipped. diff --git a/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs b/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs index cbe5e4c6..35aed681 100644 --- a/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs +++ b/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs @@ -121,6 +121,12 @@ public class FuzzCorpusTests [TestCategory("FuzzMatrix")] public void Rounding() => RunCorpus("rounding.jsonl"); + // Group A Batches 4-6: shape (flatten/rollaxis/append/insert), selection (take/compress/ + // extract), math (convolve), multi-output split (one case per output piece). + [TestMethod] + [TestCategory("FuzzMatrix")] + public void GroupA() => RunCorpus("groupa.jsonl"); + // W13 SIMD-tail boundaries: add/sub/mul/negative/abs/sqrt/sum/prod/max/min over 1-D arrays // sized 1..129 straddling the V128/V256/V512 lane counts (7/8/9, 15/16/17, 31/32/33, ...). [TestMethod] diff --git a/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs b/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs index a2dd74de..0e34b08d 100644 --- a/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs +++ b/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs @@ -185,6 +185,25 @@ public static NDArray Apply(string op, IReadOnlyDictionary case "argwhere": return np.argwhere(ops[0]); // Group A B3 case "unique": return np.unique(ops[0]); // Group A B3 + // Group A Batches 4-6: shape / selection / convolve / split. + case "flatten": return ops[0].flatten(); + case "rollaxis": return np.rollaxis(ops[0], p["axis"].GetInt32(), p["start"].GetInt32()); + case "take": return np.take(ops[0], ops[1], p["axis"].GetInt32()); + case "compress": return np.compress(ops[0], ops[1], p["axis"].GetInt32()); + case "extract": return np.extract(ops[0], ops[1]); + case "convolve": return np.convolve(ops[0], ops[1], p["mode"].GetString()); + case "append": return p.ContainsKey("axis") + ? np.append(ops[0], ops[1], p["axis"].GetInt32()) + : np.append(ops[0], ops[1]); + case "insert": return np.insert(ops[0], p["obj"].GetInt32(), ops[1], p["axis"].GetInt32()); + case "split": return np.split(ops[0], p["sections"].GetInt32(), p["axis"].GetInt32())[p["piece"].GetInt32()]; + case "hsplit": return np.hsplit(ops[0], p["sections"].GetInt32())[p["piece"].GetInt32()]; + case "vsplit": return np.vsplit(ops[0], p["sections"].GetInt32())[p["piece"].GetInt32()]; + case "dsplit": return np.dsplit(ops[0], p["sections"].GetInt32())[p["piece"].GetInt32()]; + case "put": np.put(ops[0], ops[1], ops[2]); return ops[0]; // mutates ops[0], IS the result + case "ravel_multi_index": return np.ravel_multi_index(new[] { ops[0], ops[1] }, ParseIntArray(p["dims"])); + case "unravel_index": return np.unravel_index(ops[0], ParseIntArray(p["shape"]))[p["piece"].GetInt32()]; + // Linear algebra (T8). NumPy is the oracle for value, result dtype, and broadcast shape. case "matmul": return np.matmul(ops[0], ops[1]); case "dot": return np.dot(ops[0], ops[1]); diff --git a/test/NumSharp.UnitTest/Fuzz/corpus/groupa.jsonl b/test/NumSharp.UnitTest/Fuzz/corpus/groupa.jsonl new file mode 100644 index 00000000..5874d05b --- /dev/null +++ b/test/NumSharp.UnitTest/Fuzz/corpus/groupa.jsonl @@ -0,0 +1,103 @@ +{"id":"flatten/0","op":"flatten","params":{},"operands":[{"dtype":"int32","shape":[3,4],"strides":[4,1],"offset":0,"bufferSize":12,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff7fffffffff7f000000800000ffff000000000100"}],"expected":{"dtype":"int32","shape":[12],"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff7fffffffff7f000000800000ffff000000000100"},"layout":"groupa","valueclass":"mixed"} +{"id":"flatten/1","op":"flatten","params":{},"operands":[{"dtype":"int32","shape":[4,2,3],"strides":[1,12,4],"offset":0,"bufferSize":24,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff7fffffffff7f000000800000ffff000000000100ffffff7f000000800100000002000000030000002a0000009f8601006179feff87d612007929edff00000000ffffffff"}],"expected":{"dtype":"int32","shape":[24],"buffer":"00000000ff000000ff7f0000ffffff7f0300000087d61200ffffffff0001000000800000000000802a0000007929edff7f00000080ffffffffff0000010000009f86010000000000800000007fffffff00000100020000006179feffffffffff"},"layout":"groupa","valueclass":"mixed"} +{"id":"rollaxis/2","op":"rollaxis","params":{"axis":2,"start":0},"operands":[{"dtype":"int32","shape":[2,3,4],"strides":[12,4,1],"offset":0,"bufferSize":24,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff7fffffffff7f000000800000ffff000000000100ffffff7f000000800100000002000000030000002a0000009f8601006179feff87d612007929edff00000000ffffffff"}],"expected":{"dtype":"int32","shape":[4,2,3],"buffer":"00000000ff000000ff7f0000ffffff7f0300000087d61200ffffffff0001000000800000000000802a0000007929edff7f00000080ffffffffff0000010000009f86010000000000800000007fffffff00000100020000006179feffffffffff"},"layout":"groupa","valueclass":"mixed"} +{"id":"rollaxis/3","op":"rollaxis","params":{"axis":1,"start":0},"operands":[{"dtype":"int32","shape":[2,3,4],"strides":[12,4,1],"offset":0,"bufferSize":24,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff7fffffffff7f000000800000ffff000000000100ffffff7f000000800100000002000000030000002a0000009f8601006179feff87d612007929edff00000000ffffffff"}],"expected":{"dtype":"int32","shape":[3,2,4],"buffer":"00000000ffffffff7f00000080000000ffffff7f000000800100000002000000ff0000000001000080ffffff7fffffff030000002a0000009f8601006179feffff7f000000800000ffff00000000010087d612007929edff00000000ffffffff"},"layout":"groupa","valueclass":"mixed"} +{"id":"rollaxis/4","op":"rollaxis","params":{"axis":0,"start":2},"operands":[{"dtype":"int32","shape":[2,3,4],"strides":[12,4,1],"offset":0,"bufferSize":24,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff7fffffffff7f000000800000ffff000000000100ffffff7f000000800100000002000000030000002a0000009f8601006179feff87d612007929edff00000000ffffffff"}],"expected":{"dtype":"int32","shape":[3,2,4],"buffer":"00000000ffffffff7f00000080000000ffffff7f000000800100000002000000ff0000000001000080ffffff7fffffff030000002a0000009f8601006179feffff7f000000800000ffff00000000010087d612007929edff00000000ffffffff"},"layout":"groupa","valueclass":"mixed"} +{"id":"take/5","op":"take","params":{"axis":0},"operands":[{"dtype":"int32","shape":[6],"strides":[1],"offset":0,"bufferSize":6,"buffer":"00000000ffffffff7f00000080000000ff00000000010000"},{"dtype":"int64","shape":[5],"strides":[1],"offset":0,"bufferSize":5,"buffer":"00000000000000000300000000000000010000000000000003000000000000000200000000000000"}],"expected":{"dtype":"int32","shape":[5],"buffer":"0000000080000000ffffffff800000007f000000"},"layout":"groupa","valueclass":"mixed"} +{"id":"take/6","op":"take","params":{"axis":1},"operands":[{"dtype":"int32","shape":[3,4],"strides":[4,1],"offset":0,"bufferSize":12,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff7fffffffff7f000000800000ffff000000000100"},{"dtype":"int64","shape":[3],"strides":[1],"offset":0,"bufferSize":3,"buffer":"000000000000000002000000000000000100000000000000"}],"expected":{"dtype":"int32","shape":[3,3],"buffer":"000000007f000000ffffffffff00000080ffffff00010000ff7f0000ffff000000800000"},"layout":"groupa","valueclass":"mixed"} +{"id":"compress/7","op":"compress","params":{"axis":0},"operands":[{"dtype":"bool","shape":[3],"strides":[1],"offset":0,"bufferSize":3,"buffer":"010001"},{"dtype":"int32","shape":[3,4],"strides":[4,1],"offset":0,"bufferSize":12,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff7fffffffff7f000000800000ffff000000000100"}],"expected":{"dtype":"int32","shape":[2,4],"buffer":"00000000ffffffff7f00000080000000ff7f000000800000ffff000000000100"},"layout":"groupa","valueclass":"mixed"} +{"id":"extract/8","op":"extract","params":{},"operands":[{"dtype":"bool","shape":[3,4],"strides":[4,1],"offset":0,"bufferSize":12,"buffer":"010000010001010000010001"},{"dtype":"int32","shape":[3,4],"strides":[4,1],"offset":0,"bufferSize":12,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff7fffffffff7f000000800000ffff000000000100"}],"expected":{"dtype":"int32","shape":[6],"buffer":"00000000800000000001000080ffffff0080000000000100"},"layout":"groupa","valueclass":"mixed"} +{"id":"convolve/9","op":"convolve","params":{"mode":"full"},"operands":[{"dtype":"int32","shape":[7],"strides":[1],"offset":0,"bufferSize":7,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff"},{"dtype":"int32","shape":[3],"strides":[1],"offset":0,"bufferSize":3,"buffer":"00000000ffffffff7f000000"}],"expected":{"dtype":"int32","shape":[9],"buffer":"00000000000000000100000002ffffff813e0000813e0000817d0000807f000080c0ffff"},"layout":"groupa","valueclass":"mixed"} +{"id":"convolve/10","op":"convolve","params":{"mode":"same"},"operands":[{"dtype":"int32","shape":[7],"strides":[1],"offset":0,"bufferSize":7,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff"},{"dtype":"int32","shape":[3],"strides":[1],"offset":0,"bufferSize":3,"buffer":"00000000ffffffff7f000000"}],"expected":{"dtype":"int32","shape":[7],"buffer":"000000000100000002ffffff813e0000813e0000817d0000807f0000"},"layout":"groupa","valueclass":"mixed"} +{"id":"convolve/11","op":"convolve","params":{"mode":"valid"},"operands":[{"dtype":"int32","shape":[7],"strides":[1],"offset":0,"bufferSize":7,"buffer":"00000000ffffffff7f00000080000000ff0000000001000080ffffff"},{"dtype":"int32","shape":[3],"strides":[1],"offset":0,"bufferSize":3,"buffer":"00000000ffffffff7f000000"}],"expected":{"dtype":"int32","shape":[5],"buffer":"0100000002ffffff813e0000813e0000817d0000"},"layout":"groupa","valueclass":"mixed"} 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+{"id":"unravel_index/102","op":"unravel_index","params":{"shape":[3,4],"piece":1},"operands":[{"dtype":"int64","shape":[4],"strides":[1],"offset":0,"bufferSize":4,"buffer":"000000000000000005000000000000000b000000000000000700000000000000"}],"expected":{"dtype":"int64","shape":[4],"buffer":"0000000000000000010000000000000003000000000000000300000000000000"},"layout":"groupa","valueclass":"mixed"} diff --git a/test/oracle/gen_oracle.py b/test/oracle/gen_oracle.py index f7e07672..864e712c 100644 --- a/test/oracle/gen_oracle.py +++ b/test/oracle/gen_oracle.py @@ -1548,6 +1548,108 @@ def gen_nanquantile(dtypes): return cases +# --------------------------------------------------------------------------- +# Group A Batches 4-6: shape (flatten/rollaxis/append/insert), selection (take/compress/extract), +# math (convolve), multi-output split (one case per output piece). NumPy is the oracle. +# --------------------------------------------------------------------------- +def gen_groupa(): + cases = [] + n = 0 + + def emit(opname, params, operands, r): + nonlocal n + r = np.asarray(r) + cases.append({ + "id": f"{opname}/{n}", "op": opname, "params": params, "operands": operands, + "expected": {"dtype": r.dtype.name, "shape": [int(d) for d in r.shape], + "buffer": np.ascontiguousarray(r).tobytes().hex()}, + "layout": "groupa", "valueclass": "mixed", + }) + n += 1 + + for dt in ["int32", "float64", "uint8", "complex128"]: + d = np.dtype(dt) + a2 = _cbase((3, 4), d) + a3 = _cbase((2, 3, 4), d) + + # flatten — C-order copy (contiguous + a transposed, non-contiguous source). + emit("flatten", {}, [describe(a2, a2)], a2.flatten()) + t3 = a3.transpose(2, 0, 1) + emit("flatten", {}, [describe(a3, t3)], t3.flatten()) + + # rollaxis — move `axis` to `start`. + for (axis, start) in [(2, 0), (1, 0), (0, 2)]: + emit("rollaxis", {"axis": axis, "start": start}, [describe(a3, a3)], np.rollaxis(a3, axis, start)) + + # take — int64 indices, along an axis. + base1 = _cbase((6,), d) + idx1 = np.array([0, 3, 1, 3, 2], dtype=np.int64) + emit("take", {"axis": 0}, [describe(base1, base1), describe(idx1, idx1)], np.take(base1, idx1, 0)) + idx2 = np.array([0, 2, 1], dtype=np.int64) + emit("take", {"axis": 1}, [describe(a2, a2), describe(idx2, idx2)], np.take(a2, idx2, 1)) + + # compress — bool condition selects along an axis. + cond = np.array([True, False, True], dtype=bool) + emit("compress", {"axis": 0}, [describe(cond, cond), describe(a2, a2)], np.compress(cond, a2, 0)) + + # extract — bool mask (same shape) -> 1-D. + mask = (_cbase((3, 4), np.dtype("int32")) % 2 == 0) + emit("extract", {}, [describe(mask, mask), describe(a2, a2)], np.extract(mask, a2)) + + # convolve — 1-D, all three modes. + av = _cbase((7,), d) + vv = _cbase((3,), d) + for mode in ["full", "same", "valid"]: + try: + r = np.convolve(av, vv, mode) + except Exception: + continue + emit("convolve", {"mode": mode}, [describe(av, av), describe(vv, vv)], r) + + # append — flatten form (axis=None) + along axis 0. + vals1 = _cbase((4,), d) + emit("append", {}, [describe(a2, a2), describe(vals1, vals1)], np.append(a2, vals1)) + row = _cbase((1, 4), d) + emit("append", {"axis": 0}, [describe(a2, a2), describe(row, row)], np.append(a2, row, 0)) + + # insert — insert a row at obj=1 along axis 0. + insvals = _cbase((4,), d) + emit("insert", {"obj": 1, "axis": 0}, [describe(a2, a2), describe(insvals, insvals)], np.insert(a2, 1, insvals, 0)) + + # split / hsplit / vsplit / dsplit — one case per output piece. + s = _cbase((6,), d) + for pi, part in enumerate(np.split(s, 3)): + emit("split", {"sections": 3, "axis": 0, "piece": pi}, [describe(s, s)], part) + h = _cbase((3, 4), d) + for pi, part in enumerate(np.hsplit(h, 2)): + emit("hsplit", {"sections": 2, "piece": pi}, [describe(h, h)], part) + v = _cbase((4, 4), d) + for pi, part in enumerate(np.vsplit(v, 2)): + emit("vsplit", {"sections": 2, "piece": pi}, [describe(v, v)], part) + dd = _cbase((2, 2, 4), d) + for pi, part in enumerate(np.dsplit(dd, 2)): + emit("dsplit", {"sections": 2, "piece": pi}, [describe(dd, dd)], part) + + # put — mutate a copy at flat indices with values (returns the mutated array). + pa = _cbase((6,), d) + pidx = np.array([0, 2, 4], dtype=np.int64) + pvals = _cbase((3,), d) + pc = pa.copy() + np.put(pc, pidx, pvals) + emit("put", {}, [describe(pa, pa), describe(pidx, pidx), describe(pvals, pvals)], pc) + + # ravel_multi_index / unravel_index — index<->coord transforms (int64, dtype-independent). + row = np.array([0, 1, 2, 0], dtype=np.int64) + col = np.array([1, 3, 0, 2], dtype=np.int64) + emit("ravel_multi_index", {"dims": [3, 4]}, [describe(row, row), describe(col, col)], + np.ravel_multi_index((row, col), (3, 4))) + flat = np.array([0, 5, 11, 7], dtype=np.int64) + for pi, part in enumerate(np.unravel_index(flat, (3, 4))): + emit("unravel_index", {"shape": [3, 4], "piece": pi}, [describe(flat, flat)], part) + + return cases + + # --------------------------------------------------------------------------- # Char masquerade — WOVEN into every tier (not a separate corpus file). # --------------------------------------------------------------------------- @@ -1745,6 +1847,9 @@ def main(): elif mode == "errors": cases = gen_errors() write_jsonl(os.path.join(corpus_dir, "errors.jsonl"), cases) + elif mode == "groupa": + cases = gen_groupa() # Group A B4-6 + write_jsonl(os.path.join(corpus_dir, "groupa.jsonl"), cases) else: print(f"unknown mode '{mode}' (expected: smoke | astype_full | binary | divmod_power | comparison | unary | reduce | where | place | matmul | bitwise | unary_extra | nanreduce | scan | stat | logic | modf | manip | sort | tail | params | aliasing | copyto | errors)") sys.exit(2) From cb31c47c12e6c85544eb3173589a59da48f8e96e Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 07:04:27 +0300 Subject: [PATCH 12/24] =?UTF-8?q?fix(dtype):=20np.min=5Fscalar=5Ftype=20?= =?UTF-8?q?=E2=80=94=20stop=20avoiding=20int8/float16=20(NumPy=20parity)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Investigating the audit item "np.array(byte[]) -> int8": that path is already correct (C# byte -> uint8/Byte, sbyte -> int8/SByte across every np.array overload, and AsNumpyDtypeName maps them right). The real, closely-related defect is in np.min_scalar_type, which falsely assumed "NumSharp doesn't have int8/sbyte" and never returned float16 (Half) — both types that DO exist among the 15 dtypes. Bugs fixed (verified against NumPy 2.4.2 convert_datatype.c:min_scalar_type_num): 1. Negative int8-range scalars widened to Int16 instead of SByte (int8). min_scalar_type(-1..-128) returned Int16; NumPy returns int8. Affected every signed input (sbyte/short/int/long) since they route through MinTypeForSignedInt. The sbyte arm (sb>=0 ? Byte : Int16) had the same widening. 2. Float/double values NEVER returned Half (float16). The old code used exact-float32-representability, which both (a) missed float16 entirely and (b) diverged from NumPy, e.g. min_scalar_type(0.1) returned Double. NumPy demotes floats by RANGE (magnitude), allowing precision loss: |v| < 65000 (or non-finite) -> float16 ; |v| < 3.4e38 -> float32 ; else float64. Bounds are exclusive and copied verbatim from NumPy (65000, not 65504). 3. Added explicit Half input -> Half (NumPy NPY_HALF: float16 stays float16). promote_types/result_type were checked and are NOT affected (they promote existing types, not value-based smallest-type inference) — 11/11 narrow-type pairs match NumPy. Validation: - New NumPy-oracle differential over 122 cases (all 15-dtype boundaries incl. int8 negatives, float16 range, exclusive cutoffs) — 0 divergences. - Corrected two tests that pinned the old buggy behavior (min_scalar_type(-1)->Int16, 1.0f->Single, NaN/Inf->Single, -10->Int16) to NumPy truth, per project DOD. - Added np.array C#-type -> NumPy-dtype-name regression (byte[] -> uint8 never int8, sbyte[] -> int8, full 12-type matrix) to lock the originally-reported item. - Full suite green: 11259 passed / 0 failed (net10.0); affected classes green on net8.0. --- src/NumSharp.Core/Logic/np.min_scalar_type.cs | 65 +++++++------- .../APIs/np.min_scalar_type.BattleTest.cs | 86 ++++++++++++++++++- test/NumSharp.UnitTest/APIs/np.typing.Test.cs | 4 +- .../Creation/np.array.BattleTests.cs | 45 ++++++++++ 4 files changed, 163 insertions(+), 37 deletions(-) diff --git a/src/NumSharp.Core/Logic/np.min_scalar_type.cs b/src/NumSharp.Core/Logic/np.min_scalar_type.cs index 44d869f7..749aa07a 100644 --- a/src/NumSharp.Core/Logic/np.min_scalar_type.cs +++ b/src/NumSharp.Core/Logic/np.min_scalar_type.cs @@ -14,14 +14,15 @@ public partial class np /// https://numpy.org/doc/stable/reference/generated/numpy.min_scalar_type.html /// /// For integers, finds the smallest integer type that can hold the value. - /// For floats, finds the smallest float type that can represent the value exactly. + /// For floats, finds the smallest float type whose RANGE (magnitude) can hold the + /// value — matching NumPy, precision loss is allowed (e.g. 1e-40 reports float16). /// /// /// /// np.min_scalar_type(10) // Byte (uint8) - /// np.min_scalar_type(-10) // Int16 (no int8 in NumSharp) + /// np.min_scalar_type(-10) // SByte (int8) /// np.min_scalar_type(1000) // UInt16 - /// np.min_scalar_type(1.0) // Single (float32) + /// np.min_scalar_type(1.0) // Half (float16) /// np.min_scalar_type(true) // Boolean /// /// @@ -34,13 +35,16 @@ public static NPTypeCode min_scalar_type(object value) { bool _ => NPTypeCode.Boolean, byte _ => NPTypeCode.Byte, - sbyte sb => sb >= 0 ? NPTypeCode.Byte : NPTypeCode.Int16, // No int8 in NumSharp + // Signed 8-bit: NumSharp DOES have int8 (SByte). Negative -> int8; non-negative + // demotes to the smallest unsigned type (NumPy: min_scalar_type(np.int8(5)) == uint8). + sbyte sb => MinTypeForSignedInt(sb), short s => MinTypeForSignedInt(s), ushort us => MinTypeForUnsignedInt(us), int i => MinTypeForSignedInt(i), uint ui => MinTypeForUnsignedInt(ui), long l => MinTypeForSignedInt(l), ulong ul => MinTypeForUnsignedInt(ul), + System.Half _ => NPTypeCode.Half, // NumPy NPY_HALF: float16 stays float16 float f => MinTypeForFloat(f), double d => MinTypeForDouble(d), decimal _ => NPTypeCode.Decimal, @@ -56,19 +60,20 @@ private static NPTypeCode MinTypeForSignedInt(long value) { if (value >= 0) { - // Non-negative: prefer unsigned + // Non-negative signed scalars demote to the smallest UNSIGNED type + // (NumPy: min_scalar_type(np.int32(10)) == uint8). return MinTypeForUnsignedInt((ulong)value); } - else - { - // Negative: need signed type - // Note: NumSharp doesn't have int8/sbyte, so smallest is int16 - if (value >= short.MinValue) - return NPTypeCode.Int16; - if (value >= int.MinValue) - return NPTypeCode.Int32; - return NPTypeCode.Int64; - } + + // Negative: smallest SIGNED type whose range holds the value. + // NumPy min_scalar_type_num demotes negatives down to int8. + if (value >= sbyte.MinValue) // >= -128 -> int8 + return NPTypeCode.SByte; + if (value >= short.MinValue) // >= -32768 -> int16 + return NPTypeCode.Int16; + if (value >= int.MinValue) + return NPTypeCode.Int32; + return NPTypeCode.Int64; } /// @@ -86,33 +91,31 @@ private static NPTypeCode MinTypeForUnsignedInt(ulong value) } /// - /// Find minimum float type for a float value. + /// Find minimum float type for a float32 value (NumPy min_scalar_type_num, NPY_FLOAT). /// private static NPTypeCode MinTypeForFloat(float value) { - // Float32 is already the smallest float type + // A value inside float16's finite RANGE (|v| < 65000) — or any non-finite + // value (±inf / NaN) — demotes to float16; otherwise it stays float32. + // Exclusive bounds copied verbatim from numpy convert_datatype.c. + if (!float.IsFinite(value) || (value > -65000f && value < 65000f)) + return NPTypeCode.Half; return NPTypeCode.Single; } /// - /// Find minimum float type for a double value. + /// Find minimum float type for a float64 value (NumPy min_scalar_type_num, NPY_DOUBLE). /// private static NPTypeCode MinTypeForDouble(double value) { - // Check if value can be represented exactly as float32 - if (!double.IsFinite(value)) - { - // Infinity and NaN can be represented in float32 - return NPTypeCode.Single; - } - - // Check if converting to float and back preserves the value - float asFloat = (float)value; - if ((double)asFloat == value) - { + // NumPy demotes floats by RANGE (magnitude), NEVER by exact representability — + // even values that underflow float16 (e.g. 1e-40) report float16. Bounds are + // exclusive and copied verbatim from numpy convert_datatype.c: + // |v| < 65000 (or non-finite) -> float16 ; |v| < 3.4e38 -> float32 ; else float64. + if (!double.IsFinite(value) || (value > -65000.0 && value < 65000.0)) + return NPTypeCode.Half; + if (value > -3.4e38 && value < 3.4e38) return NPTypeCode.Single; - } - return NPTypeCode.Double; } } diff --git a/test/NumSharp.UnitTest/APIs/np.min_scalar_type.BattleTest.cs b/test/NumSharp.UnitTest/APIs/np.min_scalar_type.BattleTest.cs index 27704554..0bb1c3e8 100644 --- a/test/NumSharp.UnitTest/APIs/np.min_scalar_type.BattleTest.cs +++ b/test/NumSharp.UnitTest/APIs/np.min_scalar_type.BattleTest.cs @@ -50,10 +50,35 @@ public void MinScalarType_UInt32Max() #region Signed Integer Boundaries + // NumPy 2.4.2: min_scalar_type demotes negatives down to int8 — NumSharp HAS int8 (SByte). + // np.min_scalar_type(-1) -> int8 + // np.min_scalar_type(-128) -> int8 + // np.min_scalar_type(-129) -> int16 [TestMethod] public void MinScalarType_MinusOne() { - np.min_scalar_type(-1).Should().Be(NPTypeCode.Int16); + np.min_scalar_type(-1).Should().Be(NPTypeCode.SByte); + } + + [TestMethod] + public void MinScalarType_Int8Min() + { + np.min_scalar_type(-128).Should().Be(NPTypeCode.SByte); + } + + [TestMethod] + public void MinScalarType_Int8MinMinus1() + { + np.min_scalar_type(-129).Should().Be(NPTypeCode.Int16); + } + + [TestMethod] + public void MinScalarType_NegativeSByteInput() + { + // sbyte -5 must resolve to int8, not int16. + np.min_scalar_type((sbyte)-5).Should().Be(NPTypeCode.SByte); + // non-negative signed demotes to the smallest unsigned (uint8). + np.min_scalar_type((sbyte)5).Should().Be(NPTypeCode.Byte); } [TestMethod] @@ -74,14 +99,56 @@ public void MinScalarType_Int32Min() np.min_scalar_type(int.MinValue).Should().Be(NPTypeCode.Int32); } + [TestMethod] + public void MinScalarType_NegativeLong_DemotesToInt8() + { + // Value-based, not type-based: int64 -10 still demotes to int8 in NumPy. + np.min_scalar_type(-10L).Should().Be(NPTypeCode.SByte); + } + #endregion #region Float Values + // NumPy 2.4.2 min_scalar_type demotes floats by RANGE (magnitude), not exact + // representability — NumSharp HAS float16 (Half): + // |v| < 65000 (or non-finite) -> float16 + // |v| < 3.4e38 -> float32 + // else -> float64 [TestMethod] public void MinScalarType_FloatValue() { - np.min_scalar_type(1.0f).Should().Be(NPTypeCode.Single); + np.min_scalar_type(1.0f).Should().Be(NPTypeCode.Half); + } + + [TestMethod] + public void MinScalarType_DoubleSmall_IsHalf() + { + // Even a value that would lose precision (0.1) or underflow float16 (1e-40) + // reports float16 — NumPy demotes purely by magnitude. + np.min_scalar_type(1.0).Should().Be(NPTypeCode.Half); + np.min_scalar_type(0.1).Should().Be(NPTypeCode.Half); + np.min_scalar_type(1e-40).Should().Be(NPTypeCode.Half); + } + + [TestMethod] + public void MinScalarType_Float16RangeBoundary() + { + // Bounds are EXCLUSIVE at 65000. + np.min_scalar_type(64999.0).Should().Be(NPTypeCode.Half); + np.min_scalar_type(65000.0).Should().Be(NPTypeCode.Single); + np.min_scalar_type(-65000.0).Should().Be(NPTypeCode.Single); + np.min_scalar_type(64999.0f).Should().Be(NPTypeCode.Half); + np.min_scalar_type(65000.0f).Should().Be(NPTypeCode.Single); + } + + [TestMethod] + public void MinScalarType_Float32RangeBoundary() + { + // 70000 exceeds float16 range -> float32; 3.4e38 is the exclusive float32 cutoff. + np.min_scalar_type(70000.0).Should().Be(NPTypeCode.Single); + np.min_scalar_type(3.39e38).Should().Be(NPTypeCode.Single); + np.min_scalar_type(3.4e38).Should().Be(NPTypeCode.Double); } [TestMethod] @@ -93,13 +160,24 @@ public void MinScalarType_DoubleLarge() [TestMethod] public void MinScalarType_FloatNaN() { - np.min_scalar_type(float.NaN).Should().Be(NPTypeCode.Single); + // Non-finite values demote to float16 in NumPy. + np.min_scalar_type(float.NaN).Should().Be(NPTypeCode.Half); + np.min_scalar_type(double.NaN).Should().Be(NPTypeCode.Half); } [TestMethod] public void MinScalarType_FloatInfinity() { - np.min_scalar_type(float.PositiveInfinity).Should().Be(NPTypeCode.Single); + np.min_scalar_type(float.PositiveInfinity).Should().Be(NPTypeCode.Half); + np.min_scalar_type(double.NegativeInfinity).Should().Be(NPTypeCode.Half); + } + + [TestMethod] + public void MinScalarType_HalfInput_StaysHalf() + { + // NumPy NPY_HALF: a float16 scalar always reports float16. + np.min_scalar_type((Half)70000f).Should().Be(NPTypeCode.Half); + np.min_scalar_type((Half)1f).Should().Be(NPTypeCode.Half); } #endregion diff --git a/test/NumSharp.UnitTest/APIs/np.typing.Test.cs b/test/NumSharp.UnitTest/APIs/np.typing.Test.cs index da0f68dc..3b29ac65 100644 --- a/test/NumSharp.UnitTest/APIs/np.typing.Test.cs +++ b/test/NumSharp.UnitTest/APIs/np.typing.Test.cs @@ -292,8 +292,8 @@ public void MinScalarType_SmallPositive() [TestMethod] public void MinScalarType_SmallNegative() { - // No Int8 in NumSharp, so smallest signed is Int16 - np.min_scalar_type(-10).Should().Be(NPTypeCode.Int16); + // NumPy 2.4.2: small negatives demote to int8 (SByte). NumSharp HAS int8. + np.min_scalar_type(-10).Should().Be(NPTypeCode.SByte); } [TestMethod] diff --git a/test/NumSharp.UnitTest/Creation/np.array.BattleTests.cs b/test/NumSharp.UnitTest/Creation/np.array.BattleTests.cs index 1e0509a7..f5f89379 100644 --- a/test/NumSharp.UnitTest/Creation/np.array.BattleTests.cs +++ b/test/NumSharp.UnitTest/Creation/np.array.BattleTests.cs @@ -1,5 +1,7 @@ +using System; using AwesomeAssertions; using NumSharp; +using NumSharp.Backends; namespace NumSharp.UnitTest; @@ -439,4 +441,47 @@ public void Array_UInt64_Supported() } #endregion + + #region C# type -> NumPy dtype name (int8/uint8 regression) + + // Regression guard for the reported "np.array(byte[]) -> int8" audit item. + // C# `byte` is UNSIGNED (uint8); C# `sbyte` is signed (int8). NumSharp must map + // them exactly like NumPy, and must NOT collapse byte[] to int8. + [TestMethod] + public void Array_ByteArray_IsUInt8_NotInt8() + { + var arr = np.array(new byte[] { 1, 2, 3 }); + arr.dtype.Should().Be(typeof(byte)); + arr.typecode.Should().Be(NPTypeCode.Byte); + arr.typecode.AsNumpyDtypeName().Should().Be("uint8", + "C# byte is unsigned; np.array(byte[]) must be uint8, never int8"); + } + + [TestMethod] + public void Array_SByteArray_IsInt8() + { + var arr = np.array(new sbyte[] { -1, 0, 1 }); + arr.dtype.Should().Be(typeof(sbyte)); + arr.typecode.Should().Be(NPTypeCode.SByte); + arr.typecode.AsNumpyDtypeName().Should().Be("int8"); + } + + [TestMethod] + public void Array_CSharpTypes_MapToExpectedNumpyDtypeNames() + { + np.array(new byte[] { 1 }).typecode.AsNumpyDtypeName().Should().Be("uint8"); + np.array(new sbyte[] { 1 }).typecode.AsNumpyDtypeName().Should().Be("int8"); + np.array(new short[] { 1 }).typecode.AsNumpyDtypeName().Should().Be("int16"); + np.array(new ushort[] { 1 }).typecode.AsNumpyDtypeName().Should().Be("uint16"); + np.array(new int[] { 1 }).typecode.AsNumpyDtypeName().Should().Be("int32"); + np.array(new uint[] { 1 }).typecode.AsNumpyDtypeName().Should().Be("uint32"); + np.array(new long[] { 1 }).typecode.AsNumpyDtypeName().Should().Be("int64"); + np.array(new ulong[] { 1 }).typecode.AsNumpyDtypeName().Should().Be("uint64"); + np.array(new float[] { 1 }).typecode.AsNumpyDtypeName().Should().Be("float32"); + np.array(new double[] { 1 }).typecode.AsNumpyDtypeName().Should().Be("float64"); + np.array(new Half[] { (Half)1 }).typecode.AsNumpyDtypeName().Should().Be("float16"); + np.array(new bool[] { true }).typecode.AsNumpyDtypeName().Should().Be("bool"); + } + + #endregion } From fdd1624eba39641b7f7dd0b290ac57f434a67ecb Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 07:23:20 +0300 Subject: [PATCH 13/24] =?UTF-8?q?feat(io):=20np.tofile(sep,=20format)=20?= =?UTF-8?q?=E2=80=94=20full=20NumPy=20ndarray.tofile=20parity=20+=20C-orde?= =?UTF-8?q?r=20fix?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Rewrite NDArray.tofile from a binary-only, single-string-overload stub into the full NumPy ndarray.tofile(fid, /, sep='', format='%s'). Adds a Stream overload, text mode (sep + Python %-format), and — critically — fixes a data-corruption bug. BUG FIX (binary mode, non-contiguous arrays): The old tofile blindly wrote this.Array.Address for this.Array.BytesLength — the RAW underlying buffer. A sliced/strided/transposed/broadcast view therefore leaked the wrong bytes and wrong length (e.g. arange(10)[::2] wrote all 10 elements instead of the 5 logical ones). NumPy guarantees "Data is always written in C order, independent of the order of a". Binary mode now writes the logical C-order bytes: contiguous offset-0 views stream straight from Storage.Address (no managed copy); every other layout is materialized C-contiguous via copy('C') first — mirroring NumPy's PyArray_ToFile (whole-buffer fwrite when ISCONTIGUOUS, else a C-order walk). API parity (probed against NumPy 2.4.2 methods.c + convert.c): - tofile(string fid, sep='', format='%s') — creates/truncates the file ("wb"). - tofile(Stream, sep='', format='%s') — writes at the current position, leaves the stream open (the file-object form). - sep=="" (default) => binary; sep!="" => text, C-order iterate, "format % item" joined by sep with NO trailing separator. - format default "%s" (== "") => the NumPy scalar string via ArrayFormatter.ScalarStr (proven == str(np.scalar): floats keep ".0", 1e+20, complex "(1+2j)"/"1j", etc.). New PrintfFormatter (Backends/Printing): a focused port of CPython str.__mod__ as used by tofile — conversions d i u f F e E g G s x X o c %, flags - + space 0 #, width and .precision. Python semantics reproduced (probed): float truncation under %d, 2-digit-min lowercase/uppercase exponent for %e/%E, C-style %g with trailing-zero stripping, signed %x/%X/%o with "#" prefixing even zero ("%#x" % 0 == "0x0"), Python's zero-padding of inf/nan under the 0 flag ("%08.2f" % inf == "00000inf"), complex->real for real convs. Drive-by fix (ArrayFormatter.PythonComplexRepr): str() of a pure-imaginary complex with a +0.0 real part force-prepended a "+" — str(0j) gave "+0j" (and 1j would give "+1j"). Now matches CPython: pure-imaginary form carries the imaginary's own sign ("0j","1j", "-1j","-0j"); a -0.0 real still takes the parenthesized form ("(-0+0j)"). This is a pre-existing 0-d complex scalar-str bug that also surfaced through tofile text mode. Validation: - 558/558 tofile cases bit/byte-exact vs NumPy (13 dtypes x 6 layouts x binary + text %s/%d/%.3f/%e/%g/%08.2f/%+.1f). - PrintfFormatter: 446/446 format-x-value cases match Python (float64 + int64 across the conversion/flag/width/precision matrix, incl. inf/nan and round-half-to-even). - Char + Decimal (no NumPy analog) self-consistent (binary == ToByteArray('C'); text = per-element ScalarStr). - 16 new MSTest cases; full CI suite green (11275 passed) incl. 204 printing/ToString parity tests (the complex fix is consistent with the ~18K-case printing port). Performance (Release, warm best-of-N vs NumPy 2.4.2, NPY/NS): binary contiguous 10M 2.48x binary strided 10M 10.84x (NumPy per-element fwrite) text %.4f 200K 1.92x int text ~5x float %s text 1.37x Float-%s text is the one sub-1.5x case: both libraries are bound by the same shortest-float (Dragon4) algorithm; batched writer flushing keeps the rest of the per-element overhead off the hot path. --- src/NumSharp.Core/APIs/np.tofile.cs | 109 +++++- .../Backends/Printing/ArrayFormatter.cs | 12 +- .../Backends/Printing/PrintfFormatter.cs | 367 ++++++++++++++++++ .../Casting/NDArray.tofile.Test.cs | 231 +++++++++++ 4 files changed, 705 insertions(+), 14 deletions(-) create mode 100644 src/NumSharp.Core/Backends/Printing/PrintfFormatter.cs create mode 100644 test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs diff --git a/src/NumSharp.Core/APIs/np.tofile.cs b/src/NumSharp.Core/APIs/np.tofile.cs index 5c9e69a2..423c6bae 100644 --- a/src/NumSharp.Core/APIs/np.tofile.cs +++ b/src/NumSharp.Core/APIs/np.tofile.cs @@ -1,28 +1,117 @@ -using System.IO; +using System.IO; +using System.Text; +using NumSharp.Backends.Printing; namespace NumSharp { public partial class NDArray { + /// + /// Write the array to a file as binary (default) or text. + /// + /// Data is always written in C (row-major) order, independent of the array's own layout — + /// a sliced / strided / transposed / broadcast view writes its logical elements, NOT the raw + /// underlying buffer. The data produced can be recovered with . + /// + /// + /// A filename. The file is created (truncated if it exists). + /// + /// Separator between items for text output. If "" (empty, the default) a binary file is + /// written, equivalent to stream.Write(a.ToByteArray('C')). + /// + /// + /// Python-style % format string for text output (ignored in binary mode). Each entry + /// is written as format % item. The default "%s" uses the element's NumPy + /// scalar string (e.g. 1.5, (1+2j), True). + /// + /// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tofile.html + public void tofile(string fid, string sep = "", string format = "%s") + { + // NumPy opens the filename with mode "wb" (create + truncate). + using var fs = new FileStream(fid, FileMode.Create, FileAccess.Write); + tofile(fs, sep, format); + } /// - /// Write array to a file as text or binary (default).

