test_03.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4debbbc1-6650-4962-bdea-38c9698a3343",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import math\n",
    "from time import time_ns\n",
    "from pprint import pprint\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import numba as nb\n",
    "from numba import cuda  # must be explicitly imported\n",
    "from tqdm import tqdm\n",
    "\n",
    "if False:  # numba unhappy when used with target=cuda\n",
    "    sin = np.sin\n",
    "    cos = np.cos\n",
    "else:\n",
    "    sin = math.sin\n",
    "    cos = math.cos\n",
    "\n",
    "COMPLEXITY = 2 ** 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "252d057e-976c-4a03-9a33-7e306e78f48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def base_dummy(scalar: float) -> float:\n",
    "    res: float = 0.0\n",
    "    for idx in range(COMPLEXITY):\n",
    "        if idx % 2 == round(scalar) % 2:\n",
    "            res += sin(idx)\n",
    "        else:\n",
    "            res -= cos(idx)\n",
    "    return res\n",
    "\n",
    "def base(d: np.ndarray) -> np.ndarray:\n",
    "    r = np.zeros(d.shape, dtype = d.dtype)\n",
    "    for idx in range(d.shape[0]):\n",
    "        r[idx] = base_dummy(d[idx])\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a7f25f4-6341-4aa2-8d4f-646e7e29488c",
   "metadata": {},
   "outputs": [],
   "source": [
    "@nb.njit\n",
    "def njited_dummy(scalar: float) -> float:\n",
    "    res: float = 0.0\n",
    "    for idx in range(COMPLEXITY):\n",
    "        if idx % 2 == round(scalar) % 2:\n",
    "            res += sin(idx)\n",
    "        else:\n",
    "            res -= cos(idx)\n",
    "    return res\n",
    "\n",
    "@nb.njit\n",
    "def njited(d: np.ndarray) -> np.ndarray:\n",
    "    r = np.zeros(d.shape, dtype = d.dtype)\n",
    "    for idx in range(d.shape[0]):\n",
    "        r[idx] = njited_dummy(d[idx])\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b08f4d7-7dee-4153-beff-6e543139bcf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "@nb.njit(inline = 'always')\n",
    "def njited_inline_dummy(scalar: float) -> float:\n",
    "    res: float = 0.0\n",
    "    for idx in range(COMPLEXITY):\n",
    "        if idx % 2 == round(scalar) % 2:\n",
    "            res += sin(idx)\n",
    "        else:\n",
    "            res -= cos(idx)\n",
    "    return res\n",
    "\n",
    "@nb.njit\n",
    "def njited_inline(d: np.ndarray) -> np.ndarray:\n",
    "    r = np.zeros(d.shape, dtype = d.dtype)\n",
    "    for idx in range(d.shape[0]):\n",
    "        r[idx] = njited_inline_dummy(d[idx])\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5551c71a-1880-49a6-b04d-f11728e2df7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "@nb.njit\n",
    "def njited_parallel_dummy(scalar: float) -> float:\n",
    "    res: float = 0.0\n",
    "    for idx in range(COMPLEXITY):\n",
    "        if idx % 2 == round(scalar) % 2:\n",
    "            res += sin(idx)\n",
    "        else:\n",
    "            res -= cos(idx)\n",
    "    return res\n",
    "\n",
    "@nb.njit(parallel = True)\n",
    "def njited_parallel(d: np.ndarray) -> np.ndarray:\n",
    "    r = np.zeros(d.shape, dtype = d.dtype)\n",
    "    for idx in nb.prange(d.shape[0]):\n",
    "        r[idx] = njited_parallel_dummy(d[idx])\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70c82ac7-c5f8-4891-958b-7490f7d35cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "@nb.njit\n",
    "def vectorized_dummy(scalar: float) -> float:\n",
    "    res: float = 0.0\n",
    "    for idx in range(COMPLEXITY):\n",
    "        if idx % 2 == round(scalar) % 2:\n",
    "            res += sin(idx)\n",
    "        else:\n",
    "            res -= cos(idx)\n",
    "    return res\n",
    "\n",
    "@nb.vectorize\n",
    "def vectorized(d: float) -> float:\n",
    "    return vectorized_dummy(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "043c6db3-3852-4a75-b64c-794ef4c6a9d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "@nb.njit('f8(f8)')\n",
    "def vectorized_parallel_dummy(scalar: float) -> float:\n",
    "    res: float = 0.0\n",
    "    for idx in range(COMPLEXITY):\n",
    "        if idx % 2 == round(scalar) % 2:\n",
    "            res += sin(idx)\n",
    "        else:\n",
    "            res -= cos(idx)\n",
    "    return res\n",
    "\n",
    "@nb.vectorize('f8(f8)', target = 'parallel')\n",
    "def vectorized_parallel(d: float) -> float:\n",
    "    return vectorized_parallel_dummy(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "265e71c1-6212-480e-ba1b-19e3bbd6942d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @nb.njit('f8(f8)') -> Untyped global name 'vectorized_cuda_dummy': using cpu function on device but its compilation is disabled\n",
    "# @nb.vectorize('f8(f8)', target = 'cuda') > Untyped global name 'vectorized_cuda_dummy': Cannot determine Numba type of <class 'numba.cuda.dispatcher.CUDAUFuncDispatcher'>\n",
