{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cbd42ada-5780-4c1f-8c01-30b928148499",
   "metadata": {},
   "source": [
    "# Multiprocessing on GPU\n",
    "In this tutorial, the idea is to parallelize the reading of several HDF5 files across different processes and to perform the azimuthal integration on the GPU together with Bitshuffle-LZ4 decompression.\n",
    "The scale of this problem involves 1000 files containing 1000 frames each of 4Mpx resolution—that's one million frames total. The reduced data will be 1000×1000×1000 in single-precision float format.\n",
    "This is an extreme case where we'll explore how to utilize all available computer resources. The first step is to determine the system topology:\n",
    "\n",
    "**Lenovo P620 Workstation**\n",
    "- 1 processor: AMD Threadripper PRO 3975WX, 32 cores with hyperthreading (64 threads) across 4 NUMA sub-domains. 450W TDP.\n",
    "- 2 GPUs:\n",
    "  * NVIDIA RTX A5000 (8192 CUDA-cores, 24 GB GDDR6, 16xPCIe4, 230W)\n",
    "  * NVIDIA Quadro P2200 (1280 CUDA-cores, 5GB GDDR5, 16xPCIe3, 75W)\n",
    "\n",
    "**Disclaimer:**\n",
    "- We use the `multiprocess` library instead of `multiprocessing` to demonstrate this procedure within a Jupyter notebook.\n",
    "- We use `SharedArray` to provide a natural way of sharing the result array in memory between processes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64ae7c41-c094-4b79-96ec-020bff748f40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T08:56:25.979714Z",
     "iopub.status.busy": "2026-06-14T08:56:25.979607Z",
     "iopub.status.idle": "2026-06-14T08:56:26.617298Z",
     "shell.execute_reply": "2026-06-14T08:56:26.616609Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Invalid MIT-MAGIC-COOKIE-1 key\r\n",
      "Exporting format `png' to file `/tmp/topo.png'\r\n",
      "Failed to open /tmp/topo.png for writing (File exists)\r\n"
     ]
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# use `widget` for better user experience; `inline` is for documentation generation\n",
    "\n",
    "#Topology of the computer:\n",
    "!lstopo /tmp/topo.png\n",
    "from IPython.display import Image\n",
    "Image(filename='/tmp/topo.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8dd653b1-3040-4f08-9bfe-1b1178cfcde4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T08:56:26.619565Z",
     "iopub.status.busy": "2026-06-14T08:56:26.619299Z",
     "iopub.status.idle": "2026-06-14T08:56:26.849807Z",
     "shell.execute_reply": "2026-06-14T08:56:26.849238Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pyFAI version:  2026.6.0-dev0\n",
      "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\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import concurrent\n",
    "import time\n",
    "import json\n",
    "from matplotlib.pyplot import subplots\n",
    "import multiprocess\n",
    "from multiprocess import Process, Value, Array\n",
    "if \"mpctx\" in globals():\n",
    "    mpctx = multiprocess.get_context('spawn')\n",
    "else:\n",
    "    multiprocess.set_start_method('spawn')\n",
    "    mpctx = multiprocess.get_context('spawn')\n",
    "import numpy\n",
    "import hdf5plugin\n",
    "import h5py\n",
    "import pyFAI\n",
    "print(\"pyFAI version: \", pyFAI.version)\n",
    "import SharedArray\n",
    "from silx.opencl import ocl\n",
    "from silx.opencl.codec.bitshuffle_lz4 import BitshuffleLz4\n",
    "import inspect\n",
    "import collections\n",
    "Item = collections.namedtuple(\"Item\", \"index filename\")\n",
    "MAIN_PROCESS = multiprocess.parent_process() is None\n",
    "print(ocl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c6aaba7-377d-4478-87b4-45b1b85c7ce8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T08:56:26.851105Z",
     "iopub.status.busy": "2026-06-14T08:56:26.850874Z",
     "iopub.status.idle": "2026-06-14T08:56:26.854062Z",
     "shell.execute_reply": "2026-06-14T08:56:26.853613Z"
    }
   },
   "outputs": [],
   "source": [
    "#This cell contains the parameters for all the processing\n",
    "params = {\n",
    "    \"DEVICES\": [[0,0], [0,0], [0,0], [0,0], [0,0], [0,0],   # 6 instances on the large GPU\n",
    "                [0,1], [0,1], [0,1],                        # 3 instances on the small GPU\n",
    "                [2,0]],                                     # 1 instance on the CPU-cores\n",
    "    \"NWORKERS\": 10,\n",
    "    \"FRAME_PER_FILE\": 1000,\n",
    "    \"NFILES\" : 1000,\n",
    "    \"NBINS\" : 1000,\n",
    "    \"DETECTOR\":\"Eiger_4M\",\n",