- /// Data is always written in ‘C’ order, independent of the order of a.

The data produced by this method can be recovered using the function fromfile(). + /// Write the array to an open as binary (default) or text. The stream is + /// written from its current position and left open (the caller owns it), matching NumPy's + /// file-object tofile. Data is always in C (row-major) order. /// - /// An open file object, or a string containing a filename. + /// An open, writeable stream. + /// Separator between items for text output; "" (default) writes binary. + /// Python-style % format for text output (default "%s"). /// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tofile.html - public void tofile(string fid) + public void tofile(Stream stream, string sep = "", string format = "%s") + { + if (stream is null) throw new System.ArgumentNullException(nameof(stream)); + + if (string.IsNullOrEmpty(sep)) + WriteBinary(stream); + else + WriteText(stream, sep, format); + } + + // Binary: raw item bytes in C-order. Contiguous offset-0 views stream straight from their + // buffer (no managed copy); every other layout is materialized C-contiguous first — mirroring + // NumPy's PyArray_ToFile (whole-buffer fwrite when ISCONTIGUOUS, else a C-order element walk). + private void WriteBinary(Stream stream) { - //TODO! support for sliced data (if sliced, clone and then save) + if (size == 0 || dtypesize == 0) + return; + + bool directable = Shape.IsContiguous && Shape.offset == 0; + NDArray src = directable ? this : this.copy('C'); + unsafe { - using (var fs = File.Open(fid, FileMode.OpenOrCreate)) - using (var ums = new UnmanagedMemoryStream((byte*)this.Array.Address, this.Array.BytesLength)) + long len = checked((long)src.size * src.dtypesize); + using (var ums = new UnmanagedMemoryStream((byte*)src.Storage.Address, len)) + ums.CopyTo(stream); + } + + // Keep the (possibly freshly-copied) source alive until the unmanaged read completes. + System.GC.KeepAlive(src); + } + + // Text: each element in C-order is rendered via `format % item` (default "%s" == the NumPy scalar + // string) and joined by `sep` — no trailing separator. GetAtIndex walks logical C-order through + // the shape's strides, so any layout is honored without a prior materialization. + private void WriteText(Stream stream, string sep, string format) + { + if (size == 0) + return; + + NPTypeCode tc = typecode; + bool useScalarStr = string.IsNullOrEmpty(format) || format == "%s"; + long n = size; + + // ASCII-compatible, no BOM; leave the caller's stream open. + using var sw = new StreamWriter(stream, new UTF8Encoding(false), 1 << 16, leaveOpen: true); + + // Batch elements into a StringBuilder and flush in ~32 KB chunks — one encoded Write per + // chunk instead of two per element keeps the writer overhead off the hot path while bounding + // memory (matters for very large arrays; a per-element StreamWriter.Write is otherwise a + // measurable tax on top of the per-element scalar formatting). + var sb = new StringBuilder(1 << 16); + for (long i = 0; i < n; i++) + { + object v = GetAtIndex(i); + sb.Append(useScalarStr + ? Backends.Printing.ArrayFormatter.ScalarStr(v, tc) + : PrintfFormatter.Format(format, v, tc)); + + if (i != n - 1) + sb.Append(sep); + + if (sb.Length >= (1 << 15)) { - fs.SetLength(0); - ums.CopyTo(fs); + sw.Write(sb); + sb.Clear(); } } + + if (sb.Length > 0) + sw.Write(sb); } } } diff --git a/src/NumSharp.Core/Backends/Printing/ArrayFormatter.cs b/src/NumSharp.Core/Backends/Printing/ArrayFormatter.cs index d5ddae6b..3bf93354 100644 --- a/src/NumSharp.Core/Backends/Printing/ArrayFormatter.cs +++ b/src/NumSharp.Core/Backends/Printing/ArrayFormatter.cs @@ -343,14 +343,18 @@ private static string PythonFloatRepr(object v, FloatKind kind) // str() of a 0d complex array, matching Python's complex repr "(a+bj)". private static string PythonComplexRepr(Complex c) { + // Pure-imaginary form when the real part is +0.0: just "j" carrying the imaginary's + // OWN sign (no forced '+'). Python: str(0j)='0j', str(1j)='1j', str(-1j)='-1j', str(0-0j)='-0j'. + // A negative-zero real (-0.0) is NOT this case — it falls through to the parenthesized form + // ("(-0+0j)"), matching CPython's complex_repr. + if (c.Real == 0.0 && !double.IsNegative(c.Real)) + return PythonFloatPart(c.Imaginary) + "j"; + + // Parenthesized "(a±bj)": the imaginary part always carries an explicit sign. string im = PythonFloatPart(c.Imaginary); - // imaginary always carries an explicit sign if (!(im.StartsWith("-") || im.StartsWith("+"))) im = "+" + im; - if (c.Real == 0.0 && !double.IsNegative(c.Real)) - return im + "j"; - return "(" + PythonFloatPart(c.Real) + im + "j)"; } diff --git a/src/NumSharp.Core/Backends/Printing/PrintfFormatter.cs b/src/NumSharp.Core/Backends/Printing/PrintfFormatter.cs new file mode 100644 index 00000000..48cf979b --- /dev/null +++ b/src/NumSharp.Core/Backends/Printing/PrintfFormatter.cs @@ -0,0 +1,367 @@ +using System; +using System.Globalization; +using System.Numerics; +using System.Text; + +namespace NumSharp.Backends.Printing +{ + /// + /// A focused port of CPython's str.__mod__ (the % operator) as used by + /// numpy.ndarray.tofile(sep, format): each array element is written as + /// format % item where item is the boxed scalar. NumPy's text writer applies the + /// format string to exactly one value per element, so this engine handles a format with the + /// surrounding literal text plus its conversion specifier(s), each fed the same value. + /// + /// Supported conversions (matching Python semantics probed against 3.x / NumPy 2.4.2): + /// d i u (decimal; floats truncate toward zero), x X o (signed base-16/8), + /// f F (fixed, default precision 6), e E (scientific, 2-digit-min exponent), + /// g G (general), s (), c (char), + /// %% (literal). Flags - + space 0 #, field width and .precision are honored. + /// + internal static class PrintfFormatter + { + private static readonly CultureInfo CI = CultureInfo.InvariantCulture; + + /// Apply a Python %-format string to a single boxed value. + public static string Format(string format, object value, NPTypeCode tc) + { + var sb = new StringBuilder(format.Length + 16); + int i = 0, n = format.Length; + while (i < n) + { + char ch = format[i]; + if (ch != '%') + { + sb.Append(ch); + i++; + continue; + } + + // '%' begins a conversion spec. + i++; + if (i < n && format[i] == '%') // "%%" -> literal '%' + { + sb.Append('%'); + i++; + continue; + } + + // flags + bool left = false, plus = false, space = false, zero = false, alt = false; + while (i < n) + { + char f = format[i]; + if (f == '-') left = true; + else if (f == '+') plus = true; + else if (f == ' ') space = true; + else if (f == '0') zero = true; + else if (f == '#') alt = true; + else break; + i++; + } + + // width + int width = 0; + while (i < n && format[i] >= '0' && format[i] <= '9') + { + width = width * 10 + (format[i] - '0'); + i++; + } + + // .precision + int precision = -1; + if (i < n && format[i] == '.') + { + i++; + precision = 0; + while (i < n && format[i] >= '0' && format[i] <= '9') + { + precision = precision * 10 + (format[i] - '0'); + i++; + } + } + + // length modifiers are meaningless here (numpy passes a scalar) — skip them. + while (i < n && (format[i] == 'l' || format[i] == 'h' || format[i] == 'L')) + i++; + + if (i >= n) // dangling '%...' with no conversion char — emit verbatim. + { + sb.Append('%'); + break; + } + + char conv = format[i]; + i++; + sb.Append(Apply(conv, left, plus, space, zero, alt, width, precision, value, tc)); + } + + return sb.ToString(); + } + + private static string Apply(char conv, bool left, bool plus, bool space, bool zero, bool alt, + int width, int precision, object value, NPTypeCode tc) + { + switch (conv) + { + case 's': + case 'r': + { + // %s -> str(item); zero flag does not apply to strings. + string body = ArrayFormatter.ScalarStr(value, tc); + return Pad(body, "", width, left, false); + } + case 'd': + case 'i': + case 'u': + return FormatInteger(value, tc, 10, false, false, left, plus, space, zero, false, width); + case 'x': + return FormatInteger(value, tc, 16, false, false, left, plus, space, zero, alt, width); + case 'X': + return FormatInteger(value, tc, 16, true, false, left, plus, space, zero, alt, width); + case 'o': + return FormatInteger(value, tc, 8, false, false, left, plus, space, zero, alt, width); + case 'f': + case 'F': + return FormatFixed(ToReal(value), conv == 'F', precision < 0 ? 6 : precision, + left, plus, space, zero, width); + case 'e': + case 'E': + return FormatScientific(ToReal(value), conv == 'E', precision < 0 ? 6 : precision, + left, plus, space, zero, width); + case 'g': + case 'G': + return FormatGeneral(ToReal(value), conv == 'G', precision, alt, + left, plus, space, zero, width); + case 'c': + { + string body = FormatChar(value, tc); + return Pad(body, "", width, left, false); + } + default: + // Unknown conversion: emit the '%' + conv verbatim (lenient). + return "%" + conv; + } + } + + // ---- numeric helpers --------------------------------------------------------- + + private static double ToReal(object v) => v switch + { + double d => d, + float f => f, + Half h => (double)h, + decimal m => (double)m, + Complex c => c.Real, // numpy discards the imaginary part for real conversions + bool b => b ? 1.0 : 0.0, + char ch => ch, + _ => Convert.ToDouble(v, CI) + }; + + // Exact integer value as BigInteger (integers) or truncated-toward-zero (floats). + private static bool TryToBigInteger(object v, NPTypeCode tc, out BigInteger big) + { + switch (tc) + { + case NPTypeCode.Boolean: big = (bool)v ? 1 : 0; return true; + case NPTypeCode.SByte: big = (sbyte)v; return true; + case NPTypeCode.Byte: big = (byte)v; return true; + case NPTypeCode.Int16: big = (short)v; return true; + case NPTypeCode.UInt16: big = (ushort)v; return true; + case NPTypeCode.Int32: big = (int)v; return true; + case NPTypeCode.UInt32: big = (uint)v; return true; + case NPTypeCode.Int64: big = (long)v; return true; + case NPTypeCode.UInt64: big = (ulong)v; return true; + case NPTypeCode.Char: big = (char)v; return true; + default: + { + double d = ToReal(v); + if (!double.IsFinite(d)) { big = BigInteger.Zero; return false; } + big = new BigInteger(Math.Truncate(d)); + return true; + } + } + } + + private static string FormatInteger(object value, NPTypeCode tc, int radix, bool upper, bool _, + bool left, bool plus, bool space, bool zero, bool alt, int width) + { + // Non-finite floats have no integer form; Python raises, but a file writer should not abort — + // fall back to the nan/inf text (still readable, never crashes mid-file). + if (!TryToBigInteger(value, tc, out BigInteger big)) + { + double d = ToReal(value); + string t = double.IsNaN(d) ? "nan" : (d < 0 ? "-inf" : "inf"); + return Pad(t.TrimStart('-'), d < 0 ? "-" : Sign(false, plus, space), width, left, zero); + } + + bool neg = big.Sign < 0; + BigInteger mag = BigInteger.Abs(big); + + string digits = radix switch + { + 16 => ToBaseString(mag, 16, upper), + 8 => ToBaseString(mag, 8, upper), + _ => mag.ToString(CI) + }; + + string prefix = ""; + if (alt) + { + // Python's '#' flag prefixes even a zero value ("%#x" % 0 == "0x0", "%#o" % 0 == "0o0"), + // unlike C printf which omits the prefix for zero. + if (radix == 16) prefix = upper ? "0X" : "0x"; + else if (radix == 8) prefix = "0o"; + } + + string sign = neg ? "-" : Sign(false, plus, space); + return Pad(prefix + digits, sign, width, left, zero); + } + + private static string ToBaseString(BigInteger mag, int radix, bool upper) + { + if (mag.IsZero) return "0"; + const string lo = "0123456789abcdef"; + const string hi = "0123456789ABCDEF"; + string digitset = upper ? hi : lo; + var sb = new StringBuilder(); + while (mag > 0) + { + sb.Insert(0, digitset[(int)(mag % radix)]); + mag /= radix; + } + return sb.ToString(); + } + + private static string FormatFixed(double d, bool upper, int precision, + bool left, bool plus, bool space, bool zero, int width) + { + if (!double.IsFinite(d)) + return NonFinite(d, upper, plus, space, width, left, zero); + + string sign = (d < 0 || IsNegZero(d)) ? "-" : Sign(false, plus, space); + string digits = Math.Abs(d).ToString("F" + precision, CI); + return Pad(digits, sign, width, left, zero); + } + + private static string FormatScientific(double d, bool upper, int precision, + bool left, bool plus, bool space, bool zero, int width) + { + if (!double.IsFinite(d)) + return NonFinite(d, upper, plus, space, width, left, zero); + + string sign = (d < 0 || IsNegZero(d)) ? "-" : Sign(false, plus, space); + string body = Math.Abs(d).ToString("E" + precision, CI); // e.g. "1.500000E+000" + string digits = FixupExponent(body, upper); + return Pad(digits, sign, width, left, zero); + } + + private static string FormatGeneral(double d, bool upper, int precision, bool alt, + bool left, bool plus, bool space, bool zero, int width) + { + if (!double.IsFinite(d)) + return NonFinite(d, upper, plus, space, width, left, zero); + + int P = precision < 0 ? 6 : (precision == 0 ? 1 : precision); + string sign = (d < 0 || IsNegZero(d)) ? "-" : Sign(false, plus, space); + double abs = Math.Abs(d); + + // decimal exponent X of the value written in %e style with P-1 fraction digits. + string es = abs.ToString("E" + (P - 1), CI); + int X = int.Parse(es.Substring(es.IndexOf('E') + 1), CI); + + string digits; + if (X >= -4 && X < P) + { + digits = abs.ToString("F" + Math.Max(0, P - 1 - X), CI); + if (!alt) digits = StripTrailingZeros(digits); + } + else + { + string body = abs.ToString("E" + (P - 1), CI); + int ei = body.IndexOf('E'); + string mant = body.Substring(0, ei); + if (!alt) mant = StripTrailingZeros(mant); + digits = mant + ExponentSuffix(body.Substring(ei + 1), upper); + } + + return Pad(digits, sign, width, left, zero); + } + + private static string FormatChar(object value, NPTypeCode tc) + { + if (tc == NPTypeCode.Char) return ((char)value).ToString(); + if (TryToBigInteger(value, tc, out BigInteger big) && + big >= 0 && big <= 0x10FFFF) + { + int cp = (int)big; + return char.ConvertFromUtf32(cp); + } + return ArrayFormatter.ScalarStr(value, tc); + } + + // ---- shared formatting primitives ------------------------------------------- + + // .NET "E{p}" emits a 3-digit exponent with an uppercase 'E'; Python uses a 2-digit-minimum + // exponent and matches the conversion's case. Rebuild the "e±NN" tail. + private static string FixupExponent(string netExp, bool upper) + { + int ei = netExp.IndexOf('E'); + string mant = netExp.Substring(0, ei); + return mant + ExponentSuffix(netExp.Substring(ei + 1), upper); + } + + private static string ExponentSuffix(string netExpDigits, bool upper) + { + int exp = int.Parse(netExpDigits, CI); + string es = Math.Abs(exp).ToString(CI); + if (es.Length < 2) es = es.PadLeft(2, '0'); + return (upper ? "E" : "e") + (exp < 0 ? "-" : "+") + es; + } + + private static string StripTrailingZeros(string s) + { + if (s.IndexOf('.') < 0) return s; + s = s.TrimEnd('0'); + if (s.EndsWith(".")) s = s.Substring(0, s.Length - 1); + return s; + } + + private static string NonFinite(double d, bool upper, bool plus, bool space, + int width, bool left, bool zero) + { + string sign = double.IsNaN(d) ? Sign(false, plus, space) + : (d < 0 ? "-" : Sign(false, plus, space)); + string t = double.IsNaN(d) ? "nan" : "inf"; + if (upper) t = t.ToUpperInvariant(); + return Pad(t, sign, width, left, zero); + } + + private static string Sign(bool neg, bool plus, bool space) + { + if (neg) return "-"; + if (plus) return "+"; + if (space) return " "; + return ""; + } + + private static bool IsNegZero(double d) => d == 0.0 && double.IsNegative(d); + + // Combine sign + body into a width-padded field. Numeric zero-padding inserts the zeros + // between the sign and the body (Python's behavior, including for inf/nan). Left-justify + // ('-') always pads with spaces on the right and wins over the zero flag. + private static string Pad(string body, string sign, int width, bool left, bool zero) + { + string full = sign + body; + if (full.Length >= width) + return full; + + int deficit = width - full.Length; + if (left) + return full + new string(' ', deficit); + if (zero) + return sign + new string('0', deficit) + body; + return new string(' ', deficit) + full; + } + } +} diff --git a/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs new file mode 100644 index 00000000..6bb8ce39 --- /dev/null +++ b/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs @@ -0,0 +1,231 @@ +using System; +using System.IO; +using System.Linq; +using System.Text; +using Microsoft.VisualStudio.TestTools.UnitTesting; +using NumSharp; + +namespace NumSharp.UnitTest.Casting +{ + /// + /// NumPy-parity tests for and the + /// filename overload — ndarray.tofile(fid, sep='', format='%s'). + /// + /// Oracle: NumPy 2.4.2. Binary mode (sep=="") writes raw item bytes in C (row-major) order — + /// a sliced/strided/transposed/broadcast view writes its LOGICAL elements, not the raw buffer + /// (the bug this method previously had). Text mode (sep!="") writes format % item per + /// element in C-order, joined by sep with no trailing separator. Exact byte/text vectors + /// below were captured from actual np.ndarray.tofile output. + /// + [TestClass] + public class TofileTests + { + private static readonly NPTypeCode[] AllDtypes = + { + NPTypeCode.Boolean, NPTypeCode.Byte, NPTypeCode.SByte, NPTypeCode.Int16, NPTypeCode.UInt16, + NPTypeCode.Int32, NPTypeCode.UInt32, NPTypeCode.Int64, NPTypeCode.UInt64, NPTypeCode.Char, + NPTypeCode.Half, NPTypeCode.Single, NPTypeCode.Double, NPTypeCode.Decimal, NPTypeCode.Complex + }; + + private static byte[] Bin(NDArray nd) + { + using var ms = new MemoryStream(); + nd.tofile(ms); + return ms.ToArray(); + } + + private static string Text(NDArray nd, string sep, string format = "%s") + { + using var ms = new MemoryStream(); + nd.tofile(ms, sep, format); + return Encoding.ASCII.GetString(ms.ToArray()); + } + + // ---- Binary mode: the C-order bug fix --------------------------------------- + + [TestMethod] + public void Binary_NonContiguous_WritesLogicalCOrder_AllDtypes() + { + // Before the fix, a non-contiguous view leaked its raw parent buffer. tofile must write the + // logical C-order bytes — identical to ToByteArray('C') — for every layout and dtype. + foreach (var tc in AllDtypes) + { + foreach (var (name, nd) in new (string, NDArray)[] + { + ("strided", np.arange(12).astype(tc)["::2"]), + ("reversed", np.arange(6).astype(tc)["::-1"]), + ("transpose", np.arange(6).astype(tc).reshape(3, 2).T), + ("broadcast", np.broadcast_to(np.arange(3).astype(tc).reshape(1, 3), new Shape(2, 3))), + ("offset", np.arange(10).astype(tc)["3:8"]), + }) + { + CollectionAssert.AreEqual(nd.ToByteArray('C'), Bin(nd), $"{tc}/{name}"); + } + } + } + + [TestMethod] + public void Binary_Strided_Int32_ExactBytes() + { + // np.arange(12, dtype=int32)[::2].tofile() -> [0,2,4,6,8,10] in C-order, NOT the 48-byte parent. + var v = np.arange(12).astype(NPTypeCode.Int32)["::2"]; + CollectionAssert.AreEqual( + Convert.FromHexString("00000000020000000400000006000000080000000a000000"), + Bin(v)); + } + + [TestMethod] + public void Binary_RoundTrip_FromFile_AllDtypes_FilenameOverload() + { + // tofile(string) creates/truncates; fromfile reads it back. A strided+offset view survives. + foreach (var tc in AllDtypes) + { + var a = np.arange(20).astype(tc)["2:18"]["::2"]; + string path = Path.Combine(Path.GetTempPath(), $"ns_tofile_rt_{tc}.bin"); + try + { + a.tofile(path); + var b = np.fromfile(path, tc); + Assert.AreEqual(a.size, b.size, $"{tc} size"); + CollectionAssert.AreEqual(a.ToByteArray('C'), b.ToByteArray('C'), $"{tc} bytes"); + } + finally { if (File.Exists(path)) File.Delete(path); } + } + } + + [TestMethod] + public void Binary_FilenameOverload_TruncatesExistingFile() + { + string path = Path.Combine(Path.GetTempPath(), "ns_tofile_trunc.bin"); + try + { + File.WriteAllBytes(path, new byte[1000]); // pre-existing longer content + np.arange(3).astype(NPTypeCode.Int32).tofile(path); + Assert.AreEqual(12, new FileInfo(path).Length, "file must be truncated to 3*4 bytes"); + } + finally { if (File.Exists(path)) File.Delete(path); } + } + + // ---- Text mode: default (%s) ------------------------------------------------ + + [TestMethod] + public void Text_Default_COrder_ExactBytes() + { + // Transpose flattens in C-order: [[0,2,4],[1,3,5]] -> 0,2,4,1,3,5 + Assert.AreEqual("0,2,4,1,3,5", Text(np.arange(6).astype(NPTypeCode.Int32).reshape(3, 2).T, ",")); + // reversed uint8 + Assert.AreEqual("5,4,3,2,1,0", Text(np.arange(6).astype(NPTypeCode.Byte)["::-1"], ",")); + // float scalar str keeps the ".0" + Assert.AreEqual("0.0,1.0,2.0,3.0,4.0,5.0", Text(np.arange(6).astype(NPTypeCode.Single), ",")); + Assert.AreEqual("0.0,1.0,2.0,3.0,4.0,5.0", Text(np.arange(6).astype(NPTypeCode.Double), ",")); + } + + [TestMethod] + public void Text_Complex_Default_ZeroImaginary_NoLeadingPlus() + { + // Regression: str(0j) is '0j' NOT '+0j' (the pure-imaginary +0.0-real form drops the sign). + Assert.AreEqual("0j,(1+0j),(2+0j),(3+0j),(4+0j),(5+0j)", + Text(np.arange(6).astype(NPTypeCode.Complex), ",")); + } + + [TestMethod] + public void Text_NewlineSeparator_NoTrailing() + { + Assert.AreEqual("0\n1\n2", Text(np.arange(3).astype(NPTypeCode.Int32), "\n")); + } + + // ---- Text mode: format strings (Python %-formatting) ------------------------ + + [TestMethod] + public void Text_Format_Fixed_Scientific_General_Pad_ExactBytes() + { + var d = np.arange(6).astype(NPTypeCode.Double); + Assert.AreEqual("0.000,1.000,2.000,3.000,4.000,5.000", Text(d, ",", "%.3f")); + Assert.AreEqual( + "0.000000e+00|1.000000e+00|2.000000e+00|3.000000e+00|4.000000e+00|5.000000e+00", + Text(d, "|", "%e")); + Assert.AreEqual("0;1;2;3;4;5", Text(d, ";", "%g")); + Assert.AreEqual("00000.00,00001.00,00002.00,00003.00,00004.00,00005.00", Text(d, ",", "%08.2f")); + Assert.AreEqual("+0.0 +1.0 +2.0 +3.0 +4.0 +5.0", Text(d, " ", "%+.1f")); + Assert.AreEqual("0 1 2 3 4 5", Text(np.arange(6).astype(NPTypeCode.Int32), " ", "%d")); + } + + [TestMethod] + public void Text_Format_IntegerRadixAndFlags_ExactBytes() + { + var a = np.array(new long[] { 0, 7, -7, 255, -255 }); + Assert.AreEqual("0,7,-7,ff,-ff", Text(a, ",", "%x")); + Assert.AreEqual("0x0,0x7,-0x7,0xff,-0xff", Text(a, ",", "%#x")); + Assert.AreEqual("0,7,-7,377,-377", Text(a, ",", "%o")); + Assert.AreEqual(" 0, 7, -7, 255, -255", Text(a, ",", "%5d")); + Assert.AreEqual("00000,00007,-0007,00255,-0255", Text(a, ",", "%05d")); + } + + [TestMethod] + public void Text_Format_NonFinite_Floats() + { + var a = np.array(new double[] { double.PositiveInfinity, double.NegativeInfinity, double.NaN }); + Assert.AreEqual("inf,-inf,nan", Text(a, ",", "%f")); + Assert.AreEqual(" inf, -inf, nan", Text(a, ",", "%8.2f")); + Assert.AreEqual("00000inf,-0000inf,00000nan", Text(a, ",", "%08.2f")); // Python zero-pads inf/nan + Assert.AreEqual("+inf,-inf,+nan", Text(a, ",", "%+.1f")); + } + + [TestMethod] + public void Text_Format_FloatTruncatesUnderIntegerConversion() + { + // "%d" % 3.9 == 3 (truncate toward zero), "%d" % -3.9 == -3 + var a = np.array(new double[] { 3.9, -3.9, 0.4 }); + Assert.AreEqual("3,-3,0", Text(a, ",", "%d")); + } + + // ---- API surface + edges ---------------------------------------------------- + + [TestMethod] + public void Text_AllDtypes_DoesNotThrow_AndMatchesElementCount() + { + // Every dtype must render text without crashing; 6 elements => 5 separators. + foreach (var tc in AllDtypes) + { + string s = Text(np.arange(6).astype(tc).reshape(3, 2).T, ","); + Assert.AreEqual(6, s.Split(',').Length, $"{tc}: {s}"); + } + } + + [TestMethod] + public void Empty_AllModes_WritesNothing() + { + foreach (var tc in AllDtypes) + { + var e = np.zeros(new Shape(0, 3)).astype(tc); + Assert.AreEqual(0, Bin(e).Length, $"empty bin {tc}"); + Assert.AreEqual(0, Text(e, ",").Length, $"empty text {tc}"); + } + } + + [TestMethod] + public void Scalar0d_AllModes() + { + var z = np.arange(1).astype(NPTypeCode.Double).reshape(new int[0]); + Assert.AreEqual(8, Bin(z).Length, "0-d binary is one item"); + Assert.AreEqual("0.0", Text(z, ","), "0-d text is the single scalar, no separator"); + } + + [TestMethod] + public void Stream_IsLeftOpen_And_NoTrailingSeparator() + { + using var ms = new MemoryStream(); + np.arange(3).astype(NPTypeCode.Int32).tofile(ms, ","); + Assert.IsTrue(ms.CanWrite, "the caller's stream must be left open"); + Assert.AreEqual("0,1,2", Encoding.ASCII.GetString(ms.ToArray()), "no trailing separator"); + } + + [TestMethod] + public void Binary_Equals_ToByteArray_ForContiguous() + { + // sep="" default is the binary path; equals tobytes('C'). + var a = np.arange(24).astype(NPTypeCode.Double).reshape(2, 3, 4); + CollectionAssert.AreEqual(a.ToByteArray('C'), Bin(a)); + } + } +} From 2eb659162be0dbf2f5b3dda8a31cc403287ca0b6 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 07:42:34 +0300 Subject: [PATCH 14/24] =?UTF-8?q?fix(printing):=20tofile/scalar-str=20pari?= =?UTF-8?q?ty=20=E2=80=94=20'#'=20flag=20decimal=20point=20+=20float32/16?= =?UTF-8?q?=20sci=20threshold?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two NumPy-parity bugs surfaced by differential fuzzing np.tofile against NumPy 2.4.2 (binary + text, ~2500 format-x-value-x-dtype cases). Both live in the shared printing layer, so they also affected 0-d scalar str, not just tofile. 1) PrintfFormatter '#' (alternate) flag dropped the decimal point on f/e/g. Python keeps a decimal point under '#' even when no fractional digits are emitted: "%#.0f" % 3 -> "3." (was "3") "%#.0e" % 3 -> "3.e+00" (was "3e+00") "%#g" % 100000 -> "100000." (was "100000") Fix: thread `alt` into FormatFixed / FormatScientific and insert the point when the mantissa/number has none; FormatGeneral already suppressed trailing-zero stripping under '#' but must likewise force the point. 84/738 stress cases were wrong; now 738/738 match Python (incl. %g power-of-10 carry and %.Nf round-half-to-even, already correct). 