{ "cells": [ { "cell_type": "markdown", "id": "aba2ed24-0160-49f3-9001-5d8a09fb23b5", "metadata": {}, "source": [ "# Image decompression and azimuthal integration on the GPU\n", "\n", "This tutorial explains how it is possible to speed-up azimuthal integration by speeding-up the critical part: the data transfer to the GPU.\n", "\n", "For this tutorial, a very recent version of `silx` is needed, newer than fall 2022 (available in release 1.2)\n", "\n", "**Credits:**\n", "\n", "* Thomas Vincent (ESRF) for the HDF5 direct chunk read and the Jupyter-slurm\n", "* Jon Wright (ESRF) for the initial prototype of the bitshuffle-LZ4 decompression on the GPU\n", "* Pierre Paleo (ESRF) for struggling with this kind of stuff with GPUs\n", "\n", "**Nota:** a (fast) GPU is needed for this tutorial with OpenCL properly setup !\n", "\n", "The example taken here is the same as the multithreading example: 4096 frames of Eiger_4M" ] }, { "cell_type": "code", "execution_count": 1, "id": "35d7f634-2c20-4ed0-8e2e-555de196df8d", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "# use `widget` for better user experience; `inline` is for documentation generation" ] }, { "cell_type": "code", "execution_count": 2, "id": "50803786-8f10-46d8-8fdc-405a59c235cf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OpenCL devices:\n", "[0] NVIDIA CUDA: (0,0) NVIDIA RTX A5000, (0,1) Quadro P2200\n", "[1] Portable Computing Language: (1,0) cpu-haswell-AMD Ryzen Threadripper PRO 3975WX 32-Cores\n", "[2] Intel(R) OpenCL: (2,0) AMD Ryzen Threadripper PRO 3975WX 32-Cores" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sys, os, collections, struct, time, resource\n", "import numpy, pyFAI\n", "import h5py, hdf5plugin\n", "from matplotlib.pyplot import subplots\n", "import bitshuffle\n", "import pyopencl.array as cla\n", "import silx\n", "from silx.opencl import ocl\n", "from silx.opencl.codec.bitshuffle_lz4 import BitshuffleLz4\n", "start_time = time.time()\n", "ocl" ] }, { "cell_type": "code", "execution_count": 3, "id": "9e4395dc-8944-4276-ac98-66d8174e48d4", "metadata": {}, "outputs": [], "source": [ "#Here we select the OpenCL device\n", "target = (0,0)" ] }, { "cell_type": "markdown", "id": "8d74f81c-7f0e-4af2-a672-59b5bd972377", "metadata": {}, "source": [ "## Setup the enviroment:\n", "\n", "This is a purely virtual experiment, we will use an Eiger 4M detector with data integrated over 1000 bins. Those parameters can be tuned.\n", "\n", "Random data are generated, to keep this file fairly small, it is generated with small numbers which compress nicely. The speed of the drive where you will put the file is likely to have a huge impact !" ] }, { "cell_type": "code", "execution_count": 4, "id": "c578b3e4-5912-4a02-9b4c-349872469324", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "HDF5PluginConfig(build_config=HDF5PluginBuildConfig(openmp=False, native=False, bmi2=False, sse2=True, ssse3=False, avx2=False, avx512=False, cpp11=True, cpp14=True, cpp20=True, ipp=False, filter_file_extension='.so', embedded_filters=('blosc', 'blosc2', 'bshuf', 'bzip2', 'fcidecomp', 'lz4', 'sperr', 'sz', 'sz3', 'zfp', 'zstd')), registered_filters={'bshuf': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5bshuf.so', 'blosc': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5blosc.so', 'blosc2': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5blosc2.so', 'bzip2': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5bzip2.so', 'fcidecomp': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5fcidecomp.so', 'lz4': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5lz4.so', 'sperr': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5sperr.so', 'sz': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5sz.so', 'sz3': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5sz3.so', 'zfp': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5zfp.so', 'zstd': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5zstd.so'})" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det = pyFAI.detector_factory(\"eiger_4M\")\n", "shape = det.shape\n", "dtype = numpy.dtype(\"uint32\")\n", "filename = \"/tmp/big.h5\"\n", "nbins = 1000\n", "cmp = hdf5plugin.Bitshuffle()\n", "hdf5plugin.get_config()" ] }, { "cell_type": "code", "execution_count": 