Merge pull request #223 from IntelPython/feature/add_npbench_benchmarks

Add npbench benchmarks

Author: ZzEeKkAa

dpbench/benchmarks/npbench/azimint_hist/azimint_hist.py

@@ -0,0 +1,13 @@
+# SPDX-FileCopyrightText: 2014 Jérôme Kieffer et al.
+# SPDX-FileCopyrightText: 2021 ETH Zurich and the NPBench authors
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+
+def initialize(N):
+    from numpy.random import default_rng
+
+    rng = default_rng(42)
+    data, radius = rng.random((N,)), rng.random((N,))
+    return data, radius

dpbench/benchmarks/npbench/azimint_hist/azimint_hist_cupy.py

@@ -0,0 +1,20 @@
+# SPDX-FileCopyrightText: 2014 Jérôme Kieffer et al.
+# SPDX-FileCopyrightText: 2021 ETH Zurich and the NPBench authors
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+
+"""
+Jérôme Kieffer and Giannis Ashiotis. Pyfai: a python library for
+high performance azimuthal integration on gpu, 2014. In Proceedings of the
+7th European Conference on Python in Science (EuroSciPy 2014).
+"""
+
+import cupy as np
+
+
+def azimint_hist(data, radius, npt):
+    histu = np.histogram(radius, npt)[0]
+    histw = np.histogram(radius, npt, weights=data)[0]
+    return histw / histu

dpbench/benchmarks/npbench/azimint_hist/azimint_hist_dace.py

@@ -0,0 +1,75 @@
+# SPDX-FileCopyrightText: 2014 Jérôme Kieffer et al.
+# SPDX-FileCopyrightText: 2021 ETH Zurich and the NPBench authors
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+
+"""
+Jérôme Kieffer and Giannis Ashiotis. Pyfai: a python library for
+high performance azimuthal integration on gpu, 2014. In Proceedings of the
+7th European Conference on Python in Science (EuroSciPy 2014).
+"""
+
+import dace as dc
+import numpy as np
+
+N, bins, npt = (dc.symbol(s, dtype=dc.int64) for s in ("N", "bins", "npt"))
+
+
+@dc.program
+def get_bin_edges(a: dc.float64[N], bin_edges: dc.float64[bins + 1]):
+    a_min = np.amin(a)
+    a_max = np.amax(a)
+    delta = (a_max - a_min) / bins
+    for i in dc.map[0:bins]:
+        bin_edges[i] = a_min + i * delta
+
+    bin_edges[bins] = a_max  # Avoid roundoff error on last point
+
+
+@dc.program
+def compute_bin(x: dc.float64, bin_edges: dc.float64[bins + 1]):
+    # assuming uniform bins for now
+    a_min = bin_edges[0]
+    a_max = bin_edges[bins]
+    return dc.int64(bins * (x - a_min) / (a_max - a_min))
+
+
+@dc.program
+def histogram(a: dc.float64[N], bin_edges: dc.float64[bins + 1]):
+    hist = np.ndarray((bins,), dtype=np.int64)
+    hist[:] = 0
+    get_bin_edges(a, bin_edges)
+
+    for i in dc.map[0:N]:
+        bin = min(compute_bin(a[i], bin_edges), bins - 1)
+        hist[bin] += 1
+
+    return hist
+
+
+@dc.program
+def histogram_weights(
+    a: dc.float64[N], bin_edges: dc.float64[bins + 1], weights: dc.float64[N]
+):
+    hist = np.ndarray((bins,), dtype=weights.dtype)
+    hist[:] = 0
+    get_bin_edges(a, bin_edges)
+
+    for i in dc.map[0:N]:
+        bin = min(compute_bin(a[i], bin_edges), bins - 1)
+        hist[bin] += weights[i]
+
+    return hist
+
+
+@dc.program
+def azimint_hist(data: dc.float64[N], radius: dc.float64[N]):
+    # histu = np.histogram(radius, npt)[0]
+    bin_edges_u = np.ndarray((npt + 1,), dtype=np.float64)
+    histu = histogram(radius, bin_edges_u)
+    # histw = np.histogram(radius, npt, weights=data)[0]
+    bin_edges_w = np.ndarray((npt + 1,), dtype=np.float64)
+    histw = histogram_weights(radius, bin_edges_w, data)
+    return histw / histu

dpbench/benchmarks/npbench/azimint_hist/azimint_hist_legate.py

@@ -0,0 +1,19 @@
+# SPDX-FileCopyrightText: 2014 Jérôme Kieffer et al.
+# SPDX-FileCopyrightText: 2021 ETH Zurich and the NPBench authors
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Jérôme Kieffer and Giannis Ashiotis. Pyfai: a python library for
+high performance azimuthal integration on gpu, 2014. In Proceedings of the
+7th European Conference on Python in Science (EuroSciPy 2014).
+"""
+
+import legate.numpy as np
+
+
+def azimint_hist(data, radius, npt):
+    histu = np.histogram(radius, npt)[0]
+    histw = np.histogram(radius, npt, weights=data)[0]
+    return histw / histu