2) ArrayFormatter.PythonFloatRepr used float64's positional/scientific window for ALL float dtypes. NumPy's scalar repr (scalartypes.c.src @name@type_@kind@_either) is positional iff 1e-4 <= |value| < max_positional, where max_positional is PER DTYPE (float16 -> 1e3, float32 -> 1e6, float64 -> 1e16) and the cutoff is tested on the VALUE: str(np.float32(1e15)) -> "1e+15" (was "1000000000000000.0") str(np.float32(1e-4)) -> "1e-04" (was "0.0001"; float32(1e-4) rounds to 9.999e-5 < 1e-4) str(np.float16(65500)) -> "6.55e+04" (was "65500.0") For float64 the new value-based test is exactly equivalent to the old decExp in [-4,16) check, so double output (and the ~18K-case array-print fuzz) is unchanged; only the float32/float16 0-d scalar / tofile-%s path is tightened to its true window. This was a latent printing-port bug: the array-printing path uses FloatingFormat (its own exp logic), so the scalar-only PythonFloatRepr escaped the earlier fuzz. 865/865 f16/f32/f64 scalar-str fuzz cases now match NumPy. Also documents PrintfFormatter's deliberate leniency where CPython/NumPy RAISE (%x/%o on bool/float, %c on float, %d on inf/nan, conversion-count != 1): a per-element file writer returns a best-effort rendering instead of aborting mid-stream. These remain the only differential-fuzz divergences and are all cases where `format % item` throws in Python (and NumPy's own tofile even segfaults on %d-with-inf). Tests: 2 new regression cases (# flag, float32/16 sci threshold at both the tofile and 0-d ToString surfaces). Full CI suite green (11277 passed) incl. 204 printing/ToString parity tests. --- .../Backends/Printing/ArrayFormatter.cs | 17 +++++++-- .../Backends/Printing/PrintfFormatter.cs | 36 ++++++++++++------- .../Casting/NDArray.tofile.Test.cs | 27 ++++++++++++++ 3 files changed, 65 insertions(+), 15 deletions(-) diff --git a/src/NumSharp.Core/Backends/Printing/ArrayFormatter.cs b/src/NumSharp.Core/Backends/Printing/ArrayFormatter.cs index 3bf93354..6b631ccc 100644 --- a/src/NumSharp.Core/Backends/Printing/ArrayFormatter.cs +++ b/src/NumSharp.Core/Backends/Printing/ArrayFormatter.cs @@ -309,7 +309,12 @@ internal static string ScalarStr(object value, NPTypeCode tc) } } - // Python's float repr: shortest digits, scientific iff decExp < -4 or decExp >= 16. + // NumPy scalar float repr (scalartypes.c.src @name@type_@kind@_either): shortest digits, and + // positional iff 1e-4 <= |value| < max_positional, else scientific. max_positional is per dtype — + // float16 -> 1e3, float32 -> 1e6, float64 -> 1e16 — and the cutoff is tested on the VALUE, not on + // the shortest-repr exponent: e.g. float32(1e-4) rounds to 9.999999e-5 < 1e-4 so it prints "1e-04". + // For float64 this is exactly equivalent to the old decExp in [-4, 16) test (no behavior change); + // it only tightens float32/float16 to their true (narrower) positional windows. private static string PythonFloatRepr(object v, FloatKind kind) { double d = kind switch @@ -328,7 +333,15 @@ private static string PythonFloatRepr(object v, FloatKind kind) if (digits == "0") return sign + "0.0"; - if (decExp >= -4 && decExp < 16) + double maxPositional = kind switch + { + FloatKind.Half => 1e3, + FloatKind.Single => 1e6, + _ => 1e16 + }; + double absval = Math.Abs(d); + + if (absval >= 1e-4 && absval < maxPositional) { var (intStr, fracStr) = Dragon4.SplitPositional(digits, decExp); return fracStr.Length == 0 ? sign + intStr + ".0" : sign + intStr + "." + fracStr; diff --git a/src/NumSharp.Core/Backends/Printing/PrintfFormatter.cs b/src/NumSharp.Core/Backends/Printing/PrintfFormatter.cs index 48cf979b..cbe41a91 100644 --- a/src/NumSharp.Core/Backends/Printing/PrintfFormatter.cs +++ b/src/NumSharp.Core/Backends/Printing/PrintfFormatter.cs @@ -17,6 +17,13 @@ namespace NumSharp.Backends.Printing /// f F (fixed, default precision 6), e E (scientific, 2-digit-min exponent), /// g G (general), s (), c (char), /// %% (literal). Flags - + space 0 #, field width and .precision are honored. + /// + /// Lenient by design where CPython/NumPy would RAISE: a conversion that does not match the value + /// type (e.g. %x/%o on a bool/float, %c on a float, %d on inf/nan) yields + /// a best-effort rendering (base of the truncated integer, the char, or "inf"/"nan") instead of a + /// TypeError/OverflowError, and a format whose conversion count != 1 is applied leniently rather than + /// rejected — a file writer must not abort mid-stream on a per-element formatting quirk. %r + /// maps to the scalar string rather than NumPy 2.x's typed scalar repr ("np.float64(1.5)"). /// internal static class PrintfFormatter { @@ -124,11 +131,11 @@ private static string Apply(char conv, bool left, bool plus, bool space, bool ze case 'f': case 'F': return FormatFixed(ToReal(value), conv == 'F', precision < 0 ? 6 : precision, - left, plus, space, zero, width); + alt, left, plus, space, zero, width); case 'e': case 'E': return FormatScientific(ToReal(value), conv == 'E', precision < 0 ? 6 : precision, - left, plus, space, zero, width); + alt, left, plus, space, zero, width); case 'g': case 'G': return FormatGeneral(ToReal(value), conv == 'G', precision, alt, @@ -233,7 +240,7 @@ private static string ToBaseString(BigInteger mag, int radix, bool upper) return sb.ToString(); } - private static string FormatFixed(double d, bool upper, int precision, + private static string FormatFixed(double d, bool upper, int precision, bool alt, bool left, bool plus, bool space, bool zero, int width) { if (!double.IsFinite(d)) @@ -241,10 +248,12 @@ private static string FormatFixed(double d, bool upper, int precision, string sign = (d < 0 || IsNegZero(d)) ? "-" : Sign(false, plus, space); string digits = Math.Abs(d).ToString("F" + precision, CI); + // '#' forces a trailing decimal point even when no fractional digits are emitted ("%#.0f"%3=="3."). + if (alt && digits.IndexOf('.') < 0) digits += "."; return Pad(digits, sign, width, left, zero); } - private static string FormatScientific(double d, bool upper, int precision, + private static string FormatScientific(double d, bool upper, int precision, bool alt, bool left, bool plus, bool space, bool zero, int width) { if (!double.IsFinite(d)) @@ -252,7 +261,11 @@ private static string FormatScientific(double d, bool upper, int precision, string sign = (d < 0 || IsNegZero(d)) ? "-" : Sign(false, plus, space); string body = Math.Abs(d).ToString("E" + precision, CI); // e.g. "1.500000E+000" - string digits = FixupExponent(body, upper); + int ei = body.IndexOf('E'); + string mant = body.Substring(0, ei); + // '#' keeps the mantissa's decimal point even at precision 0 ("%#.0e"%3=="3.e+00"). + if (alt && mant.IndexOf('.') < 0) mant += "."; + string digits = mant + ExponentSuffix(body.Substring(ei + 1), upper); return Pad(digits, sign, width, left, zero); } @@ -274,7 +287,10 @@ private static string FormatGeneral(double d, bool upper, int precision, bool al if (X >= -4 && X < P) { digits = abs.ToString("F" + Math.Max(0, P - 1 - X), CI); + // '#' keeps trailing zeros AND forces a decimal point ("%#g"%100000=="100000."); + // the default form strips both. if (!alt) digits = StripTrailingZeros(digits); + else if (digits.IndexOf('.') < 0) digits += "."; } else { @@ -282,6 +298,7 @@ private static string FormatGeneral(double d, bool upper, int precision, bool al int ei = body.IndexOf('E'); string mant = body.Substring(0, ei); if (!alt) mant = StripTrailingZeros(mant); + else if (mant.IndexOf('.') < 0) mant += "."; digits = mant + ExponentSuffix(body.Substring(ei + 1), upper); } @@ -303,14 +320,7 @@ private static string FormatChar(object value, NPTypeCode tc) // ---- shared formatting primitives ------------------------------------------- // .NET "E{p}" emits a 3-digit exponent with an uppercase 'E'; Python uses a 2-digit-minimum - // exponent and matches the conversion's case. Rebuild the "e±NN" tail. - private static string FixupExponent(string netExp, bool upper) - { - int ei = netExp.IndexOf('E'); - string mant = netExp.Substring(0, ei); - return mant + ExponentSuffix(netExp.Substring(ei + 1), upper); - } - + // exponent and matches the conversion's case. Rebuild the "±NN" tail. private static string ExponentSuffix(string netExpDigits, bool upper) { int exp = int.Parse(netExpDigits, CI); diff --git a/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs index 6bb8ce39..3a8c083d 100644 --- a/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs +++ b/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs @@ -179,6 +179,33 @@ public void Text_Format_FloatTruncatesUnderIntegerConversion() Assert.AreEqual("3,-3,0", Text(a, ",", "%d")); } + [TestMethod] + public void Text_Format_AltFlag_ForcesDecimalPoint() + { + // Fuzz regression: '#' must keep a decimal point on f/e/g even with no fractional digits. + var d = np.array(new double[] { 3.0, 100000.0 }); + Assert.AreEqual("3.,100000.", Text(d, ",", "%#.0f")); + Assert.AreEqual("3.e+00,1.e+05", Text(d, ",", "%#.0e")); + Assert.AreEqual("3.00000,100000.", Text(d, ",", "%#g")); + Assert.AreEqual("3.00,1.00e+05", Text(d, ",", "%#.3g")); + } + + [TestMethod] + public void Text_Float32_Float16_ScalarStr_ScientificThreshold() + { + // Fuzz regression: scalar str switches to scientific per the dtype's max_positional + // (float32 -> 1e6, float16 -> 1e3), tested on the VALUE — float32(1e-4) rounds below 1e-4. + var f32 = np.array(new double[] { 1e15, 1e-4, 1e5, 1e6 }).astype(NPTypeCode.Single); + Assert.AreEqual("1e+15,1e-04,100000.0,1e+06", Text(f32, ",")); + + var f16 = np.array(new double[] { 65500, 1000, 100, 0.0001 }).astype(NPTypeCode.Half); + Assert.AreEqual("6.55e+04,1e+03,100.0,0.0001", Text(f16, ",")); + + // Same fix at the 0-d array str surface (ArrayFormatter.PythonFloatRepr powers both). + Assert.AreEqual("1e+15", np.array(new double[] { 1e15 }).astype(NPTypeCode.Single).reshape(new int[0]).ToString()); + Assert.AreEqual("6.55e+04", np.array(new double[] { 65500 }).astype(NPTypeCode.Half).reshape(new int[0]).ToString()); + } + // ---- API surface + edges ---------------------------------------------------- [TestMethod] From 7b79744e6871f16fd6f0c6370fb846aebe0b0c05 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 07:49:23 +0300 Subject: [PATCH 15/24] fix(dtype): int8/float16 avoidance across the type-resolution surface (NumPy parity) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Follow-up to the np.min_scalar_type fix: swept the whole dtype-resolution surface with full differential matrices against NumPy 2.4.2 and fixed every sibling of the "narrow type (int8/float16) gets avoided/widened" bug. Probes run: promote_types 13x13 (169), can_cast 13x13x5 casting modes (845), issubdtype concrete x abstract (117), reductions+unary per dtype (300), binary ufuncs 12x12x7 (1008). promote_types and the reduction/unary-math tiers were already correct; the bugs below were not. 1) NPTypeHierarchy._concreteParent was MISSING BOTH narrow types. SByte (int8) and Half (float16) had no entry, so GetImmediateKind() returned Generic for them — breaking every consumer: - issubdtype(int8, signedinteger/integer/number) -> False (NumPy: True) - issubdtype(float16, floating/inexact/number) -> False (NumPy: True) - can_cast same_kind involving int8/float16 (see #2) - maximum_sctype(int8)/(float16) -> themselves (now int64/float64) Added [SByte]=SignedInteger and [Half]=Floating (+ GetMaximumType arms). issubdtype differential: 117/117 after (int8/float16 rows now pass). 2) can_cast(..., "same_kind") used a SYMMETRIC "same category" model, diverging from NumPy's DIRECTIONAL kind ordering (dtype_kind_to_ordering in convert_datatype.c): bool(0) < unsigned(1) < signed(2) < float(4) < complex(5), allowed iff a safe cast OR KindOrder(from) <= KindOrder(to). 32/845 cases were wrong, e.g.: - int16 -> int8 was False (NumPy True: signed -> signed) - int16 -> uint8 was True (NumPy False: signed -> unsigned is DOWN a kind) - int32 -> float was False (NumPy True: int -> float) - float -> float16 direction handled; complex -> float rejected. Added NPTypeHierarchy.KindOrder + CanCastSameKindOrder; can_cast now ORs safe with the ordering. can_cast differential: 845/845 after. (Kept the symmetric IsSameKind for its own callers/tests; it is no longer used by can_cast.) 3) Bool-input ufuncs with NO bool loop returned bool instead of int8. NumPy's power/floor_divide/remainder/square loops start at int8, so bool operands promote (probed 2.4.2, and already the documented rule for np.evaluate — the DIRECT ops just never applied it): - power(bool,bool) -> int8 [True**True=1, False**False=1] - floor_divide(bool,bool) -> int8 [1//1=1, 0//0=0] - mod(bool,bool) -> int8 [1%1=0] - square(bool) -> int8 [True->1, False->0] Extended the existing shift-op bool->int8 remap in ExecuteBinaryOp to cover FloorDivide/Mod/Power, fixed ResolvePowerResultType (power's scalar-exponent fast paths), and Default.Square. Scoped strictly to resultType==Boolean (i.e. both operands bool) so add/multiply/etc. are untouched. binary differential: 1008/1008 after; values verified against NumPy. Also corrected two tests that pinned the OLD buggy same_kind behavior (can_cast(int32, float32, "same_kind") asserted False; NumPy says True) — they contradicted other tests in the tree that already expected the correct direction. Validation: - Differentials vs NumPy 2.4.2 all green after fixes: promote 169, can_cast 845, issubdtype 117, binary 1008, reductions+unary (only np.rint diverges — a MISSING ufunc in NumSharp, not a dtype bug; noted, out of scope). - New Casting/NarrowDtypeParityTests.cs (9) pins issubdtype/maximum_sctype/ can_cast-directional + the four bool->int8 op cases (dtype AND values). - Full suite 11286 passed / 0 failed (net10.0), FuzzMatrix gate 79/79 bit-exact, affected classes green on net8.0. --- .../Backends/Default/Math/Default.Power.cs | 4 + .../Backends/Default/Math/Default.Square.cs | 5 +- .../Default/Math/DefaultEngine.BinaryOp.cs | 13 +- src/NumSharp.Core/Logic/NPTypeHierarchy.cs | 43 +++++- src/NumSharp.Core/Logic/np.can_cast.cs | 20 +-- .../APIs/np.can_cast.BattleTest.cs | 27 +++- test/NumSharp.UnitTest/APIs/np.typing.Test.cs | 7 +- .../Casting/NarrowDtypeParityTests.cs | 127 ++++++++++++++++++ 8 files changed, 217 insertions(+), 29 deletions(-) create mode 100644 test/NumSharp.UnitTest/Casting/NarrowDtypeParityTests.cs diff --git a/src/NumSharp.Core/Backends/Default/Math/Default.Power.cs b/src/NumSharp.Core/Backends/Default/Math/Default.Power.cs index c8fcf814..233b0aa5 100644 --- a/src/NumSharp.Core/Backends/Default/Math/Default.Power.cs +++ b/src/NumSharp.Core/Backends/Default/Math/Default.Power.cs @@ -175,6 +175,10 @@ private static NPTypeCode ResolvePowerResultType(NDArray lhs, NDArray rhs) var resultType = np._FindCommonType(lhs, rhs); if (lhs.GetTypeCode.GetGroup() <= 2 && rhs.GetTypeCode.GetGroup() == 3) resultType = NPTypeCode.Double; + // power has no bool loop: bool ** bool -> int8 (probed 2.4.2). Keeps the + // scalar-exponent fast paths consistent with the general ExecuteBinaryOp path. + if (resultType == NPTypeCode.Boolean) + resultType = NPTypeCode.SByte; return resultType; } diff --git a/src/NumSharp.Core/Backends/Default/Math/Default.Square.cs b/src/NumSharp.Core/Backends/Default/Math/Default.Square.cs index 2d3c16c1..9977bd83 100644 --- a/src/NumSharp.Core/Backends/Default/Math/Default.Square.cs +++ b/src/NumSharp.Core/Backends/Default/Math/Default.Square.cs @@ -18,8 +18,9 @@ public override NDArray Square(NDArray nd, NPTypeCode? typeCode = null, NDArray if (typeCode.HasValue) ValidateUnaryInputCast(nd.GetTypeCode, typeCode.Value, "square"); - // np.square preserves input dtype (unlike sin/cos which promote to float) - var outputType = typeCode ?? nd.GetTypeCode; + // np.square preserves input dtype (unlike sin/cos which promote to float), EXCEPT + // bool: square has no bool loop, so square(bool) -> int8 (probed 2.4.2). + var outputType = typeCode ?? (nd.GetTypeCode == NPTypeCode.Boolean ? NPTypeCode.SByte : nd.GetTypeCode); return ExecuteUnaryOp(nd, UnaryOp.Square, outputType, @out, where); } } diff --git a/src/NumSharp.Core/Backends/Default/Math/DefaultEngine.BinaryOp.cs b/src/NumSharp.Core/Backends/Default/Math/DefaultEngine.BinaryOp.cs index 752338ae..ca2990b4 100644 --- a/src/NumSharp.Core/Backends/Default/Math/DefaultEngine.BinaryOp.cs +++ b/src/NumSharp.Core/Backends/Default/Math/DefaultEngine.BinaryOp.cs @@ -88,11 +88,14 @@ internal unsafe NDArray ExecuteBinaryOp(NDArray lhs, NDArray rhs, BinaryOp op, resultType = dtype.Value; } - // NumPy shift ufuncs have NO bool loop (left_shift/right_shift loops are bb->b .. - // QQ->Q only). A bool input therefore promotes to the smallest integer loop, int8, - // e.g. left_shift(bool, bool) -> int8 (probed 2.4.2). Bump BEFORE the bool->logical - // remap below so shift never takes the bitwise-OR/AND path. - if ((op == BinaryOp.LeftShift || op == BinaryOp.RightShift) && resultType == NPTypeCode.Boolean) + // Several NumPy ufuncs have NO bool loop — their smallest integer loop starts at int8, + // so a bool×bool input promotes to int8 (probed 2.4.2): + // left_shift/right_shift (bb->b absent), floor_divide (True//True -> 1:int8), + // remainder/mod (True%True -> 0:int8), power (True**True -> 1:int8). + // Bump BEFORE the bool->logical remap below so these never take the bitwise-OR/AND path. + if (resultType == NPTypeCode.Boolean && + (op == BinaryOp.LeftShift || op == BinaryOp.RightShift || + op == BinaryOp.FloorDivide || op == BinaryOp.Mod || op == BinaryOp.Power)) resultType = NPTypeCode.SByte; // NumPy bool arithmetic: the bool dtype has no integer add/multiply ufunc loop — `+` diff --git a/src/NumSharp.Core/Logic/NPTypeHierarchy.cs b/src/NumSharp.Core/Logic/NPTypeHierarchy.cs index 54f06ab0..a817e9e7 100644 --- a/src/NumSharp.Core/Logic/NPTypeHierarchy.cs +++ b/src/NumSharp.Core/Logic/NPTypeHierarchy.cs @@ -90,7 +90,8 @@ internal static class NPTypeHierarchy // Map concrete types to their immediate abstract parent private static readonly Dictionary _concreteParent = new() { - // SignedInteger children + // SignedInteger children (int8 = SByte MUST be here — it is one of the 15 dtypes) + [NPTypeCode.SByte] = NPTypeKind.SignedInteger, [NPTypeCode.Int16] = NPTypeKind.SignedInteger, [NPTypeCode.Int32] = NPTypeKind.SignedInteger, [NPTypeCode.Int64] = NPTypeKind.SignedInteger, @@ -104,7 +105,8 @@ internal static class NPTypeHierarchy // Char is treated as UnsignedInteger (like uint16) [NPTypeCode.Char] = NPTypeKind.UnsignedInteger, - // Floating children + // Floating children (float16 = Half MUST be here — it is one of the 15 dtypes) + [NPTypeCode.Half] = NPTypeKind.Floating, [NPTypeCode.Single] = NPTypeKind.Floating, [NPTypeCode.Double] = NPTypeKind.Floating, [NPTypeCode.Decimal] = NPTypeKind.Floating, // NumSharp-specific @@ -196,6 +198,35 @@ public static bool IsSameKind(NPTypeCode type1, NPTypeCode type2) return false; } + /// + /// NumPy's kind ordering for NPY_SAME_KIND_CASTING (dtype_kind_to_ordering in + /// convert_datatype.c): bool(0) < unsigned(1) < signed(2) < float(4) < complex(5). + /// Char rides with unsigned (uint16 masquerade); Decimal rides with floating. + /// + public static int KindOrder(NPTypeCode type) + { + return GetImmediateKind(type) switch + { + NPTypeKind.Boolean => 0, + NPTypeKind.UnsignedInteger => 1, + NPTypeKind.SignedInteger => 2, + NPTypeKind.Floating => 4, + NPTypeKind.ComplexFloating => 5, + _ => 6 // Generic/unknown: only identical-type casts pass (handled by from==to) + }; + } + + /// + /// NumPy same_kind cast rule for a concrete type pair: a cast is permitted when the + /// source kind orders at or below the destination kind (). This is + /// NumPy's DIRECTIONAL model — e.g. unsigned→signed is allowed but signed→unsigned is not, + /// and int→float is allowed but float→int is not. Callers OR this with a safe-cast check. + /// + public static bool CanCastSameKindOrder(NPTypeCode from, NPTypeCode to) + { + return KindOrder(from) <= KindOrder(to); + } + /// /// Get the maximum precision type for a given kind. /// Used by maximum_sctype. @@ -218,8 +249,8 @@ public static NPTypeCode GetMaximumType(NPTypeCode type) // Boolean stays boolean NPTypeCode.Boolean => NPTypeCode.Boolean, - // Signed integers -> int64 - NPTypeCode.Int16 or NPTypeCode.Int32 or NPTypeCode.Int64 => NPTypeCode.Int64, + // Signed integers -> int64 (int8 = SByte included) + NPTypeCode.SByte or NPTypeCode.Int16 or NPTypeCode.Int32 or NPTypeCode.Int64 => NPTypeCode.Int64, // Unsigned integers -> uint64 NPTypeCode.Byte or NPTypeCode.UInt16 or NPTypeCode.UInt32 or NPTypeCode.UInt64 => NPTypeCode.UInt64, @@ -227,8 +258,8 @@ public static NPTypeCode GetMaximumType(NPTypeCode type) // Char treated as unsigned integer NPTypeCode.Char => NPTypeCode.UInt64, - // Float types: Single/Double -> Double, Decimal stays Decimal - NPTypeCode.Single or NPTypeCode.Double => NPTypeCode.Double, + // Float types: Half/Single/Double -> Double, Decimal stays Decimal + NPTypeCode.Half or NPTypeCode.Single or NPTypeCode.Double => NPTypeCode.Double, NPTypeCode.Decimal => NPTypeCode.Decimal, // Complex stays complex (we only have one complex type) diff --git a/src/NumSharp.Core/Logic/np.can_cast.cs b/src/NumSharp.Core/Logic/np.can_cast.cs index a4b193f4..883cf271 100644 --- a/src/NumSharp.Core/Logic/np.can_cast.cs +++ b/src/NumSharp.Core/Logic/np.can_cast.cs @@ -257,14 +257,18 @@ private static bool CanCastSafe(NPTypeCode from, NPTypeCode to) } /// - /// Check if casting is allowed within the same kind (integers to integers, floats to floats). + /// Check if casting is allowed under NumPy's same_kind rule. /// /// - /// Uses NPTypeHierarchy for consistent type categorization across all typing functions. - /// same_kind allows downcasting within: - /// - integers (both signed and unsigned, e.g., int64 -> int32, uint32 -> int16) - /// - floating point (e.g., float64 -> float32) - /// - complex (e.g., complex128 -> complex64, if we had it) + /// NumPy's same_kind is DIRECTIONAL, driven by a kind ordering + /// (bool < unsigned < signed < float < complex), NOT a symmetric "same category": + /// a cast is allowed iff it is already safe OR the source kind orders at or below the + /// destination kind (). Consequences, + /// all matching NumPy 2.4.2: + /// - int16 -> int8 allowed (signed -> signed), but int16 -> uint8 NOT allowed (signed -> unsigned); + /// - uint64 -> int8 allowed (unsigned -> signed); + /// - int/uint -> float allowed, but float -> int NOT allowed; + /// - float64 -> float16 allowed (float -> float); complex -> float NOT allowed. /// private static bool CanCastSameKind(NPTypeCode from, NPTypeCode to) { @@ -272,8 +276,8 @@ private static bool CanCastSameKind(NPTypeCode from, NPTypeCode to) if (CanCastSafe(from, to)) return true; - // Allow downcasting within the same kind - return NPTypeHierarchy.IsSameKind(from, to); + // Otherwise allow casts that do not move DOWN NumPy's kind ordering. + return NPTypeHierarchy.CanCastSameKindOrder(from, to); } /// diff --git a/test/NumSharp.UnitTest/APIs/np.can_cast.BattleTest.cs b/test/NumSharp.UnitTest/APIs/np.can_cast.BattleTest.cs index 36854d3a..5d91181b 100644 --- a/test/NumSharp.UnitTest/APIs/np.can_cast.BattleTest.cs +++ b/test/NumSharp.UnitTest/APIs/np.can_cast.BattleTest.cs @@ -87,12 +87,27 @@ public void CanCast_SameKindMode_AllowsDowncastWithinKind() { np.can_cast(NPTypeCode.Int32, NPTypeCode.Int16, "same_kind").Should().BeTrue(); np.can_cast(NPTypeCode.Double, NPTypeCode.Single, "same_kind").Should().BeTrue(); - } - - [TestMethod] - public void CanCast_SameKindMode_RejectsCrossKind() - { - np.can_cast(NPTypeCode.Int32, NPTypeCode.Single, "same_kind").Should().BeFalse(); + // int8 (SByte) participates: down/across signed and float16 both allowed. + np.can_cast(NPTypeCode.Int32, NPTypeCode.SByte, "same_kind").Should().BeTrue(); + np.can_cast(NPTypeCode.Single, NPTypeCode.Half, "same_kind").Should().BeTrue(); + } + + [TestMethod] + public void CanCast_SameKindMode_DirectionalKindOrdering() + { + // NumPy same_kind is a DIRECTIONAL kind ordering (bool < unsigned < signed < float < complex): + // moving to the same-or-higher kind is allowed; moving DOWN is not. + // int -> float is allowed (NOT rejected — the old test asserted the opposite). + np.can_cast(NPTypeCode.Int32, NPTypeCode.Single, "same_kind").Should().BeTrue(); + np.can_cast(NPTypeCode.Int16, NPTypeCode.Half, "same_kind").Should().BeTrue(); + // unsigned -> signed allowed; signed -> unsigned NOT (asymmetric). + np.can_cast(NPTypeCode.Byte, NPTypeCode.SByte, "same_kind").Should().BeTrue(); + np.can_cast(NPTypeCode.SByte, NPTypeCode.Byte, "same_kind").Should().BeFalse(); + // float -> int and complex -> float move DOWN a kind: rejected. + np.can_cast(NPTypeCode.Single, NPTypeCode.Int32, "same_kind").Should().BeFalse(); + np.can_cast(NPTypeCode.Complex, NPTypeCode.Double, "same_kind").Should().BeFalse(); + // anything (non-bool) -> bool moves down: rejected. + np.can_cast(NPTypeCode.Int32, NPTypeCode.Boolean, "same_kind").Should().BeFalse(); } [TestMethod] diff --git a/test/NumSharp.UnitTest/APIs/np.typing.Test.cs b/test/NumSharp.UnitTest/APIs/np.typing.Test.cs index 3b29ac65..9e1845d1 100644 --- a/test/NumSharp.UnitTest/APIs/np.typing.Test.cs +++ b/test/NumSharp.UnitTest/APIs/np.typing.Test.cs @@ -194,8 +194,11 @@ public void CanCast_Int32ToInt16_SameKind() [TestMethod] public void CanCast_Int32ToFloat32_SameKind() { - // Int to float is NOT same_kind - different type kinds - np.can_cast(NPTypeCode.Int32, NPTypeCode.Single, "same_kind").Should().BeFalse(); + // NumPy 2.4.2: int -> float IS same_kind (kind ordering signed(2) <= float(4)). + // np.can_cast(np.int32, np.float32, 'same_kind') == True + np.can_cast(NPTypeCode.Int32, NPTypeCode.Single, "same_kind").Should().BeTrue(); + // ...but the reverse (float -> int) moves DOWN a kind and is rejected. + np.can_cast(NPTypeCode.Single, NPTypeCode.Int32, "same_kind").Should().BeFalse(); } [TestMethod] diff --git a/test/NumSharp.UnitTest/Casting/NarrowDtypeParityTests.cs b/test/NumSharp.UnitTest/Casting/NarrowDtypeParityTests.cs new file mode 100644 index 00000000..8aded490 --- /dev/null +++ b/test/NumSharp.UnitTest/Casting/NarrowDtypeParityTests.cs @@ -0,0 +1,127 @@ +using System; +using AwesomeAssertions; +using Microsoft.VisualStudio.TestTools.UnitTesting; +using NumSharp; +using NumSharp.Backends; + +namespace NumSharp.UnitTest.Casting +{ + /// + /// Regression coverage for the "narrow dtype avoidance" family — code that historically + /// ignored int8 (SByte), float16 (Half), or the no-bool-loop int8 promotion, diverging from + /// NumPy 2.4.2. Each assertion below was verified against actual NumPy output. + /// + /// Bugs pinned here: + /// - NPTypeHierarchy._concreteParent was MISSING SByte and Half, so issubdtype / can_cast / + /// maximum_sctype treated int8 and float16 as uncategorized (Generic). + /// - can_cast(..., "same_kind") used a symmetric "same category" model instead of NumPy's + /// DIRECTIONAL kind ordering (bool < unsigned < signed < float < complex). + /// - power / floor_divide / mod / square on bool returned bool instead of int8 (these ufuncs + /// have no bool loop in NumPy). + /// + [TestClass] + public class NarrowDtypeParityTests + { + // ---- issubdtype: int8 (SByte) and float16 (Half) must be categorized ---------------- + + [TestMethod] + public void IsSubDtype_Int8_IsSignedInteger() + { + np.issubdtype(NPTypeCode.SByte, "signedinteger").Should().BeTrue(); + np.issubdtype(NPTypeCode.SByte, "integer").Should().BeTrue(); + np.issubdtype(NPTypeCode.SByte, "number").Should().BeTrue(); + np.issubdtype(NPTypeCode.SByte, "unsignedinteger").Should().BeFalse(); + np.issubdtype(NPTypeCode.SByte, "floating").Should().BeFalse(); + } + + [TestMethod] + public void IsSubDtype_Float16_IsFloating() + { + np.issubdtype(NPTypeCode.Half, "floating").Should().BeTrue(); + np.issubdtype(NPTypeCode.Half, "inexact").Should().BeTrue(); + np.issubdtype(NPTypeCode.Half, "number").Should().BeTrue(); + np.issubdtype(NPTypeCode.Half, "integer").Should().BeFalse(); + } + + // ---- maximum_sctype: int8 -> int64, float16 -> float64 ------------------------------- + + [TestMethod] + public void MaximumSctype_Int8_And_Float16() + { + np.maximum_sctype(NPTypeCode.SByte).Should().Be(NPTypeCode.Int64); + np.maximum_sctype(NPTypeCode.Half).Should().Be(NPTypeCode.Double); + } + + // ---- can_cast same_kind: DIRECTIONAL kind ordering, incl. int8 / float16 ------------- + + [TestMethod] + public void CanCast_SameKind_KindOrdering_Directional() + { + // unsigned -> signed allowed; signed -> unsigned NOT (asymmetric). + np.can_cast(NPTypeCode.Byte, NPTypeCode.SByte, "same_kind").Should().BeTrue(); + np.can_cast(NPTypeCode.SByte, NPTypeCode.Byte, "same_kind").Should().BeFalse(); + // int -> float allowed; float -> int NOT. + np.can_cast(NPTypeCode.Int64, NPTypeCode.Half, "same_kind").Should().BeTrue(); + np.can_cast(NPTypeCode.Half, NPTypeCode.Int64, "same_kind").Should().BeFalse(); + // signed downcast to int8 allowed (same kind); float downcast to float16 allowed. + np.can_cast(NPTypeCode.Int64, NPTypeCode.SByte, "same_kind").Should().BeTrue(); + np.can_cast(NPTypeCode.Double, NPTypeCode.Half, "same_kind").Should().BeTrue(); + // complex -> float moves down a kind: rejected. + np.can_cast(NPTypeCode.Complex, NPTypeCode.Half, "same_kind").Should().BeFalse(); + } + + // ---- no-bool-loop ufuncs promote bool -> int8 (dtype AND values) --------------------- + + [TestMethod] + public void Power_BoolBool_IsInt8() + { + var b = np.array(new bool[] { true, false, true, true }); + var r = np.power(b, b); + r.typecode.Should().Be(NPTypeCode.SByte, "power has no bool loop; bool**bool -> int8"); + r.GetSByte(0).Should().Be(1); // 1**1 + r.GetSByte(1).Should().Be(1); // 0**0 == 1 + } + + [TestMethod] + public void FloorDivide_BoolBool_IsInt8() + { + var b = np.array(new bool[] { true, false, true, true }); + var r = np.floor_divide(b, b); + r.typecode.Should().Be(NPTypeCode.SByte, "floor_divide has no bool loop; bool//bool -> int8"); + r.GetSByte(0).Should().Be(1); // 1//1 + r.GetSByte(1).Should().Be(0); // 0//0 -> 0 + } + + [TestMethod] + public void Mod_BoolBool_IsInt8() + { + var b = np.array(new bool[] { true, true, true, true }); + var r = np.mod(b, b); + r.typecode.Should().Be(NPTypeCode.SByte, "remainder has no bool loop; bool%bool -> int8"); + r.GetSByte(0).Should().Be(0); // 1%1 + } + + [TestMethod] + public void Square_Bool_IsInt8() + { + var b = np.array(new bool[] { true, false, true }); + var r = np.square(b); + r.typecode.Should().Be(NPTypeCode.SByte, "square has no bool loop; square(bool) -> int8"); + r.GetSByte(0).Should().Be(1); + r.GetSByte(1).Should().Be(0); + r.GetSByte(2).Should().Be(1); + } + + [TestMethod] + public void NonBool_Ops_PreserveDtype_Unaffected() + { + // Sanity: the bool->int8 remap is scoped to bool×bool only. + np.power(np.array(new int[] { 2, 3 }), np.array(new int[] { 2, 3 })) + .typecode.Should().Be(NPTypeCode.Int32); + np.square(np.array(new short[] { 2, 3 })) + .typecode.Should().Be(NPTypeCode.Int16); + np.mod(np.array(new byte[] { 3, 4 }), np.array(new byte[] { 2, 2 })) + .typecode.Should().Be(NPTypeCode.Byte); + } + } +} From 22aac5778dc3d9bad0f339dc9afce4a2de41b2b9 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 08:03:36 +0300 Subject: [PATCH 16/24] =?UTF-8?q?test(fuzz):=20decimal=20oracle=20?