5, "id": "b883ae1c-0549-40cd-9753-ff2d11cb6448", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of frames the computer can host in memory: 30143.226\n", "Limit process to 64GB memory, i.e. ~ 3800 frames\n" ] } ], "source": [ "mem_bytes = os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES')\n", "target_bytes = 64 * 1<<30 # 64GB\n", "print(f\"Number of frames the computer can host in memory: {mem_bytes/(numpy.prod(shape)*dtype.itemsize):.3f}\")\n", "if os.environ.get('SLURM_MEM_PER_NODE'):\n", " print(f\"Number of frames the computer can host in memory with SLURM restrictions: {int(os.environ['SLURM_MEM_PER_NODE'])*(1<<20)/(numpy.prod(shape)*dtype.itemsize):.3f}\")\n", "elif mem_bytes>target_bytes:\n", " print(\"Limit process to 64GB memory, i.e. ~ 3800 frames\")\n", " soft, hard = resource.getrlimit(resource.RLIMIT_AS)\n", " resource.setrlimit(resource.RLIMIT_AS, (target_bytes, target_bytes))" ] }, { "cell_type": "code", "execution_count": 6, "id": "a2c253af-504f-4909-b333-235c218b00e2", "metadata": {}, "outputs": [], "source": [ "#The computer being limited to 64G of RAM, the number of frames actually possible is 3800.\n", "nbframes = 4096 # slightly larger than the maximum achievable ! Such a dataset should not host in memory." ] }, { "cell_type": "code", "execution_count": 7, "id": "1b5e3e71-4c5a-4c67-96b3-91e602deb027", "metadata": {}, "outputs": [], "source": [ "#Prepare a frame with little count so that it compresses well\n", "geo = {\"detector\": det, \n", " \"wavelength\": 1e-10, \n", " \"rot3\":0} #work around a bug https://github.com/silx-kit/pyFAI/pull/1749\n", "ai = pyFAI.load(geo)\n", "omega = ai.solidAngleArray()\n", "q = numpy.arange(15)\n", "img = ai.calcfrom1d(q, 100/(1+q*q))\n", "frame = numpy.random.poisson(img).astype(dtype)" ] }, { "cell_type": "code", "execution_count": 8, "id": "b4fb2aa7-4158-488f-80e0-690283858f8f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# display the image\n", "fig,ax = subplots()\n", "ax.imshow(frame)" ] }, { "cell_type": "code", "execution_count": 9, "id": "fdcb69d3-da9d-4215-919f-a2fc648b845e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Performances of the different algorithms for azimuthal integration of Eiger 4M image on the CPU\n", "Using algorithm histogram : 589 ms ± 2.32 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "Using algorithm csc : 40.6 ms ± 150 μs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "Using algorithm csr : 25.9 ms ± 385 μs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "Performances of the different algorithms for azimuthal integration of Eiger 4M image on the GPU\n", "Using algorithm csr : 4.84 ms ± 16.7 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "print(\"Performances of the different algorithms for azimuthal integration of Eiger 4M image on the CPU\")\n", "for algo in (\"histogram\", \"csc\", \"csr\"):\n", " print(f\"Using algorithm {algo:10s}:\", end=\" \")\n", " %timeit ai.integrate1d(img, nbins, method=(\"full\", algo, \"cython\"))\n", "print(\"Performances of the different algorithms for azimuthal integration of Eiger 4M image on the GPU\")\n", "print(f\"Using algorithm {algo:10s}:\", end=\" \")\n", "%timeit ai.integrate1d(img, nbins, method=(\"full\", algo, \"opencl\", target))" ] }, { "cell_type": "markdown", "id": "9bc9ef05-f255-4fa5-b7b7-6e66f750a09d", "metadata": {}, "source": [ "**Note:** The full pixel splitting is time consuming and handicaps the histogram algorithm while both sparse-matrix methods are much faster since they cache this calculation in the sparse matrix.\n", "\n", "On the Power9 computer the CPU is much slower than the GPU !" ] }, { "cell_type": "code", "execution_count": 10, "id": "7ee60d8e-4bcc-458c-bd3d-e48724881142", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.71 ms ± 15.4 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "\n", "OpenCL kernel profiling statistics in milliseconds for: OCL_CSR_Integrator\n", " Kernel name (count): min median max mean std\n", " copy H->D image ( 811): 1.407 1.426 1.622 1.429 0.016\n", " memset_ng ( 811): 0.009 0.011 0.024 0.012 0.001\n", " corrections4a ( 811): 0.179 0.181 0.186 0.181 0.001\n", " csr_integrate4 ( 811): 0.394 0.396 0.399 0.396 0.001\n", " copy D->H avgint ( 811): 0.002 0.002 0.002 0.002 0.000\n", " copy D->H std ( 811): 0.002 0.002 0.002 0.002 0.000\n", " copy D->H sem ( 811): 0.001 0.001 0.002 0.001 0.000\n", " copy D->H merged8 ( 811): 0.002 0.003 0.003 0.003 0.000\n", "________________________________________________________________________________\n", " Total OpenCL execution time : 1642.501ms\n" ] } ], "source": [ "# How is the time spend when integrating on GPU ?