Merge branch 'flatironinstitute:master' into interp-vectorization

Author: DiamonDinoia

.github/workflows/python_build_win.ps1

@@ -15,7 +15,7 @@ Add-Content -Path make.inc -Value "CXXFLAGS+= -march=x86-64"
 Set-Variable libvcruntime140_a -Value ([IO.Path]::Combine((Split-Path -Path $PYTHON), "libs", 'libvcruntime140.a'))
 Copy-Item -Path .\.github\workflows\libvcruntime140.a -Destination $libvcruntime140_a
 
-python -m pip install --upgrade setuptools wheel numpy pip
+python -m pip install --upgrade setuptools==70.1.1 wheel numpy pip
 if (-not $?) {throw "Failed pip install"}
 
 # mingw gcc compiler pacth to work with python

docs/c_gpu.rst

@@ -5,7 +5,7 @@ There are four steps needed to call cuFINUFFT from C: 1) making a plan, 2) setti
 The simplest case is to call them in order, i.e., 1234.
 However, it is possible to repeat 3 with new strength or coefficient data, or to repeat 2 to choose new nonuniform points in order to do one or more step 3's again, before destroying.
 For instance, 123334 and 1232334 are allowed.
-If non-standard algorithm options are desired, an extra function is needed before making the plan (see bottom of this page).
+If non-standard algorithm options are desired, an extra function is needed before making the plan; see bottom of this page for options.
 
 This API matches very closely that of the plan interface to FINUFFT (in turn modeled on those of FFTW and NFFT).
 Here is the full documentation for these functions.
@@ -289,8 +289,8 @@ This deallocates all arrays inside the ``plan`` struct, freeing all internal mem
 Note: the plan (being just a pointer to the plan struct) is not actually "destroyed"; rather, its internal struct is destroyed.
 There is no need for further deallocation of the plan.
 
-Non-standard options
-~~~~~~~~~~~~~~~~~~~~
+Options for GPU code
+--------------------
 
 The last argument in the above plan stage accepts a pointer to an options structure, which is the same in both single and double precision.
 To create such a structure, use:
@@ -300,7 +300,59 @@ To create such a structure, use:
     cufinufft_opts opts;
     cufinufft_default_opts(&opts);
 
-Then you may change fields of ``opts`` by hand, finally pass ``&opts`` in as the last argument to ``cufinufft_makeplan`` or ``cufinufftf_makeplan``.
-The options fields are currently only documented in the ``include/cufinufft_opts.h``.
+Then you may change fields of ``opts`` by hand, finally pass ``&opts`` in as the last argument to ``cufinufft_makeplan`` or ``cufinufftf_makeplan``. Here are the options, with the important user-controllable ones documented. For their default values, see below.
 