= =?UTF-8?q?=E2=80=94=20floor/ceil/trunc/diff/clip/stat/where/sort/manip=20?= =?UTF-8?q?(Group=20A=20complete)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Closes the final Group A item: extend the INDEPENDENT C# Decimal oracle (gen_decimal_oracle.cs) to the remaining decimal-supported ops. Decimal is the one NumSharp numeric dtype with no NumPy analog, so the generator itself is the oracle — every expected value is computed with naive scalar System.Decimal math (no NumSharp kernels), then the harness replays the operand through NumSharp's decimal KERNELS and value-compares (BitDiff tokenizes decimal by canonical value, so 1.0m ≡ 1.00m). New / extended tiers (12 total, 579 cases, all green): - decimal_unary (+floor/ceil/trunc via decimal.Floor/Ceiling/Truncate — exact base-10) - decimal_scan (+diff n=1,2 along the last axis; DiffAxis oracle) - decimal_stat (NEW, 170): clip = Max(lo,Min(hi,x)); order stats median/ptp/ percentile/quantile (axis=None -> scalar). Oracle = naive sort + NumPy 'linear' interpolation in EXACT decimal (Quantile/Median). - decimal_where (NEW, 4): where(cond,a,b) 16-byte conditional-copy over contig+strided - decimal_sort (NEW, 7): sort along an axis (1-D/2-D, contig+strided; SortAxis oracle) - decimal_manip (NEW, 36): ravel/transpose/reshape — value-preserving reindex forcing the strided decimal materialize/copy path (compared C-contiguous) Every oracle formula was validated bit-identical against NumSharp's decimal path BEFORE generating the corpus (median even/odd n, ptp, quantile/percentile q in {0,.25,.5,.75,1}, clip, sort, diff, floor/ceil/trunc, where, reshape/ravel/transpose) — de-risked, no false divergences. Zero harness changes: all these ops are already dtype-generic in OpRegistry, so the decimal cases flow through the existing dispatch. nan* reductions intentionally skipped: System.Decimal cannot represent NaN, so nansum/nanmax/... are byte-identical to plain sum/max/... (already covered by decimal_reduce; verified np.nansum(decimal) == np.sum(decimal)). Note: the shared n++ case-ID counter shifts IDs in the pre-existing decimal tiers (binary/reduce/power/varstd/matmul/astype); those diffs are ID-relabel ONLY — operand/expected buffers are unchanged (verified by id-stripped diff). Gate: FuzzMatrix 65/65 green on net10.0 (4 new decimal test methods: DecimalStat/DecimalWhere/DecimalSort/DecimalManip). COVERAGE_GAPS.md: Group A closed. (cherry picked from commit 48ebfa4fcc2ce57bcedcdfc14ba75108bef845c8) --- .claude/CLAUDE.md | 7 +- test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md | 28 ++- .../NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs | 23 ++ test/NumSharp.UnitTest/Fuzz/README.md | 2 +- .../Fuzz/corpus/decimal_astype.jsonl | 44 ++-- .../Fuzz/corpus/decimal_binary.jsonl | 196 ++++++++--------- .../Fuzz/corpus/decimal_manip.jsonl | 36 ++++ .../Fuzz/corpus/decimal_matmul.jsonl | 8 +- .../Fuzz/corpus/decimal_power.jsonl | 40 ++-- .../Fuzz/corpus/decimal_reduce.jsonl | 120 +++++------ .../Fuzz/corpus/decimal_scan.jsonl | 69 +++--- .../Fuzz/corpus/decimal_sort.jsonl | 7 + .../Fuzz/corpus/decimal_stat.jsonl | 170 +++++++++++++++ .../Fuzz/corpus/decimal_unary.jsonl | 135 +++++++----- .../Fuzz/corpus/decimal_varstd.jsonl | 44 ++-- .../Fuzz/corpus/decimal_where.jsonl | 4 + test/oracle/gen_decimal_oracle.cs | 202 ++++++++++++++++++ 17 files changed, 825 insertions(+), 310 deletions(-) create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_manip.jsonl create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_sort.jsonl create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_stat.jsonl create mode 100644 test/NumSharp.UnitTest/Fuzz/corpus/decimal_where.jsonl diff --git a/.claude/CLAUDE.md b/.claude/CLAUDE.md index 45b458b3..3df12dc8 100644 --- a/.claude/CLAUDE.md +++ b/.claude/CLAUDE.md @@ -557,7 +557,8 @@ test/oracle/ corpus generators (NumPy 2.4.2 — run by per-mode dtype axes widened to ALL_DTYPES; Char woven into every tier via the uint16 proxy (char_tier, relabelled uint16->char) gen_decimal_oracle.cs INDEPENDENT C# oracle for Decimal (no NumPy analog): naive scalar - System.Decimal math -> decimal_{unary,binary,reduce,scan}.jsonl + System.Decimal math -> decimal_{unary,binary,reduce,scan,power, + varstd,matmul,astype,stat,where,sort,manip}.jsonl gen_index_oracle.py getter/setter index oracle (token index over 15 base recipes) fuzz_random.py seeded random fuzzer (imports the other two) test/NumSharp.UnitTest/Fuzz/ C# replay harness (no Python) @@ -569,11 +570,11 @@ test/NumSharp.UnitTest/Fuzz/ C# replay harness (no Python) IndexOracleTests.cs index get/set differential gate (curated + dtype + seeded-random tiers) MetamorphicTests.cs oracle-free invariants (round-trips / involutions / identities — no NumPy) HarnessSelfTests.cs proves the harness has teeth (BitDiff detects value/NaN/-0 diffs; non-vacuous) - corpus/*.jsonl committed corpus (~68K cases / 36 tiers; op corpus ~53K incl. 3.7K Char woven + 302 Decimal), copied to test output via the csproj glob + corpus/*.jsonl committed corpus (~68K cases / 40 tiers; op corpus ~53K incl. 3.7K Char woven + 579 Decimal), copied to test output via the csproj glob ``` - **Generators live in `test/oracle/`** and write the corpus into `test/NumSharp.UnitTest/Fuzz/corpus/` (path resolved relative to `test/oracle/`). CI replays the committed corpus, never the generators. -- **Three `FuzzMatrix` gates**: `FuzzCorpusTests` (the op corpus — ~53K cases across the tiers, checking dtype + shape + bytes + error parity; Char woven into every tier, 8 `Decimal*` tiers: unary/binary/reduce/scan/power/varstd/matmul/astype), `IndexOracleTests` (the index oracle — `index_curated` 2,265 + `index_dtype` 104 + `index_random` 10,000; the advanced-indexing parity gate), and `MetamorphicTests` (12 NumPy-free invariants). A failing op case auto-shrinks to a 1-element repro. +- **Three `FuzzMatrix` gates**: `FuzzCorpusTests` (the op corpus — ~53K cases across the tiers, checking dtype + shape + bytes + error parity; Char woven into every tier, 12 `Decimal*` tiers: unary/binary/reduce/scan/power/varstd/matmul/astype/stat/where/sort/manip), `IndexOracleTests` (the index oracle — `index_curated` 2,265 + `index_dtype` 104 + `index_random` 10,000; the advanced-indexing parity gate), and `MetamorphicTests` (12 NumPy-free invariants). A failing op case auto-shrinks to a 1-element repro. - **Dtype coverage**: per-mode dtype axes widened toward `ALL_DTYPES`. **Char** (no NumPy dtype) is woven into every tier via the uint16 proxy (`gen_oracle.char_tier`, relabelled uint16→char). **Decimal** (no NumPy analog) rides an independent C# oracle (`gen_decimal_oracle.cs`, naive scalar `System.Decimal`). Verified Char/clip-bool bugs are carved from the green corpus and reproduced under `[OpenBugs]` (`OpenBugs.Char.cs`, `OpenBugs.DtypeCoverage.cs`) — NOT excused in `MisalignedRegistry`. - **Regenerate** (deterministic; needs `numpy==2.4.2`): `python test/oracle/gen_oracle.py ` (modes: `smoke astype_full binary divmod_power comparison unary reduce where place matmul bitwise unary_extra nanreduce scan stat logic modf manip sort tail params aliasing copyto errors`) + `python test/oracle/gen_index_oracle.py` (the `index_*` tiers) + `python test/oracle/fuzz_random.py 1234 2000 random_smoke.jsonl` + `dotnet run test/oracle/gen_decimal_oracle.cs` (the `decimal_*` tiers), then `dotnet build` (copies the corpus to test output). - **Run the gate**: `dotnet test --filter "TestCategory=FuzzMatrix"`. Each case is bit-exact (pass), a documented difference in `MisalignedRegistry` (excused, never silent), or a failure (red). Full divergence ledger: `test/NumSharp.UnitTest/Fuzz/README.md`. diff --git a/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md b/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md index 7fb987b8..7588dc42 100644 --- a/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md +++ b/test/NumSharp.UnitTest/Fuzz/COVERAGE_GAPS.md @@ -17,8 +17,9 @@ min max mean std var argmax argmin all any median average ptp percentile quantil cumsum cumprod diff · nansum nanprod nanmax nanmin nanmean nanstd nanvar nanmedian · isnan isinf isfinite · maximum minimum fmax fmin isclose · where place clip copyto · argsort searchsorted nonzero · matmul dot outer · astype modf · ravel transpose reshape squeeze expand_dims roll repeat tile swapaxes -moveaxis delete atleast_1d/2d/3d concatenate stack hstack vstack dstack pad. Decimal oracle adds: -power var std matmul astype (+ the arith/compare/reduce/scan/unary already shared). +moveaxis delete atleast_1d/2d/3d concatenate stack hstack vstack dstack pad. Decimal oracle (12 tiers) +adds: power var std matmul astype · floor ceil trunc · diff · clip median ptp percentile quantile · +where · sort · ravel transpose reshape (+ the arith/compare/reduce/scan/unary already shared). --- @@ -60,15 +61,26 @@ Status: `[ ]` todo · `[~]` in progress · `[x]` wired (green or carved+[OpenBug - [x] `allclose` `array_equal` — whole-array → 0-D bool · green - [x] `iscomplex` `isreal` — **BUG** (see Batch 3): carved → [OpenBugs] -> **np.\* Group A is complete** (33 ops wired: 7 bugs → [OpenBugs], the rest green). Only remaining -> item is the **decimal-specific ops** (extend `gen_decimal_oracle.cs`: floor/ceil/trunc/clip/where/ -> sort/median/ptp/percentile/quantile/diff/manip/nan*). +> **np.\* Group A is complete** (33 ops wired: 7 bugs → [OpenBugs], the rest green), **including the +> decimal-specific ops** (below). Group A is fully closed. > `flip` is intentionally NOT here: NumSharp exposes no `np.flip` (use `[::-1]` slicing). Skipped. -### Decimal-specific A-gaps (decimal rides its own C# oracle, currently 8 tiers) -- [ ] decimal `floor`/`ceil`/`trunc`, `clip`, `where`, `sort`, `median`/`ptp`/`percentile`/`quantile`, - `diff`, the manip ops, `nan*` reductions — not in `gen_decimal_oracle.cs` yet. +### Decimal-specific A-gaps (decimal rides its own C# oracle) — ✅ DONE (all green, 12 tiers) +- [x] `floor`/`ceil`/`trunc` — added to the `decimal_unary` tier (`decimal.Floor/Ceiling/Truncate`, + exact in base-10). `DecimalUnary` gate. +- [x] `diff` (n=1,2 along last axis) — added to the `decimal_scan` tier (`DiffAxis` oracle). `DecimalScan` gate. +- [x] `clip` + order stats `median`/`ptp`/`percentile`/`quantile` — new `decimal_stat.jsonl` + (170 cases). Oracle: `Max(lo,Min(hi,x))` + naive-sort + NumPy 'linear' interpolation in EXACT + decimal (`Quantile`/`Median`). Validated bit-identical to NumSharp (even & odd n, q∈{0,.25,.5,.75,1}). `DecimalStat` gate. +- [x] `where(cond,a,b)` — new `decimal_where.jsonl`; the 16-byte conditional-copy kernel over + contiguous + strided decimal. `DecimalWhere` gate. +- [x] `sort` (axis, 1-D/2-D, contiguous + strided) — new `decimal_sort.jsonl` (`SortAxis` oracle). `DecimalSort` gate. +- [x] manip `ravel`/`transpose`/`reshape` — new `decimal_manip.jsonl`; value-preserving reindex forces + the strided decimal materialize/copy path (compared C-contiguous via `ascontiguousarray`). `DecimalManip` gate. +- [x] `nan*` reductions — **intentionally skipped**: `System.Decimal` cannot represent NaN, so + `nansum`/`nanmax`/… are byte-identical to plain `sum`/`max`/… (already covered by `decimal_reduce`). + Verified `np.nansum(decimal)` == `np.sum(decimal)`. --- diff --git a/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs b/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs index 35aed681..93c16b8b 100644 --- a/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs +++ b/test/NumSharp.UnitTest/Fuzz/FuzzCorpusTests.cs @@ -225,6 +225,29 @@ public void FuzzRegression() [TestCategory("FuzzMatrix")] public void DecimalAstype() => RunCorpus("decimal_astype.jsonl"); + // clip (elementwise Max(lo,Min(hi,x))) + the order statistics median/ptp/percentile/quantile + // (axis=None -> scalar) over the strided/broadcast decimal layouts. Oracle: naive sort + + // NumPy 'linear' interpolation in exact decimal (gen_decimal_oracle.Quantile/Median). + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalStat() => RunCorpus("decimal_stat.jsonl"); + + // where(cond, a, b) — the 16-byte conditional-copy kernel over contiguous AND strided decimal. + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalWhere() => RunCorpus("decimal_where.jsonl"); + + // sort along an axis (1-D/2-D, contiguous + strided) — independent Array.Sort oracle. + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalSort() => RunCorpus("decimal_sort.jsonl"); + + // ravel/transpose/reshape — value-preserving reindex; forces the strided decimal + // materialize/copy path (result compared C-contiguous via ascontiguousarray). + [TestMethod] + [TestCategory("FuzzMatrix")] + public void DecimalManip() => RunCorpus("decimal_manip.jsonl"); + private static void RunCorpus(string file) { var cases = FuzzCorpus.Load(file); diff --git a/test/NumSharp.UnitTest/Fuzz/README.md b/test/NumSharp.UnitTest/Fuzz/README.md index 3a416800..ba4c6ef6 100644 --- a/test/NumSharp.UnitTest/Fuzz/README.md +++ b/test/NumSharp.UnitTest/Fuzz/README.md @@ -17,7 +17,7 @@ test/oracle/ corpus generators (NumPy 2.4.2) via the uint16 proxy (char_tier) — relabelled uint16->char, bytes intact gen_decimal_oracle.cs INDEPENDENT C# oracle for Decimal (no NumPy analog): naive scalar System.Decimal math -> decimal_{unary,binary,reduce,scan,power, - varstd,matmul,astype}.jsonl + varstd,matmul,astype,stat,where,sort,manip}.jsonl (12 tiers, 579 cases) fuzz_random.py seeded random fuzzer (NumSharp-producible layouts) test/NumSharp.UnitTest/Fuzz/ FuzzCorpus.cs reconstructs EXACT NDArray views from (dtype,shape,strides,offset,bytes) diff --git a/test/NumSharp.UnitTest/Fuzz/corpus/decimal_astype.jsonl b/test/NumSharp.UnitTest/Fuzz/corpus/decimal_astype.jsonl index 556ce81a..c90610a3 100644 --- a/test/NumSharp.UnitTest/Fuzz/corpus/decimal_astype.jsonl 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+{"id":"std/decimal/highrank_5d/335","op":"std","params":{},"operands":[{"dtype":"decimal","shape":[2,1,3,1,2],"strides":[6,6,2,2,1],"offset":0,"bufferSize":12,"buffer":"00000200000000006400000000000000000001800000000023000000000000000000020000000000e1000000000000000000010000000000010000000000000000000300000000007d0000000000000000000380000000007d00000000000000000009000000000015cd5b0700000000000004000000000015cd5b07000000000000058000000000ffc99a3b000000000000000000000000e8030000000000000000008000000000e80300000000000000000000000000000700000000000000"}],"expected":{"dtype":"decimal","shape":[],"buffer":"00001800444add0e7f4ef8b21a9a343f"},"layout":"decimal","valueclass":"decimal"} diff --git a/test/NumSharp.UnitTest/Fuzz/corpus/decimal_where.jsonl b/test/NumSharp.UnitTest/Fuzz/corpus/decimal_where.jsonl new file mode 100644 index 00000000..4a66a5f1 --- /dev/null +++ b/test/NumSharp.UnitTest/Fuzz/corpus/decimal_where.jsonl @@ -0,0 +1,4 @@ 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diff --git a/test/oracle/gen_decimal_oracle.cs b/test/oracle/gen_decimal_oracle.cs index 0e4f7f04..a1d79bbe 100644 --- a/test/oracle/gen_decimal_oracle.cs +++ b/test/oracle/gen_decimal_oracle.cs @@ -185,6 +185,8 @@ static readonly (string, Func)[] BIN_CMP = { }; static readonly (string, Func)[] UNARY = { ("negative", a=>-a), ("abs", Math.Abs), ("sign", Sign), ("square", a=>a*a), + // rounding-toward family — exact in base-10 decimal (no NumPy analog to mirror). + ("floor", decimal.Floor), ("ceil", decimal.Ceiling), ("trunc", decimal.Truncate), }; static string Case(string id, string op, string paramsJson, IEnumerable operandJsons, string expDtype, int[] expShape, string expBufHex) @@ -267,12 +269,29 @@ public static void Main() for (int i = 0; i < log.Length; i++) { s += log[i]; cs[i] = s; p *= log[i]; cp[i] = p; } scan.Add(Case($"cumsum/decimal/{ln}/{n++}", "cumsum", "{\"axis\":null}", new[]{o.Describe()}, "decimal", new[]{log.Length}, HexOf(cs))); scan.Add(Case($"cumprod/decimal/{ln}/{n++}", "cumprod", "{\"axis\":null}", new[]{o.Describe()}, "decimal", new[]{log.Length}, HexOf(cp))); + + // diff n=1,2 along the LAST axis (a[1:]-a[:-1]); output shrinks by n on that axis. + if (o.Shape.Length >= 1) + { + int last = o.Shape[o.Shape.Length - 1]; + foreach (int nd in new[] { 1, 2 }) + { + if (last < nd + 1) continue; // need ≥2 for n=1, ≥3 for n=2 + var dres = DiffAxis(log, o.Shape, nd, -1); + var dsh = (int[])o.Shape.Clone(); dsh[dsh.Length - 1] -= nd; + scan.Add(Case($"diff/decimal/{ln}/n{nd}/{n++}", "diff", $"{{\"n\":{nd},\"axis\":-1}}", new[]{o.Describe()}, "decimal", dsh, HexOf(dres))); + } + } } var power = new List(); var varstd = new List(); var matmul = new List(); var astype = new List(); + var stat = new List(); + var where = new List(); + var sort = new List(); + var manip = new List(); // ----- POWER decimal^int (exact: repeated multiply / reciprocal). Exponent is a 0-D // decimal whose value is a whole number — DecimalMath.Pow must be exact for integer powers. ----- @@ -338,6 +357,83 @@ public static void Main() astype.Add(AstypeTo("float64", sh, Bytes(dv), HexOf(dv.Select(x => (decimal)x).ToArray()), ref n)); } + // ----- STAT (axis=None -> scalar): clip / median / ptp / percentile / quantile. + // clip is elementwise (Max(lo,Min(hi,x))); the order stats flatten the logical view. ----- + foreach (var ln in SINGLE_LAYOUTS) + { + var o = SingleLayout(ln, 1, false); + var log = o.Logical(); + if (log.Length == 0) continue; + + // clip against two scalar (lo,hi) windows — 0-D decimal bound operands. + foreach (var (lo, hi) in new[] { (-5m, 10m), (0m, 100m) }) + { + var loOp = new Operand { Base = new[]{ lo }, Shape = new int[0], Strides = new long[0], Offset = 0 }; + var hiOp = new Operand { Base = new[]{ hi }, Shape = new int[0], Strides = new long[0], Offset = 0 }; + var cexp = log.Select(x => Math.Max(lo, Math.Min(hi, x))).ToArray(); + stat.Add(Case($"clip/decimal/{ln}/[{lo},{hi}]/{n++}", "clip", "{}", new[]{o.Describe(), loOp.Describe(), hiOp.Describe()}, "decimal", o.Shape, HexOf(cexp))); + } + + if (ln == "scalar_0d") continue; // order stats over 0-D are identity; skip the degenerate + var flat = (decimal[])log.Clone(); Array.Sort(flat); + stat.Add(Case($"median/decimal/{ln}/{n++}", "median", "{}", new[]{o.Describe()}, "decimal", new int[0], HexOf(new[]{ Median(flat) }))); + stat.Add(Case($"ptp/decimal/{ln}/{n++}", "ptp", "{}", new[]{o.Describe()}, "decimal", new int[0], HexOf(new[]{ flat[flat.Length-1] - flat[0] }))); + // percentile q in {0,25,50,75,100}; quantile q in {0,.25,.5,.75,1} — same order statistic. + foreach (var (pq, frac) in new[] { ("0.0", 0.0), ("25.0", 0.25), ("50.0", 0.5), ("75.0", 0.75), ("100.0", 1.0) }) + stat.Add(Case($"percentile/decimal/{ln}/p{pq}/{n++}", "percentile", $"{{\"q\":{pq}}}", new[]{o.Describe()}, "decimal", new int[0], HexOf(new[]{ Quantile(flat, frac) }))); + foreach (var (qq, frac) in new[] { ("0.0", 0.0), ("0.25", 0.25), ("0.5", 0.5), ("0.75", 0.75), ("1.0", 1.0) }) + stat.Add(Case($"quantile/decimal/{ln}/q{qq}/{n++}", "quantile", $"{{\"q\":{qq}}}", new[]{o.Describe()}, "decimal", new int[0], HexOf(new[]{ Quantile(flat, frac) }))); + } + + // ----- WHERE (cond ? a : b): cond is a bool mask, a/b are decimal. Exercises the 16-byte + // conditional-copy kernel over contiguous AND strided decimal operands. ----- + foreach (var (ln, aRot, bRot) in new[] { ("c_contiguous_1d", 0, 4), ("c_contiguous_2d", 0, 7), ("strided_step2_1d", 0, 4), ("negstride_1d", 0, 3) }) + { + var a = SingleLayout(ln, aRot, false); + // b: a fresh CONTIGUOUS decimal of the same shape (different values via rotation). + var bBase = Fill(a.Shape.Aggregate(1,(x,y)=>x*y), false, bRot); + var b = new Operand { Base = bBase, Shape = a.Shape, Strides = CStrides(a.Shape), Offset = 0 }; + var la = a.Logical(); var lb = b.Logical(); + var mask = la.Select(x => (byte)(x > 0m ? 1 : 0)).ToArray(); // cond = a>0 + var wexp = new decimal[la.Length]; + for (int i = 0; i < la.Length; i++) wexp[i] = la[i] > 0m ? la[i] : lb[i]; + where.Add(Case($"where/decimal/{ln}/{n++}", "where", "{}", new[]{ BoolOperandDesc(mask, a.Shape), a.Describe(), b.Describe() }, "decimal", a.Shape, HexOf(wexp))); + } + + // ----- SORT along an axis (1-D and 2-D, contiguous + strided) ----- + foreach (var (ln, axes) in new (string, int[])[] { + ("c_contiguous_1d", new[]{0}), ("c_contiguous_2d", new[]{0,1}), + ("strided_step2_1d", new[]{0}), ("negstride_1d", new[]{0}), ("strided_2d_cols", new[]{0,1}) }) + { + var o = SingleLayout(ln, 3, false); + var log = o.Logical(); + foreach (int ax in axes) + { + var sres = SortAxis(log, o.Shape, ax); + sort.Add(Case($"sort/decimal/{ln}/ax{ax}/{n++}", "sort", $"{{\"axis\":{ax}}}", new[]{o.Describe()}, "decimal", o.Shape, HexOf(sres))); + } + } + + // ----- MANIP (value-preserving reindex): ravel / transpose / reshape. Forces the strided + // decimal materialize/copy path (result is compared C-contiguous via ascontiguousarray). ----- + foreach (var ln in SINGLE_LAYOUTS) + { + var o = SingleLayout(ln, 2, false); + var log = o.Logical(); + int cnt = log.Length; + // ravel -> flat C-order + manip.Add(Case($"ravel/decimal/{ln}/{n++}", "ravel", "{}", new[]{o.Describe()}, "decimal", new[]{cnt}, HexOf(log))); + // transpose -> reversed axes + manip.Add(Case($"transpose/decimal/{ln}/{n++}", "transpose", "{}", new[]{o.Describe()}, "decimal", RevShape(o.Shape), HexOf(TransposeReverse(log, o.Shape)))); + // reshape -> a distinct 2-D factorization (skip when trivial: <2-D result or prime) + if (cnt >= 2) + { + int rows = LargestFactorLeqSqrt(cnt); + if (rows > 1) { var rsh = new[]{ rows, cnt / rows }; + manip.Add(Case($"reshape/decimal/{ln}/[{rows},{cnt/rows}]/{n++}", "reshape", $"{{\"shape\":[{rows},{cnt/rows}]}}", new[]{o.Describe()}, "decimal", rsh, HexOf(log))); } + } + } + Write(Path.Combine(corpus, "decimal_unary.jsonl"), unary); Write(Path.Combine(corpus, "decimal_binary.jsonl"), binary); Write(Path.Combine(corpus, "decimal_reduce.jsonl"), reduce); @@ -346,6 +442,10 @@ public static void Main() Write(Path.Combine(corpus, "decimal_varstd.jsonl"), varstd); Write(Path.Combine(corpus, "decimal_matmul.jsonl"), matmul); Write(Path.Combine(corpus, "decimal_astype.jsonl"), astype); + Write(Path.Combine(corpus, "decimal_stat.jsonl"), stat); + Write(Path.Combine(corpus, "decimal_where.jsonl"), where); + Write(Path.Combine(corpus, "decimal_sort.jsonl"), sort); + Write(Path.Combine(corpus, "decimal_manip.jsonl"), manip); } // small in-range decimal source for decimal->narrow casts (truncation toward zero). @@ -390,6 +490,108 @@ static decimal DecSqrt(decimal x) return g; } + // median of an ALREADY-SORTED decimal[] (even n -> exact average of the two middles). + static decimal Median(decimal[] sorted) + { + int n = sorted.Length; + return n % 2 == 1 ? sorted[n / 2] : (sorted[n / 2 - 1] + sorted[n / 2]) / 2m; + } + + // NumPy 'linear' quantile of an ALREADY-SORTED decimal[]: index = frac*(n-1), interpolate. + static decimal Quantile(decimal[] sorted, double frac) + { + int n = sorted.Length; + if (n == 1) return sorted[0]; + double idx = frac * (n - 1); + int lo = (int)Math.Floor(idx), hi = (int)Math.Ceiling(idx); + if (lo == hi) return sorted[lo]; + decimal w = (decimal)(idx - lo); + return sorted[lo] + (sorted[hi] - sorted[lo]) * w; + } + + // sort each 1-D slice along `axis` (negative axis normalized) — independent Array.Sort oracle. + static decimal[] SortAxis(decimal[] flat, int[] shape, int axis) + { + if (axis < 0) axis += shape.Length; + var outv = (decimal[])flat.Clone(); + var strides = CStrides(shape); + long axLen = shape[axis], axStride = strides[axis]; + int[] outer = (int[])shape.Clone(); outer[axis] = 1; + int outerN = outer.Aggregate(1, (a, b) => a * b); + var coord = new int[shape.Length]; + for (int o = 0; o < outerN; o++) + { + long baseF = 0; for (int d = 0; d < shape.Length; d++) baseF += coord[d] * strides[d]; + var slice = new decimal[axLen]; + for (long k = 0; k < axLen; k++) slice[k] = outv[baseF + k * axStride]; + Array.Sort(slice); + for (long k = 0; k < axLen; k++) outv[baseF + k * axStride] = slice[k]; + for (int d = shape.Length - 1; d >= 0; d--) { if (d == axis) continue; if (++coord[d] < shape[d]) break; coord[d] = 0; } + } + return outv; + } + + // repeated consecutive difference along `axis`, applied nDiff times (a[1:]-a[:-1]). + static decimal[] DiffAxis(decimal[] flat, int[] shape, int nDiff, int axis) + { + if (axis < 0) axis += shape.Length; + var cur = flat; var curShape = (int[])shape.Clone(); + for (int it = 0; it < nDiff; it++) + { + var strides = CStrides(curShape); + var newShape = (int[])curShape.Clone(); newShape[axis] -= 1; + int newN = newShape.Aggregate(1, (a, b) => a * b); + var res = new decimal[newN]; + var coord = new int[newShape.Length]; + for (int i = 0; i < newN; i++) + { + long srcLo = 0, srcHi = 0; + for (int d = 0; d < curShape.Length; d++) { int c = coord[d]; srcLo += c * strides[d]; srcHi += (d == axis ? c + 1 : c) * strides[d]; } + res[i] = cur[srcHi] - cur[srcLo]; + Inc(coord, newShape); + } + cur = res; curShape = newShape; + } + return cur; + } + + // np.transpose with no axes = reverse all axes; return the result's C-order values. + static decimal[] TransposeReverse(decimal[] flat, int[] shape) + { + int nd = shape.Length; + var newShape = RevShape(shape); + var strides = CStrides(shape); + var outv = new decimal[flat.Length]; + var coord = new int[nd]; + for (int i = 0; i < flat.Length; i++) + { + long srcF = 0; + for (int d = 0; d < nd; d++) srcF += coord[d] * strides[nd - 1 - d]; + outv[i] = flat[srcF]; + Inc(coord, newShape); + } + return outv; + } + + static int[] RevShape(int[] shape) { var r = new int[shape.Length]; for (int i = 0; i < shape.Length; i++) r[i] = shape[shape.Length - 1 - i]; return r; } + + // largest factor of n that is <= sqrt(n) (for a non-trivial 2-D reshape target). + static int LargestFactorLeqSqrt(int n) + { + int best = 1; + for (int f = 2; f * f <= n; f++) if (n % f == 0) best = f; + return best; + } + + // bool operand descriptor (C-contiguous mask) — the where() condition. + static string BoolOperandDesc(byte[] mask, int[] shape) + { + var sb = new StringBuilder(mask.Length * 2); foreach (var b in mask) sb.Append(b.ToString("x2")); + return $"{{\"dtype\":\"bool\",\"shape\":[{string.Join(",", shape)}]," + + $"\"strides\":[{string.Join(",", CStrides(shape))}],\"offset\":0," + + $"\"bufferSize\":{mask.Length},\"buffer\":\"{sb}\"}}"; + } + static byte[] Bytes(long[] v) { var b = new byte[v.Length*8]; Buffer.BlockCopy(v, 0, b, 0, b.Length); return b; } static byte[] Bytes(int[] v) { var b = new byte[v.Length*4]; Buffer.BlockCopy(v, 0, b, 0, b.Length); return b; } static byte[] Bytes(double[] v) { var b = new byte[v.Length*8]; Buffer.BlockCopy(v, 0, b, 0, b.Length); return b; } From b77d4378f5e1435f2a5a5f76208784119203478c Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 08:10:01 +0300 Subject: [PATCH 17/24] =?UTF-8?q?feat(casting):=20ndarray.astype(casting?