\n", "res0 = ai.integrate1d(frame, nbins, method=(\"full\", \"csr\", \"opencl\", target))\n", "engine = ai.engines[res0.method].engine\n", "engine.events = []\n", "engine.set_profiling(True)\n", "omega_crc = engine.on_device[\"solidangle\"]\n", "%timeit engine.integrate_ng(img, solidangle=omega, solidangle_checksum=omega_crc)\n", "print(\"\\n\".join(engine.log_profile(stats=True)))\n", "engine.set_profiling(False)\n", "engine.events = []" ] }, { "cell_type": "markdown", "id": "23f8dfd8-f55c-4148-b783-f238081ba0af", "metadata": {}, "source": [ "**Note:** Most of the time is spent in the transfer from the CPU to the GPU." ] }, { "cell_type": "code", "execution_count": 11, "id": "4716f9cb-de3d-44cf-979e-12b442cceb8b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -r1 -n1 -o -q\n", "#Saving of a HDF5 file with many frames ...\n", "with h5py.File(filename, \"w\") as h:\n", " ds = h.create_dataset(\"data\", shape=(nbframes,)+shape, chunks=(1,)+shape, dtype=dtype, **cmp) \n", " for i in range(nbframes):\n", " ds[i] = frame + i%500 #Each frame has a different value to prevent caching effects" ] }, { "cell_type": "code", "execution_count": 12, "id": "918ce131-3486-4263-8584-c78c8c2b88d8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File size 9.213 GB with a compression ratio of 7.429x\n", "Write speed: 1004.457 MB/s of uncompressed data, or 55.981 fps.\n" ] } ], "source": [ "timing_write = _\n", "size=os.stat(filename).st_size\n", "print(f\"File size {size/(1024**3):.3f} GB with a compression ratio of {nbframes*numpy.prod(shape)*dtype.itemsize/size:.3f}x\")\n", "print(f\"Write speed: {nbframes*numpy.prod(shape)*dtype.itemsize/(1e6*timing_write.best):.3f} MB/s of uncompressed data, or {nbframes/timing_write.best:.3f} fps.\")" ] }, { "cell_type": "code", "execution_count": 13, "id": "e559f3a9-4890-47ef-959d-6f0b099963c9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -r1 -n1 -o -q\n", "#Reading all frames and decompressing them\n", "buffer = numpy.zeros(shape, dtype=dtype)\n", "with h5py.File(filename, \"r\") as h:\n", " ds = h[\"data\"]\n", " for i in range(nbframes):\n", " ds.read_direct(buffer, numpy.s_[i,:,:], numpy.s_[:,:])" ] }, { "cell_type": "code", "execution_count": 14, "id": "3f6a4b10-8fe5-4450-8272-4a98b7a22de4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Read speed: 1721.759 MB/s of uncompressed data, or 95.958 fps.\n" ] } ], "source": [ "timing_read1 = _\n", "print(f\"Read speed: {nbframes*numpy.prod(shape)*dtype.itemsize/(1e6*timing_read1.best):.3f} MB/s of uncompressed data, or {nbframes/timing_read1.best:.3f} fps.\")" ] }, { "cell_type": "code", "execution_count": 15, "id": "557956b5-5eec-44ea-8f13-0d76c6d88200", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compression ratio: 9.095x\n", "2.43 ms ± 417 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "Decompression speed: 515.661 fps\n" ] } ], "source": [ "# Time for decompressing one frame:\n", "chunk = bitshuffle.compress_lz4(frame,0)\n", "print(f\"Compression ratio: {frame.nbytes/len(chunk):.3f}x\")\n", "timing_decompress = %timeit -o bitshuffle.decompress_lz4(chunk, frame.shape, frame.dtype, 0)\n", "print(f\"Decompression speed: {1/timing_decompress.best:.3f} fps\")" ] }, { "cell_type": "code", "execution_count": 16, "id": "9727f861-eeec-4ec6-8c8c-57e99618474a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -r1 -n1 -o -q\n", "#Reading all frames without decompressing them\n", "with h5py.File(filename, \"r\") as h:\n", " ds = h[\"data\"]\n", " for i in range(ds.id.get_num_chunks()):\n", " filter_mask, chunk = ds.id.read_direct_chunk(ds.id.get_chunk_info(i).chunk_offset)" ] }, { "cell_type": "code", "execution_count": 17, "id": "a8f957e5-f3cc-41a4-9502-dc98f5e31dd7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Read speed: 6922.496 MB/s of compressed data.\n", "HDF5 read speed (without decompression): 2866.371 fps.\n", "HDF5 read speed (with decompression, theoritical): 437.038 fps.