{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee97b6d3-ed97-486a-8662-8be5868b4c9d",
   "metadata": {},
   "source": [
    "# Image Decompression and Azimuthal Integration on the GPU\n",
    "\n",
    "This tutorial explains how to accelerate azimuthal integration by optimizing the critical bottleneck: data transfer to the GPU.\n",
    "For this tutorial, a recent version of `silx` is required (newer than Fall 2022, available in release 1.2 or later).\n",
    "\n",
    "**Credits:**\n",
    "- Thomas Vincent (ESRF): HDF5 direct chunk read and Jupyter-Slurm integration\n",
    "- Jon Wright (ESRF): Initial prototype of Bitshuffle-LZ4 decompression on the GPU\n",
    "- Pierre Paleo (ESRF): Support with GPU-related challenges\n",
    "\n",
    "**Note:** A capable GPU is required for this tutorial, with OpenCL properly configured!\n",
    "\n",
    "The example used here is the same as the multithreading tutorial: 4096 frames from Eiger_4M.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "35d7f634-2c20-4ed0-8e2e-555de196df8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:55:46.787807Z",
     "iopub.status.busy": "2026-09-15T08:55:46.787731Z",
     "iopub.status.idle": "2026-09-15T08:55:47.129784Z",
     "shell.execute_reply": "2026-09-15T08:55:47.129201Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:55:47.131821Z",
     "iopub.status.busy": "2026-09-15T08:55:47.131596Z",
     "iopub.status.idle": "2026-09-15T08:55:47.398406Z",
     "shell.execute_reply": "2026-09-15T08:55:47.398061Z"
    }
   },
   "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\n",
    "import os\n",
    "import time\n",
    "import resource\n",
    "import numpy\n",
    "import pyFAI\n",
    "import h5py\n",
    "import 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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:55:47.399833Z",
     "iopub.status.busy": "2026-09-15T08:55:47.399646Z",
     "iopub.status.idle": "2026-09-15T08:55:47.401440Z",
     "shell.execute_reply": "2026-09-15T08:55:47.401098Z"
    }
   },
   "outputs": [],
   "source": [
    "#Here we select the OpenCL device\n",
    "target = (0,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65959281-21f0-43c6-a6ce-cd2233a8ed74",
   "metadata": {},
   "source": [
    "## Setting Up the Environment\n",
    "\n",
    "This is a purely virtual experiment. We will simulate an Eiger 4M detector with data integrated over 1000 bins. These parameters can be adjusted.\n",
    "\n",
    "Random data are generated with small values that compress well, keeping the file size reasonably small. The speed of the drive where the file is stored will likely have a significant impact!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c578b3e4-5912-4a02-9b4c-349872469324",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:55:47.402380Z",
     "iopub.status.busy": "2026-09-15T08:55:47.402298Z",
     "iopub.status.idle": "2026-09-15T08:55:48.292981Z",
     "shell.execute_reply": "2026-09-15T08:55:48.292036Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'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', 'htj2k': '/users/kieffer/.venv/py313/lib/python3.13/site-packages/hdf5plugin/plugins/libh5htj2k.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'}\n",
      "python:  3.13.1 | packaged by conda-forge | (main, Jan 13 2025, 09:53:10) [GCC 13.3.0]\n",
      "Silx:  3.1.0\n",
      "pyFAI:  2026.9.0\n"
     ]
    }
   ],
   "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",
    "print(hdf5plugin.get_config().registered_filters)\n",
    "print(\"python: \", sys.version)\n",
    "print(\"Silx: \", silx.version)\n",
    "print(\"pyFAI: \", pyFAI.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b883ae1c-0549-40cd-9753-ff2d11cb6448",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:55:48.295423Z",
     "iopub.status.busy": "2026-09-15T08:55:48.295053Z",
     "iopub.status.idle": "2026-09-15T08:55:48.298990Z",
     "shell.execute_reply": "2026-09-15T08:55:48.298372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of frames the computer can host in memory: 30144.536\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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:55:48.300852Z",
     "iopub.status.busy": "2026-09-15T08:55:48.300763Z",
     "iopub.status.idle": "2026-09-15T08:55:48.303048Z",
     "shell.execute_reply": "2026-09-15T08:55:48.302324Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:55:48.304562Z",
     "iopub.status.busy": "2026-09-15T08:55:48.304476Z",
     "iopub.status.idle": "2026-09-15T08:55:48.822777Z",
     "shell.execute_reply": "2026-09-15T08:55:48.822031Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:55:48.825180Z",
     "iopub.status.busy": "2026-09-15T08:55:48.824945Z",
     "iopub.status.idle": "2026-09-15T08:55:49.273358Z",
     "shell.execute_reply": "2026-09-15T08:55:49.272678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fac32e2eba0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:55:49.275007Z",
     "iopub.status.busy": "2026-09-15T08:55:49.274909Z",
     "iopub.status.idle": "2026-09-15T08:55:58.972167Z",
     "shell.execute_reply": "2026-09-15T08:55:58.970878Z"
    }
   },
