test_06.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b33da213-730d-46ea-92e0-95a34553e926",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "633b48ab-562f-4d9b-94f3-1d32080a23b0",
   "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": "markdown",
   "id": "a760945e-8c6b-4b22-8b01-9973586e50ee",
   "metadata": {},
   "source": [
    "# Base implementation of the test\n",
    "\n",
    "Pure Python plus `numpy`, intentionally iterative approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "366522d1-451f-4875-86c1-94549a65fcde",
   "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": "markdown",
   "id": "baacef19-e0f3-4576-aaee-90f1904b04eb",
   "metadata": {},
   "source": [
    "# Using `numba.vectorized`, target `cpu`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0963eff5-d688-4686-85a8-633f6018d9c7",
   "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": "markdown",
   "id": "4a31e2e4-1347-4f87-b7b6-cbbae929bc8d",
   "metadata": {},
   "source": [
    "# Using `numba.guvectorized`, target `cpu`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66c8133c-bd59-4b67-a7bb-bff11f53c0a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "@nb.njit('f8(f8)')\n",
    "def guvectorized_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.guvectorize('void(f8[:],f8[:])', '()->()')\n",
    "def guvectorized(d: float, r: float):\n",
    "    r[0] = guvectorized_dummy(d[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f783e84-0fe2-419a-8e7b-ccd20930745f",
   "metadata": {},
   "source": [
    "# Using `numba.vectorized`, target `parallel`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ef822a7-d3d2-4f73-a20c-4fbb7ce68a22",
   "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": "markdown",
   "id": "7c3cc6d6-7eea-4009-bbca-bdbb47fe9b49",
   "metadata": {},
   "source": [
    "# Using `numba.guvectorized`, target `parallel`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3375fca3-df60-422d-99de-ebf8b6c855e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "@nb.njit('f8(f8)')\n",
    "def guvectorized_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.guvectorize('void(f8[:],f8[:])', '()->()', target = 'parallel')\n",
    "def guvectorized_parallel(d: float, r: float):\n",
    "    r[0] = guvectorized_parallel_dummy(d[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93633d6e-07fa-4cd1-a45a-ca3e0144ef79",
   "metadata": {},
   "source": [
    "# Using `numba.vectorized`, target `cuda`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "51e9c635-f7bf-46e9-bc50-f598ac290a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "@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": "markdown",
   "id": "96bb92b7-48b3-406a-ba61-c108608afcb8",
   "metadata": {},
   "source": [
    "# Using `numba.guvectorized`, target `cuda`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec2b51ae-4ecc-45f0-870f-3deb4fb93bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "@cuda.jit('f8(f8)', device = True, inline = True)\n",
    "def guvectorized_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.guvectorize('void(f8[:],f8[:])', '()->()', target = 'cuda')\n",
    "def guvectorized_cuda(d: float, r: float):\n",
    "    r[0] = guvectorized_cuda_dummy(d[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f6b35c-c8bd-4fd0-9e80-8555e8d47daa",
   "metadata": {},
   "source": [
    "# Verification of results of all functions against base implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e687611-e325-4dbc-ad4a-d23a8c081b6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "funcs = [\n",
    "    base,\n",
    "    vectorized,\n",
    "    vectorized_parallel,\n",
    "    vectorized_cuda,\n",
    "    guvectorized,\n",
    "    guvectorized_parallel,\n",
    "    guvectorized_cuda,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0207051e-5077-4b13-b2c7-50259ddf8333",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 1 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).astype('f8')\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": "markdown",
   "id": "7be6d6b6-1a1a-44d7-9b85-9c15961c829a",
   "metadata": {},
   "source": [
    "# Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f5136fd-d9ae-486a-85bf-3afe1f69c591",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 41%|█████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                           | 11/27 [00:07<00:10,  1.48it/s]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 2 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 48%|████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                            | 13/27 [00:08<00:10,  1.38it/s]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 3 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                    | 14/27 [00:09<00:10,  1.29it/s]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 4 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                             | 15/27 [00:10<00:10,  1.16it/s]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 5 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                     | 16/27 [00:11<00:10,  1.08it/s]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 7 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                             | 17/27 [00:12<00:10,  1.01s/it]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 10 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                     | 18/27 [00:14<00:10,  