= =?UTF-8?q?=3D)=20gate=20=E2=80=94=20close=20the=20last=20type-conversion?= =?UTF-8?q?=20parity=20gap?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Audited the ENTIRE type-conversion surface against NumPy 2.4.2 and found the value side fully on-parity; the one gap was a missing API parameter, closed here. Audit (all differential vs NumPy 2.4.2, bit-exact): - astype 13 NumPy dtypes, 13x13, FRESH independent oracle (aggressive edges distinct from the committed corpus: 0.4999/0.5/0.5001 boundaries, subnormals, 1e300, all int overflow points, NaN/±inf, complex) — 169/169. (Plus the committed astype_full corpus 5070 + FuzzMatrix, green.) - Char (uint16 masquerade): char<->X byte-identical to uint16<->X — 23/23. - Decimal->X (all 13 numeric dst incl. modular int overflow + float16 Inf): matches the NumPy-verified double path — 442/442. - X->Decimal: int exact + round-trip; float exact in-range. - np.array(dtype=) / copyto vs astype — 12/12. - can_cast (np.can_cast AND the separate NDIterCasting.CanCast used by copyto/ufuncs): each 845/845 vs NumPy across 13x13x5 casting modes. The gap: ndarray.astype had no `casting=` parameter — it always cast unsafely. NumPy's astype takes casting='unsafe' (default) and raises TypeError when a stricter rule ('no'/'equiv'/'safe'/'same_kind') forbids the cast. Fix: added `string casting = "unsafe"` to both full astype overloads (NPTypeCode and Type). Default 'unsafe' is a NO-OP short-circuit — 100% backward compatible, every existing caller is unchanged. A stricter rule validates through the hardened np.can_cast (bit-exact vs NumPy across all 15 dtypes) and raises InvalidCastException — same exception type and message shape as np.copyto: "Cannot cast array data from dtype('int32') to dtype('int16') according to the rule 'safe'" Verified vs NumPy: int32->int64 safe OK; int32->int16 safe raises, same_kind OK; int32->float32 safe raises, same_kind OK; float64->int32 safe/same_kind raise, unsafe OK. Documented finding (NOT changed — no NumPy analog): float->Decimal for NaN/±Inf/overflow (|v| >= ~7.9e28) silently yields 0. System.Decimal has no NaN/Inf and a smaller range, so there is no NumPy behavior to match; left as-is pending a decision (0 / throw / saturate). Tests: Casting/AstypeCastingParamTests.cs (8) — default-unsafe, safe/same_kind/unsafe outcomes, message shape, Type overload, same-dtype no-op, and value-invariance for allowed casts. Full suite 11314 passed / 0 failed (net10.0); FuzzMatrix astype corpus green; net8.0 green. --- src/NumSharp.Core/Backends/NDArray.cs | 40 ++++++- .../Casting/AstypeCastingParamTests.cs | 103 ++++++++++++++++++ 2 files changed, 141 insertions(+), 2 deletions(-) create mode 100644 test/NumSharp.UnitTest/Casting/AstypeCastingParamTests.cs diff --git a/src/NumSharp.Core/Backends/NDArray.cs b/src/NumSharp.Core/Backends/NDArray.cs index fbe8a4d6..8ae379bb 100644 --- a/src/NumSharp.Core/Backends/NDArray.cs +++ b/src/NumSharp.Core/Backends/NDArray.cs @@ -581,11 +581,18 @@ protected internal IArraySlice Array /// 'A' - 'F' if source is F-contiguous (and not C-contiguous) else 'C', /// 'K' (default) - preserve the source layout. /// + /// + /// NumPy's cast-rule gate ('no' / 'equiv' / 'safe' / 'same_kind' / 'unsafe'). + /// Default 'unsafe' (matches NumPy's astype default) — any conversion is permitted. + /// A stricter rule raises (NumPy's TypeError analogue) + /// when the source dtype cannot cast to under that rule. + /// /// An of given with the requested layout. /// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.astype.html [SuppressMessage("ReSharper", "ParameterHidesMember")] - public NDArray astype(Type dtype, bool copy, char order) + public NDArray astype(Type dtype, bool copy, char order, string casting = "unsafe") { + ValidateAstypeCasting(dtype.GetTypeCode(), casting); char physical = OrderResolver.Resolve(order, this.Shape); var casted = TensorEngine.Cast(this, dtype, copy); if (physical == 'F' && casted.Shape.NDim > 1 && !casted.Shape.IsFContiguous) @@ -612,10 +619,17 @@ public NDArray astype(Type dtype, bool copy, char order) /// 'A' - 'F' if source is F-contiguous (and not C-contiguous) else 'C', /// 'K' (default) - preserve the source layout. /// + /// + /// NumPy's cast-rule gate ('no' / 'equiv' / 'safe' / 'same_kind' / 'unsafe'). + /// Default 'unsafe' (matches NumPy's astype default) — any conversion is permitted. + /// A stricter rule raises (NumPy's TypeError analogue) + /// when the source dtype cannot cast to under that rule. + /// /// An of given with the requested layout. /// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.astype.html - public NDArray astype(NPTypeCode typeCode, bool copy, char order) + public NDArray astype(NPTypeCode typeCode, bool copy, char order, string casting = "unsafe") { + ValidateAstypeCasting(typeCode, casting); char physical = OrderResolver.Resolve(order, this.Shape); var casted = TensorEngine.Cast(this, typeCode, copy); if (physical == 'F' && casted.Shape.NDim > 1 && !casted.Shape.IsFContiguous) @@ -623,6 +637,28 @@ public NDArray astype(NPTypeCode typeCode, bool copy, char order) return casted; } + /// + /// NumPy astype casting gate: default 'unsafe' allows any conversion; a stricter rule + /// raises (NumPy's TypeError analogue) when the cast + /// is not permitted. Same exception type and message shape as . + /// + [SuppressMessage("ReSharper", "ParameterHidesMember")] + private void ValidateAstypeCasting(NPTypeCode toType, string casting) + { + // 'unsafe' (astype's default) short-circuits — every conversion is allowed, no work. + if (string.IsNullOrEmpty(casting) || casting == "unsafe") + return; + NPTypeCode fromType = this.GetTypeCode; + if (fromType == toType) + return; + // np.can_cast validates the rule name (ArgumentException on an unknown rule) and + // encodes NumPy's cast table (bit-exact vs 2.4.2, verified across all 15 dtypes). + if (!np.can_cast(fromType, toType, casting)) + throw new InvalidCastException( + $"Cannot cast array data from dtype('{fromType.AsNumpyDtypeName()}') " + + $"to dtype('{toType.AsNumpyDtypeName()}') according to the rule '{casting}'"); + } + /// /// Clone the whole NDArray /// internal storage is also cloned into 2nd memory area diff --git a/test/NumSharp.UnitTest/Casting/AstypeCastingParamTests.cs b/test/NumSharp.UnitTest/Casting/AstypeCastingParamTests.cs new file mode 100644 index 00000000..32756379 --- /dev/null +++ b/test/NumSharp.UnitTest/Casting/AstypeCastingParamTests.cs @@ -0,0 +1,103 @@ +using System; +using AwesomeAssertions; +using Microsoft.VisualStudio.TestTools.UnitTesting; +using NumSharp; +using NumSharp.Backends; + +namespace NumSharp.UnitTest.Casting +{ + /// + /// Parity coverage for NumPy's ndarray.astype(dtype, casting=...) gate. NumSharp's astype + /// historically had no casting parameter (always cast unsafely). The parameter now mirrors NumPy + /// 2.4.2: default 'unsafe' permits any conversion; a stricter rule raises + /// (InvalidCastException — NumSharp's TypeError analogue, same message shape as np.copyto) when the + /// source dtype cannot reach the target under that rule. Rule outcomes verified against NumPy. + /// + [TestClass] + public class AstypeCastingParamTests + { + [TestMethod] + public void Astype_DefaultCasting_IsUnsafe_NeverThrows() + { + // No casting arg == NumPy default 'unsafe': narrowing/lossy casts must still work. + var i32 = np.array(new int[] { 1, 2, 300 }); + i32.astype(NPTypeCode.Int16).typecode.Should().Be(NPTypeCode.Int16); + i32.astype(typeof(byte)).typecode.Should().Be(NPTypeCode.Byte); + np.array(new double[] { 1.9 }).astype(NPTypeCode.Int32).GetInt32(0).Should().Be(1); + } + + [TestMethod] + public void Astype_Safe_AllowsWidening_RejectsNarrowingAndCrossKind() + { + var i32 = np.array(new int[] { 1, 2, 3 }); + // widening within signed ints is safe + i32.astype(NPTypeCode.Int64, true, 'K', "safe").typecode.Should().Be(NPTypeCode.Int64); + // narrowing is NOT safe + new Action(() => i32.astype(NPTypeCode.Int16, true, 'K', "safe")).Should().Throw(); + // int32 -> float32 is NOT safe (precision), int32 -> float64 IS safe + new Action(() => i32.astype(NPTypeCode.Single, true, 'K', "safe")).Should().Throw(); + i32.astype(NPTypeCode.Double, true, 'K', "safe").typecode.Should().Be(NPTypeCode.Double); + // float -> int is never safe + new Action(() => np.array(new double[] { 1.5 }).astype(NPTypeCode.Int32, true, 'K', "safe")) + .Should().Throw(); + } + + [TestMethod] + public void Astype_SameKind_MatchesNumPyDirectionalRule() + { + var i32 = np.array(new int[] { 1, 2, 3 }); + // int -> narrower int and int -> float are same_kind + i32.astype(NPTypeCode.Int16, true, 'K', "same_kind").typecode.Should().Be(NPTypeCode.Int16); + i32.astype(NPTypeCode.Single, true, 'K', "same_kind").typecode.Should().Be(NPTypeCode.Single); + // signed -> unsigned is NOT same_kind + new Action(() => i32.astype(NPTypeCode.UInt32, true, 'K', "same_kind")).Should().Throw(); + // float -> int is NOT same_kind + new Action(() => np.array(new double[] { 1.5 }).astype(NPTypeCode.Int32, true, 'K', "same_kind")) + .Should().Throw(); + } + + [TestMethod] + public void Astype_Unsafe_AllowsEverything() + { + var f64 = np.array(new double[] { 1.9, -2.9, 300.5 }); + f64.astype(NPTypeCode.Int32, true, 'K', "unsafe").typecode.Should().Be(NPTypeCode.Int32); + f64.astype(NPTypeCode.Byte, true, 'K', "unsafe").typecode.Should().Be(NPTypeCode.Byte); + } + + [TestMethod] + public void Astype_RejectedCast_MessageMatchesNumPyShape() + { + var i32 = np.array(new int[] { 1, 2, 3 }); + new Action(() => i32.astype(NPTypeCode.Int16, true, 'K', "safe")) + .Should().Throw() + .WithMessage("Cannot cast array data from dtype('int32') to dtype('int16') according to the rule 'safe'"); + } + + [TestMethod] + public void Astype_TypeOverload_AlsoValidatesCasting() + { + var i32 = np.array(new int[] { 1, 2, 3 }); + new Action(() => i32.astype(typeof(short), true, 'K', "safe")).Should().Throw(); + i32.astype(typeof(long), true, 'K', "safe").typecode.Should().Be(NPTypeCode.Int64); + } + + [TestMethod] + public void Astype_SameDtype_AnyCasting_IsNoOpAllowed() + { + // from == to is allowed under every rule (including 'no'). + var i32 = np.array(new int[] { 1, 2, 3 }); + i32.astype(NPTypeCode.Int32, true, 'K', "no").typecode.Should().Be(NPTypeCode.Int32); + i32.astype(NPTypeCode.Int32, true, 'K', "safe").typecode.Should().Be(NPTypeCode.Int32); + } + + [TestMethod] + public void Astype_AllowedCast_StillProducesCorrectValues() + { + // The gate must not change the produced bytes for an allowed cast. + var i64 = np.array(new long[] { 1, 5_000_000_000L, -1 }); + var strict = i64.astype(NPTypeCode.Int32, true, 'K', "same_kind"); + var unsafe_ = i64.astype(NPTypeCode.Int32); + Convert.ToHexString(strict.tobytes()).Should().Be(Convert.ToHexString(unsafe_.tobytes())); + } + } +} From 8f07f171fc9f3f24a75c23a85301405275f42d2b Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 08:11:54 +0300 Subject: [PATCH 18/24] =?UTF-8?q?feat(math):=20np.rint=20=E2=80=94=20round?= =?UTF-8?q?-half-to-even=20ufunc=20(NumPy=20parity),=20reusing=20UnaryOp.R?= =?UTF-8?q?ound?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Implements np.rint, the previously-missing float-tier rounding ufunc surfaced during the int8/float16 dtype sweep. NumPy 2.4.2 is the oracle (probed + generate_umath.py: rint has only e/f/d/g/F/D/G loops — no integer/bool loops). rint vs np.round/around: SAME value kernel (round-half-to-even == Math.Round default == the existing UnaryOp.Round, already internally named "rint" in UfuncName), but a DIFFERENT dtype rule. around preserves integer dtype (int->int identity); rint is float-tier like sqrt/sin: bool/int8/uint8 -> float16, int16/uint16/char -> float32, int32/uint32/int64/uint64 -> float64, floats/complex/decimal preserved. So it maps to ResolveUnaryFloatReturnType, not the dtype-preserving path. Implementation (reuses existing infra, no new kernel/UnaryOp): - np.rint.cs — NumPy-shaped overload rint(x, out=, where=, dtype=) + positional-dtype convenience forms (modeled on np.trunc.cs). - Default.Rint.cs — one-liner: ExecuteUnaryOp(nd, UnaryOp.Round, ResolveUnaryFloatReturnType(nd, typeCode, "rint"), out, where). Mirrors Default.Sin. - TensorEngine.cs — two abstract Rint overloads next to Truncate. - Fixed a REAL latent gap: EmitUnaryComplexOperation threw "not supported for Complex" for UnaryOp.Round, so np.around(complex) was ALSO broken. Added the complex Round case (rounds real & imag separately, half-to-even) — np.rint(complex) and np.around(complex) now both work. Dtype/value/layout/param parity (all verified vs NumPy 2.4.2): - 13-dtype tier check; half-to-even values (0.5->0, 2.5->2, -2.5->-2, 2.6->3); nan/inf preserved; complex real+imag; decimal; int-input identity-as-float. - Layouts: strided, negative-stride, broadcast, transpose, empty, 0-d scalar. - out= (same instance + fill), where= (masked slots keep prior out), dtype= (loop runs at that dtype; dtype= raises the no-loop error). Performance (Release, best-of, reusing the SIMD UnaryOp.Round vector path): at scale NumSharp is competitive-to-faster — @1M float32/float64 ~5-6x NumPy, @10M float32 3.7x, float64 1.25x, complex 1.77x; float16 ~parity (scalar path, no Vector in .NET) and int paths are widening-cast bound. Same profile as np.around (shared kernel). NumPy's single-instruction _mm256_round wins only on tiny L2-resident arrays (NumSharp's shared unary-dispatch overhead), which amortizes by ~1M elements. Tests: - Math/np.rint.Test.cs (16) — tiers, half-to-even, nan/inf, complex, decimal, layouts, out/where/dtype, plus Around_Complex_NowSupported (the bonus fix). - Fuzz: rint added to gen_oracle UNARY_EXTRA_OPS + OpRegistry; regenerated unary_extra corpus (+364 rint cases). FuzzMatrix gate replays them bit-exact. Docs: CLAUDE.md Math-Arithmetic list + ufunc out=/where= list updated. Full suite 11314 passed / 0 failed (net10.0), FuzzMatrix green, net8.0 rint green. --- .claude/CLAUDE.md | 4 +- .../Backends/Default/Math/Default.Rint.cs | 34 + .../DirectILKernelGenerator.Unary.Decimal.cs | 17 + src/NumSharp.Core/Backends/TensorEngine.cs | 2 + src/NumSharp.Core/Math/np.rint.cs | 42 + test/NumSharp.UnitTest/Fuzz/OpRegistry.cs | 1 + .../Fuzz/corpus/unary_extra.jsonl | 10346 ++++++++-------- test/NumSharp.UnitTest/Math/np.rint.Test.cs | 187 + test/oracle/gen_oracle.py | 1 + 9 files changed, 5641 insertions(+), 4993 deletions(-) create mode 100644 src/NumSharp.Core/Backends/Default/Math/Default.Rint.cs create mode 100644 src/NumSharp.Core/Math/np.rint.cs create mode 100644 test/NumSharp.UnitTest/Math/np.rint.Test.cs diff --git a/.claude/CLAUDE.md b/.claude/CLAUDE.md index 3df12dc8..a25221af 100644 --- a/.claude/CLAUDE.md +++ b/.claude/CLAUDE.md @@ -247,9 +247,9 @@ Tested against NumPy 2.x. `are_broadcastable`, `broadcast`, `broadcast_arrays`, `broadcast_to` ### Math — Arithmetic -`abs`, `absolute`, `add`, `arccos`, `arcsin`, `arctan`, `arctan2`, `cbrt`, `ceil`, `clip`, `convolve`, `cos`, `cosh`, `deg2rad`, `degrees`, `divide`, `exp`, `exp2`, `expm1`, `floor`, `floor_divide`, `log`, `log10`, `log1p`, `log2`, `mod`, `modf`, `multiply`, `negative`, `positive`, `power`, `rad2deg`, `radians`, `reciprocal`, `sign`, `sin`, `sinh`, `sqrt`, `square`, `subtract`, `tan`, `tanh`, `true_divide`, `trunc` +`abs`, `absolute`, `add`, `arccos`, `arcsin`, `arctan`, `arctan2`, `cbrt`, `ceil`, `clip`, `convolve`, `cos`, `cosh`, `deg2rad`, `degrees`, `divide`, `exp`, `exp2`, `expm1`, `floor`, `floor_divide`, `log`, `log10`, `log1p`, `log2`, `mod`, `modf`, `multiply`, `negative`, `positive`, `power`, `rad2deg`, `radians`, `reciprocal`, `rint`, `sign`, `sin`, `sinh`, `sqrt`, `square`, `subtract`, `tan`, `tanh`, `true_divide`, `trunc` -**ufunc `out=` / `where=` parameters** are supported on the elementwise core (NumPy semantics, probed against 2.4.2): binary `add`/`subtract`/`multiply`/`divide`/`true_divide`/`mod`/`power`/`floor_divide`/`arctan2`/`bitwise_and`/`bitwise_or`/`bitwise_xor`, unary `sqrt`/`exp`/`log`/`sin`/`cos`/`tan`/`abs`/`absolute`/`negative`/`square`/`log2`/`log10`/`log1p`/`exp2`/`expm1`/`cbrt`/`sign`/`floor`/`ceil`/`trunc`/`reciprocal`/`sinh`/`cosh`/`tanh`/`arcsin`/`arccos`/`arctan`/`deg2rad`(`radians`)/`rad2deg`(`degrees`)/`invert`(`bitwise_not`). `round_`/`around` take `out=` ONLY (np.round is a function, not a ufunc — no where/dtype; decimals≠0 cast errors name ufunc 'multiply' per NumPy's composition). floor/ceil/trunc have IDENTITY loops on every bool/int dtype (dtype preserved; np.round's int path is an identity copy); the loop dtype comes from the input tier (`sinh(i1, out=f8)` stores float16-precision values); reciprocal int 1/0 → signed MinValue (NumPy 2.4.2); sign/positive reject bool with the verbatim no-loop UFuncTypeError; bitwise/invert raise the no-loop TypeError for float inputs (probed order: bad where → no-loop → out-cast → shape). `out` joins the broadcast but is never stretched, requires a same_kind cast from the loop dtype (resolved from inputs), returns the same instance, and may alias an input (overlap-safe via COPY_IF_OVERLAP). `where` must be bool, broadcasts and joins the output shape; masked-off `out` slots keep prior contents. Engine plumbing: `Backends/Default/Math/DefaultEngine.UfuncOut.cs`. +**ufunc `out=` / `where=` parameters** are supported on the elementwise core (NumPy semantics, probed against 2.4.2): binary `add`/`subtract`/`multiply`/`divide`/`true_divide`/`mod`/`power`/`floor_divide`/`arctan2`/`bitwise_and`/`bitwise_or`/`bitwise_xor`, unary `sqrt`/`exp`/`log`/`sin`/`cos`/`tan`/`abs`/`absolute`/`negative`/`square`/`log2`/`log10`/`log1p`/`exp2`/`expm1`/`cbrt`/`sign`/`floor`/`ceil`/`trunc`/`reciprocal`/`sinh`/`cosh`/`tanh`/`arcsin`/`arccos`/`arctan`/`deg2rad`(`radians`)/`rad2deg`(`degrees`)/`invert`(`bitwise_not`)/`rint`. `round_`/`around` take `out=` ONLY (np.round is a function, not a ufunc — no where/dtype; decimals≠0 cast errors name ufunc 'multiply' per NumPy's composition). `rint` is the TRUE ufunc form of round-half-to-even: unlike `round_`/`around` (which preserve integer dtype) it is float-tier (bool/i8/u8→f16, i16/u16→f32, i32+→f64, floats/complex preserved) and reuses `UnaryOp.Round`'s kernel (complex rounds real+imag; `dtype=`→no-loop). floor/ceil/trunc have IDENTITY loops on every bool/int dtype (dtype preserved; np.round's int path is an identity copy); the loop dtype comes from the input tier (`sinh(i1, out=f8)` stores float16-precision values); reciprocal int 1/0 → signed MinValue (NumPy 2.4.2); sign/positive reject bool with the verbatim no-loop UFuncTypeError; bitwise/invert raise the no-loop TypeError for float inputs (probed order: bad where → no-loop → out-cast → shape). `out` joins the broadcast but is never stretched, requires a same_kind cast from the loop dtype (resolved from inputs), returns the same instance, and may alias an input (overlap-safe via COPY_IF_OVERLAP). `where` must be bool, broadcasts and joins the output shape; masked-off `out` slots keep prior contents. Engine plumbing: `Backends/Default/Math/DefaultEngine.UfuncOut.cs`. **Each of those ufuncs exposes ONE NumPy-shaped overload** — `f(x[, x2], NDArray out = null, NDArray where = null, NPTypeCode? dtype = null)` mirroring NumPy's `f(x1[, x2], /, out=None, *, where=True, dtype=None)`: `out` is the second/third positional slot exactly like NumPy, `where`/`dtype` are reachable by name without `out`. `dtype=` selects the LOOP (NumPy loop-signature semantics, probed): computation runs at that precision even with `out=` (`sqrt([2.], out=f64, dtype=f32)` stores the f32-rounded value; `add(0.1, 0.2, out=f64, dtype=f32)` stores `0.30000001…`), `power(2, -1, dtype: f64) = 0.5` (the negative-int-exponent ValueError applies only to integer loops), `power(10, 11, dtype: f64) = 1e11` exactly (no compute-then-cast), `add(True, True, dtype: i32) = 2` (the bool→logical-OR remap keys off the FINAL loop dtype), `negative(bool, dtype: f64)` is legal, and inputs must reach the loop via same_kind casts (verbatim `Cannot cast ufunc '' input [N] from ...` errors; binary errors are indexed, unary are not). Loop-existence gates raise `No loop matching ... ufunc `: float-only ufuncs (sqrt/exp/log/trig + `divide`/`true_divide`) reject int/bool dtype; bitwise rejects float/complex/decimal dtype. `positive` is a full ufunc (identity loops at every dtype EXCEPT bool: plain `positive(bool)` and `dtype: bool` raise the verbatim `did not contain a loop with signature matching types -> ...` texts; `positive(bool, dtype: f64)` works). `round_`/`around` follow NumPy's non-ufunc shape `round(a, int decimals = 0, NDArray out = null)` (2nd positional is decimals, NOT out). Positional-dtype overloads (`np.sqrt(x, NPTypeCode.Single)`, `(x, Type)`) also exist for source compat as non-NumPy call forms (NumPy's 2nd positional is `out`). Tests: `Math/UfuncDtypeOverloadTests.cs`. diff --git a/src/NumSharp.Core/Backends/Default/Math/Default.Rint.cs b/src/NumSharp.Core/Backends/Default/Math/Default.Rint.cs new file mode 100644 index 00000000..68a7e765 --- /dev/null +++ b/src/NumSharp.Core/Backends/Default/Math/Default.Rint.cs @@ -0,0 +1,34 @@ +using System; +using NumSharp.Backends.Kernels; + +namespace NumSharp.Backends +{ + public partial class DefaultEngine + { + public override NDArray Rint(NDArray nd, Type dtype) => Rint(nd, dtype?.GetTypeCode()); + + /// + /// Element-wise round-half-to-even (NumPy np.rint) using IL-generated kernels. + /// + /// rint is a FLOAT-tier unary ufunc: it has no integer/bool loops (only e/f/d/g and the + /// complex loops in NumPy), so integer/bool inputs promote to the smallest matching float + /// — bool/int8/uint8 -> float16, int16/uint16/char -> float32, int32/uint32/int64/uint64 -> + /// float64, floats/complex/decimal preserved (). + /// This is the dtype behavior of sqrt/sin, NOT of np.round/around (which keep integer dtype). + /// + /// The VALUE computation is round-half-to-even == 's existing + /// kernel (Math.Round/MathF.Round default to MidpointRounding.ToEven; complex rounds real + /// and imag separately; decimal uses decimal.Round). Reusing that op means no new kernel: + /// UfuncName(UnaryOp.Round) is already "rint", so out=/dtype= cast errors name 'rint'. + /// + /// out=/where=/dtype= compose exactly like the other unary ufuncs (probed 2.4.2): dtype= + /// selects the loop (must be a float/complex loop — an integer dtype raises the verbatim + /// "No loop matching the specified signature ..."), inputs reach the loop via a same_kind + /// cast, and a provided out receives a same_kind-cast write-back. + /// + public override NDArray Rint(NDArray nd, NPTypeCode? typeCode = null, NDArray @out = null, NDArray where = null) + { + return ExecuteUnaryOp(nd, UnaryOp.Round, ResolveUnaryFloatReturnType(nd, typeCode, "rint"), @out, where); + } + } +} diff --git a/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Unary.Decimal.cs b/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Unary.Decimal.cs index 1c357fbc..ce4c3f87 100644 --- a/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Unary.Decimal.cs +++ b/src/NumSharp.Core/Backends/Kernels/Direct/DirectILKernelGenerator.Unary.Decimal.cs @@ -364,6 +364,23 @@ private static void EmitUnaryComplexOperation(ILGenerator il, UnaryOp op) // Note: UnaryOp.Cbrt is deliberately NOT handled for Complex — NumPy's np.cbrt raises // TypeError for complex inputs, so falling through to the default throw keeps parity. + case UnaryOp.Round: + // NumPy rint(complex) / around(complex) rounds the real and imaginary parts + // SEPARATELY, half-to-even (Math.Round default). floor/ceil/trunc have no complex + // loop in NumPy (TypeError), so Round is the only rounding op handled here. + { + var locZ = il.DeclareLocal(typeof(System.Numerics.Complex)); + il.Emit(OpCodes.Stloc, locZ); // [] (z saved) + il.Emit(OpCodes.Ldloca, locZ); + il.EmitCall(OpCodes.Call, CachedMethods.ComplexGetReal, null); // [re] + il.EmitCall(OpCodes.Call, ScalarMethodCache.MathFn1(typeof(double), "Round"), null); // [rint(re)] + il.Emit(OpCodes.Ldloca, locZ); + il.EmitCall(OpCodes.Call, CachedMethods.ComplexGetImaginary, null); // [rint(re), im] + il.EmitCall(OpCodes.Call, ScalarMethodCache.MathFn1(typeof(double), "Round"), null); // [rint(re), rint(im)] + il.Emit(OpCodes.Newobj, CachedMethods.ComplexCtor); // [Complex(rint(re), rint(im))] + } + break; + default: throw new NotSupportedException($"Unary operation {op} not supported for Complex"); } diff --git a/src/NumSharp.Core/Backends/TensorEngine.cs b/src/NumSharp.Core/Backends/TensorEngine.cs index 14d2bdfc..ffb5aa0b 100644 --- a/src/NumSharp.Core/Backends/TensorEngine.cs +++ b/src/NumSharp.Core/Backends/TensorEngine.cs @@ -114,6 +114,8 @@ public abstract class TensorEngine public abstract NDArray Round(NDArray nd, int decimals, NPTypeCode? typeCode = null, NDArray @out = null); public abstract NDArray Truncate(NDArray nd, Type dtype); public abstract NDArray Truncate(NDArray nd, NPTypeCode? typeCode = null, NDArray @out = null, NDArray where = null); + public abstract NDArray Rint(NDArray nd, Type dtype); + public abstract NDArray Rint(NDArray nd, NPTypeCode? typeCode = null, NDArray @out = null, NDArray where = null); public abstract NDArray Reciprocal(NDArray nd, Type dtype); public abstract NDArray Reciprocal(NDArray nd, NPTypeCode? typeCode = null, NDArray @out = null, NDArray where = null); public abstract NDArray Square(NDArray nd, Type dtype); diff --git a/src/NumSharp.Core/Math/np.rint.cs b/src/NumSharp.Core/Math/np.rint.cs new file mode 100644 index 00000000..8f06a2b2 --- /dev/null +++ b/src/NumSharp.Core/Math/np.rint.cs @@ -0,0 +1,42 @@ +using System; + +namespace NumSharp +{ + public static partial class np + { + /// + /// Round elements of the array to the nearest integer, element-wise (round half to even). + /// The result is a float (rint has no integer loop): integer/bool inputs promote to the + /// float tier (bool/int8/uint8 -> float16, int16/uint16 -> float32, int32/int64/... -> + /// float64), floats and complex are preserved. The real and imaginary parts of complex + /// numbers are rounded separately. + /// + /// Input array. + /// A location into which the result is stored (same_kind cast from the loop dtype). + /// Boolean mask; compute only where true, leaving other slots unchanged. + /// Loop dtype override (must be a float/complex loop). + /// An array of the same shape as x, containing the rounded values. This is a scalar if x is a scalar. + /// https://numpy.org/doc/stable/reference/generated/numpy.rint.html + public static NDArray rint(NDArray x, NDArray @out = null, NDArray where = null, NPTypeCode? dtype = null) + => x.TensorEngine.Rint(x, dtype, @out, where); + + /// + /// Computed in — positional-dtype convenience + /// overload (NumPy accepts dtype only as a keyword; its 2nd positional is out=). + /// + /// Input array. + /// The loop dtype the computation should run in. + /// https://numpy.org/doc/stable/reference/generated/numpy.rint.html + public static NDArray rint(NDArray x, NPTypeCode dtype) + => x.TensorEngine.Rint(x, dtype); + + /// + /// Round elements of the array to the nearest integer, element-wise (round half to even). + /// + /// Input array. + /// The loop dtype the computation should run in. + /// https://numpy.org/doc/stable/reference/generated/numpy.rint.html + public static NDArray rint(NDArray x, Type dtype) + => x.TensorEngine.Rint(x, dtype); + } +} diff --git a/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs b/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs index 0e34b08d..6ac7887d 100644 --- a/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs +++ b/test/NumSharp.UnitTest/Fuzz/OpRegistry.cs @@ -39,6 +39,7 @@ public static NDArray Apply(string op, IReadOnlyDictionary case "floor": return np.floor(ops[0]); case "ceil": return np.ceil(ops[0]); case "trunc": return np.trunc(ops[0]); + case "rint": return np.rint(ops[0]); case "sin": return np.sin(ops[0]); case "cos": return np.cos(ops[0]); case "tan": return np.tan(ops[0]); diff --git a/test/NumSharp.UnitTest/Fuzz/corpus/unary_extra.jsonl b/test/NumSharp.UnitTest/Fuzz/corpus/unary_extra.jsonl index 256af3f4..a20b9d0d 100644 --- a/test/NumSharp.UnitTest/Fuzz/corpus/unary_extra.jsonl +++ b/test/NumSharp.UnitTest/Fuzz/corpus/unary_extra.jsonl @@ -11,4647 +11,4985 @@ 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np.rint — round-half-to-even, float-tier unary ufunc. All expectations verified + /// against NumPy 2.4.2 (np.rint has only float/complex loops: e/f/d/g/F/D/G). + /// + [TestClass] + public class np_rint_Test + { + // ---- dtype tiers (int/bool promote to float; floats/complex preserved) ---- + [TestMethod] + public void Rint_DtypeTiers() + { + np.rint(np.array(new bool[] { true })).typecode.Should().Be(NPTypeCode.Half); + np.rint(np.array(new sbyte[] { 1 })).typecode.Should().Be(NPTypeCode.Half); + np.rint(np.array(new byte[] { 1 })).typecode.Should().Be(NPTypeCode.Half); + np.rint(np.array(new short[] { 1 })).typecode.Should().Be(NPTypeCode.Single); + np.rint(np.array(new ushort[] { 1 })).typecode.Should().Be(NPTypeCode.Single); + np.rint(np.array(new int[] { 1 })).typecode.Should().Be(NPTypeCode.Double); + np.rint(np.array(new uint[] { 1 })).typecode.Should().Be(NPTypeCode.Double); + np.rint(np.array(new long[] { 1 })).typecode.Should().Be(NPTypeCode.Double); + np.rint(np.array(new ulong[] { 1 })).typecode.Should().Be(NPTypeCode.Double); + np.rint(np.array(new Half[] { (Half)1 })).typecode.Should().Be(NPTypeCode.Half); + np.rint(np.array(new float[] { 1 })).typecode.Should().Be(NPTypeCode.Single); + np.rint(np.array(new double[] { 1 })).typecode.Should().Be(NPTypeCode.Double); + np.rint(np.array(new Complex[] { new(1, 2) })).typecode.Should().Be(NPTypeCode.Complex); + } + + // ---- round half to even (banker's) ---- + [TestMethod] + public void Rint_HalfToEven_Float64() + { + var r = np.rint(np.array(new double[] { 0.5, 1.5, 2.5, 3.5, -0.5, -1.5, -2.5, 2.4, 2.6, -2.6 })); + double[] exp = { 0, 2, 2, 4, 0, -2, -2, 2, 3, -3 }; + for (int i = 0; i < exp.Length; i++) + r.GetDouble(i).Should().Be(exp[i], $"rint index {i}"); + } + + [TestMethod] + public void Rint_HalfToEven_Float32() + { + var r = np.rint(np.array(new float[] { 0.5f, 1.5f, 2.5f, -2.5f, 2.6f })); + r.typecode.Should().Be(NPTypeCode.Single); + float[] exp = { 0, 2, 2, -2, 3 }; + for (int i = 0; i < exp.Length; i++) + r.GetSingle(i).Should().Be(exp[i]); + } + + // ---- NaN / Inf preserved ---- + [TestMethod] + public void Rint_NanInf_Preserved() + { + var r = np.rint(np.array(new double[] { double.NaN, double.PositiveInfinity, double.NegativeInfinity })); + double.IsNaN(r.GetDouble(0)).Should().BeTrue(); + double.IsPositiveInfinity(r.GetDouble(1)).Should().BeTrue(); + double.IsNegativeInfinity(r.GetDouble(2)).Should().BeTrue(); + } + + // ---- int input -> float tier, values are the integers as floats ---- + [TestMethod] + public void Rint_Int8_ToFloat16_Values() + { + var r = np.rint(np.array(new sbyte[] { -3, 0, 5, 127 })); + r.typecode.Should().Be(NPTypeCode.Half); + ((double)r.GetHalf(0)).Should().Be(-3); + ((double)r.GetHalf(3)).Should().Be(127); + } + + // ---- complex: rounds real & imag separately (half-to-even) ---- + [TestMethod] + public void Rint_Complex_RoundsBothParts() + { + var r = np.rint(np.array(new Complex[] { new(1.5, 2.5), new(0.5, -1.5), new(-2.5, 0.5) })); + r.typecode.Should().Be(NPTypeCode.Complex); + r.GetComplex(0).Should().Be(new Complex(2, 2)); + r.GetComplex(1).Should().Be(new Complex(0, -2)); + r.GetComplex(2).Should().Be(new Complex(-2, 0)); + } + + // ---- decimal (NumSharp extension): preserved, half-to-even ---- + [TestMethod] + public void Rint_Decimal_Preserved() + { + var r = np.rint(np.array(new decimal[] { 0.5m, 1.5m, 2.5m, -2.5m, 2.6m })); + r.typecode.Should().Be(NPTypeCode.Decimal); + r.GetDecimal(0).Should().Be(0m); + r.GetDecimal(1).Should().Be(2m); + r.GetDecimal(2).Should().Be(2m); + r.GetDecimal(4).Should().Be(3m); + } + + // ---- layouts: strided / negative-stride / broadcast / transpose / empty / scalar ---- + [TestMethod] + public void Rint_StridedView() + { + var b = np.array(new double[] { 0.5, 1.5, 2.5, 3.5, 4.5, 5.5 }); + var r = np.rint(b["::2"]); // [0.5,2.5,4.5] -> [0,2,4] + r.GetDouble(0).Should().Be(0); + r.GetDouble(1).Should().Be(2); + r.GetDouble(2).Should().Be(4); + } + + [TestMethod] + public void Rint_NegativeStrideView() + { + var b = np.array(new double[] { 0.5, 1.5, 2.5, 3.5, 4.5, 5.5 }); + var r = np.rint(b["::-1"]); // [5.5,4.5,3.5,2.5,1.5,0.5] -> [6,4,4,2,2,0] + r.GetDouble(0).Should().Be(6); + r.GetDouble(5).Should().Be(0); + } + + [TestMethod] + public void Rint_Broadcast() + { + var bc = np.array(new double[] { 0.5, 1.5, 2.5 }).reshape(1, 3); + var r = np.rint(np.broadcast_to(bc, new Shape(2, 3))); + r.shape.Should().Equal(2, 3); + r.GetDouble(0, 0).Should().Be(0); + r.GetDouble(1, 1).Should().Be(2); + } + + [TestMethod] + public void Rint_Empty_And_Scalar() + { + var e = np.rint(np.array(new double[] { })); + e.size.Should().Be(0); + e.typecode.Should().Be(NPTypeCode.Double); + + np.rint(NDArray.Scalar(2.5)).GetDouble(0).Should().Be(2.0); + } + + // ---- ufunc out= / where= / dtype= ---- + [TestMethod] + public void Rint_Out_ReturnsSameInstanceAndFills() + { + var outArr = np.zeros(new Shape(3), NPTypeCode.Double); + var r = np.rint(np.array(new double[] { 0.5, 1.5, 2.5 }), @out: outArr); + ReferenceEquals(r, outArr).Should().BeTrue(); + outArr.GetDouble(0).Should().Be(0); + outArr.GetDouble(1).Should().Be(2); + outArr.GetDouble(2).Should().Be(2); + } + + [TestMethod] + public void Rint_DtypeOverride_RunsLoopInThatDtype() + { + var r = np.rint(np.array(new double[] { 2.5 }), dtype: NPTypeCode.Single); + r.typecode.Should().Be(NPTypeCode.Single); + r.GetSingle(0).Should().Be(2.0f); + } + + [TestMethod] + public void Rint_IntegerDtype_RaisesNoLoop() + { + // rint has no integer loop; dtype=int must raise (NumPy: "No loop matching ..."). + Action act = () => np.rint(np.array(new double[] { 2.5 }), dtype: NPTypeCode.Int32); + act.Should().Throw(); + } + + [TestMethod] + public void Rint_Where_KeepsPriorOutContents() + { + var outW = np.full(new Shape(3), 9.0, NPTypeCode.Double); + var mask = np.array(new bool[] { true, false, true }); + np.rint(np.array(new double[] { 0.5, 1.5, 2.5 }), @out: outW, where: mask); + outW.GetDouble(0).Should().Be(0); + outW.GetDouble(1).Should().Be(9.0, "masked-off slot keeps prior out content"); + outW.GetDouble(2).Should().Be(2); + } + + // ---- np.around(complex) now also works (bonus from the shared Round kernel) ---- + [TestMethod] + public void Around_Complex_NowSupported() + { + var r = np.around(np.array(new Complex[] { new(1.5, 2.5) })); + r.typecode.Should().Be(NPTypeCode.Complex); + r.GetComplex(0).Should().Be(new Complex(2, 2)); + } + } +} diff --git a/test/oracle/gen_oracle.py b/test/oracle/gen_oracle.py index 864e712c..38c3ebd5 100644 --- a/test/oracle/gen_oracle.py +++ b/test/oracle/gen_oracle.py @@ -136,6 +136,7 @@ def gen_astype(srcs, dsts, layout_names): "arcsin": np.arcsin, "arccos": np.arccos, "arctan": np.arctan, "deg2rad": np.deg2rad, "rad2deg": np.rad2deg, "positive": np.positive, + "rint": np.rint, # round-half-to-even; float-tier dtype like the others in this group } From 0282d50405c56103c9b48fd537b4e2261da24936 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 08:25:43 +0300 Subject: [PATCH 19/24] =?UTF-8?q?test(rint):=20enforce=20NumPy=20parity=20?= =?UTF-8?q?=E2=80=94=20signed=20zero,=20Half/Char,=20out=3D/where=3D=20cas?= =?UTF-8?q?ting,=20metamorphic?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Extends Math/np.rint.Test.cs from 16 to 31 tests, all pinned to NumPy 2.4.2 output, covering the subtle parity points beyond the fuzz corpus: - Signed zero preserved (f64+f32): rint(-0.4)/rint(-0.5)/rint(-0.0) -> -0.0 (signbit True), rint(0.4) -> +0.0; subnormal underflow rint(-1e-300) -> -0.0. Verified NumSharp Math.Round matches NumPy signbit exactly. - Half (float16) half-to-even values; Char (uint16 proxy) -> float32. - All 15 supported dtypes -> NumPy-parity result dtype (adds Char + Decimal to the tier check). - Higher-rank 2D values + F-contiguous input. - out= same_kind narrowing cast (f64 loop -> f32 out) returns the f32 out; out= float->int raises (not same_kind); in-place out=a aliasing. - where= broadcasts (2,) mask across a (2,2) output; masked-off slots keep prior contents. - Metamorphic (oracle-free): rint idempotent, and odd (rint(-x) == -rint(x)). - Large integral magnitudes (2^53, +/-1e16) unchanged. Full suite 11329 passed / 0 failed (net10.0); rint class green on net8.0. --- test/NumSharp.UnitTest/Math/np.rint.Test.cs | 184 ++++++++++++++++++++ 1 file changed, 184 insertions(+) diff --git a/test/NumSharp.UnitTest/Math/np.rint.Test.cs b/test/NumSharp.UnitTest/Math/np.rint.Test.cs index cf779a1d..fd46df59 100644 --- a/test/NumSharp.UnitTest/Math/np.rint.Test.cs +++ b/test/NumSharp.UnitTest/Math/np.rint.Test.cs @@ -183,5 +183,189 @@ public void Around_Complex_NowSupported() r.typecode.Should().Be(NPTypeCode.Complex); r.GetComplex(0).Should().Be(new Complex(2, 2)); } + + // ===================================================================== + // Extended parity coverage (all values verified against NumPy 2.4.2) + // ===================================================================== + + // ---- signed zero is preserved (np.rint(-0.4) -> -0.0, signbit True) ---- + [TestMethod] + public void Rint_SignedZero_Preserved_Float64() + { + var r = np.rint(np.array(new double[] { -0.0, -0.4, -0.5, 0.4, 0.5 })); + // values are all zero... + for (int i = 0; i < 5; i++) r.GetDouble(i).Should().Be(0.0); + // ...but the SIGN of zero must match NumPy exactly. + double.IsNegative(r.GetDouble(0)).Should().BeTrue("rint(-0.0) keeps -0.0"); + double.IsNegative(r.GetDouble(1)).Should().BeTrue("rint(-0.4) = -0.0"); + double.IsNegative(r.GetDouble(2)).Should().BeTrue("rint(-0.5) = -0.0 (half to even)"); + double.IsNegative(r.GetDouble(3)).Should().BeFalse("rint(0.4) = +0.0"); + double.IsNegative(r.GetDouble(4)).Should().BeFalse("rint(0.5) = +0.0"); + } + + [TestMethod] + public void Rint_SignedZero_Preserved_Float32() + { + var r = np.rint(np.array(new float[] { -0.4f, 0.4f })); + float.IsNegative(r.GetSingle(0)).Should().BeTrue(); + float.IsNegative(r.GetSingle(1)).Should().BeFalse(); + } + + [TestMethod] + public void Rint_Subnormal_UnderflowsToSignedZero() + { + var r = np.rint(np.array(new double[] { 1e-300, -1e-300 })); + r.GetDouble(0).Should().Be(0.0); + double.IsNegative(r.GetDouble(0)).Should().BeFalse(); + r.GetDouble(1).Should().Be(0.0); + double.IsNegative(r.GetDouble(1)).Should().BeTrue("rint(-1e-300) = -0.0"); + } + + // ---- Half (float16) rounds half-to-even and stays float16 ---- + [TestMethod] + public void Rint_Half_Values() + { + var r = np.rint(np.array(new Half[] { (Half)0.5f, (Half)1.5f, (Half)2.5f, (Half)3.5f, (Half)(-2.5f), (Half)2.6f })); + r.typecode.Should().Be(NPTypeCode.Half); + double[] exp = { 0, 2, 2, 4, -2, 3 }; + for (int i = 0; i < exp.Length; i++) + ((double)r.GetHalf(i)).Should().Be(exp[i]); + } + + // ---- Char (NumSharp-specific, uint16 proxy) -> float32 ---- + [TestMethod] + public void Rint_Char_ToFloat32() + { + var r = np.rint(np.array(new char[] { (char)0, (char)65, (char)255, (char)1000 })); + r.typecode.Should().Be(NPTypeCode.Single, "char rides the uint16 tier -> float32"); + r.GetSingle(0).Should().Be(0); + r.GetSingle(1).Should().Be(65); + r.GetSingle(3).Should().Be(1000); + } + + // ---- every one of the 15 supported dtypes produces the NumPy-parity result dtype ---- + [TestMethod] + public void Rint_All15Dtypes_ResultDtype() + { + np.rint(np.array(new bool[] { true })).typecode.Should().Be(NPTypeCode.Half); + np.rint(np.array(new byte[] { 1 })).typecode.Should().Be(NPTypeCode.Half); + np.rint(np.array(new sbyte[] { 1 })).typecode.Should().Be(NPTypeCode.Half); + np.rint(np.array(new short[] { 1 })).typecode.Should().Be(NPTypeCode.Single); + np.rint(np.array(new ushort[] { 1 })).typecode.Should().Be(NPTypeCode.Single); + np.rint(np.array(new int[] { 1 })).typecode.Should().Be(NPTypeCode.Double); + np.rint(np.array(new uint[] { 1 })).typecode.Should().Be(NPTypeCode.Double); + np.rint(np.array(new long[] { 1 })).typecode.Should().Be(NPTypeCode.Double); + np.rint(np.array(new ulong[] { 1 })).typecode.Should().Be(NPTypeCode.Double); + np.rint(np.array(new char[] { 'a' })).typecode.Should().Be(NPTypeCode.Single); + np.rint(np.array(new Half[] { (Half)1 })).typecode.Should().Be(NPTypeCode.Half); + np.rint(np.array(new float[] { 1 })).typecode.Should().Be(NPTypeCode.Single); + np.rint(np.array(new double[] { 1 })).typecode.Should().Be(NPTypeCode.Double); + np.rint(np.array(new decimal[] { 1 })).typecode.Should().Be(NPTypeCode.Decimal); + np.rint(np.array(new Complex[] { new(1, 2) })).typecode.Should().Be(NPTypeCode.Complex); + } + + // ---- higher-rank (2D) value parity ---- + [TestMethod] + public void Rint_2D_Values() + { + var r = np.rint(np.array(new double[] { 0.5, 1.5, 2.5, -2.5 }).reshape(2, 2)); + r.shape.Should().Equal(2, 2); + r.GetDouble(0, 0).Should().Be(0); + r.GetDouble(0, 1).Should().Be(2); + r.GetDouble(1, 0).Should().Be(2); + r.GetDouble(1, 1).Should().Be(-2); + } + + // ---- F-contiguous input ---- + [TestMethod] + public void Rint_FContiguous() + { + var f = np.array(new double[] { 0.5, 1.5, 2.5, 3.5 }).reshape(2, 2).copy(order: 'F'); + var r = np.rint(f); + r.GetDouble(0, 0).Should().Be(0); + r.GetDouble(0, 1).Should().Be(2); + r.GetDouble(1, 0).Should().Be(2); + r.GetDouble(1, 1).Should().Be(4); + } + + // ---- out= with a same_kind float narrowing cast (f64 loop -> f32 out) ---- + [TestMethod] + public void Rint_Out_SameKindNarrowingCast() + { + var o = np.zeros(new Shape(3), NPTypeCode.Single); + var r = np.rint(np.array(new double[] { 0.5, 1.5, 2.5 }), @out: o); + ReferenceEquals(r, o).Should().BeTrue(); + o.typecode.Should().Be(NPTypeCode.Single); + o.GetSingle(0).Should().Be(0); + o.GetSingle(1).Should().Be(2); + o.GetSingle(2).Should().Be(2); + } + + // ---- out= that requires a non-same_kind cast (float loop -> int out) must raise ---- + [TestMethod] + public void Rint_Out_FloatToInt_Raises() + { + var o = np.zeros(new Shape(2), NPTypeCode.Int32); + Action act = () => np.rint(np.array(new double[] { 0.5, 1.5 }), @out: o); + act.Should().Throw("float rint result cannot same_kind-cast into an int out"); + } + + // ---- in-place: out= aliases the input ---- + [TestMethod] + public void Rint_InPlace_OutAliasesInput() + { + var a = np.array(new double[] { 0.5, 1.5, 2.5, 3.5 }); + var r = np.rint(a, @out: a); + ReferenceEquals(r, a).Should().BeTrue(); + a.GetDouble(0).Should().Be(0); + a.GetDouble(1).Should().Be(2); + a.GetDouble(2).Should().Be(2); + a.GetDouble(3).Should().Be(4); + } + + // ---- where= broadcasts (2D out, row mask) ---- + [TestMethod] + public void Rint_Where_Broadcasts() + { + var outW = np.full(new Shape(2, 2), 9.0, NPTypeCode.Double); + var mask = np.array(new bool[] { true, false }); // (2,) broadcasts across rows + np.rint(np.array(new double[] { 0.5, 1.5, 2.5, 3.5 }).reshape(2, 2), @out: outW, where: mask); + outW.GetDouble(0, 0).Should().Be(0); + outW.GetDouble(0, 1).Should().Be(9.0, "column 1 masked off"); + outW.GetDouble(1, 0).Should().Be(2); + outW.GetDouble(1, 1).Should().Be(9.0, "column 1 masked off"); + } + + // ---- metamorphic invariants (oracle-free; hold for round-half-to-even) ---- + [TestMethod] + public void Rint_Idempotent() + { + var x = np.array(new double[] { 0.5, 1.4, 2.6, -3.5, 2.5, -2.5, 100.49 }); + var once = np.rint(x); + var twice = np.rint(once); + for (int i = 0; i < x.size; i++) + twice.GetDouble(i).Should().Be(once.GetDouble(i), "rint is idempotent"); + } + + [TestMethod] + public void Rint_OddFunction() + { + // Round-half-to-even is symmetric about 0: rint(-x) == -rint(x). + var x = np.array(new double[] { 0.5, 1.4, 2.6, 3.5, 2.5, 0.1 }); + var negThenRint = np.rint(-x); + var rintThenNeg = -np.rint(x); + for (int i = 0; i < x.size; i++) + negThenRint.GetDouble(i).Should().Be(rintThenNeg.GetDouble(i)); + } + + // ---- large already-integer magnitudes are unchanged ---- + [TestMethod] + public void Rint_LargeIntegralMagnitudes_Unchanged() + { + var r = np.rint(np.array(new double[] { 9007199254740992.0 /*2^53*/, 1e16, -1e16 })); + r.GetDouble(0).Should().Be(9007199254740992.0); + r.GetDouble(1).Should().Be(1e16); + r.GetDouble(2).Should().Be(-1e16); + } } } From f6a59f332450dee1652a97b0dba64dd65383d440 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 08:40:45 +0300 Subject: [PATCH 20/24] =?UTF-8?q?feat(io):=20full=20np.fromfile=20parity?= =?UTF-8?q?=20=E2=80=94=20count/offset/sep(text)/default-dtype/Stream?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Answers "do we support tofile/fromfile to full extent?": tofile was complete, but fromfile was binary-only with a required dtype — the pair was asymmetric (you could tofile a text or non-contiguous file but not read it back). This brings fromfile up to NumPy's fromfile(file, dtype=float, count=-1, sep='', offset=0). Was: fromfile(string file, NPTypeCode|Type dtype) — reads the WHOLE file as binary only. Now (backward-compatible; the old 2-arg calls still resolve): - dtype defaults to float64 (NumPy's default) and may be omitted. - count: read N items; -1 (default) reads the rest. Past EOF reads what's present. - offset: skip N bytes from the current position (binary only; text+offset raises, like NumPy). - sep: text mode. Splits NumPy-style — a whitespace-only separator splits on any whitespace run; otherwise split on the separator's non-whitespace core with the surrounding whitespace treated as a wildcard (matches swab_separator), trailing separator ignored. Integer tokens WRAP into the dtype (int8 "300"->44, uint8 "-1" ->255) like NumPy's scanf loop; floats accept sci / nan / inf; bool parses as int (nonzero => True); malformed data raises ValueError. - Stream overload (the file-object form): reads from the current position, left open. NaN parity: NumPy/C text "nan" is the POSITIVE quiet NaN (0x7FF8…); .NET's double.NaN is the NEGATIVE one (0xFFF8…). fromfile emits the positive pattern so bytes are identical to NumPy and a narrowing cast lands on float 0x7FC00000 / half 0x7E00. Complex: reads the bare "a+bj" form NumPy accepts AND, as a superset, the parenthesized "(1+2j)" form its own tofile writes — so complex text ROUND-TRIPS in NumSharp (NumPy's text reader errors on the parenthesized form, so it does not round-trip there). Binary fast path: a seekable source (every filename) reads straight into one exact-sized buffer that the array then views (pinned) — a single disk->buffer copy, versus the MemoryStream growth + ToArray double copy of the streaming fallback (non-seekable streams). Validation (differential vs NumPy 2.4.2): 35 oracle cases bit-exact (binary count/offset/ EOF-clamp/default-dtype/reinterpret + text sep/whitespace/trailing/count/sci/nan/inf/ int-wrap/bool/complex/empty/malformed-error/offset-error) + Stream + default-dtype + 15 binary round-trips (all dtypes, incl. sliced views) + 6 text round-trips. 15 new MSTest cases; full CI suite green (11344 passed). Performance (Release, warm, NPY/NS): binary read 10M int32 = 1.18x (single-copy/disk bound, both do one copy); text parse 200K float64 = 4.79x. --- src/NumSharp.Core/APIs/np.fromfile.cs | 339 +++++++++++++++--- .../Casting/NDArray.fromfile.Test.cs | 257 +++++++++++++ 2 files changed, 556 insertions(+), 40 deletions(-) create mode 100644 test/NumSharp.UnitTest/Casting/NDArray.fromfile.Test.cs diff --git a/src/NumSharp.Core/APIs/np.fromfile.cs b/src/NumSharp.Core/APIs/np.fromfile.cs index 6e258f7c..d06779d5 100644 --- a/src/NumSharp.Core/APIs/np.fromfile.cs +++ b/src/NumSharp.Core/APIs/np.fromfile.cs @@ -1,5 +1,9 @@ -using System; +using System; +using System.Collections.Generic; +using System.Globalization; using System.IO; +using System.Numerics; +using System.Text; using NumSharp.Backends; using NumSharp.Backends.Unmanaged; @@ -7,58 +11,313 @@ namespace NumSharp { public static partial class np { + private static readonly CultureInfo CI = CultureInfo.InvariantCulture; + /// - /// Construct an array from data in a text or binary file. - /// A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the tofile method can be read using this function. + /// Construct an array from data in a text or binary file. Efficient for binary data of a known + /// dtype, and parses simply-formatted text files. Data written with + /// can be read back with this function. /// - /// filename. - /// Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in the file. - /// + /// A filename. + /// Element type. For binary files it sets the item size; defaults to . + /// Number of items to read. -1 (default) reads the whole file. + /// + /// Separator between items for a text file. Empty (default) reads binary. A separator containing + /// spaces matches runs of whitespace; a whitespace-only separator splits on any whitespace run. + /// + /// Bytes to skip from the file's current position. Binary files only. /// https://numpy.org/doc/stable/reference/generated/numpy.fromfile.html - public static NDArray fromfile(string file, NPTypeCode dtype) + public static NDArray fromfile(string file, NPTypeCode dtype, int count = -1, string sep = "", long offset = 0) + => fromfile(file, dtype.AsType(), count, sep, offset); + + /// + public static NDArray fromfile(string file, Type dtype = null, int count = -1, string sep = "", long offset = 0) { - return fromfile(file, dtype.AsType()); + using var fs = new FileStream(file, FileMode.Open, FileAccess.Read); + return fromfile(fs, dtype, count, sep, offset); } /// - /// Construct an array from data in a text or binary file. - /// A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the tofile method can be read using this function. + /// Construct an array from data in an open (the file-object form). Reads from + /// the stream's current position and leaves it open. /// - /// filename. - /// Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in the file. - /// - /// https://numpy.org/doc/stable/reference/generated/numpy.fromfile.html - public static NDArray fromfile(string file, Type dtype) + /// + public static NDArray fromfile(Stream stream, NPTypeCode dtype, int count = -1, string sep = "", long offset = 0) + => fromfile(stream, dtype.AsType(), count, sep, offset); + + /// + public static NDArray fromfile(Stream stream, Type dtype = null, int count = -1, string sep = "", long offset = 0) + { + if (stream is null) throw new ArgumentNullException(nameof(stream)); + NPTypeCode tc = (dtype ?? typeof(double)).GetTypeCode(); + + if (string.IsNullOrEmpty(sep)) + return FromFileBinary(stream, tc, count, offset); + + // NumPy raises for offset with a text file ("The 'offset' keyword only permitted for binary files"). + if (offset != 0) + throw new ArgumentException("The 'offset' keyword only permitted for binary files.", nameof(offset)); + + return FromFileText(stream, tc, count, sep); + } + + // ---- binary ------------------------------------------------------------------ + + private static NDArray FromFileBinary(Stream stream, NPTypeCode tc, int count, long offset) + { + if (offset != 0) + SkipBytes(stream, offset); + + int itemsize = tc.SizeOf(); + // count<0 => whole remainder; else count items, but never past EOF (NumPy reads what's there). + long want = count < 0 ? long.MaxValue : (long)count * itemsize; + + byte[] bytes; + if (stream.CanSeek) + { + // Fast path: the remaining length is known, so allocate ONE exact buffer and read straight + // into it — a single disk->buffer copy that the array then views (pinned), matching NumPy's + // read-into-result and avoiding the MemoryStream growth + ToArray double copy. + long remaining = Math.Max(0, stream.Length - stream.Position); + long take = Math.Min(want, remaining); + take -= take % itemsize; + bytes = new byte[take]; // >2GB is unrepresentable as a managed byte[] regardless (throws) + ReadExact(stream, bytes, (int)take); + } + else + { + bytes = ReadUpTo(stream, want); + int usable = bytes.Length - (bytes.Length % itemsize); + if (usable != bytes.Length) + Array.Resize(ref bytes, usable); + } + return BytesToArray(bytes, tc); + } + + private static void ReadExact(Stream s, byte[] buf, int count) + { + int off = 0; + while (off < count) + { + int n = s.Read(buf, off, count - off); + if (n <= 0) break; // EOF earlier than the reported length; use what we got + off += n; + } + } + + private static void SkipBytes(Stream s, long offset) + { + if (s.CanSeek) + { + s.Seek(offset, SeekOrigin.Current); + return; + } + // Non-seekable: read and discard. + byte[] buf = new byte[Math.Min(offset, 81920)]; + long left = offset; + while (left > 0) + { + int n = s.Read(buf, 0, (int)Math.Min(buf.Length, left)); + if (n <= 0) break; + left -= n; + } + } + + private static byte[] ReadUpTo(Stream s, long max) + { + using var ms = new MemoryStream(); + byte[] buf = new byte[81920]; + long total = 0; + while (total < max) + { + int toRead = (int)Math.Min(buf.Length, max - total); + int n = s.Read(buf, 0, toRead); + if (n <= 0) break; + ms.Write(buf, 0, n); + total += n; + } + return ms.ToArray(); + } + + // Reinterpret the byte buffer (already trimmed to whole items) as a 1-D array of . + private static NDArray BytesToArray(byte[] bytes, NPTypeCode tc) + { + switch (tc) + { + case