   "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 : "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "505 ms ± 945 μs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "Using algorithm csc       : "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35.9 ms ± 360 μs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "Using algorithm csr       : "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.8 ms ± 4.39 ms 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       : "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.45 ms ± 93.8 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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:55:58.974568Z",
     "iopub.status.busy": "2026-09-15T08:55:58.974374Z",
     "iopub.status.idle": "2026-09-15T08:56:02.476613Z",
     "shell.execute_reply": "2026-09-15T08:56:02.475864Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.29 ms ± 62.8 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):    0.993    1.034    1.219    1.041    0.026\n",
      "                                         memset_ng (  811):    0.004    0.014    0.033    0.014    0.001\n",
      "                                     corrections4a (  811):    0.179    0.181    0.196    0.181    0.001\n",
      "                                    csr_integrate4 (  811):    0.394    0.396    0.399    0.397    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    0.002    0.003    0.002    0.000\n",
      "________________________________________________________________________________\n",
      "                       Total OpenCL execution time        : 1329.307ms\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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:56:02.478855Z",
     "iopub.status.busy": "2026-09-15T08:56:02.478749Z",
     "iopub.status.idle": "2026-09-15T08:57:12.021775Z",
     "shell.execute_reply": "2026-09-15T08:57:12.020668Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 1min 9s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -r1 -n1 -o -q\n",
    "#Saving of a HDF5 file with many frames ...\n",
    "\n",
    "# if not os.path.exists(filename):\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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:57:12.024397Z",
     "iopub.status.busy": "2026-09-15T08:57:12.024193Z",
     "iopub.status.idle": "2026-09-15T08:57:12.029416Z",
     "shell.execute_reply": "2026-09-15T08:57:12.028688Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File size 9.212 GB with a compression ratio of 7.430x\n",
      "Write speed: 1056.936 MB/s of uncompressed data, or 58.906 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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:57:12.031440Z",
     "iopub.status.busy": "2026-09-15T08:57:12.031281Z",
     "iopub.status.idle": "2026-09-15T08:57:54.893925Z",
     "shell.execute_reply": "2026-09-15T08:57:54.892980Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 42.9 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)>"
      ]
     },
     "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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:57:54.896148Z",
     "iopub.status.busy": "2026-09-15T08:57:54.896037Z",
     "iopub.status.idle": "2026-09-15T08:57:54.899537Z",
     "shell.execute_reply": "2026-09-15T08:57:54.898874Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read speed: 1714.935 MB/s of uncompressed data, or 95.578 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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:57:54.901036Z",
     "iopub.status.busy": "2026-09-15T08:57:54.900951Z",
     "iopub.status.idle": "2026-09-15T08:57:56.427717Z",
     "shell.execute_reply": "2026-09-15T08:57:56.427278Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compression ratio: 9.098x\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.66 ms ± 1.07 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "Decompression speed: 889.239 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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:57:56.431161Z",
     "iopub.status.busy": "2026-09-15T08:57:56.431060Z",
     "iopub.status.idle": "2026-09-15T08:57:57.754267Z",
     "shell.execute_reply": "2026-09-15T08:57:57.753564Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 1.32 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)>"
      ]
     },
     "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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:57:57.755989Z",
     "iopub.status.busy": "2026-09-15T08:57:57.755898Z",
     "iopub.status.idle": "2026-09-15T08:57:57.759109Z",
     "shell.execute_reply": "2026-09-15T08:57:57.758350Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read speed: 7498.607 MB/s of compressed data.\n",
      "HDF5 read speed (without decompression): 3105.318 fps.\n",
      "HDF5 read speed (with decompression, theoritical): 691.283 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": "01bd52e8-a173-4242-a711-cd4cbcd2165c",
   "metadata": {},
   "source": [
    "## Preparing the Azimuthal Integrator\n",
    "\n",
    "To unleash the full performance of the azimuthal integrator, specifically its ability to handle GPU arrays, the OpenCL integrator must be extracted from `AzimuthalIntegrator`. The integrator used here employs sparse matrix multiplication with a CSR (Compressed Sparse Row) representation, optimized to run on the GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a4f8923c-88ac-435e-8428-0fbbf7bf066f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:57:57.760712Z",
     "iopub.status.busy": "2026-09-15T08:57:57.760626Z",
     "iopub.status.idle": "2026-09-15T08:58:08.342048Z",