    "@cuda.jit('f8(f8)', device = True, inline = True)\n",
    "def vectorized_cuda_dummy(scalar: float) -> float:\n",
    "    res: float = 0.0\n",
    "    for idx in range(COMPLEXITY):\n",
    "        if idx % 2 == round(scalar) % 2:\n",
    "            res += sin(idx)\n",
    "        else:\n",
    "            res -= cos(idx)\n",
    "    return res\n",
    "\n",
    "@nb.vectorize('f8(f8)', target = 'cuda')\n",
    "def vectorized_cuda(d: float) -> float:\n",
    "    return vectorized_cuda_dummy(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "230903c4-9948-43bd-ba8d-07c5da8ecdcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "funcs = [\n",
    "    base,\n",
    "    njited,\n",
    "    njited_inline,\n",
    "    njited_parallel,\n",
    "    vectorized,\n",
    "    vectorized_parallel,\n",
    "    vectorized_cuda,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0207051e-5077-4b13-b2c7-50259ddf8333",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (1) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n"
     ]
    }
   ],
   "source": [
    "def test_all(size = 100):\n",
    "    \n",
    "    data = np.random.random(size) * 128\n",
    "    res_base = funcs[0](data)\n",
    "    \n",
    "    for func in funcs[1:]:\n",
    "        res = func(data)\n",
    "        assert np.allclose(res_base, res)\n",
    "        \n",
    "test_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3dc7b8f9-b5b3-464e-b3fe-3bdb127faee9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 28%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                                                                                                                                                                                | 11/39 [00:05<00:15,  1.81it/s]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (2) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 33%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                                                                                                                                                                            | 13/39 [00:07<00:16,  1.62it/s]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (3) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 36%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                                                                                                                                                                 | 14/39 [00:07<00:16,  1.49it/s]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (4) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 38%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                                                                                                                                                       | 15/39 [00:08<00:17,  1.36it/s]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (5) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 41%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                                                                                                                                             | 16/39 [00:09<00:19,  1.21it/s]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (7) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 44%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                                                                                                                                  | 17/39 [00:10<00:20,  1.05it/s]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (10) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 46%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                                                                                                                        | 18/39 [00:12<00:23,  1.13s/it]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (13) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 49%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                                                                                              | 19/39 [00:14<00:27,  1.38s/it]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (19) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                                                                                                   | 20/39 [00:17<00:33,  1.74s/it]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (26) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                                                                         | 21/39 [00:20<00:40,  2.25s/it]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (37) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 56%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                                                               | 22/39 [00:25<00:50,  2.96s/it]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (52) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 59%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                                                                     | 23/39 [00:31<01:03,  3.96s/it]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (73) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                                          | 24/39 [00:40<01:20,  5.36s/it]/env310/lib/python3.10/site-packages/numba/cuda/compiler.py:726: NumbaPerformanceWarning: Grid size (103) < 2 * SM count (128) will likely result in GPU under utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [1:22:38<00:00, 127.15s/it]\n"