    "    \"pathname\" : \"/tmp/big_%04d.h5\",\n",
    "    \"pathmask\" : \"/tmp/big_????.h5\",\n",
    "    \"dtype\" : \"float32\",\n",
    "    \"SHARED_NAME\" : \"shm://multigpu\",\n",
    "    \"array_shape\" : [1000, 1000, 1000],\n",
    "    }\n",
    "with open(\"param.json\", \"w\") as w: w.write(json.dumps(params, indent=2))\n",
    "for k,v in params.items():\n",
    "    globals()[k] = v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f58cd73-78b5-4b6e-a6cd-2d553d35f3fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T08:56:26.855143Z",
     "iopub.status.busy": "2026-06-14T08:56:26.855033Z",
     "iopub.status.idle": "2026-06-14T08:56:26.857116Z",
     "shell.execute_reply": "2026-06-14T08:56:26.856635Z"
    }
   },
   "outputs": [],
   "source": [
    "def build_integrator(detector=DETECTOR):\n",
    "    \"Build an azimuthal integrator with a dummy geometry\"\n",
    "    geo = {\"detector\": detector, \n",
    "           \"wavelength\": 1e-10} \n",
    "    ai = pyFAI.load(geo)\n",
    "    return ai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d4a5604-4c2d-4bdb-aea8-63114f9e43c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T08:56:26.858144Z",
     "iopub.status.busy": "2026-06-14T08:56:26.858032Z",
     "iopub.status.idle": "2026-06-14T08:56:26.865112Z",
     "shell.execute_reply": "2026-06-14T08:56:26.864515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/tmp/big_0000.h5',\n",
       " '/tmp/big_0001.h5',\n",
       " '/tmp/big_0002.h5',\n",
       " '/tmp/big_0003.h5',\n",
       " '/tmp/big_0004.h5']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a set of files\n",
    "def generate_one_frame(ai, unit=\"q_nm^-1\", dtype=\"uint32\"):\n",
    "    \"\"\"Prepare a frame with little count so that it compresses well\"\"\"\n",
    "    qmax = ai.array_from_unit(unit=unit).max()\n",
    "    q = numpy.linspace(0, qmax, 100)\n",
    "    img = ai.calcfrom1d(q, 100/(1+q*q))\n",
    "    frame = numpy.random.poisson(img).astype(dtype)\n",
    "    return frame\n",
    "\n",
    "def generate_files(img):\n",
    "    cmp = hdf5plugin.Bitshuffle()\n",
    "    filename = pathname%0\n",
    "    shape = img.shape\n",
    "    with h5py.File(filename, \"w\") as h:\n",
    "        ds = h.create_dataset(\"data\", shape=(FRAME_PER_FILE,)+shape, chunks=(1,)+shape, dtype=img.dtype, **cmp) \n",
    "        for i in range(FRAME_PER_FILE):\n",
    "            ds[i] = img + i%500 #Each frame has a different value to prevent caching effects\n",
    "    res = [filename]\n",
    "    for i in range(1, NFILES):\n",
    "        new_file = pathname%i\n",
    "        os.link(filename,new_file)\n",
    "        res.append(new_file)\n",
    "    return res\n",
    "\n",
    "# Create a set of files with dummy data in them:\n",
    "if len(glob.glob(pathmask)) == NFILES: \n",
    "    input_files = glob.glob(pathmask)\n",
    "    input_files.sort()\n",
    "else:\n",
    "    for f in glob.glob(pathmask):\n",
    "        os.remove(f)\n",
    "    input_files = generate_files(generate_one_frame(build_integrator(DETECTOR)))\n",
    "input_files[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "89a5845f-1ce6-41e9-9b1a-4ce532058d4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T08:56:26.866489Z",
     "iopub.status.busy": "2026-06-14T08:56:26.866380Z",
     "iopub.status.idle": "2026-06-14T08:56:26.869145Z",
     "shell.execute_reply": "2026-06-14T08:56:26.868620Z"
    }
   },
   "outputs": [],
   "source": [
    "#This is allows to create and destroy shared numpy arrays\n",
    "\n",
    "def create_shared_array(shape, dtype=\"float32\", name=SHARED_NAME, create=False):\n",
    "    if create:\n",
    "        ary = SharedArray.create(name, shape, dtype=dtype)\n",
    "    else:\n",
    "        ary = SharedArray.attach(name)\n",
    "    return ary\n",
    "\n",
    "def release_shared(name=SHARED_NAME):\n",
    "    if MAIN_PROCESS:\n",
    "        SharedArray.delete(name)\n",
    "\n",
    "result_array = create_shared_array(array_shape, dtype, SHARED_NAME, create=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc73341d-85d8-4a60-9085-3a556312a8a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T08:56:26.870812Z",
     "iopub.status.busy": "2026-06-14T08:56:26.870704Z",
     "iopub.status.idle": "2026-06-14T08:56:26.875471Z",
     "shell.execute_reply": "2026-06-14T08:56:26.874952Z"
    }
   },
   "outputs": [],
   "source": [
    "def worker(rank, queue, shm_name, counter):\n",
    "    \"\"\"Function representing one worker, used in a pool of worker.\n",