\n" ] } ], "source": [ "timing_read2 = _\n", "print(f\"Read speed: {size/(1e6*timing_read2.best):.3f} MB/s of compressed data.\")\n", "print(f\"HDF5 read speed (without decompression): {nbframes/timing_read2.best:.3f} fps.\")\n", "print(f\"HDF5 read speed (with decompression, theoritical): {nbframes/(timing_read2.best+timing_decompress.best*nbframes):.3f} fps.\")" ] }, { "cell_type": "markdown", "id": "03d4e618-db18-4ebd-9a70-9c1559f7acf2", "metadata": {}, "source": [ "## Prepare the azimuthal integrator\n", "To unleash the full performances of the azimuthal integrator, here the ability to deal with GPU arrays, one needs to extract the OpenCL integrator from AzimuthalIntegator. The integrator used here is a sparse matrix multiplication one with a CSR representation, tuned to run on the GPU." ] }, { "cell_type": "code", "execution_count": 18, "id": "a4f8923c-88ac-435e-8428-0fbbf7bf066f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.08 ms ± 18.3 μs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n", "807 μs ± 404 ns per loop (mean ± std. dev. of 3 runs, 1,000 loops each)\n", "The maximum achievable integration speed on this device is 1239.869 fps when data are in the GPU memory,\n", "but only 485.766 fps when data are still in the CPU memory !\n" ] } ], "source": [ "res0 = ai.integrate1d(frame, nbins, method=(\"full\", \"csr\", \"opencl\", target))\n", "engine = ai.engines[res0.method].engine\n", "#This is how the engine works. First send the image on the GPU:\n", "\n", "timing_integration_from_mem = %timeit -r3 -o engine.integrate_ng(frame, solidangle=omega, solidangle_checksum=omega_crc)\n", "\n", "frame_d = cla.to_device(engine.queue, frame)\n", "omega_crc = engine.on_device[\"solidangle\"]\n", "\n", "res1 = engine.integrate_ng(frame_d, solidangle=omega, solidangle_checksum=omega_crc)\n", "assert numpy.allclose(res0.intensity, res1.intensity) # validates the equivalence of both approaches:\n", "timing_integration = %timeit -r3 -o engine.integrate_ng(frame_d, solidangle=omega, solidangle_checksum=omega_crc)\n", "print(f\"The maximum achievable integration speed on this device is {1/timing_integration.best:.3f} fps when data are in the GPU memory,\"\n", " f\"\\nbut only {1/timing_integration_from_mem.best:.3f} fps when data are still in the CPU memory !\")" ] }, { "cell_type": "code", "execution_count": 19, "id": "7d75885a-0313-47fb-95e3-667448cdd658", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The maximum theoritical throughput considering reading, decompression and integration is 311.137 fps.\n", "But in practice, most people achieve at best 80.130 fps, partially due to a poor implementation of decompression in HDF5.\n" ] } ], "source": [ "timimg_sum_theo = timing_integration.best + timing_read2.best/nbframes + timing_integration_from_mem.best\n", "timimg_sum_prac = timing_read1.best/nbframes + timing_integration_from_mem.best\n", "print(f\"The maximum theoritical throughput considering reading, decompression and integration is {1/timimg_sum_theo:.3f} fps.\\n\"\n", " f\"But in practice, most people achieve at best {1/timimg_sum_prac:.3f} fps, \"\n", " \"partially due to a poor implementation of decompression in HDF5.\")" ] }, { "cell_type": "markdown", "id": "296b9c59-a957-4e75-ac29-808fc4c4a952", "metadata": {}, "source": [ "**Summary:**\n", "* Read speed: 2908 fps\n", "* Read + decompress: 96/406 fps\n", "* Read + decompress + integrate: 80/312 fps.\n", "\n", "## Using the decompression on the GPU\n", "\n", "This feature requires silx 1.2 !" ] }, { "cell_type": "code", "execution_count": 20, "id": "1613555d-8006-450c-90c2-be4cbd820ad7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'3.0.0-a0'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "silx.version" ] }, { "cell_type": "code", "execution_count": 21, "id": "5f9f2220-0931-497f-b5df-993341202c0e", "metadata": {}, "outputs": [], "source": [ "# Read one chunk\n", "with h5py.File(filename, \"r\") as h:\n", " ds = h[\"data\"]\n", " i=0\n", " filter_mask, chunk = ds.id.read_direct_chunk(ds.id.get_chunk_info(i).chunk_offset)" ] }, { "cell_type": "code", "execution_count": 22, "id": "c1fea01a-beaa-4727-9147-59cef1d4dd0b", "metadata": {}, "outputs": [], "source": [ "gpu_decompressor = BitshuffleLz4(len(chunk), frame.size, dtype=frame.dtype, ctx=engine.ctx)" ] }, { "cell_type": "code", "execution_count": 