     "shell.execute_reply": "2026-09-15T08:58:08.341315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.76 ms ± 1.89 μs per loop (mean ± std. dev. of 3 runs, 1,000 loops each)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "802 μs ± 680 ns per loop (mean ± std. dev. of 3 runs, 1,000 loops each)\n",
      "The maximum achievable integration speed on this device is 1248.718 fps when data are in the GPU memory,\n",
      "but only 567.571 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": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:58:08.343987Z",
     "iopub.status.busy": "2026-09-15T08:58:08.343892Z",
     "iopub.status.idle": "2026-09-15T08:58:08.346880Z",
     "shell.execute_reply": "2026-09-15T08:58:08.346264Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum theoritical throughput considering reading, decompression and integration is 346.651 fps.\n",
      "But in practice, most people achieve at best 81.803 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": "5f9f2220-0931-497f-b5df-993341202c0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:58:08.348477Z",
     "iopub.status.busy": "2026-09-15T08:58:08.348392Z",
     "iopub.status.idle": "2026-09-15T08:58:08.352094Z",
     "shell.execute_reply": "2026-09-15T08:58:08.351523Z"
    }
   },
   "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": 21,
   "id": "c1fea01a-beaa-4727-9147-59cef1d4dd0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:58:08.353844Z",
     "iopub.status.busy": "2026-09-15T08:58:08.353757Z",
     "iopub.status.idle": "2026-09-15T08:58:08.368594Z",
     "shell.execute_reply": "2026-09-15T08:58:08.367861Z"
    }
   },
   "outputs": [],
   "source": [
    "gpu_decompressor = BitshuffleLz4(len(chunk), frame.size, dtype=frame.dtype, ctx=engine.ctx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "06356a14-9ba5-4d72-b0c4-652626c46de5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:58:08.370124Z",
     "iopub.status.busy": "2026-09-15T08:58:08.370033Z",
     "iopub.status.idle": "2026-09-15T08:59:09.425943Z",
     "shell.execute_reply": "2026-09-15T08:59:09.424963Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workgroup size    1 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.8 ms ± 4.95 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "Workgroup size    2 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.55 ms ± 19.5 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "Workgroup size    4 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.91 ms ± 1.06 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "Workgroup size    8 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6 ms ± 4.78 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size   16 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "943 μs ± 195 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size   32 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "614 μs ± 673 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size   64 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "460 μs ± 689 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size  128 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "393 μs ± 137 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size  256 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "393 μs ± 383 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size  512 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "493 μs ± 1.6 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size 1024 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "697 μs ± 1.75 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "\n",
      "Best performances (3.928e-04s) obtained with WG=256\n",
      "\n",
      "Decompression of data on the GPU occures at 2546.064 fps while it is 889.239 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<<i\n",
    "    print(f\"Workgroup size {j:4d} : \", end=\" \")\n",
    "    res = %timeit -o gpu_decompressor.decompress(chunk, wg=j)\n",
    "    if res.best<best[0]:\n",
    "        best = (res.best, j)\n",
    "        \n",
    "print(f\"\\nBest performances ({best[0]:.3e}s) obtained with WG={best[1]}\")\n",
    "gpu_decompressor.block_size = best[1]\n",
    "\n",
    "print(f\"\\nDecompression of data on the GPU occures at {1/best[0]:.3f} fps \"\n",
    "      f\"while it is {1/timing_decompress.best:.3f} fps when performed on the CPU.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bf55c1f0-45c4-42b8-9081-2e3c797a895f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T08:59:09.427917Z",
     "iopub.status.busy": "2026-09-15T08:59:09.427800Z",
     "iopub.status.idle": "2026-09-15T09:00:25.121432Z",
     "shell.execute_reply": "2026-09-15T09:00:25.120548Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workgroup size   1 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.84 ms ± 1.25 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "Workgroup size   2 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.54 ms ± 1.27 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "Workgroup size   4 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.26 ms ± 4.01 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "Workgroup size   8 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.06 ms ± 4.2 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "Workgroup size  16 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.22 ms ± 1.39 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size  32 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "818 μs ± 1.01 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size  64 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "709 μs ± 949 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size 128 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "712 μs ± 1.17 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size 256 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "687 μs ± 1.51 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size 512 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "654 μs ± 582 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "Workgroup size 1024 :  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "661 μs ± 1.18 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "\n",