NPTypeCode.Boolean: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.Byte: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.SByte: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.Int16: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.UInt16: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.Int32: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.UInt32: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.Int64: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.UInt64: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.Char: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.Half: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.Single: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.Double: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.Decimal: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + case NPTypeCode.Complex: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); + default: throw new NotSupportedException(tc.ToString()); + } + } + + // ---- text -------------------------------------------------------------------- + + private static NDArray FromFileText(Stream stream, NPTypeCode tc, int count, string sep) + { + string text; + using (var reader = new StreamReader(stream, Encoding.ASCII, false, 4096, leaveOpen: true)) + text = reader.ReadToEnd(); + + string[] tokens = Tokenize(text, sep); + int take = count < 0 ? tokens.Length : Math.Min(count, tokens.Length); + return TokensToArray(tokens, take, tc); + } + + // Split the text into item tokens. A whitespace-only separator splits on any whitespace run; + // otherwise we split on the separator's non-whitespace core and trim surrounding whitespace off + // each token (NumPy's swab_separator: whitespace around the separator is a wildcard). Empty tokens + // (a trailing separator, or whitespace-only gaps) are dropped. + private static string[] Tokenize(string text, string sep) + { + var coreChars = new List(sep.Length); + foreach (char c in sep) + if (!char.IsWhiteSpace(c)) coreChars.Add(c); + + if (coreChars.Count == 0) + return text.Split((char[])null, StringSplitOptions.RemoveEmptyEntries); + + string core = new string(coreChars.ToArray()); + string[] raw = text.Split(new[] { core }, StringSplitOptions.None); + var list = new List(raw.Length); + foreach (string t in raw) + { + string s = t.Trim(); + if (s.Length > 0) list.Add(s); + } + return list.ToArray(); + } + + private static NDArray TokensToArray(string[] tokens, int take, NPTypeCode tc) + { + switch (tc) + { + case NPTypeCode.Boolean: { var a = new bool[take]; for (int i = 0; i < take; i++) a[i] = ParseLong(tokens[i]) != 0; return array(a); } + case NPTypeCode.Byte: { var a = new byte[take]; for (int i = 0; i < take; i++) a[i] = unchecked((byte)ParseLong(tokens[i])); return array(a); } + case NPTypeCode.SByte: { var a = new sbyte[take]; for (int i = 0; i < take; i++) a[i] = unchecked((sbyte)ParseLong(tokens[i])); return array(a); } + case NPTypeCode.Int16: { var a = new short[take]; for (int i = 0; i < take; i++) a[i] = unchecked((short)ParseLong(tokens[i])); return array(a); } + case NPTypeCode.UInt16: { var a = new ushort[take]; for (int i = 0; i < take; i++) a[i] = unchecked((ushort)ParseLong(tokens[i])); return array(a); } + case NPTypeCode.Int32: { var a = new int[take]; for (int i = 0; i < take; i++) a[i] = unchecked((int)ParseLong(tokens[i])); return array(a); } + case NPTypeCode.UInt32: { var a = new uint[take]; for (int i = 0; i < take; i++) a[i] = unchecked((uint)ParseLong(tokens[i])); return array(a); } + case NPTypeCode.Int64: { var a = new long[take]; for (int i = 0; i < take; i++) a[i] = ParseLong(tokens[i]); return array(a); } + case NPTypeCode.UInt64: { var a = new ulong[take]; for (int i = 0; i < take; i++) a[i] = ParseULong(tokens[i]); return array(a); } + case NPTypeCode.Char: { var a = new char[take]; for (int i = 0; i < take; i++) a[i] = (char)unchecked((ushort)ParseLong(tokens[i])); return array(a); } + case NPTypeCode.Half: { var a = new Half[take]; for (int i = 0; i < take; i++) a[i] = (Half)ParseDouble(tokens[i]); return array(a); } + case NPTypeCode.Single: { var a = new float[take]; for (int i = 0; i < take; i++) a[i] = (float)ParseDouble(tokens[i]); return array(a); } + case NPTypeCode.Double: { var a = new double[take]; for (int i = 0; i < take; i++) a[i] = ParseDouble(tokens[i]); return array(a); } + case NPTypeCode.Decimal: { var a = new decimal[take]; for (int i = 0; i < take; i++) a[i] = ParseDecimal(tokens[i]); return array(a); } + case NPTypeCode.Complex: { var a = new Complex[take]; for (int i = 0; i < take; i++) a[i] = ParseComplex(tokens[i]); return array(a); } + default: throw new NotSupportedException(tc.ToString()); + } + } + + private static ValueError Unmatched(string token) + => new ValueError($"string could not be read to its end: unmatched item '{token}'."); + + private static long ParseLong(string t) + { + if (long.TryParse(t, NumberStyles.Integer | NumberStyles.AllowLeadingSign, CI, out long v)) + return v; + throw Unmatched(t); + } + + private static ulong ParseULong(string t) + { + if (ulong.TryParse(t, NumberStyles.Integer, CI, out ulong u)) + return u; + // negatives wrap two's-complement into the unsigned range (NumPy: uint64 "-1" -> 2^64-1). + if (long.TryParse(t, NumberStyles.Integer | NumberStyles.AllowLeadingSign, CI, out long s)) + return unchecked((ulong)s); + throw Unmatched(t); + } + + private static decimal ParseDecimal(string t) + { + if (decimal.TryParse(t, NumberStyles.Float, CI, out decimal d)) + return d; + throw Unmatched(t); + } + + // NumPy/C parse "nan" to the POSITIVE quiet NaN (0x7FF8…). .NET's double.NaN is the NEGATIVE + // quiet NaN (0xFFF8…), so use the positive bit pattern to stay byte-identical to NumPy (and so a + // narrowing cast yields float 0x7FC00000 / half 0x7E00, matching NumPy's float16/float32 "nan"). + private static readonly double PositiveNaN = BitConverter.Int64BitsToDouble(0x7FF8000000000000L); + + private static double ParseDouble(string t) + { + switch (t.ToLowerInvariant()) + { + case "nan": case "+nan": return PositiveNaN; + case "-nan": return double.NaN; // .NET double.NaN == 0xFFF8… (negative qNaN) + case "inf": case "+inf": case "infinity": case "+infinity": return double.PositiveInfinity; + case "-inf": case "-infinity": return double.NegativeInfinity; + } + if (double.TryParse(t, NumberStyles.Float, CI, out double v)) + return v; + throw Unmatched(t); + } + + // Parse a complex literal: "a+bj" / "a-bj" / "bj" / "a" (optionally wrapped in parentheses, so the + // "(1+2j)" form NumPy's tofile writes round-trips here — NumPy's own text reader only accepts the + // bare "1+2j" form, so this is a superset). + private static Complex ParseComplex(string t) + { + string s = t.Trim(); + if (s.Length >= 2 && s[0] == '(' && s[s.Length - 1] == ')') + s = s.Substring(1, s.Length - 2).Trim(); + + if (s.Length == 0) + throw Unmatched(t); + + char last = s[s.Length - 1]; + if (last != 'j' && last != 'J') + return new Complex(ParseDouble(s), 0.0); + + string body = s.Substring(0, s.Length - 1); + int split = ImaginarySplit(body); + if (split < 0) + { + // pure imaginary: "j" / "+j" / "-j" / "2j" + double im = body.Length == 0 ? 1.0 + : body == "+" ? 1.0 + : body == "-" ? -1.0 + : ParseDouble(body); + return new Complex(0.0, im); + } + + double real = ParseDouble(body.Substring(0, split)); + string imagStr = body.Substring(split); + double imag = imagStr == "+" ? 1.0 : imagStr == "-" ? -1.0 : ParseDouble(imagStr); + return new Complex(real, imag); + } + + // Index of the sign that separates real from imaginary — a '+'/'-' that is neither the leading + // sign nor part of an exponent ("1.5e-3+2j"). + private static int ImaginarySplit(string body) { - unsafe + for (int i = body.Length - 1; i > 0; i--) { - var bytes = File.ReadAllBytes(file); - switch (dtype.GetTypeCode()) + char c = body[i]; + if (c == '+' || c == '-') { - // %foreach supported_dtypes, supported_dtypes_lowercase% - // case NPTypeCode.#1: return new NDArray(new ArraySlice<#2>(UnmanagedMemoryBlock<#2>.FromBuffer(bytes, false))); - // % - // default: - // throw new NotSupportedException(); - case NPTypeCode.Boolean: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.Byte: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.SByte: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.Int16: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.UInt16: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.Int32: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.UInt32: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.Int64: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.UInt64: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.Char: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.Half: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.Double: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.Single: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.Decimal: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - case NPTypeCode.Complex: return new NDArray(new ArraySlice(UnmanagedMemoryBlock.FromBuffer(bytes, false))); - default: - throw new NotSupportedException(); + char prev = body[i - 1]; + if (prev == 'e' || prev == 'E') continue; // exponent sign + return i; } } + return -1; } } } diff --git a/test/NumSharp.UnitTest/Casting/NDArray.fromfile.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.fromfile.Test.cs new file mode 100644 index 00000000..381c6448 --- /dev/null +++ b/test/NumSharp.UnitTest/Casting/NDArray.fromfile.Test.cs @@ -0,0 +1,257 @@ +using System; +using System.IO; +using System.Linq; +using System.Numerics; +using System.Text; +using Microsoft.VisualStudio.TestTools.UnitTesting; +using NumSharp; + +namespace NumSharp.UnitTest.Casting +{ + /// + /// NumPy-parity tests for and its + /// Stream / default-dtype overloads — np.fromfile(file, dtype=float, count=-1, sep='', offset=0). + /// + /// Verified against NumPy 2.4.2. Binary reads reinterpret raw bytes (count/offset honored, reads what + /// is present past EOF); text reads parse sep-separated items in NumPy's whitespace-tolerant way, + /// wrapping integers into the dtype and rejecting malformed data. Round-trips with + /// . + /// + [TestClass] + public class FromfileTests + { + private static readonly NPTypeCode[] AllDtypes = + { + NPTypeCode.Boolean, NPTypeCode.Byte, NPTypeCode.SByte, NPTypeCode.Int16, NPTypeCode.UInt16, + NPTypeCode.Int32, NPTypeCode.UInt32, NPTypeCode.Int64, NPTypeCode.UInt64, NPTypeCode.Char, + NPTypeCode.Half, NPTypeCode.Single, NPTypeCode.Double, NPTypeCode.Decimal, NPTypeCode.Complex + }; + + private static string TempWithText(string content) + { + string p = Path.Combine(Path.GetTempPath(), "ns_ff_" + Guid.NewGuid().ToString("N") + ".txt"); + File.WriteAllText(p, content); + return p; + } + + // ---- binary: count / offset / EOF / default dtype / Stream ------------------ + + [TestMethod] + public void Binary_Count_Offset_EofClamp() + { + string p = Path.Combine(Path.GetTempPath(), "ns_ff_bin.bin"); + np.arange(12).astype(NPTypeCode.Int32).tofile(p); + try + { + CollectionAssert.AreEqual(new[] { 0, 1, 2, 3 }, np.fromfile(p, NPTypeCode.Int32, count: 4).ToArray()); + CollectionAssert.AreEqual(Enumerable.Range(2, 10).ToArray(), np.fromfile(p, NPTypeCode.Int32, offset: 8).ToArray()); + CollectionAssert.AreEqual(new[] { 2, 3 }, np.fromfile(p, NPTypeCode.Int32, count: 2, offset: 8).ToArray()); + // count past EOF reads what is present (no error). + Assert.AreEqual(12, np.fromfile(p, NPTypeCode.Int32, count: 100).size); + } + finally { File.Delete(p); } + } + + [TestMethod] + public void Binary_DefaultDtype_IsFloat64() + { + string p = Path.Combine(Path.GetTempPath(), "ns_ff_def.bin"); + np.arange(4).astype(NPTypeCode.Double).tofile(p); + try + { + var r = np.fromfile(p); // no dtype -> float64 + Assert.AreEqual(NPTypeCode.Double, r.typecode); + CollectionAssert.AreEqual(new double[] { 0, 1, 2, 3 }, r.ToArray()); + } + finally { File.Delete(p); } + } + + [TestMethod] + public void Binary_StreamOverload_ReadsFromCurrentPosition() + { + string p = Path.Combine(Path.GetTempPath(), "ns_ff_stream.bin"); + np.arange(6).astype(NPTypeCode.Int32).tofile(p); + try + { + using var fs = File.OpenRead(p); + fs.Seek(4, SeekOrigin.Begin); + var r = np.fromfile(fs, NPTypeCode.Int32, count: 2); + CollectionAssert.AreEqual(new[] { 1, 2 }, r.ToArray()); + Assert.IsTrue(fs.CanRead, "stream must be left open"); + } + finally { File.Delete(p); } + } + + [TestMethod] + public void Binary_Empty_ReturnsEmpty() + { + string p = Path.Combine(Path.GetTempPath(), "ns_ff_empty.bin"); + File.WriteAllBytes(p, Array.Empty()); + try { Assert.AreEqual(0, np.fromfile(p, NPTypeCode.Int32).size); } + finally { File.Delete(p); } + } + + // ---- text: separators & whitespace ------------------------------------------ + + [TestMethod] + public void Text_Sep_Whitespace_TrailingSep() + { + string p = TempWithText("1,2,3,4,5"); + try { CollectionAssert.AreEqual(new[] { 1, 2, 3, 4, 5 }, np.fromfile(p, NPTypeCode.Int32, sep: ",").ToArray()); } + finally { File.Delete(p); } + + p = TempWithText("1, 2 , 3,4 , 5"); // whitespace around items and separators + try { CollectionAssert.AreEqual(new[] { 1, 2, 3, 4, 5 }, np.fromfile(p, NPTypeCode.Int32, sep: ",").ToArray()); } + finally { File.Delete(p); } + + p = TempWithText("1,2,3,"); // trailing separator ignored + try { CollectionAssert.AreEqual(new[] { 1, 2, 3 }, np.fromfile(p, NPTypeCode.Int32, sep: ",").ToArray()); } + finally { File.Delete(p); } + + p = TempWithText("1 2\t3\n4 5"); // whitespace-only separator splits on any whitespace run + try { CollectionAssert.AreEqual(new[] { 1, 2, 3, 4, 5 }, np.fromfile(p, NPTypeCode.Int32, sep: " ").ToArray()); } + finally { File.Delete(p); } + } + + [TestMethod] + public void Text_Count_LimitsItems() + { + string p = TempWithText("1,2,3,4,5"); + try { CollectionAssert.AreEqual(new[] { 1, 2, 3 }, np.fromfile(p, NPTypeCode.Int32, count: 3, sep: ",").ToArray()); } + finally { File.Delete(p); } + } + + [TestMethod] + public void Text_Float_Scientific_NanInf() + { + string p = TempWithText("1.5,2.25,3.0,0.1"); + try { CollectionAssert.AreEqual(new[] { 1.5, 2.25, 3.0, 0.1 }, np.fromfile(p, NPTypeCode.Double, sep: ",").ToArray()); } + finally { File.Delete(p); } + + p = TempWithText("1.5e3,2.5e-2,1e10"); + try { CollectionAssert.AreEqual(new[] { 1500.0, 0.025, 1e10 }, np.fromfile(p, NPTypeCode.Double, sep: ",").ToArray()); } + finally { File.Delete(p); } + + p = TempWithText("nan,inf,-inf,1.5"); + try + { + var r = np.fromfile(p, NPTypeCode.Double, sep: ",").ToArray(); + Assert.IsTrue(double.IsNaN(r[0]) && double.IsPositiveInfinity(r[1]) && double.IsNegativeInfinity(r[2]) && r[3] == 1.5); + // NumPy's canonical text NaN is the POSITIVE quiet NaN (0x7FF8…), not .NET's default. + Assert.AreEqual(0x7FF8000000000000L, BitConverter.DoubleToInt64Bits(r[0])); + } + finally { File.Delete(p); } + } + + [TestMethod] + public void Text_Integer_WrapsIntoDtype() + { + // "%d"-style parse wraps modulo the dtype width, like NumPy (int8 300 -> 44, uint8 -1 -> 255). + string p = TempWithText("300,44,-1"); + try { CollectionAssert.AreEqual(new sbyte[] { 44, 44, -1 }, np.fromfile(p, NPTypeCode.SByte, sep: ",").ToArray()); } + finally { File.Delete(p); } + + p = TempWithText("-1,256,255"); + try { CollectionAssert.AreEqual(new byte[] { 255, 0, 255 }, np.fromfile(p, NPTypeCode.Byte, sep: ",").ToArray()); } + finally { File.Delete(p); } + } + + [TestMethod] + public void Text_Bool_ParsesAsInt() + { + // NumPy reads bool text as an int (nonzero => True); "True"/"False" would raise. + string p = TempWithText("1,0,2,0"); + try { CollectionAssert.AreEqual(new[] { true, false, true, false }, np.fromfile(p, NPTypeCode.Boolean, sep: ",").ToArray()); } + finally { File.Delete(p); } + } + + [TestMethod] + public void Text_Complex_BareForm_And_ParenthesizedSuperset() + { + // NumPy reads the bare "a+bj" form (whitespace-separated). + string p = TempWithText("1+2j 3-4j 5j"); + try + { + CollectionAssert.AreEqual( + new[] { new Complex(1, 2), new Complex(3, -4), new Complex(0, 5) }, + np.fromfile(p, NPTypeCode.Complex, sep: " ").ToArray()); + } + finally { File.Delete(p); } + + // Superset: NumSharp also reads the parenthesized "(1+2j)" form its own tofile writes + // (NumPy's text reader errors on this, so tofile/fromfile round-trips complex here, unlike NumPy). + p = TempWithText("(1+2j),(3+4j)"); + try + { + CollectionAssert.AreEqual( + new[] { new Complex(1, 2), new Complex(3, 4) }, + np.fromfile(p, NPTypeCode.Complex, sep: ",").ToArray()); + } + finally { File.Delete(p); } + } + + [TestMethod] + public void Text_Empty_ReturnsEmpty() + { + string p = TempWithText(""); + try { Assert.AreEqual(0, np.fromfile(p, NPTypeCode.Int32, sep: ",").size); } + finally { File.Delete(p); } + } + + [TestMethod] + public void Text_Malformed_Throws() + { + string p = TempWithText("1,2,x,4"); + try { Assert.ThrowsException(() => np.fromfile(p, NPTypeCode.Int32, sep: ",")); } + finally { File.Delete(p); } + } + + [TestMethod] + public void Text_OffsetNotPermitted_Throws() + { + string p = TempWithText("1,2,3"); + try { Assert.ThrowsException(() => np.fromfile(p, NPTypeCode.Int32, sep: ",", offset: 4)); } + finally { File.Delete(p); } + } + + // ---- round-trips ------------------------------------------------------------ + + [TestMethod] + public void RoundTrip_Binary_AllDtypes_IncludingViews() + { + foreach (var tc in AllDtypes) + { + var a = np.arange(10).astype(tc)["1:9"]; // offset view -> tofile writes logical C-order + string p = Path.Combine(Path.GetTempPath(), $"ns_ffrt_{tc}.bin"); + try + { + a.tofile(p); + var b = np.fromfile(p, tc); + Assert.AreEqual(a.size, b.size, $"{tc} size"); + CollectionAssert.AreEqual(a.ToByteArray('C'), b.ToByteArray('C'), $"{tc} bytes"); + } + finally { File.Delete(p); } + } + } + + [TestMethod] + public void RoundTrip_Text_IntAndFloatDtypes() + { + // int + float dtypes round-trip through text; bool ("True"/"False") and complex parens are the + // documented asymmetries covered elsewhere. + foreach (var tc in new[] { NPTypeCode.Int16, NPTypeCode.Int32, NPTypeCode.Int64, NPTypeCode.UInt32, + NPTypeCode.Half, NPTypeCode.Single, NPTypeCode.Double }) + { + var a = np.arange(6).astype(tc); + string p = Path.Combine(Path.GetTempPath(), $"ns_ffrtt_{tc}.txt"); + try + { + a.tofile(p, ","); + var b = np.fromfile(p, tc, sep: ","); + CollectionAssert.AreEqual(a.ToByteArray('C'), b.ToByteArray('C'), $"{tc} text round-trip"); + } + finally { File.Delete(p); } + } + } + } +} From 5d38769e17ef949948d9844d000cb8d41e779c64 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 19:07:42 +0300 Subject: [PATCH 21/24] =?UTF-8?q?refactor(casting)!:=20dissolve=20legacy?= =?UTF-8?q?=20NDArray.ToByteArray=20=E2=80=94=20tobytes=20is=20the=20sole?= =?UTF-8?q?=20NumPy=20byte=20export?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Per "Breaking Changes OK to match NumPy": remove the pre-parity backward-compat shim so the NumPy-named tobytes is the one and only byte-export API. Source - NdArray.ToByteArray.cs -> NdArray.tobytes.cs: move the implementation into tobytes(char order = 'C') and DELETE the public ToByteArray(char) method (tobytes was previously just a thin alias delegating to it). Body is byte-for-byte identical, so behavior is unchanged; this is a pure rename/removal. - np.tofile.cs: doc-comment reference ToByteArray('C') -> tobytes('C'). Old-implementation test removed (user-directed) - Delete test/NumSharp.UnitTest/APIs/np.tofromfile.Test.cs — the 2019-era uint8/uint16 tofile<->fromfile round-trip. Its coverage is fully subsumed (all 15 dtypes + views) by NDArray.tofile.Test.cs (RoundTrip_*_AllDtypes) and NDArray.fromfile.Test.cs (RoundTrip_Binary_AllDtypes_IncludingViews). Tests migrated ToByteArray -> tobytes (coverage preserved, not reduced) - NDArray.ToByteArray.Test.cs -> NDArray.tobytes.Contract.Test.cs (class ToByteArrayTests -> TobytesContractTests): the C6 non-contiguous-view regression suite, unchanged in substance. - NDArray.tobytes.Order.Test.cs: DefaultOrder_IsC_AndAliasMatches -> DefaultOrder_IsC (the alias-equality assertions are moot once the alias is gone; the default==C check stays). - NDArray.ToArray.Test.cs, NDArray.tofile.Test.cs, NDArray.fromfile.Test.cs, NumpyByteContractTests.cs: call sites updated to tobytes. Note: fromfile's Type + NPTypeCode overload pair is KEPT — it is the established NumSharp idiom (frombuffer carries the identical pair) and NumPy's fromfile(file, dtype, ...) signature is a superset of the old 2-arg call, so it cannot be "de-compatibilized" without breaking parity. Validation: net10.0 and net8.0 both Passed 11342 / Failed 0 / Skipped 11 (CI filter TestCategory!=OpenBugs and !=HighMemory and !=LargeMemoryTest). --- src/NumSharp.Core/APIs/np.tofile.cs | 2 +- ...rray.ToByteArray.cs => NdArray.tobytes.cs} | 17 ++----- .../APIs/np.tofromfile.Test.cs | 49 ------------------- .../Casting/NDArray.ToArray.Test.cs | 4 +- .../Casting/NDArray.fromfile.Test.cs | 4 +- ...st.cs => NDArray.tobytes.Contract.Test.cs} | 32 ++++++------ .../Casting/NDArray.tobytes.Order.Test.cs | 9 ++-- .../Casting/NDArray.tofile.Test.cs | 10 ++-- .../Interop/NumpyByteContractTests.cs | 20 ++++---- 9 files changed, 43 insertions(+), 104 deletions(-) rename src/NumSharp.Core/Casting/{NdArray.ToByteArray.cs => NdArray.tobytes.cs} (81%) delete mode 100644 test/NumSharp.UnitTest/APIs/np.tofromfile.Test.cs rename test/NumSharp.UnitTest/Casting/{NDArray.ToByteArray.Test.cs => NDArray.tobytes.Contract.Test.cs} (89%) diff --git a/src/NumSharp.Core/APIs/np.tofile.cs b/src/NumSharp.Core/APIs/np.tofile.cs index 423c6bae..565e60e2 100644 --- a/src/NumSharp.Core/APIs/np.tofile.cs +++ b/src/NumSharp.Core/APIs/np.tofile.cs @@ -17,7 +17,7 @@ public partial class NDArray /// A filename. The file is created (truncated if it exists). /// /// Separator between items for text output. If "" (empty, the default) a binary file is - /// written, equivalent to stream.Write(a.ToByteArray('C')). + /// written, equivalent to stream.Write(a.tobytes('C')). /// /// /// Python-style % format string for text output (ignored in binary mode). Each entry diff --git a/src/NumSharp.Core/Casting/NdArray.ToByteArray.cs b/src/NumSharp.Core/Casting/NdArray.tobytes.cs similarity index 81% rename from src/NumSharp.Core/Casting/NdArray.ToByteArray.cs rename to src/NumSharp.Core/Casting/NdArray.tobytes.cs index ac9e0caf..182ad7c0 100644 --- a/src/NumSharp.Core/Casting/NdArray.ToByteArray.cs +++ b/src/NumSharp.Core/Casting/NdArray.tobytes.cs @@ -5,9 +5,9 @@ namespace NumSharp public partial class NDArray { /// - /// Copy the array's elements into a newly allocated byte array, honoring the requested - /// memory . Mirrors NumPy's ndarray.tobytes(order='C'): the - /// result is the logical array (strides, offset and broadcasting resolved), NOT the + /// Construct a byte array containing the raw data bytes of the array in the requested memory + /// (default C-order). Mirrors NumPy's ndarray.tobytes(order='C'): + /// the result is the logical array (strides, offset and broadcasting resolved), NOT the /// raw underlying buffer. A view whose memory does not already lay its logical elements out /// in the requested order (sliced/strided/transposed/broadcast, or C-order requested on an /// F-contiguous view and vice-versa) is materialized into a fresh contiguous buffer first, @@ -26,7 +26,7 @@ public partial class NDArray /// A fresh, detached array of length size * dtypesize. /// Thrown when is not one of C/F/A/K. /// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tobytes.html - public byte[] ToByteArray(char order = 'C') + public byte[] tobytes(char order = 'C') { if (size == 0) return System.Array.Empty(); @@ -72,14 +72,5 @@ public byte[] ToByteArray(char order = 'C') return bytes; } } - - /// - /// NumPy-named alias of : construct a byte array containing the - /// raw data bytes of the array in the requested (default C-order). - /// - /// 'C' (default), 'F', 'A', or 'K' — see . - /// A fresh, detached array of length size * dtypesize. - /// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tobytes.html - public byte[] tobytes(char order = 'C') => ToByteArray(order); } } diff --git a/test/NumSharp.UnitTest/APIs/np.tofromfile.Test.cs b/test/NumSharp.UnitTest/APIs/np.tofromfile.Test.cs deleted file mode 100644 index d30bf0e2..00000000 --- a/test/NumSharp.UnitTest/APIs/np.tofromfile.Test.cs +++ /dev/null @@ -1,49 +0,0 @@ -using Microsoft.VisualStudio.TestTools.UnitTesting; -using NumSharp; -using System; -using System.Collections.Generic; -using System.Text; -using System.Linq; - -namespace NumSharp.UnitTest.APIs -{ - [TestClass] - public class NumpyToFromFileTest : TestClass - { - [TestMethod] - public void NumpyToFromFileTestByte1() - { - var testString = "Hallo World!"; - byte[] rawData = Encoding.ASCII.GetBytes(testString); - - - NDArray ndArray = new NDArray(rawData); - - var testFileName = @"test." + nameof(NumpyToFromFileTestByte1); - ndArray.tofile(testFileName); - var loadedArray = np.fromfile(testFileName, np.uint8); - - Assert.AreEqual(np.uint8, loadedArray.dtype); - AssertAreEqual(ndArray.shape, loadedArray.shape); - AssertAreEqual(rawData, loadedArray.Array); - } - - [TestMethod] - public void NumpyToFromFileTestUShort1() - { - var testString = "Hallo World!"; - var testData = Encoding.ASCII.GetBytes(testString); - ushort[] testArray = Array.ConvertAll(testData, d => (ushort)d); - - NDArray ndArray = new NDArray(testArray); - - var testFileName = @"test." + nameof(NumpyToFromFileTestUShort1); - ndArray.tofile(testFileName); - var loadedArray = np.fromfile(testFileName, np.uint16); - - Assert.AreEqual(np.uint16, loadedArray.dtype); - AssertAreEqual(ndArray.shape, loadedArray.shape); - AssertAreEqual(testArray, loadedArray.Array); - } - } -} diff --git a/test/NumSharp.UnitTest/Casting/NDArray.ToArray.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.ToArray.Test.cs index 3ceef86a..26efebae 100644 --- a/test/NumSharp.UnitTest/Casting/NDArray.ToArray.Test.cs +++ b/test/NumSharp.UnitTest/Casting/NDArray.ToArray.Test.cs @@ -22,10 +22,10 @@ public void ToDotNetArray() } [TestMethod] - public void ToByteArray() + public void Tobytes() { var nd = np.array(new int[][] {new int[] {3, 1}, new int[] {2, 1}}); - var bytes = nd.ToByteArray(); + var bytes = nd.tobytes(); bytes.Length.Should().Be((int)(nd.size * sizeof(int))); Assert.IsTrue(Enumerable.SequenceEqual(new byte[] {3, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0}, bytes)); diff --git a/test/NumSharp.UnitTest/Casting/NDArray.fromfile.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.fromfile.Test.cs index 381c6448..227413f0 100644 --- a/test/NumSharp.UnitTest/Casting/NDArray.fromfile.Test.cs +++ b/test/NumSharp.UnitTest/Casting/NDArray.fromfile.Test.cs @@ -228,7 +228,7 @@ public void RoundTrip_Binary_AllDtypes_IncludingViews() a.tofile(p); var b = np.fromfile(p, tc); Assert.AreEqual(a.size, b.size, $"{tc} size"); - CollectionAssert.AreEqual(a.ToByteArray('C'), b.ToByteArray('C'), $"{tc} bytes"); + CollectionAssert.AreEqual(a.tobytes('C'), b.tobytes('C'), $"{tc} bytes"); } finally { File.Delete(p); } } @@ -248,7 +248,7 @@ public void RoundTrip_Text_IntAndFloatDtypes() { a.tofile(p, ","); var b = np.fromfile(p, tc, sep: ","); - CollectionAssert.AreEqual(a.ToByteArray('C'), b.ToByteArray('C'), $"{tc} text round-trip"); + CollectionAssert.AreEqual(a.tobytes('C'), b.tobytes('C'), $"{tc} text round-trip"); } finally { File.Delete(p); } } diff --git a/test/NumSharp.UnitTest/Casting/NDArray.ToByteArray.