dpbench/benchmarks/npbench/azimint_hist/azimint_hist_numba_n.py

@@ -0,0 +1,61 @@
+# SPDX-FileCopyrightText: 2014 Jérôme Kieffer et al.
+# SPDX-FileCopyrightText: 2021 ETH Zurich and the NPBench authors
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Jérôme Kieffer and Giannis Ashiotis. Pyfai: a python library for
+high performance azimuthal integration on gpu, 2014. In Proceedings of the
+7th European Conference on Python in Science (EuroSciPy 2014).
+"""
+
+import numba as nb
+import numpy as np
+
+
+@nb.jit(nopython=True, parallel=False, fastmath=True)
+def get_bin_edges(a, bins):
+    bin_edges = np.zeros((bins + 1,), dtype=np.float64)
+    a_min = a.min()
+    a_max = a.max()
+    delta = (a_max - a_min) / bins
+    for i in range(bin_edges.shape[0]):
+        bin_edges[i] = a_min + i * delta
+
+    bin_edges[-1] = a_max  # Avoid roundoff error on last point
+    return bin_edges
+
+
+@nb.jit(nopython=True, fastmath=True)
+def compute_bin(x, bin_edges):
+    # assuming uniform bins for now
+    n = bin_edges.shape[0] - 1
+    a_min = bin_edges[0]
+    a_max = bin_edges[-1]
+
+    # special case to mirror NumPy behavior for last bin
+    if x == a_max:
+        return n - 1  # a_max always in last bin
+
+    return int(n * (x - a_min) / (a_max - a_min))
+
+
+@nb.jit(nopython=True, parallel=False, fastmath=True)
+def histogram(a, bins, weights):
+    hist = np.zeros((bins,), dtype=a.dtype)
+    bin_edges = get_bin_edges(a, bins)
+
+    for i in range(a.shape[0]):
+        bin = compute_bin(a[i], bin_edges)
+        hist[bin] += weights[i]
+
+    return hist, bin_edges
+
+
+@nb.jit(nopython=True, parallel=False, fastmath=True)
+def azimint_hist(data, radius, npt):
+    histu = np.histogram(radius, npt)[0]
+    # histw = np.histogram(radius, npt, weights=data)[0]
+    histw = histogram(radius, npt, weights=data)[0]
+    return histw / histu

dpbench/benchmarks/npbench/azimint_hist/azimint_hist_numba_np.py

@@ -0,0 +1,61 @@
+# SPDX-FileCopyrightText: 2014 Jérôme Kieffer et al.
+# SPDX-FileCopyrightText: 2021 ETH Zurich and the NPBench authors
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Jérôme Kieffer and Giannis Ashiotis. Pyfai: a python library for
+high performance azimuthal integration on gpu, 2014. In Proceedings of the
+7th European Conference on Python in Science (EuroSciPy 2014).
+"""
+
+import numba as nb
+import numpy as np
+
+
+@nb.jit(nopython=True, parallel=True, fastmath=True)
+def get_bin_edges_parallel(a, bins):
+    bin_edges = np.zeros((bins + 1,), dtype=np.float64)
+    a_min = a.min()
+    a_max = a.max()
+    delta = (a_max - a_min) / bins
+    for i in range(bin_edges.shape[0]):
+        bin_edges[i] = a_min + i * delta
+
+    bin_edges[-1] = a_max  # Avoid roundoff error on last point
+    return bin_edges
+
+
+@nb.jit(nopython=True, fastmath=True)
+def compute_bin(x, bin_edges):
+    # assuming uniform bins for now
+    n = bin_edges.shape[0] - 1
+    a_min = bin_edges[0]
+    a_max = bin_edges[-1]
+
+    # special case to mirror NumPy behavior for last bin
+    if x == a_max:
+        return n - 1  # a_max always in last bin
+
+    return int(n * (x - a_min) / (a_max - a_min))
+
+
+@nb.jit(nopython=True, parallel=True, fastmath=True)
+def histogram_parallel(a, bins, weights):
+    hist = np.zeros((bins,), dtype=a.dtype)
+    bin_edges = get_bin_edges_parallel(a, bins)
+
+    for i in range(a.shape[0]):
+        bin = compute_bin(a[i], bin_edges)
+        hist[bin] += weights[i]
+
+    return hist, bin_edges
+
+
+@nb.jit(nopython=True, parallel=True, fastmath=True)
+def azimint_hist(data, radius, npt):
+    histu = np.histogram(radius, npt)[0]
+    # histw = np.histogram(radius, npt, weights=data)[0]
+    histw = histogram_parallel(radius, npt, weights=data)[0]
+    return histw / histu