      "Best performances (6.538e-04s) obtained with WG=512\n",
      "With data on the CPU:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.65 ms ± 22.2 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "#Tune the integrator for the fastest speed:\n",
    "\n",
    "def new_engine(engine, wg):\n",
    "    return engine.__class__((engine._data, engine._indices, engine._indptr), \n",
    "                              engine.size, empty=engine.empty, unit=engine.unit, \n",
    "                              bin_centers=engine.bin_centers, azim_centers = engine.azim_centers,  \n",
    "                              ctx=engine.ctx, block_size=wg)\n",
    "\n",
    "best = (numpy.finfo(\"float32\").max, None)\n",
    "for i in range(0, 11):\n",
    "    j = 1<<i\n",
    "    print(f\"Workgroup size {j:3d} : \", end=\" \")\n",
    "    ne = new_engine(engine, j)\n",
    "    ne.integrate_ng(frame_d, solidangle=omega, solidangle_checksum=omega_crc)\n",
    "    res = %timeit -o ne.integrate_ng(frame_d, solidangle=omega, solidangle_checksum=omega_crc)\n",
    "    if res.best<best[0]:\n",
    "        best = (res.best, j)\n",
    "\n",
    "print(f\"\\nBest performances ({best[0]:.3e}s) obtained with WG={best[1]}\")\n",
    "engine = new_engine(engine, best[1])\n",
    "print(\"With data on the CPU:\")\n",
    "timing_integrate_cpu = %timeit -o engine.integrate_ng(frame, solidangle=omega, solidangle_checksum=omega_crc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "469d47f3-1fd8-4c86-ab38-308dc96d98da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:00:25.123229Z",
     "iopub.status.busy": "2026-09-15T09:00:25.123129Z",
     "iopub.status.idle": "2026-09-15T09:00:25.125918Z",
     "shell.execute_reply": "2026-09-15T09:00:25.125370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azimuthal integration of data on the GPU occures at 1529.565 fps\n",
      "while it is 613.121 fps when data comes from the CPU.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Azimuthal integration of data on the GPU occures at {1/best[0]:.3f} fps\\n\"\n",
    "      f\"while it is {1/timing_integrate_cpu.best:.3f} fps when data comes from the CPU.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cfcc3806-a5f9-4451-b0d9-1fa2c6c3fbe4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:00:25.127859Z",
     "iopub.status.busy": "2026-09-15T09:00:25.127774Z",
     "iopub.status.idle": "2026-09-15T09:00:33.701234Z",
     "shell.execute_reply": "2026-09-15T09:00:33.700300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.06 ms ± 1.48 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "#Build a pipeline with decompression and integration on the GPU:\n",
    "%timeit engine.integrate_ng(gpu_decompressor(chunk), solidangle=omega, solidangle_checksum=omega_crc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d2b3a31a-62ef-4a2a-8ed8-6deedc3893ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:00:33.703371Z",
     "iopub.status.busy": "2026-09-15T09:00:33.703253Z",
     "iopub.status.idle": "2026-09-15T09:00:33.706156Z",
     "shell.execute_reply": "2026-09-15T09:00:33.705513Z"
    }
   },
   "outputs": [],
   "source": [
    "result = numpy.empty((nbframes, nbins), dtype=numpy.float32)\n",
    "engine.set_profiling(False)\n",
    "gpu_decompressor.set_profiling(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d187fb05-c447-49ba-9ce8-ce3e3aae63b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:00:33.707694Z",
     "iopub.status.busy": "2026-09-15T09:00:33.707607Z",
     "iopub.status.idle": "2026-09-15T09:00:40.886534Z",
     "shell.execute_reply": "2026-09-15T09:00:40.885756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 7.17 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)>"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "id": "016db1ff-fb94-494a-b798-637590c3677b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:00:40.888309Z",
     "iopub.status.busy": "2026-09-15T09:00:40.888218Z",
     "iopub.status.idle": "2026-09-15T09:00:40.890514Z",
     "shell.execute_reply": "2026-09-15T09:00:40.889885Z"
    }
   },
   "outputs": [],
   "source": [
    "timing_process_gpu = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e4033694-9cd1-4aae-b388-cadbf478363a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:00:40.891924Z",
     "iopub.status.busy": "2026-09-15T09:00:40.891840Z",
     "iopub.status.idle": "2026-09-15T09:00:40.894610Z",
     "shell.execute_reply": "2026-09-15T09:00:40.894011Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing speed when decompression occures on GPU: 570.930 fps which is better than theoritical speed by 1.647x.\n",
      "It is much better than the actual speed measured by 6.979x.\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": "code",