     ]
    }
   ],
   "source": [
    "def _name(func):\n",
    "    try:\n",
    "        return func.__name__\n",
    "    except AttributeError:  # numba#8272\n",
    "        return list(func.functions.values())[0][1].py_func.__name__[13:]\n",
    "\n",
    "def benchmark(start = 4, stop = 23, reps = 2):  # 16\n",
    "    sizes = (2 ** np.arange(start, stop + 0.5, 0.5)).astype('i8')\n",
    "    results = {_name(func): [] for func in funcs[1:]}\n",
    "    gc.disable()\n",
    "    for size in tqdm(sizes):\n",
    "        data = np.arange(0, size, 1, dtype = 'f8')\n",
    "        for func in funcs[1:]:  # exclude pure python\n",
    "            runtimes = []\n",
    "            for rep in range(reps):\n",
    "                gc.collect()\n",
    "                start = time_ns()\n",
    "                _ = func(data)\n",
    "                runtime = time_ns() - start\n",
    "                runtimes.append(runtime)\n",
    "            results[_name(func)].append(min(runtimes) * 1e-9)\n",
    "    gc.enable()\n",
    "    return sizes, results\n",
    "\n",
    "s, r = benchmark()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "844e9593-6c6a-4c56-a2b6-51d3798bb2ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_all(sizes, results):\n",
    "    fig, ax = plt.subplots(figsize = (15, 8))\n",
    "    for name, values in results.items():\n",
    "        ax.loglog(sizes, values, label = name)\n",
    "    ax.legend()\n",
    "    ax.set_xlabel('size of array')\n",
    "    ax.set_ylabel('time [s]')\n",
    "    ax.grid()\n",
    "    fig.tight_layout()\n",
    "    fig.savefig('plot_all.jpg')\n",
    "\n",
    "plot_all(s, r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7dc039df-2b49-432f-9016-65227aeaee10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_relative_speedup(sizes, results, pairs):\n",
    "    fig, ax = plt.subplots(figsize = (15, 8))\n",
    "    for base, faster in pairs:\n",
    "        ax.semilogx(\n",
    "            sizes,\n",
    "            np.array(r[base]) / np.array(r[faster]),\n",
    "            label = f'{faster:s} over {base:}',\n",
    "        )\n",
    "    ax.legend()\n",
    "    ax.set_xlabel('size of array')\n",
    "    ax.set_ylabel('relative speedup')\n",
    "    ax.grid()\n",
    "    fig.tight_layout()\n",
    "    fig.savefig('plot_relative_speedup.jpg')\n",
    "\n",
    "plot_relative_speedup(s, r, pairs = [\n",
    "    ('vectorized', 'vectorized_parallel'),\n",
    "    ('vectorized_parallel', 'vectorized_cuda'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dea13f34-7a5c-4832-b7fc-755f64de2620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     16,      22,      32,      45,      64,      90,     128,\n",
       "           181,     256,     362,     512,     724,    1024,    1448,\n",
       "          2048,    2896,    4096,    5792,    8192,   11585,   16384,\n",
       "         23170,   32768,   46340,   65536,   92681,  131072,  185363,\n",
       "        262144,  370727,  524288,  741455, 1048576, 1482910, 2097152,\n",
       "       2965820, 4194304, 5931641, 8388608])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e9e8daf7-30ec-4f58-aa2e-48bd4347749c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'njited': [0.00046608200000000004,\n",
      "            0.000621262,\n",
      "            0.000888504,\n",
      "            0.001243765,\n",
      "            0.0017790170000000001,\n",
      "            0.00247173,\n",
      "            0.0035031940000000003,\n",
      "            0.005048081,\n",
      "            0.007001219,\n",
      "            0.009921800000000001,\n",
      "            0.014022636000000002,\n",
      "            0.01982309,\n",
      "            0.028612755,\n",
      "            0.039537509000000005,\n",
      "            0.056795548,\n",
      "            0.07943224,\n",
      "            0.11271890300000001,\n",
      "            0.15754442400000002,\n",
      "            0.223591969,\n",
      "            0.317590186,\n",
      "            0.44750428900000006,\n",
      "            0.6331332740000001,\n",
      "            0.9018749620000001,\n",
      "            1.271493825,\n",
      "            1.8007542950000002,\n",
      "            2.536662789,\n",
      "            3.589324806,\n",