    "    \n",
    "    :param rank: integer, index of the worker.\n",
    "    :param queue: input queue, expects Item with index and name of the file to process\n",
    "    :param shm_name: name of the output shared memory to put integrated intensities\n",
    "    :param counter: decremented when quits\n",
    "    :return: nothing, used in a process.\n",
    "    \"\"\"\n",
    "    def new_engine(engine, wg):\n",
    "        \"Change workgroup size of an engine\"\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",
    "    #imports:\n",
    "    import pyFAI\n",
    "    import numpy\n",
    "    import SharedArray\n",
    "    from silx.opencl.codec.bitshuffle_lz4 import BitshuffleLz4\n",
    "    import h5py\n",
    "    import json\n",
    "    import sys\n",
    "    #load parameters:\n",
    "    for k,v in json.load(open(\"param.json\")).items():\n",
    "        globals()[k] = v\n",
    "    #Start up the integrator:\n",
    "    ai = pyFAI.load({\"detector\": DETECTOR, \n",
    "                     \"wavelength\": 1e-10, \n",
    "                     \"rot3\":0})\n",
    "    blank = numpy.zeros(ai.detector.shape, dtype=\"uint32\")\n",
    "    method = (\"full\", \"csr\", \"opencl\", DEVICES[rank%len(DEVICES)])\n",
    "    res = ai.integrate1d(blank, NBINS, method=method)\n",
    "    omega = ai.solidAngleArray()\n",
    "    engine = ai.engines[res.method].engine\n",
    "    \n",
    "    omega_crc = engine.on_device[\"solidangle\"]\n",
    "    engine = new_engine(engine, 512)\n",
    "   \n",
    "    gpu_decompressor = BitshuffleLz4(2000000, blank.size, dtype=blank.dtype, ctx=engine.ctx)\n",
    "    gpu_decompressor.block_size = 128\n",
    "    result_array = SharedArray.attach(SHARED_NAME)\n",
    "    with counter.get_lock():\n",
    "        counter.value += 1\n",
    "    #Worker is ready !\n",
    "    while True:\n",
    "        item = queue.get()\n",
    "        index = item.index\n",
    "        if index<0: \n",
    "            with counter.get_lock():\n",
    "                counter.value -= 1\n",
    "            return\n",
    "        with h5py.File(item.filename, \"r\") as h5:\n",
    "            ds = h5[\"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",
    "                if filter_mask == 0:\n",
    "                    # print(f\"{rank}: process frame #{i}\")\n",
    "                    dec = gpu_decompressor(chunk)\n",
    "                    intensity = engine.integrate_ng(dec, solidangle=omega, solidangle_checksum=omega_crc).intensity\n",
    "                    result_array[index, i,:] = intensity.astype(dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c80957b0-fbe3-4692-8536-a56e321b5ba4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T08:56:26.877104Z",
     "iopub.status.busy": "2026-06-14T08:56:26.876996Z",
     "iopub.status.idle": "2026-06-14T08:56:30.923524Z",
     "shell.execute_reply": "2026-06-14T08:56:30.922700Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/users/kieffer/.venv/py314/lib/python3.14/site-packages/pyopencl/cache.py:496: CompilerWarning: Non-empty compiler output encountered. Set the environment variable PYOPENCL_COMPILER_OUTPUT=1 to see more.\n",
      "  _create_built_program_from_source_cached(\n",
      "/users/kieffer/.venv/py314/lib/python3.14/site-packages/pyopencl/cache.py:500: CompilerWarning: Non-empty compiler output encountered. Set the environment variable PYOPENCL_COMPILER_OUTPUT=1 to see more.\n",
      "  prg.build(options_bytes, devices)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<Process name='worker_00' pid=138502 parent=138271 started>,\n",
       " <Process name='worker_01' pid=138503 parent=138271 started>,\n",
       " <Process name='worker_02' pid=138504 parent=138271 started>,\n",
       " <Process name='worker_03' pid=138505 parent=138271 started>,\n",
       " <Process name='worker_04' pid=138506 parent=138271 started>,\n",
       " <Process name='worker_05' pid=138507 parent=138271 started>,\n",
       " <Process name='worker_06' pid=138508 parent=138271 started>,\n",
       " <Process name='worker_07' pid=138509 parent=138271 started>,\n",
       " <Process name='worker_08' pid=138510 parent=138271 started>,\n",
       " <Process name='worker_09' pid=138511 parent=138271 started>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_pool(nbprocess, queue, shm_name, counter):\n",
    "    \"\"\"Build a pool of processes with workers, and starts them\"\"\"\n",
    "    pool = []\n",
    "    for i in range(nbprocess):\n",