1.12s/it]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 13 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                              | 19/27 [00:15<00:10,  1.28s/it]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 19 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                      | 20/27 [00:17<00:10,  1.51s/it]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 26 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                              | 21/27 [00:20<00:11,  1.85s/it]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 37 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 81%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                      | 22/27 [00:24<00:11,  2.32s/it]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 52 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                               | 23/27 [00:28<00:11,  2.99s/it]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 73 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                       | 24/27 [00:34<00:11,  3.97s/it]/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 103 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 27/27 [01:11<00:00,  2.67s/it]\n"
     ]
    }
   ],
   "source": [
    "def _name(func):\n",
    "    try:\n",
    "        return func.__name__\n",
    "    except AttributeError:  # numba#8272\n",
    "        try:\n",
    "            return list(func.functions.values())[0][1].py_func.__name__[13:]\n",
    "        except AttributeError:  # TODO\n",
    "            return list(func.kernelmap.values())[0][1].py_func.__name__[9:]\n",
    "\n",
    "def benchmark(start = 4, stop = 17, reps = 2):\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": "markdown",
   "id": "5b4966a9-e98f-47fe-bdc4-e4f23842d026",
   "metadata": {},
   "source": [
    "# Benchmark Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8794e0f3-dba4-4b54-8af6-836f1faff633",
   "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",
    "    ('guvectorized', 'guvectorized_parallel'),\n",
    "    ('guvectorized_parallel', 'guvectorized_cuda'),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e2fbc2-ef45-4d70-a69d-6829aaa61f58",
   "metadata": {},
   "source": [
    "# Benchmark Raw Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d068ac80-b158-46aa-9e0a-2789073784c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    16,     22,     32,     45,     64,     90,    128,    181,\n",
       "          256,    362,    512,    724,   1024,   1448,   2048,   2896,\n",
       "         4096,   5792,   8192,  11585,  16384,  23170,  32768,  46340,\n",
       "        65536,  92681, 131072])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5493d276-e5bd-4e53-9779-75f6f742b1e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'guvectorized': [0.000475232,\n",
      "                  0.000644803,\n",
      "                  0.000926894,\n",
      "                  0.001290296,\n",
      "                  0.001827208,\n",
      "                  0.002564212,\n",
      "                  0.003629456,\n",
      "                  0.005143163,\n",
      "                  0.007219012,\n",
      "                  0.010277637000000001,\n",
      "                  0.014538975,\n",
      "                  0.020442402000000002,\n",
      "                  0.029017391000000003,\n",
      "                  0.042557323,\n",
      "                  0.058183594000000005,\n",
      "                  0.08199935200000001,\n",
      "                  0.11591685500000001,\n",
      "                  0.16350350100000002,\n",
      "                  0.231413219,\n",
      "                  0.326877851,\n",
      "                  0.461980914,\n",
      "                  0.654348884,\n",
      "                  0.924214207,\n",
      "                  1.3068866890000002,\n",
      "                  1.8504393280000002,\n",
      "                  2.69135305,\n",
      "                  3.6946677730000004],\n",
      " 'guvectorized_cuda': [0.002970323,\n",
      "                       0.002958673,\n",
      "                       0.002948884,\n",
      "                       0.003093464,\n",
      "                       0.003108304,\n",
      "                       0.003407676,\n",
      "                       0.003965758000000001,\n",
      "                       0.005909576000000001,\n",
      "                       0.007188433,\n",
      "                       0.010177217,\n",
      "                       0.013322760000000001,\n",
      "                       0.017248448,\n",
      "                       0.017180688,\n",
      "                       0.017199598,\n",
      "                       0.017290378000000002,\n",
      "                       0.017269668000000002,\n",
      "                       0.017314029,\n",
      "                       0.017327069,\n",
      "                       0.017483520000000002,\n",
      "                       0.017326859,\n",
      "                       0.017310579,\n",
      "                       0.017340898,\n",
      "                       0.01757871,\n",
      "                       0.033736703,\n",
      "                       0.033947423000000004,\n",
      "                       0.050573949,\n",
      "                       0.067226084],\n",
      " 'guvectorized_parallel': [0.00020599000000000003,\n",
      "                           0.000186941,\n",