23, "id": "06356a14-9ba5-4d72-b0c4-652626c46de5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Workgroup size 1 : 11.4 ms ± 36.6 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "Workgroup size 2 : 6.1 ms ± 2.29 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "Workgroup size 4 : 3.51 ms ± 4.08 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "Workgroup size 8 : 2.22 ms ± 2.78 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "Workgroup size 16 : 1.56 ms ± 3.52 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "Workgroup size 32 : 1.23 ms ± 490 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "Workgroup size 64 : 1.07 ms ± 2.91 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "Workgroup size 128 : 1.01 ms ± 741 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "Workgroup size 256 : 1.01 ms ± 1.09 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "Workgroup size 512 : 1.07 ms ± 3.53 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "Workgroup size 1024 : 1.29 ms ± 4.75 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "\n", "Best performances (1.012e-03s) obtained with WG=256\n", "\n", "Decompression of data on the GPU occures at 988.022 fps while it is 515.661 fps when performed on the CPU.\n" ] } ], "source": [ "#Tune the decompressor for the fastest speed:\n", "best = numpy.finfo(\"float32\").max, None\n", "for i in range(0, 11):\n", " j = 1<" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -r1 -n1 -o -q\n", "# Process a complete stack:\n", "with h5py.File(filename, \"r\") as h:\n", " ds = h[\"data\"]\n", " for i in range(ds.id.get_num_chunks()):\n", " filter_mask, chunk = ds.id.read_direct_chunk(ds.id.get_chunk_info(i).chunk_offset)\n", " result[i] = engine.integrate_ng(gpu_decompressor(chunk), solidangle=omega, solidangle_checksum=omega_crc).intensity" ] }, { "cell_type": "code", "execution_count": 29, "id": "016db1ff-fb94-494a-b798-637590c3677b", "metadata": {}, "outputs": [], "source": [ "timing_process_gpu = _" ] }, { "cell_type": "code", "execution_count": 30, "id": "e4033694-9cd1-4aae-b388-cadbf478363a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing speed when decompression occures on GPU: 413.360 fps which is better than theoritical speed by 1.329x.\n", "It is much better than the actual speed measured by 5.159x.\n" ] } ], "source": [ "print(f\"Processing speed when decompression occures on GPU: {nbframes/timing_process_gpu.best:.3f} fps \"\n", " f\"which is better than theoritical speed by {timimg_sum_theo*nbframes/timing_process_gpu.best:.3f}x.\\n\"\n", " f\"It is much better than the actual speed measured by {timimg_sum_prac*nbframes/timing_process_gpu.best:.3f}x.\")" ] }, { "cell_type": "markdown", "id": "89f85542-5088-4469-abb2-06cde677fdda", "metadata": {}, "source": [ "## Display some results\n", "Since the input data were all synthetic and similar, no great science is expected from this... but one can ensure each frame differs slightly from the neighbors with a pattern of 500 frames. " ] }, { "cell_type": "code", "execution_count": 31, "id": "8650407f-a6ce-40ac-875e-f4f0f0c07a0d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax = subplots(figsize=(8,8))\n", "ax.imshow(result)" ] }, { "cell_type": "markdown", "id": "5013c60f-f973-405a-8f6f-9b8dd448a3d6", "metadata": {}, "source": [ "## Conclusion\n", "\n", "Bitshuffle-LZ4 data decompression can be off-loaded to the GPU, this is especially appealing when downstream processing requires also GPU-computing like azimuthal integration.\n", "\n", "The procedure is simpler than multi-threading approach: no queue, no threads, it just requires a GPU properly setup.\n", "\n", "The performances measured on a (not so recent) Nvidia A5000 on a fast CPU (4.5 GHz) provide a 5x speed up.!\n", "\n", "Those performances can be further parallelized using multiprocessing if needed (see the other tutorial in this section)." ] }, { "cell_type": "code", "execution_count": 32, "id": "cc6f2236-c77e-4add-adf6-4d0929bb5b46", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total processing time: 345.270 s\n" ] } ], "source": [ "print(f\"Total processing time: {time.time()-start_time:.3f} s\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.1" } }, "nbformat": 4, "nbformat_minor": 5 }