    "        process = Process(target=worker, name=f\"worker_{i:02d}\", args=(i, queue, shm_name, counter))\n",
    "        process.start()\n",
    "        pool.append(process)\n",
    "    while counter.value<nbprocess:\n",
    "        time.sleep(1)\n",
    "    return pool\n",
    "\n",
    "def end_pool(pool, queue):\n",
    "    \"\"\"Ends all processes from a pool by sending them a \"kill-pill\"\"\"\n",
    "    for process in pool:\n",
    "        queue.put(Item(-1, None))\n",
    "\n",
    "# Build the pool of workers\n",
    "queue = mpctx.Queue()\n",
    "counter = mpctx.Value(\"i\", 0)\n",
    "pool = build_pool(NWORKERS, queue, SHARED_NAME, counter)\n",
    "pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b9ef84b-650b-4b8e-87df-104d6aea6953",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T08:56:30.925392Z",
     "iopub.status.busy": "2026-06-14T08:56:30.925205Z",
     "iopub.status.idle": "2026-06-14T08:56:30.976975Z",
     "shell.execute_reply": "2026-06-14T08:56:30.975398Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = time.perf_counter()\n",
    "for idx, fn in enumerate(input_files):        \n",
    "    queue.put(Item(idx, fn))\n",
    "end_pool(pool, queue)\n",
    "counter.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0a1491ab-d03c-4685-8adf-0b0de570a033",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T08:56:30.988807Z",
     "iopub.status.busy": "2026-06-14T08:56:30.988667Z",
     "iopub.status.idle": "2026-06-14T09:09:10.385920Z",
     "shell.execute_reply": "2026-06-14T09:09:10.384980Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run-time: 759.5s\n"
     ]
    }
   ],
   "source": [
    "while counter.value:\n",
    "    time.sleep(1)\n",
    "run_time = time.perf_counter()-start_time\n",
    "print(f\"run-time: {run_time:.1f}s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "978b72f0-1ff1-4f19-ad5b-dcdd97e41851",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T09:09:10.388038Z",
     "iopub.status.busy": "2026-06-14T09:09:10.387809Z",
     "iopub.status.idle": "2026-06-14T09:09:10.843734Z",
     "shell.execute_reply": "2026-06-14T09:09:10.842883Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2957370ad0>"
      ]
     },
     "execution_count": 11,
     "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": [
    "fig, ax = subplots()\n",
    "ax.imshow(result_array[:,:,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7db29db0-f29c-41c1-89da-b53f93ef09db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T09:09:10.845550Z",
     "iopub.status.busy": "2026-06-14T09:09:10.845427Z",
     "iopub.status.idle": "2026-06-14T09:09:10.848685Z",
     "shell.execute_reply": "2026-06-14T09:09:10.848047Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performances: 1316.731 fps\n"
     ]
    }
   ],
   "source": [
    "print(f\"Performances: {NFILES*FRAME_PER_FILE/run_time:.3f} fps\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c584fc0b-6109-4b4e-a87b-b29ca3d87289",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T09:09:10.850261Z",
     "iopub.status.busy": "2026-06-14T09:09:10.850146Z",
     "iopub.status.idle": "2026-06-14T09:09:10.852514Z",
     "shell.execute_reply": "2026-06-14T09:09:10.851900Z"
    }
   },
   "outputs": [],
   "source": [
    "release_shared(SHARED_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364f3555-4755-4c30-9de9-8ea5acd832ad",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "It is possible to achieve 1300 frames per second on a single workstation. \n",
    "In this configuration, one million Eiger-4M frames are processed in 12 minutes.\n",
    "\n",
    "**Energy considerations for processing one million frames:**\n",
    "- The workstation consumes 700W at the wall outlet, representing 540 kJ (150 Wh) for the data reduction.\n",
    "- Equivalent processing using only a 6-core workstation takes half a day (40,682 seconds) and consumes 1,561 kJ (433 Wh).\n",
    "- CPU+GPU-based processing is thus approximately 3× more energy-efficient than CPU-only processing.\n",
    "\n",
    "**Other considerations:**\n",
    "- Here, disk reading is fast enough, so there's no need for too many processes per GPU. If disk I/O is slower, adding more processes could be beneficial.\n",
    "- Since the output array resides in shared memory, sufficient RAM must be available to accommodate it. In this case, 512 GB of memory is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5caa8246-ce34-4bac-8461-4ed4b5765029",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.14.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}