pipeline-cpus.ipynb

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    "### Accelerate real-time Python workload\n",
    "\n",
    "We start with a simple signals processing workload, and then accelerate it by several orders magnitude using the following libraries:\n",
    "\n",
    "1.  Numpy\n",
    "2.  Numba\n",
    "3.  Dask\n",
    "4.  CuPy\n",
    "5.  Numba CUDA\n",
    "\n",
    "We eventually run a real-time streaming system on multi-GPU hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def gather_from_detector():\n",
    "    return np.random.random((1000, 1000))\n",
    "\n",
    "@numba.jit\n",
    "def smooth(x):\n",
    "    out = np.empty_like(x)\n",
    "    for i in range(1, x.shape[0] - 1):\n",
    "        for j in range(1, x.shape[1] - 1):\n",
    "            out[i, j] = (x[i + -1, j + -1] + x[i + -1, j + 0] + x[i + -1, j + 1] +\n",
    "                         x[i +  0, j + -1] + x[i +  0, j + 0] + x[i +  0, j + 1] +\n",
    "                         x[i +  1, j + -1] + x[i +  1, j + 0] + x[i +  1, j + 1]) // 9\n",
    "\n",
    "    return out\n",
    "\n",
    "def save(x, filename):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "for i in range(1000):\n",
    "    img = client.submit(gather_from_detector, pure=False)\n",
    "    img = client.submit(smooth, img)\n",
    "    img = client.submit(np.fft.fft2, img)\n",
    "    future = client.submit(save, img, \"file-\" + str(i) + \"-.dat\")\n",
    "    fire_and_forget(future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client, LocalCluster, fire_and_forget\n",
    "\n",
    "cluster = LocalCluster()\n",
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "\n",
    "async def f():\n",
    "    while True:\n",
    "        img = client.submit(gather_from_detector, pure=False)\n",
    "        img = client.submit(smooth, img)\n",
    "        img = client.submit(np.fft.fft2, img)\n",
    "        future = client.submit(save, img, \"file-\" + str(i) + \"-.dat\")\n",
    "        fire_and_forget(future)\n",
    "        await asyncio.sleep(0.01)\n",
    "        \n",
    "asyncio.ensure_future(f())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.scale(8)"
   ]
  }
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pipeline-gpus.ipynb