      "            5.0768979000000005,\n",
      "            7.182482913,\n",
      "            10.149991951,\n",
      "            14.432900129,\n",
      "            20.423478127000003,\n",
      "            29.079588239000003,\n",
      "            41.110390849000005,\n",
      "            58.02083565,\n",
      "            82.11639714600001,\n",
      "            114.871757063,\n",
      "            161.97425697,\n",
      "            229.029435613],\n",
      " 'njited_inline': [0.000472842,\n",
      "                   0.0006464330000000001,\n",
      "                   0.0009215730000000001,\n",
      "                   0.0012874350000000002,\n",
      "                   0.0018483070000000002,\n",
      "                   0.0025619500000000003,\n",
      "                   0.003635975,\n",
      "                   0.005238011000000001,\n",
      "                   0.007260639,\n",
      "                   0.010459623000000001,\n",
      "                   0.014531338000000001,\n",
      "                   0.020589153000000002,\n",
      "                   0.028964976000000003,\n",
      "                   0.040970365,\n",
      "                   0.058161674000000003,\n",
      "                   0.082031431,\n",
      "                   0.11623496700000001,\n",
      "                   0.16402748,\n",
      "                   0.232250104,\n",
      "                   0.329721935,\n",
      "                   0.46747033800000004,\n",
      "                   0.658208475,\n",
      "                   0.9332967780000001,\n",
      "                   1.3128149390000001,\n",
      "                   1.8559187250000002,\n",
      "                   2.6359574350000003,\n",
      "                   3.722083242,\n",
      "                   5.248153388,\n",
      "                   7.433895424,\n",
      "                   10.512661942000001,\n",
      "                   14.890898318000001,\n",
      "                   20.989466508,\n",
      "                   30.073046350000002,\n",
      "                   42.603091235,\n",
      "                   60.259246442000006,\n",
      "                   85.118391524,\n",
      "                   118.94378311800001,\n",
      "                   167.69500051100002,\n",
      "                   237.273412524],\n",
      " 'njited_parallel': [0.00019782,\n",
      "                     0.000243851,\n",
      "                     0.000524542,\n",
      "                     0.000252011,\n",
      "                     0.0005957530000000001,\n",
      "                     0.000600963,\n",
      "                     0.00042931200000000005,\n",
      "                     0.000619023,\n",
      "                     0.001005664,\n",
      "                     0.001231315,\n",
      "                     0.001930768,\n",
      "                     0.001291166,\n",
      "                     0.001705167,\n",
      "                     0.00249552,\n",
      "                     0.0027881110000000002,\n",
      "                     0.006628716000000001,\n",
      "                     0.004892850000000001,\n",
      "                     0.006700127,\n",
      "                     0.009000316000000001,\n",
      "                     0.013527305000000002,\n",
      "                     0.018988277,\n",
      "                     0.026687187,\n",
      "                     0.038057453000000005,\n",
      "                     0.055297982,\n",
      "                     0.077007819,\n",
      "                     0.107778312,\n",
      "                     0.15411154700000002,\n",
      "                     0.216426995,\n",
      "                     0.304906055,\n",
      "                     0.42412507600000005,\n",
      "                     0.600805939,\n",
      "                     0.854091939,\n",
      "                     1.214018958,\n",
      "                     1.7201683380000001,\n",
      "                     2.4556389220000003,\n",
      "                     3.476865102,\n",
      "                     4.917010628,\n",
      "                     6.983503834,\n",
      "                     9.876017643],\n",
      " 'vectorized': [0.000464982,\n",
      "                0.000613183,\n",
      "                0.0008850440000000001,\n",
      "                0.0012376960000000002,\n",
      "                0.001773227,\n",
      "                0.002484801,\n",
      "                0.003631814,\n",
      "                0.00506539,\n",
      "                0.006993138,\n",
      "                0.009884769,\n",
      "                0.014446738,\n",
      "                0.020487882000000002,\n",
      "                0.028111883,\n",
      "                0.040767904,\n",
      "                0.05717134,\n",
      "                0.08139365700000001,\n",
      "                0.112512863,\n",
      "                0.16060944600000002,\n",