   "execution_count": 30,
   "id": "cee061dd-59be-4702-a387-c8768b546184",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:00:40.896391Z",
     "iopub.status.busy": "2026-09-15T09:00:40.896307Z",
     "iopub.status.idle": "2026-09-15T09:00:40.898573Z",
     "shell.execute_reply": "2026-09-15T09:00:40.897918Z"
    }
   },
   "outputs": [],
   "source": [
    "# %timeit engine.integrate_ng(gpu_decompresso(chunk,), solidangle=omega, solidangle_checksum=omega_crc).intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c1dab615-b744-4f45-9d39-48bed2d319c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:00:40.900022Z",
     "iopub.status.busy": "2026-09-15T09:00:40.899940Z",
     "iopub.status.idle": "2026-09-15T09:00:49.713796Z",
     "shell.execute_reply": "2026-09-15T09:00:49.713204Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.09 ms ± 4.66 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit gpu_decompressor(chunk,force_unblock_on_device=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9ea8f22d-fc73-403e-a41c-d80d2d5eebef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:00:49.715864Z",
     "iopub.status.busy": "2026-09-15T09:00:49.715765Z",
     "iopub.status.idle": "2026-09-15T09:00:52.959879Z",
     "shell.execute_reply": "2026-09-15T09:00:52.959071Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400 μs ± 549 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit gpu_decompressor(chunk,force_unblock_on_device=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7d5a0e78-9594-4dc5-bd5f-324dfc5b48f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:00:52.961752Z",
     "iopub.status.busy": "2026-09-15T09:00:52.961660Z",
     "iopub.status.idle": "2026-09-15T09:01:07.701134Z",
     "shell.execute_reply": "2026-09-15T09:01:07.700211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.82 ms ± 1.85 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit engine.integrate_ng(gpu_decompressor(chunk, force_unblock_on_device=True), solidangle=omega, solidangle_checksum=omega_crc).intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "22761f9e-71ec-467c-b176-ac6464c73b49",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:01:07.703600Z",
     "iopub.status.busy": "2026-09-15T09:01:07.703478Z",
     "iopub.status.idle": "2026-09-15T09:01:16.237000Z",
     "shell.execute_reply": "2026-09-15T09:01:16.236194Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.05 ms ± 697 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit engine.integrate_ng(gpu_decompressor(chunk, force_unblock_on_device=False), solidangle=omega, solidangle_checksum=omega_crc).intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4927b950-c79c-451b-a3c6-5de5ee4f2f61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:01:16.238994Z",
     "iopub.status.busy": "2026-09-15T09:01:16.238894Z",
     "iopub.status.idle": "2026-09-15T09:01:16.241960Z",
     "shell.execute_reply": "2026-09-15T09:01:16.241405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6296296296296295"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.76/1.08"
   ]
  },
  {
   "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": 36,
   "id": "8650407f-a6ce-40ac-875e-f4f0f0c07a0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:01:16.243947Z",
     "iopub.status.busy": "2026-09-15T09:01:16.243859Z",
     "iopub.status.idle": "2026-09-15T09:01:16.753320Z",
     "shell.execute_reply": "2026-09-15T09:01:16.752546Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fac1c533ed0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "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 offloaded to the GPU, which is especially appealing when downstream processing also requires GPU computing like azimuthal integration.\n",
    "\n",
    "The procedure is simpler than the multi-threading approach: no queues, no threads, it just requires a GPU properly set up.\n",
    "\n",
    "The performance measured on a (not so recent) NVIDIA A5000 on a fast CPU (4.5 GHz) provides a 5x speedup!\n",
    "\n",
    "Those performances can be further parallelized using multiprocessing if needed (see the other tutorial in this section)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe7207a6-8344-4d1a-bd8c-b99c3249d5b1",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Bitshuffle-LZ4 data decompression can be offloaded to the GPU, which is especially appealing when downstream processing also requires GPU computing, such as azimuthal integration.\n",
    "\n",
    "The procedure is simpler than the multi-threading approach: no queues, no threads—just a GPU properly set up.\n",
    "\n",
    "Performance measured on a (somewhat dated) NVIDIA A5000 paired with a fast CPU (4.5 GHz) provides a 6× speedup!\n",
    "\n",
    "This performance can be further improved through multiprocessing if needed (see the other tutorial in this section)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cc6f2236-c77e-4add-adf6-4d0929bb5b46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:01:16.755288Z",
     "iopub.status.busy": "2026-09-15T09:01:16.755192Z",
     "iopub.status.idle": "2026-09-15T09:01:16.757951Z",
     "shell.execute_reply": "2026-09-15T09:01:16.757408Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total processing time: 329.360 s\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total processing time: {time.time()-start_time:.3f} s\")"
   ]
  }
 ],
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