-For examples of this advanced usage, see ``test/cuda/cufinufft*.cu``
+Data handling options
+~~~~~~~~~~~~~~~~~~~~~
+
+**modeord**: Fourier coefficient frequency index ordering; see the CPU option of the same name :ref:`modeord<modeord>`.
+As a reminder, ``modeord=0`` selects increasing frequencies (negative through positive) in each dimension,
+while ``modeord=1`` selects FFT-style ordering starting at zero and wrapping over to negative frequencies half way through.
+
+**gpu_device_id**: Sets the GPU device ID. Leave at default unless you know what you're doing. [To be documented]
+
+Diagnostic options
+~~~~~~~~~~~~~~~~~~
+
+**gpu_spreadinterponly**: if ``0`` do the NUFFT as intended. If ``1``, omit the FFT and kernel FT deconvolution steps and return garbage answers.
+Nonzero value is *only* to be used to aid timing tests (although currently there are no timing codes that exploit this option), and will give wrong or undefined answers for the NUFFT transforms!
+
+
+Algorithm performance options
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+**gpu_method**: Spreader/interpolator algorithm.
+
+* ``gpu_method=0`` : makes an automatic choice of one of the below methods, based on our heuristics.
+
+* ``gpu_method=1`` : uses a nonuniform points-driven method, either unsorted which is referred to as GM in our paper, or sorted which is called GM-sort in our paper, depending on option ``gpu_sort`` below
+  
+* ``gpu_method=2`` : for spreading only, ie, type 1 transforms, uses a shared memory output-block driven method, referred to as SM in our paper. Has no effect for interpolation (type 2 transforms)
+
+* ``gpu_method>2`` : (various upsupported experimental methods due to Melody Shih, not for regular users. Eg ``3`` tests an idea of Paul Springer's to group NU points when spreading, ``4`` is a block gather method of possible interest.)
+
+**gpu_sort**: ``0`` do not sort nonuniform points, ``1`` do sort nonuniform points. Only has an effect when ``gpu_method=1`` (or if this method has been internally chosen when ``gpu_method=0``). Unlike the CPU code, there is no auto-choice since in our experience sorting is fast and always helps. It is possible for structured NU point inputs that ``gpu_sort=0`` may be the faster.
+
+**gpu_kerevalmeth**: ``0`` use direct (reference) kernel evaluation, which is not recommended for speed (however, it allows nonstandard ``opts.upsampfac`` to be used). ``1`` use Horner piecewise polynomial evaluation (recommended, and enforces ``upsampfac=2.0``)
+
+**upsampfac**: set upsampling factor. For the recommended ``kerevalmeth=1`` you must choose the standard ``upsampfac=2.0``. If you are willing to risk a slower kernel evaluation, you may set any ``upsampfac>1.0``, but this is experimental and unsupported.
+
+**gpu_maxsubprobsize**: maximum number of NU points to be handled in a single subproblem in the spreading SM method (``gpu_method=2`` only)
+
+**gpu_{o}binsize{x,y,z}**: various bisizes for sorting (GM-sort) or SM subproblem methods. Values of ``-1`` trigger the heuristically set default values. Leave at default unless you know what you're doing. [To be documented]
+
+**gpu_maxbatchsize**: ``0`` use heuristically defined batch size for vectorized (many-transforms with same NU points) interface, else set this batch size.
+
+**gpu_stream**: CUDA stream to use. Leave at default unless you know what you're doing. [To be documented]
+
+
+For all GPU option default values we refer to the source code in
+``src/cuda/cufinufft.cu:cufinufft_default_opts``):
+
+.. literalinclude:: ../src/cuda/cufinufft.cu
+   :start-after: @gpu_defopts_start
+   :end-before: @gpu_defopts_end
+
+For examples of advanced options-switching usage, see ``test/cuda/cufinufft*.cu`` and ``perftest/cuda/cuperftest.cu``.
+
+You may notice a lack of debugging/timing options in the GPU code. This is to avoid CUDA writing to stdout. Please help us out by adding some of these.

docs/julia.rst

@@ -1,9 +1,10 @@
-Julia interfaces
-================
+Julia interfaces (CPU and GPU)
+==============================
 
-Ludvig af Klinteberg, Libin Lu, and others, have built `FINUFFT.jl <https://github.com/ludvigak/FINUFFT.jl>`_, an interface from the `Julia <https://julialang.org/>`_ language. This package supports 32-bit and 64-bit precision, and automatically downloads and runs pre-built binaries of the FINUFFT library for Linux, macOS, Windows and FreeBSD (for a full list see `finufft_jll <https://github.com/JuliaBinaryWrappers/finufft_jll.jl>`_).
+Principal author Ludvig af Klinteberg and others have built and maintain `FINUFFT.jl <https://github.com/ludvigak/FINUFFT.jl>`_, an interface from the `Julia <https://julialang.org/>`_ language. This official Julia package supports 32-bit and 64-bit precision, now on both CPU and GPU (via `CUDA.jl`), via a common interface.
+The Julia package installation automatically downloads pre-built CPU binaries of the FINUFFT library for Linux, macOS, Windows and FreeBSD (for a full list see `finufft_jll <https://github.com/JuliaBinaryWrappers/finufft_jll.jl>`_), and the GPU binary for Linux (see `cufinufft_jll <https://github.com/JuliaBinaryWrappers/cufinufft_jll.jl>`_).
 