      "                0.225058895,\n",
      "                0.31846767000000004,\n",
      "                0.45307330100000004,\n",
      "                0.640574733,\n",
      "                0.9027785150000001,\n",
      "                1.2731672090000001,\n",
      "                1.7995293870000002,\n",
      "                2.552043254,\n",
      "                3.615050033,\n",
      "                5.082438648,\n",
      "                7.227804409000001,\n",
      "                10.248479952,\n",
      "                14.421841562000001,\n",
      "                20.476133234000002,\n",
      "                29.163021707000002,\n",
      "                41.22831466,\n",
      "                58.114490145000005,\n",
      "                82.442559551,\n",
      "                115.139212704,\n",
      "                162.459243168,\n",
      "                229.773278123],\n",
      " 'vectorized_cuda': [0.0029785420000000003,\n",
      "                     0.0029987620000000003,\n",
      "                     0.002988562,\n",
      "                     0.003082632,\n",
      "                     0.0031102720000000003,\n",
      "                     0.0034160840000000002,\n",
      "                     0.004063346000000001,\n",
      "                     0.005748893000000001,\n",
      "                     0.0075371610000000006,\n",
      "                     0.010202581,\n",
      "                     0.014019776000000001,\n",
      "                     0.017293469000000002,\n",
      "                     0.017182779000000002,\n",
      "                     0.017312919,\n",
      "                     0.017287869,\n",
      "                     0.01740353,\n",
      "                     0.01727626,\n",
      "                     0.0173126,\n",
      "                     0.01753249,\n",
      "                     0.01728033,\n",
      "                     0.017410529,\n",
      "                     0.017575781000000002,\n",
      "                     0.017544561,\n",
      "                     0.033848056,\n",
      "                     0.033918716,\n",
      "                     0.050517572000000004,\n",
      "                     0.06607510500000001,\n",
      "                     0.083751904,\n",
      "                     0.10902935400000001,\n",
      "                     0.14681397300000001,\n",
      "                     0.192874124,\n",
      "                     0.276152708,\n",
      "                     0.37763678100000003,\n",
      "                     0.536880377,\n",
      "                     0.754117213,\n",
      "                     1.058661351,\n",
      "                     1.495284647,\n",
      "                     2.113165665,\n",
      "                     2.9771795030000003],\n",
      " 'vectorized_parallel': [0.000240661,\n",
      "                         0.000554972,\n",
      "                         0.000505332,\n",
      "                         0.000482613,\n",
      "                         0.0005869120000000001,\n",
      "                         0.0006329020000000001,\n",
      "                         0.000441102,\n",
      "                         0.000557482,\n",
      "                         0.000920394,\n",
      "                         0.000647613,\n",
      "                         0.000707143,\n",
      "                         0.001096924,\n",
      "                         0.001982638,\n",
      "                         0.003481624,\n",
      "                         0.002849482,\n",
      "                         0.010226820000000001,\n",
      "                         0.00509152,\n",
      "                         0.006898508,\n",
      "                         0.009328238,\n",
      "                         0.013075073000000001,\n",
      "                         0.019026066,\n",
      "                         0.028094763000000002,\n",
      "                         0.040333182,\n",
      "                         0.05249406,\n",
      "                         0.075764064,\n",
      "                         0.10742496100000001,\n",
      "                         0.15821295300000002,\n",
      "                         0.213115671,\n",
      "                         0.304137461,\n",
      "                         0.42923827400000003,\n",
      "                         0.6009468680000001,\n",
      "                         0.8787029430000001,\n",
      "                         1.217733556,\n",
      "                         1.7409656310000001,\n",
      "                         2.4395021590000003,\n",
      "                         3.4770304980000004,\n",
      "                         4.924803327,\n",
      "                         7.006374046,\n",
      "                         9.806653159000001]}\n"
     ]
    }
   ],
   "source": [
    "pprint(r)"
   ]
  }
 ],
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