dpbench/benchmarks/npbench/azimint_hist/azimint_hist_numba_npr.py

@@ -0,0 +1,61 @@
+# SPDX-FileCopyrightText: 2014 Jérôme Kieffer et al.
+# SPDX-FileCopyrightText: 2021 ETH Zurich and the NPBench authors
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Jérôme Kieffer and Giannis Ashiotis. Pyfai: a python library for
+high performance azimuthal integration on gpu, 2014. In Proceedings of the
+7th European Conference on Python in Science (EuroSciPy 2014).
+"""
+
+import numba as nb
+import numpy as np
+
+
+@nb.jit(nopython=True, parallel=True, fastmath=True)
+def get_bin_edges_prange(a, bins):
+    bin_edges = np.zeros((bins + 1,), dtype=np.float64)
+    a_min = a.min()
+    a_max = a.max()
+    delta = (a_max - a_min) / bins
+    for i in nb.prange(bin_edges.shape[0]):
+        bin_edges[i] = a_min + i * delta
+
+    bin_edges[-1] = a_max  # Avoid roundoff error on last point
+    return bin_edges
+
+
+@nb.jit(nopython=True, fastmath=True)
+def compute_bin(x, bin_edges):
+    # assuming uniform bins for now
+    n = bin_edges.shape[0] - 1
+    a_min = bin_edges[0]
+    a_max = bin_edges[-1]
+
+    # special case to mirror NumPy behavior for last bin
+    if x == a_max:
+        return n - 1  # a_max always in last bin
+
+    return int(n * (x - a_min) / (a_max - a_min))
+
+
+@nb.jit(nopython=True, parallel=True, fastmath=True)
+def histogram_prange(a, bins, weights):
+    hist = np.zeros((bins,), dtype=a.dtype)
+    bin_edges = get_bin_edges_prange(a, bins)
+
+    for i in nb.prange(a.shape[0]):
+        bin = compute_bin(a[i], bin_edges)
+        hist[bin] += weights[i]
+
+    return hist, bin_edges
+
+
+@nb.jit(nopython=True, parallel=True, fastmath=True)
+def azimint_hist(data, radius, npt):
+    histu = np.histogram(radius, npt)[0]
+    # histw = np.histogram(radius, npt, weights=data)[0]
+    histw = histogram_prange(radius, npt, weights=data)[0]
+    return histw / histu

dpbench/benchmarks/npbench/azimint_hist/azimint_hist_numba_o.py

@@ -0,0 +1,21 @@
+# SPDX-FileCopyrightText: 2014 Jérôme Kieffer et al.
+# SPDX-FileCopyrightText: 2021 ETH Zurich and the NPBench authors
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Jérôme Kieffer and Giannis Ashiotis. Pyfai: a python library for
+high performance azimuthal integration on gpu, 2014. In Proceedings of the
+7th European Conference on Python in Science (EuroSciPy 2014).
+"""
+
+import numba as nb
+import numpy as np
+
+
+@nb.jit(nopython=False, forceobj=True, parallel=False, fastmath=True)
+def azimint_hist(data, radius, npt):
+    histu = np.histogram(radius, npt)[0]
+    histw = np.histogram(radius, npt, weights=data)[0]
+    return histw / histu

dpbench/benchmarks/npbench/azimint_hist/azimint_hist_numba_op.py

@@ -0,0 +1,21 @@
+# SPDX-FileCopyrightText: 2014 Jérôme Kieffer et al.
+# SPDX-FileCopyrightText: 2021 ETH Zurich and the NPBench authors
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Jérôme Kieffer and Giannis Ashiotis. Pyfai: a python library for
+high performance azimuthal integration on gpu, 2014. In Proceedings of the
+7th European Conference on Python in Science (EuroSciPy 2014).
+"""
+
+import numba as nb
+import numpy as np
+
+
+@nb.jit(nopython=False, forceobj=True, parallel=True, fastmath=True)
+def azimint_hist(data, radius, npt):
+    histu = np.histogram(radius, npt)[0]
+    histw = np.histogram(radius, npt, weights=data)[0]
+    return histw / histu

dpbench/benchmarks/npbench/azimint_hist/azimint_hist_numpy.py

@@ -0,0 +1,19 @@
+# SPDX-FileCopyrightText: 2014 Jérôme Kieffer et al.
+# SPDX-FileCopyrightText: 2021 ETH Zurich and the NPBench authors
+# SPDX-FileCopyrightText: 2022 - 2023 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Jérôme Kieffer and Giannis Ashiotis. Pyfai: a python library for
+high performance azimuthal integration on gpu, 2014. In Proceedings of the
+7th European Conference on Python in Science (EuroSciPy 2014).
+"""
+
+import numpy as np
+
+
+def azimint_hist(data, radius, npt):
+    histu = np.histogram(radius, npt)[0]
+    histw = np.histogram(radius, npt, weights=data)[0]
+    return histw / histu