-`FINUFFT.jl` has now (in 2022) itself been wrapped as part of `NFFT.jl <https://juliamath.github.io/NFFT.jl/dev/performance/>`_, which contains an "abstract" interface
+`FINUFFT.jl` has itself been wrapped as part of `NFFT.jl <https://juliamath.github.io/NFFT.jl/dev/performance/>`_, which contains an "abstract" interface
 to any NUFFT in Julia, with FINUFFT as an example.
 Their
 `performance comparison page <https://juliamath.github.io/NFFT.jl/dev/performance/>`_

docs/opts.rst

@@ -1,7 +1,7 @@
 .. _opts:
 
-Options parameters
-==================
+Options parameters (CPU)
+========================
 
 Aside from the mandatory inputs (dimension, type,
 nonuniform points, strengths or coefficients, and, in C++/C/Fortran/MATLAB,
@@ -140,7 +140,7 @@ automatically from call to call in the same executable (incidentally, also in th
 * ``spread_sort=2`` : uses a heuristic to decide whether to sort or not.
 
 The heuristic bakes in empirical findings such as: generally it is not worth sorting in 1D type 2 transforms, or when the number of nonuniform points is small.
-Do not change this from its default unless you obsever.
+Feel free to try experimenting here; if you have highly-structured nonuniform point ordering (such as coming from polar-grid or propeller-type MRI k-points) it may be advantageous not to sort.
 
 **spread_kerevalmeth**: Kernel evaluation method in spreader/interpolator.
 This should not be changed from its default value, unless you are an

makefile

@@ -471,7 +471,7 @@ ifneq ($(MINGW),ON)
 	rm -f $(STATICLIB) $(DYNLIB)
 	rm -f matlab/*.mex*
 	rm -f $(TESTS) test/results/*.out perftest/results/*.out
-	rm -f $(EXAMPLES) $(FE) $(ST) $(STF) $(GTT) $(GTTF)
+	rm -f $(EXAMPLES) $(FE) $(ST) $(STF) $(STA) $(STAF) $(GTT) $(GTTF)
 	rm -f perftest/manysmallprobs
 	rm -f examples/core test/core perftest/core $(FE_DIR)/core
 else
@@ -480,7 +480,7 @@ else
 	del matlab\*.mex*
 	for %%f in ($(subst /,\, $(TESTS))) do ((if exist %%f del %%f) & (if exist %%f.exe del %%f.exe))
 	del test\results\*.out perftest\results\*.out
-	for %%f in ($(subst /,\, $(EXAMPLES)), $(subst /,\,$(FE)), $(subst /,\,$(ST)), $(subst /,\,$(STF)), $(subst /,\,$(GTT)), $(subst /,\,$(GTTF))) do ((if exist %%f del %%f) & (if exist %%f.exe del %%f.exe))
+	for %%f in ($(subst /,\, $(EXAMPLES)), $(subst /,\,$(FE)), $(subst /,\,$(ST)), $(subst /,\,$(STF)), $(subst /,\,$(STA)), $(subst /,\,$(STAF)), $(subst /,\,$(GTT)), $(subst /,\,$(GTTF))) do ((if exist %%f del %%f) & (if exist %%f.exe del %%f.exe))
 	del perftest\manysmallprobs
 	del examples\core, test\core, perftest\core, $(subst /,\, $(FE_DIR))\core
 endif

perftest/compare_spreads.jl

@@ -4,7 +4,7 @@ using CairoMakie
 using JLD2        # for load/save arrays to file
 using UnPack
 
-fnam = "results/master-vs-svec2_gcc114_5700U_nthr1"   # outfile head
+fnam = "/home/alex/numerics/finufft/perftest/results/7-1-24-vs-ivec_gcc114_5700U_nthr1"   # outfile head
 # locations of pair of FINUFFT repos to compare...
 repo1 = "/home/alex/numerics/finufft"
 repo2 = "/home/alex/numerics/nufft/finufft-svec2"

src/cuda/cufinufft.cu

@@ -92,39 +92,36 @@ void cufinufft_default_opts(cufinufft_opts *opts)
     Options with prefix "gpu_" are used for gpu code.
 