      "                           0.002681862,\n",
      "                           0.00020110100000000002,\n",
      "                           0.000650423,\n",
      "                           0.000258101,\n",
      "                           0.004263949,\n",
      "                           0.000293981,\n",
      "                           0.00048576200000000004,\n",
      "                           0.000554343,\n",
      "                           0.0007702130000000001,\n",
      "                           0.001125955,\n",
      "                           0.001347606,\n",
      "                           0.0018525590000000002,\n",
      "                           0.008951770000000001,\n",
      "                           0.0044417300000000005,\n",
      "                           0.004835702,\n",
      "                           0.006335289,\n",
      "                           0.010182676,\n",
      "                           0.013632712000000002,\n",
      "                           0.021213607000000002,\n",
      "                           0.030328717,\n",
      "                           0.043165896,\n",
      "                           0.058807907000000006,\n",
      "                           0.08402175,\n",
      "                           0.117191181,\n",
      "                           0.16142005],\n",
      " 'vectorized': [0.000472722,\n",
      "                0.000637553,\n",
      "                0.000919554,\n",
      "                0.0012874350000000002,\n",
      "                0.001818638,\n",
      "                0.0025573220000000003,\n",
      "                0.003630167,\n",
      "                0.005124143,\n",
      "                0.007278163000000001,\n",
      "                0.010244447,\n",
      "                0.014455435,\n",
      "                0.020401432,\n",
      "                0.028896001,\n",
      "                0.040804946,\n",
      "                0.058116433,\n",
      "                0.081857041,\n",
      "                0.11554932400000001,\n",
      "                0.16374349300000002,\n",
      "                0.230586705,\n",
      "                0.327106033,\n",
      "                0.46241611600000004,\n",
      "                0.653734602,\n",
      "                0.924482419,\n",
      "                1.307456302,\n",
      "                1.8522010370000002,\n",
      "                2.684155611,\n",
      "                3.697531199],\n",
      " 'vectorized_cuda': [0.002998623,\n",
      "                     0.0029633930000000004,\n",
      "                     0.003193735,\n",
      "                     0.0031261740000000002,\n",
      "                     0.003070344,\n",
      "                     0.003458416,\n",
      "                     0.004115879,\n",
      "                     0.005720735,\n",
      "                     0.007549644,\n",
      "                     0.010281956,\n",
      "                     0.013870063,\n",
      "                     0.017287539,\n",
      "                     0.017162638,\n",
      "                     0.01752888,\n",
      "                     0.017437559000000002,\n",
      "                     0.017416918,\n",
      "                     0.017373499,\n",
      "                     0.017382959,\n",
      "                     0.017322519,\n",
      "                     0.017340039,\n",
      "                     0.017362758000000002,\n",
      "                     0.017572869,\n",
      "                     0.017366858000000002,\n",
      "                     0.033906534,\n",
      "                     0.034067565,\n",
      "                     0.050570709000000005,\n",
      "                     0.067072883],\n",
      " 'vectorized_parallel': [0.00018269,\n",
      "                         0.00031645100000000004,\n",
      "                         0.000202451,\n",
      "                         0.000324402,\n",
      "                         0.0013925160000000001,\n",
      "                         0.00030422200000000004,\n",
      "                         0.0030018930000000003,\n",
      "                         0.0005053820000000001,\n",
      "                         0.000551232,\n",
      "                         0.000576873,\n",
      "                         0.0008400040000000001,\n",
      "                         0.001336296,\n",
      "                         0.0018986790000000001,\n",
      "                         0.0016954770000000001,\n",
      "                         0.007846586000000001,\n",
      "                         0.008071616,\n",
      "                         0.00453536,\n",
      "                         0.00647931,\n",
      "                         0.009102391000000001,\n",
      "                         0.013904294000000001,\n",
      "                         0.021016954,\n",
      "                         0.026460329,\n",
      "                         0.037755451,\n",
      "                         0.060725375000000005,\n",
      "                         0.08054383500000001,\n",
      "                         0.115167721,\n",
      "                         0.16515263700000002]}\n"
     ]
    }
   ],
   "source": [
    "pprint(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab330785-b87b-43bc-9f93-a8d735ebb475",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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