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.tobytes.Contract.Test.cs similarity index 89% rename from test/NumSharp.UnitTest/Casting/NDArray.ToByteArray.Test.cs rename to test/NumSharp.UnitTest/Casting/NDArray.tobytes.Contract.Test.cs index 8e3d47b5..54390fbd 100644 --- a/test/NumSharp.UnitTest/Casting/NDArray.ToByteArray.Test.cs +++ b/test/NumSharp.UnitTest/Casting/NDArray.tobytes.Contract.Test.cs @@ -8,16 +8,16 @@ namespace NumSharp.UnitTest.Casting { /// - /// Regression + contract tests for . + /// Regression + contract tests for . /// /// NumPy parity: ndarray.tobytes() returns the LOGICAL array in C (row-major) order, /// honoring strides / offset / broadcasting — NOT the raw underlying buffer. Before the C6 fix, - /// ToByteArray() blindly copied Storage.InternalArray.BytesLength from + /// the byte export blindly copied Storage.InternalArray.BytesLength from /// Storage.Address, so a non-contiguous view leaked the whole parent buffer /// (e.g. a 5-element [::2] view of a 10-element array returned 40 bytes of the wrong data). /// [TestClass] - public class ToByteArrayTests + public class TobytesContractTests { private static readonly NPTypeCode[] AllDtypes = { @@ -49,7 +49,7 @@ public class ToByteArrayTests private static void AssertLogical(string name, NDArray nd) { - byte[] actual = nd.ToByteArray(); + byte[] actual = nd.tobytes(); Assert.AreEqual((int)(nd.size * nd.dtypesize), actual.Length, $"{name}: length must equal size*dtypesize"); CollectionAssert.AreEqual(LogicalBytes(nd), actual, $"{name}: bytes must be the logical C-order array"); } @@ -77,7 +77,7 @@ public void Scalar0d_AllDtypes() public void Empty_ReturnsEmptyArray() { foreach (var tc in AllDtypes) - Assert.AreEqual(0, np.arange(0).astype(tc).ToByteArray().Length, $"empty_{tc}"); + Assert.AreEqual(0, np.arange(0).astype(tc).tobytes().Length, $"empty_{tc}"); } // ---- Views: the C6 regressions ---------------------------------------------- @@ -148,10 +148,10 @@ public void Strided_Int32_ExactBytes() { // np.arange(10, dtype=np.int32)[::2].tobytes() -> [0,2,4,6,8] var v = np.arange(10).astype(NPTypeCode.Int32)["::2"]; - Assert.AreEqual(20, v.ToByteArray().Length, "5 elements * 4 bytes, NOT the 40-byte parent buffer"); + Assert.AreEqual(20, v.tobytes().Length, "5 elements * 4 bytes, NOT the 40-byte parent buffer"); CollectionAssert.AreEqual( new byte[] { 0,0,0,0, 2,0,0,0, 4,0,0,0, 6,0,0,0, 8,0,0,0 }, - v.ToByteArray()); + v.tobytes()); } [TestMethod] @@ -161,7 +161,7 @@ public void Transpose_Int32_ExactBytes_COrder() var t = np.arange(6).astype(NPTypeCode.Int32).reshape(2, 3).T; CollectionAssert.AreEqual( new byte[] { 0,0,0,0, 3,0,0,0, 1,0,0,0, 4,0,0,0, 2,0,0,0, 5,0,0,0 }, - t.ToByteArray()); + t.tobytes()); } [TestMethod] @@ -171,7 +171,7 @@ public void Reversed_Int32_ExactBytes() var r = np.arange(5).astype(NPTypeCode.Int32)["::-1"]; CollectionAssert.AreEqual( new byte[] { 4,0,0,0, 3,0,0,0, 2,0,0,0, 1,0,0,0, 0,0,0,0 }, - r.ToByteArray()); + r.tobytes()); } /// The original contract (a pristine contiguous array) must be unchanged. @@ -181,13 +181,13 @@ public void PristineContiguous_LegacyContract_Unchanged() var nd = np.array(new int[][] { new[] { 3, 1 }, new[] { 2, 1 } }); CollectionAssert.AreEqual( new byte[] { 3,0,0,0, 1,0,0,0, 2,0,0,0, 1,0,0,0 }, - nd.ToByteArray()); + nd.tobytes()); } // ---- Round-trip via frombuffer ---------------------------------------------- [TestMethod] - public void RoundTrip_ToByteArray_FromBuffer_AllDtypes_AllViews() + public void RoundTrip_tobytes_FromBuffer_AllDtypes_AllViews() { foreach (var tc in AllDtypes) { @@ -203,21 +203,21 @@ public void RoundTrip_ToByteArray_FromBuffer_AllDtypes_AllViews() }; foreach (var (name, nd) in views) { - byte[] bytes = nd.ToByteArray(); + byte[] bytes = nd.tobytes(); var rebuilt = np.frombuffer(bytes, tc); // A freshly built pristine array re-emits identical bytes -> the view's logical content survived. - CollectionAssert.AreEqual(bytes, rebuilt.ToByteArray(), $"{tc}/{name} round-trip"); + CollectionAssert.AreEqual(bytes, rebuilt.tobytes(), $"{tc}/{name} round-trip"); Assert.AreEqual(nd.size, rebuilt.size, $"{tc}/{name} size"); } } } - /// Mutating a ToByteArray() result must never touch the source (it is a fresh copy). + /// Mutating a tobytes() result must never touch the source (it is a fresh copy). [TestMethod] - public void ToByteArray_IsDetachedCopy() + public void Tobytes_IsDetachedCopy() { var nd = np.arange(4).astype(NPTypeCode.Int32); - byte[] bytes = nd.ToByteArray(); + byte[] bytes = nd.tobytes(); bytes[0] = 0xFF; Assert.AreEqual(0, nd.ToArray()[0], "mutating the byte[] must not affect the source array"); } diff --git a/test/NumSharp.UnitTest/Casting/NDArray.tobytes.Order.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.tobytes.Order.Test.cs index 37faab56..132644cb 100644 --- a/test/NumSharp.UnitTest/Casting/NDArray.tobytes.Order.Test.cs +++ b/test/NumSharp.UnitTest/Casting/NDArray.tobytes.Order.Test.cs @@ -5,8 +5,7 @@ namespace NumSharp.UnitTest.Casting { /// - /// NumPy-parity tests for the order parameter of - /// and its NumPy-named alias . + /// NumPy-parity tests for the order parameter of . /// /// Oracle: NumPy 2.4.2 ndarray.tobytes(order). Exact byte vectors below were captured from /// np.arange(...).astype(...).tobytes(order).hex(). The whole (15 dtype x 18 layout x 4 order) @@ -138,14 +137,12 @@ public void LowercaseOrder_EqualsUppercase_AllDtypes() // ---- API surface ----------------------------------------------------------------- [TestMethod] - public void DefaultOrder_IsC_AndAliasMatches() + public void DefaultOrder_IsC() { foreach (var tc in AllDtypes) { var a = np.arange(6).astype(tc).reshape(2, 3); - CollectionAssert.AreEqual(a.tobytes('C'), a.ToByteArray(), $"ToByteArray()==C {tc}"); - CollectionAssert.AreEqual(a.tobytes('C'), a.tobytes(), $"tobytes()==C {tc}"); - CollectionAssert.AreEqual(a.ToByteArray('F'), a.tobytes('F'), $"alias F {tc}"); + CollectionAssert.AreEqual(a.tobytes('C'), a.tobytes(), $"tobytes()==C {tc}"); } } diff --git a/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs index 3a8c083d..62cf53af 100644 --- a/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs +++ b/test/NumSharp.UnitTest/Casting/NDArray.tofile.Test.cs @@ -47,7 +47,7 @@ private static string Text(NDArray nd, string sep, string format = "%s") public void Binary_NonContiguous_WritesLogicalCOrder_AllDtypes() { // Before the fix, a non-contiguous view leaked its raw parent buffer. tofile must write the - // logical C-order bytes — identical to ToByteArray('C') — for every layout and dtype. + // logical C-order bytes — identical to tobytes('C') — for every layout and dtype. foreach (var tc in AllDtypes) { foreach (var (name, nd) in new (string, NDArray)[] @@ -59,7 +59,7 @@ public void Binary_NonContiguous_WritesLogicalCOrder_AllDtypes() ("offset", np.arange(10).astype(tc)["3:8"]), }) { - CollectionAssert.AreEqual(nd.ToByteArray('C'), Bin(nd), $"{tc}/{name}"); + CollectionAssert.AreEqual(nd.tobytes('C'), Bin(nd), $"{tc}/{name}"); } } } @@ -87,7 +87,7 @@ public void Binary_RoundTrip_FromFile_AllDtypes_FilenameOverload() a.tofile(path); var b = np.fromfile(path, tc); Assert.AreEqual(a.size, b.size, $"{tc} size"); - CollectionAssert.AreEqual(a.ToByteArray('C'), b.ToByteArray('C'), $"{tc} bytes"); + CollectionAssert.AreEqual(a.tobytes('C'), b.tobytes('C'), $"{tc} bytes"); } finally { if (File.Exists(path)) File.Delete(path); } } @@ -248,11 +248,11 @@ public void Stream_IsLeftOpen_And_NoTrailingSeparator() } [TestMethod] - public void Binary_Equals_ToByteArray_ForContiguous() + public void Binary_Equals_Tobytes_ForContiguous() { // sep="" default is the binary path; equals tobytes('C'). var a = np.arange(24).astype(NPTypeCode.Double).reshape(2, 3, 4); - CollectionAssert.AreEqual(a.ToByteArray('C'), Bin(a)); + CollectionAssert.AreEqual(a.tobytes('C'), Bin(a)); } } } diff --git a/test/NumSharp.UnitTest/Interop/NumpyByteContractTests.cs b/test/NumSharp.UnitTest/Interop/NumpyByteContractTests.cs index a5fbc793..681bede0 100644 --- a/test/NumSharp.UnitTest/Interop/NumpyByteContractTests.cs +++ b/test/NumSharp.UnitTest/Interop/NumpyByteContractTests.cs @@ -61,7 +61,7 @@ public void Half_Specials_BitExact_RoundTrip() // float16 bits: nan=0x7e00, inf=0x7c00, -inf=0xfc00, -0=0x8000, min-subnormal=0x0001, max=0x7bff byte[] bits = { 0x00,0x7e, 0x00,0x7c, 0x00,0xfc, 0x00,0x80, 0x01,0x00, 0xff,0x7b }; var h = np.frombuffer(bits, NPTypeCode.Half); - CollectionAssert.AreEqual(bits, h.ToByteArray(), "half bit pattern (incl. NaN payload/subnormal) must survive"); + CollectionAssert.AreEqual(bits, h.tobytes(), "half bit pattern (incl. NaN payload/subnormal) must survive"); var a = h.ToArray(); Assert.IsTrue(Half.IsNaN(a[0])); Assert.IsTrue(Half.IsPositiveInfinity(a[1])); @@ -77,7 +77,7 @@ public void Single_SignalingNaN_Payload_NotQuieted() byte[] sbits = { 0x01, 0x00, 0x80, 0x7f }; var s = np.frombuffer(sbits, NPTypeCode.Single); Assert.IsTrue(float.IsNaN(s.ToArray()[0])); - CollectionAssert.AreEqual(sbits, s.ToByteArray(), "signaling-NaN bits preserved"); + CollectionAssert.AreEqual(sbits, s.tobytes(), "signaling-NaN bits preserved"); } [TestMethod] @@ -86,14 +86,14 @@ public void Double_MinSubnormal_Preserved() byte[] bits = { 0x01, 0, 0, 0, 0, 0, 0, 0 }; // 0x...0001 == double.Epsilon == 5e-324 var d = np.frombuffer(bits, NPTypeCode.Double); Assert.AreEqual(double.Epsilon, d.ToArray()[0]); - CollectionAssert.AreEqual(bits, d.ToByteArray()); + CollectionAssert.AreEqual(bits, d.tobytes()); } [TestMethod] public void NegativeZero_SignBit_Preserved() { var nd = np.array(new[] { -0.0, 0.0 }); - byte[] b = nd.ToByteArray(); + byte[] b = nd.tobytes(); Assert.IsTrue(double.IsNegative(nd.ToArray()[0])); Assert.IsFalse(double.IsNegative(nd.ToArray()[1])); Assert.AreEqual(0x80, b[7], "-0.0 sign bit in the high byte (little-endian)"); @@ -110,7 +110,7 @@ public void UInt64_AboveInt64Max_RoundTrips() Buffer.BlockCopy(u, 0, bytes, 0, bytes.Length); var nd = np.frombuffer(bytes, NPTypeCode.UInt64); CollectionAssert.AreEqual(u, nd.ToArray()); - CollectionAssert.AreEqual(bytes, nd.ToByteArray()); + CollectionAssert.AreEqual(bytes, nd.tobytes()); } // ---- Complex layout ---------------------------------------------------------- @@ -125,8 +125,8 @@ public void Complex_NaNInf_Parts_BitExact_RoundTrip() new Complex(-0.0, 0.0) }); Assert.AreEqual(16, c.dtypesize, "complex128 == 2 doubles"); - var rt = np.frombuffer(c.ToByteArray(), NPTypeCode.Complex); - CollectionAssert.AreEqual(c.ToByteArray(), rt.ToByteArray()); + var rt = np.frombuffer(c.tobytes(), NPTypeCode.Complex); + CollectionAssert.AreEqual(c.tobytes(), rt.tobytes()); var a = rt.ToArray(); Assert.IsTrue(double.IsNaN(a[0].Real) && a[0].Imaginary == 1.0); Assert.IsTrue(double.IsPositiveInfinity(a[1].Real) && double.IsNegativeInfinity(a[1].Imaginary)); @@ -156,10 +156,10 @@ public void Decimal_Is16Bytes_AndReinterpretAsFloatIsGarbage() var dec = np.array(new[] { 1.5m, -2.5m }); Assert.AreEqual(NPTypeCode.Decimal, dec.typecode); Assert.AreEqual(16, dec.dtypesize, "C# decimal is 16 bytes (flags/hi/mid/lo) — NOT IEEE-754"); - Assert.AreEqual(32, dec.ToByteArray().Length); + Assert.AreEqual(32, dec.tobytes().Length); // Reinterpreting the 16-byte decimal as IEEE float64 (numpy has no decimal dtype) yields garbage. - var asDouble = np.frombuffer(dec.ToByteArray(), NPTypeCode.Double); + var asDouble = np.frombuffer(dec.tobytes(), NPTypeCode.Double); Assert.AreNotEqual(1.5, asDouble.ToArray()[0], 1e-6, "decimal bytes are not IEEE — a raw reinterpret is meaningless; values must be converted"); @@ -174,7 +174,7 @@ public void Bool_NonBinaryBytes_StoragePreserved_LikeNumpy() { // numpy preserves the raw bytes: np.frombuffer(bytes([0,1,2,3,255]), bool).tobytes() == those bytes. var b = np.frombuffer(new byte[] { 0, 1, 2, 3, 255 }, NPTypeCode.Boolean); - CollectionAssert.AreEqual(new byte[] { 0, 1, 2, 3, 255 }, b.ToByteArray()); + CollectionAssert.AreEqual(new byte[] { 0, 1, 2, 3, 255 }, b.tobytes()); } [TestMethod] From a65694964f05a22b6734889a5487680dcf23a0e4 Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 19:26:25 +0300 Subject: [PATCH 22/24] docs(memory): correct UnmanagedMemoryBlock(T*, long) ownership remark MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The XML on the `UnmanagedMemoryBlock(T* ptr, long count)` constructor claimed "Does claim ownership." — the exact opposite of what the constructor does, and a direct contradiction of its own ("Construct as a wrapper around pointer ... without claiming ownership"). The body sets `_disposer = Disposer.Null` (AllocationType.Wrap), so disposing the block never frees `ptr`; the caller owns the memory's lifetime. A reader trusting the old remark could reason their way into a double-free (expecting the block to free it) or a use-after-free (expecting the block NOT to, when it never would). Rewrote the remark to state the non-owning contract explicitly. Comment-only change; no behavioral impact. --- .../Backends/Unmanaged/UnmanagedMemoryBlock`1.cs | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/NumSharp.Core/Backends/Unmanaged/UnmanagedMemoryBlock`1.cs b/src/NumSharp.Core/Backends/Unmanaged/UnmanagedMemoryBlock`1.cs index b9360629..59da716d 100644 --- a/src/NumSharp.Core/Backends/Unmanaged/UnmanagedMemoryBlock`1.cs +++ b/src/NumSharp.Core/Backends/Unmanaged/UnmanagedMemoryBlock`1.cs @@ -49,7 +49,12 @@ public UnmanagedMemoryBlock(long count) /// /// /// The length in objects of and not in bytes. - /// Does claim ownership. + /// + /// Does NOT claim ownership: wraps with the non-owning + /// Disposer.Null marker (AllocationType.Wrap), so disposing this block + /// never frees . The caller remains responsible for the + /// lifetime of the underlying memory. + /// [MethodImpl(Optimize)] public UnmanagedMemoryBlock(T* ptr, long count) { From 64c912ebfb9fa98ba6c7c112629c964de8d310ec Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 19:26:35 +0300 Subject: [PATCH 23/24] test(casting): pin GetData/ToArray dtype-mismatch semantics MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Regression guards for typed data extraction on a dtype MISMATCH, pinning NumSharp's contract against the Numpy.NET interop finding "C3": there, `NDarray.GetData()` on an int64 array silently REINTERPRETS the raw buffer as int32 and truncates to the element count ([1, 5e9, -1] -> [1, 0, 705032704], i.e. numpy's `a.view(np.int32)[:3]` byte garbage) — data corruption. NumSharp must never behave that way. The tests lock in: * NDArray.GetData() performs a VALUE CAST equal to numpy's `a.astype(...)` — int64->int32 truncates by value ([1, 705032704, -1], NOT the reinterpret [1, 0, 705032704]); float64->int32 truncates toward zero with overflow wrap ([1, -2, -2147483648]); int32->int64 widens; matching dtype is a passthrough. * NDArray.ToArray() is STRICT and throws ArrayTypeMismatchException on mismatch (no silent conversion). Oracle values produced with NumPy 2.4.2. Category: normal (green) — these document the intended, correct behavior, not an OpenBug. --- .../NDArray.GetData.TypeMismatch.Test.cs | 90 +++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 test/NumSharp.UnitTest/Casting/NDArray.GetData.TypeMismatch.Test.cs diff --git a/test/NumSharp.UnitTest/Casting/NDArray.GetData.TypeMismatch.Test.cs b/test/NumSharp.UnitTest/Casting/NDArray.GetData.TypeMismatch.Test.cs new file mode 100644 index 00000000..9047e4da --- /dev/null +++ b/test/NumSharp.UnitTest/Casting/NDArray.GetData.TypeMismatch.Test.cs @@ -0,0 +1,90 @@ +using Microsoft.VisualStudio.TestTools.UnitTesting; +using System; +using System.Linq; +using AwesomeAssertions; + +namespace NumSharp.UnitTest +{ + /// + /// Regression guards for typed data extraction on a dtype MISMATCH. + /// + /// Context (interop finding "C3"): Numpy.NET's NDarray.GetData<int>() on an int64 array + /// silently REINTERPRETS the raw buffer as int32 and truncates to the element count, e.g. + /// [1, 5_000_000_000, -1] -> [1, 0, 705032704] — which is numpy's + /// a.view(np.int32)[:3], i.e. raw byte garbage, NOT a value conversion. That is data corruption. + /// + /// NumSharp must never behave that way. The contract these tests pin: + /// * performs a VALUE CAST that matches numpy's a.astype(...) exactly. + /// * is STRICT and throws on mismatch. + /// Oracle values were produced with NumPy 2.4.2. + /// + [TestClass] + public class GetDataTypeMismatchTests + { + private static int[] GetInts(NDArray nd) + { + var g = nd.GetData(); + var r = new int[nd.size]; + for (int i = 0; i < r.Length; i++) r[i] = g[i]; + return r; + } + + private static long[] GetLongs(NDArray nd) + { + var g = nd.GetData(); + var r = new long[nd.size]; + for (int i = 0; i < r.Length; i++) r[i] = g[i]; + return r; + } + + [TestMethod] + public void GetData_Int64ToInt32_ValueCasts_NotReinterpret() + { + var i64 = np.array(new long[] { 1, 5_000_000_000L, -1 }); + i64.typecode.Should().Be(NPTypeCode.Int64); + + var got = GetInts(i64); + + // numpy: np.array([1, 5_000_000_000, -1], np.int64).astype(np.int32).tolist() == [1, 705032704, -1] + got.Should().Equal(1, 705032704, -1); + + // Must NOT equal numpy's a.view(np.int32)[:3] == [1, 0, 705032704] — the Numpy.NET "C3" reinterpret bug. + got.Should().NotEqual(new[] { 1, 0, 705032704 }); + } + + [TestMethod] + public void GetData_Float64ToInt32_TruncatesTowardZero_MatchesNumpyAstype() + { + var f64 = np.array(new double[] { 1.9, -2.9, 3_500_000_000.0 }); + + // numpy: np.array([1.9, -2.9, 3.5e9]).astype(np.int32).tolist() == [1, -2, -2147483648] + GetInts(f64).Should().Equal(1, -2, -2147483648); + } + + [TestMethod] + public void GetData_Int32ToInt64_Widens_MatchesNumpyAstype() + { + var i32 = np.array(new int[] { 1, 2, -3 }); + + // numpy: np.array([1, 2, -3], np.int32).astype(np.int64).tolist() == [1, 2, -3] + GetLongs(i32).Should().Equal(1L, 2L, -3L); + } + + [TestMethod] + public void GetData_MatchingType_ReturnsValuesUnchanged() + { + var i64 = np.array(new long[] { 1, 5_000_000_000L, -1 }); + + GetLongs(i64).Should().Equal(1L, 5_000_000_000L, -1L); + } + + [TestMethod] + public void ToArray_TypeMismatch_Throws() + { + var i64 = np.array(new long[] { 1, 5_000_000_000L, -1 }); + + Action act = () => i64.ToArray(); + act.Should().Throw(); + } + } +} From 8d5631e1d2a77974fdcdf68da38a937d53f23cfa Mon Sep 17 00:00:00 2001 From: Eli Belash Date: Wed, 1 Jul 2026 19:26:52 +0300 Subject: [PATCH 24/24] docs(website): 15-dtype table, namespace overwrite, TOC links, benchmark coach MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit DocFX site updates: * api/index.md — "Supported Data Types" was stale at 12 numeric types. Corrected to the 15 public array dtypes: added the three that were missing (SByte -> np.int8, Half -> np.float16, Complex -> np.complex128) and a note that NPTypeCode.Empty/String and the Float alias are enum/compat values (Float resolves to Single), not additional array dtypes. * api/overwrites/NumSharp.md — new DocFX overwrite supplying a summary/remarks block for the `NumSharp` namespace landing page (NDArray, the np facade, Shape/Slice/NPTypeCode, NumPyRandom) plus a short broadcasting/slicing example. Wired in via docfx.json: registered under `overwrite`, and excluded from the `content` glob so it is applied as metadata rather than rendered as a standalone page. * toc.yml — added top-level "Benchmarks" (the dashboard page) and "Source Code" (GitHub) entries. * docs/benchmarks-dashboard.md — added the "Click Here to see breakdown" onboarding coach: a one-time, localStorage-remembered bubble that points first-time viewers at the interactive breakdown targets (2x-5x status band, Reduction suite row, dtype-heatmap tail cell). It is IntersectionObserver-driven, keyboard-dismissable, degrades gracefully when storage is disabled, and self-dismisses after ~5.6s. Also removed the now-defunct `.guide-formula` box and folded its content into the "Reading Ratios" prose (ratio = NumPy / NumSharp, higher is better), matching the repo-wide NPY/NS convention. * images/benchmark-dashboard.png — dashboard screenshot asset (also used by the README). --- docs/website-src/api/index.md | 8 +- docs/website-src/api/overwrites/NumSharp.md | 27 ++ docs/website-src/docfx.json | 7 +- docs/website-src/docs/benchmarks-dashboard.md | 244 +++++++++++++++--- docs/website-src/toc.yml | 4 + 5 files changed, 245 insertions(+), 45 deletions(-) create mode 100644 docs/website-src/api/overwrites/NumSharp.md diff --git a/docs/website-src/api/index.md b/docs/website-src/api/index.md index df6fc676..bc0298e3 100644 --- a/docs/website-src/api/index.md +++ b/docs/website-src/api/index.md @@ -361,12 +361,13 @@ These types are for advanced users extending NumSharp or understanding its inter ## Supported Data Types -NumSharp supports 12 numeric data types: +NumSharp supports 15 public array dtypes: | NPTypeCode | C# Type | NumPy Equivalent | |------------|---------|------------------| | Boolean | `bool` | `np.bool_` | | Byte | `byte` | `np.uint8` | +| SByte | `sbyte` | `np.int8` | | Int16 | `short` | `np.int16` | | UInt16 | `ushort` | `np.uint16` | | Int32 | `int` | `np.int32` | @@ -374,9 +375,14 @@ NumSharp supports 12 numeric data types: | Int64 | `long` | `np.int64` | | UInt64 | `ulong` | `np.uint64` | | Char | `char` | (no equivalent) | +| Half | `System.Half` | `np.float16` | | Single | `float` | `np.float32` | | Double | `double` | `np.float64` | | Decimal | `decimal` | (no equivalent) | +| Complex | `System.Numerics.Complex` | `np.complex128` | + +`NPTypeCode.Empty`, `NPTypeCode.String`, and the `NPTypeCode.Float` alias are enum/compatibility +values, not additional public array dtypes. `Float` resolves to `Single`. --- diff --git a/docs/website-src/api/overwrites/NumSharp.md b/docs/website-src/api/overwrites/NumSharp.md new file mode 100644 index 00000000..dbb89e8d --- /dev/null +++ b/docs/website-src/api/overwrites/NumSharp.md @@ -0,0 +1,27 @@ +--- +uid: NumSharp +summary: Primary NumSharp namespace containing the NDArray type, NumPy-style API facade, dtype metadata, slicing and shape helpers, random state, and NumPy-compatible exception types. +remarks: | + Use this namespace when writing NumSharp code: + + - is the NumPy-style static API, equivalent to Python's `import numpy as np`. + - is the main n-dimensional array container. + - , , and define shape, view, indexing, and dtype behavior. + - exposes NumPy-compatible MT19937 random generation. + + NumSharp targets NumPy 2.x API and behavioral compatibility. Arrays use unmanaged storage, + slicing returns views that share storage, broadcasting follows NumPy shape rules, and the public + NDArray dtype set covers Boolean, Byte, SByte, Int16, UInt16, Int32, UInt32, Int64, UInt64, Char, + Half, Single, Double, Decimal, and Complex. `NPTypeCode.Empty`, `NPTypeCode.String`, and the + `NPTypeCode.Float` alias are enum/compatibility values, not additional public array dtypes; + `Float` resolves to `Single`. + + ```csharp + using NumSharp; + + var a = np.arange(6).reshape(2, 3); + var b = np.array(new[] { 10, 20, 30 }); + NDArray c = a + b; // NumPy-style broadcasting + NDArray col = c[":, 1"]; // Slicing returns a view + ``` +--- diff --git a/docs/website-src/docfx.json b/docs/website-src/docfx.json index 9e52d130..5e452a46 100644 --- a/docs/website-src/docfx.json +++ b/docs/website-src/docfx.json @@ -23,7 +23,7 @@ "content": [ { "files": ["**/*.{md,yml}"], - "exclude": ["_site/**", "obj/**", "scripts/**", "filterConfig.yml", "templates/**"] + "exclude": ["_site/**", "obj/**", "scripts/**", "filterConfig.yml", "templates/**", "api/overwrites/**"] }, { "src": "../../benchmark/history/latest", @@ -31,6 +31,11 @@ "dest": "docs/reports" } ], + "overwrite": [ + { + "files": ["api/overwrites/*.md"] + } + ], "resource": [ { "files": ["images/**"] diff --git a/docs/website-src/docs/benchmarks-dashboard.md b/docs/website-src/docs/benchmarks-dashboard.md index da659149..21040567 100644 --- a/docs/website-src/docs/benchmarks-dashboard.md +++ b/docs/website-src/docs/benchmarks-dashboard.md @@ -187,35 +187,6 @@ html[data-bs-theme="dark"] .ns-bench-dashboard .dtype-count { text-transform: uppercase; } -.ns-bench-dashboard .guide-formula { - background: var(--muted-bg); - border: 1px solid var(--line); - border-radius: 8px; - display: grid; - gap: 0.08rem; - min-width: 0; - padding: 0.48rem 0.68rem; - white-space: nowrap; -} - -.ns-bench-dashboard .guide-formula span { - color: var(--quiet); - font-size: 0.74rem; - font-weight: 700; - text-transform: uppercase; -} - -.ns-bench-dashboard .guide-formula strong { - font-size: 0.92rem; - font-variant-numeric: tabular-nums; -} - -.ns-bench-dashboard .guide-formula small { - color: var(--quiet); - font-size: 0.74rem; - font-variant-numeric: tabular-nums; -} - .ns-bench-dashboard .guide-primer { border: 1px solid color-mix(in srgb, var(--line) 86%, transparent); border-radius: 8px; @@ -1208,6 +1179,55 @@ html[data-bs-theme="dark"] .ns-bench-dashboard .dtype-count { font-weight: 650; } +.ns-bench-dashboard .is-coach-target { + outline: 2px solid color-mix(in srgb, #0ea5e9 70%, var(--panel)); + outline-offset: 3px; + box-shadow: + 0 0 0 4px color-mix(in srgb, #0ea5e9 18%, transparent), + inset 0 0 0 1px color-mix(in srgb, #0ea5e9 36%, transparent); +} + +.ns-breakdown-coach { + --coach-arrow-x: 50%; + background: #0f172a; + border: 1px solid rgba(255, 255, 255, 0.18); + border-radius: 8px; + box-shadow: 0 1rem 2.2rem rgba(2, 6, 23, 0.32); + color: #ffffff; + font-size: 0.84rem; + font-weight: 750; + left: 0; + line-height: 1.25; + max-width: min(16rem, calc(100vw - 1.5rem)); + opacity: 0; + padding: 0.62rem 0.78rem; + pointer-events: none; + position: fixed; + top: 0; + transform: translateY(0.35rem); + transition: opacity 150ms ease, transform 150ms ease; + width: max-content; + z-index: 1085; +} + +.ns-breakdown-coach::before { + background: #0f172a; + border-left: 1px solid rgba(255, 255, 255, 0.18); + border-top: 1px solid rgba(255, 255, 255, 0.18); + content: ""; + height: 0.7rem; + left: var(--coach-arrow-x); + position: absolute; + top: -0.39rem; + transform: translateX(-50%) rotate(45deg); + width: 0.7rem; +} + +.ns-breakdown-coach.is-visible { + opacity: 1; + transform: translateY(0); +} + .tippy-box[data-theme~="ns-bench"] { --ns-tip-bg: var(--bs-body-bg, #ffffff); --ns-tip-fg: var(--bs-body-color, #1f2933); @@ -1470,11 +1490,6 @@ html[data-bs-theme="dark"] .tippy-box[data-theme~="ns-bench"] { gap: 0.55rem; } - .ns-bench-dashboard .guide-formula { - align-self: stretch; - width: 100%; - } - .ns-bench-dashboard .guide-grid { grid-template-columns: 1fr; } @@ -1559,11 +1574,6 @@ html[data-bs-theme="dark"] .tippy-box[data-theme~="ns-bench"] {
Operation cells

Legend & How To Read

-
- Ratio - NumPy / NumSharp - Example: x3 = NumSharp 10s, NumPy 30s -
@@ -1586,7 +1596,7 @@ html[data-bs-theme="dark"] .tippy-box[data-theme~="ns-bench"] {

Reading Ratios

-

Higher is better. 2.00x means NumSharp is twice as fast as NumPy; 0.50x means NumSharp takes about twice as long.

+

Ratio is NumPy / NumSharp. Higher is better: 3.00x means NumSharp took 10s while NumPy took 30s; 0.50x means NumSharp takes about twice as long.

@@ -1611,7 +1621,7 @@ html[data-bs-theme="dark"] .tippy-box[data-theme~="ns-bench"] { - + @@ -1647,7 +1657,7 @@ html[data-bs-theme="dark"] .tippy-box[data-theme~="ns-bench"] { 2.24x 39 / 10
-
+
Reduction 1.81x @@ -1747,7 +1757,7 @@ html[data-bs-theme="dark"] .tippy-box[data-theme~="ns-bench"] {
int320.93x111 rows
int640.75x118 rows
uint640.74x63 rows
-
bool0.30x8 rows
+
bool0.30x8 rows