     Notes:
-    Values set in this function for different type and dimensions are preferable
-    based on experiments. User can experiement with different settings by
-    replacing them after calling this function.
+    1) Values set in this function for different type and dimensions are preferable
+    based on experiments. User can experiment with different settings by
+    changing them after calling this function.
+    2) Sphinx sucks the below code block into the web docs, hence keep it clean.
 
-    Melody Shih 07/25/19; Barnett 2/5/21.
+    Melody Shih 07/25/19; Barnett 2/5/21, tidied for sphinx 7/2/24.
 */
 {
-  opts->upsampfac = 2.0;
+  // sphinx tag (don't remove): @gpu_defopts_start
+  // data handling opts...
+  opts->modeord = 0;
+  opts->gpu_device_id = 0;
 
-  /* following options are for gpu */
-  opts->gpu_sort = 1; // access nupts in an ordered way for nupts driven method
+  // diagnostic opts...
+  opts->gpu_spreadinterponly = 0;
 
+  // algorithm performance opts...
+  opts->gpu_method = 0;
+  opts->gpu_sort = 1;
+  opts->gpu_kerevalmeth = 1;
+  opts->upsampfac = 2.0;
   opts->gpu_maxsubprobsize = 1024;
   opts->gpu_obinsizex      = -1;
   opts->gpu_obinsizey      = -1;
   opts->gpu_obinsizez      = -1;
-
   opts->gpu_binsizex = -1;
   opts->gpu_binsizey = -1;
   opts->gpu_binsizez = -1;
-
-  opts->gpu_spreadinterponly = 0; // default to do the whole nufft
-
-  opts->gpu_maxbatchsize = 0;     // Heuristically set
+  opts->gpu_maxbatchsize = 0;
   opts->gpu_stream       = cudaStreamDefault;
-
-  opts->gpu_kerevalmeth = 1; // Horner
-
-  opts->gpu_method = 0;      // Auto method (2 for type 1, 2 for type 2).
-
-  // By default, only use device 0
-  opts->gpu_device_id = 0;
-
-  opts->modeord = 0;
+  // sphinx tag (don't remove): @gpu_defopts_end
 }
 }

src/finufft.cpp

@@ -550,7 +550,7 @@ void FINUFFT_DEFAULT_OPTS(finufft_opts *o)
   o->showwarn     = 1;
 
   o->nthreads           = 0;
-  o->fftw               = FFTW_ESTIMATE; //
+  o->fftw               = FFTW_ESTIMATE;
   o->spread_sort        = 2;
   o->spread_kerevalmeth = 1;
   o->spread_kerpad      = 1;

src/spreadinterp.cpp

@@ -752,6 +752,12 @@ void evaluate_kernel_vector(FLT *ker, FLT *args, const finufft_spread_opts &opts
     if (!(opts.flags & TF_OMIT_EVALUATE_EXPONENTIAL))
       for (int i = 0; i < Npad; i++) // Loop 2: Compute exponentials
         ker[i] = exp(ker[i]);
+    if (opts.kerpad) {
+      // padded part should be zero, in spread_subproblem_nd_kernels, there are
+      // out of bound writes to trg arrays
+      for (int i = N; i < Npad; ++i)
+        ker[i] = 0.0;
+    }
   } else {
     for (int i = 0; i < N; i++) // dummy for timing only
       ker[i] = 1.0;

tools/finufft/build-wheels-linux.sh

@@ -25,7 +25,6 @@ versions=("cp36-cp36m"
           "cp310-cp310"
           "cp311-cp311"
           "cp312-cp312"
-          "pp38-pypy38_pp73"
           "pp39-pypy39_pp73")
 
 pys=()