test_04.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)->(n)')\n",
    "def guvectorized(d: float, r: float):\n",
    "    for idx in range(d.shape[0]):\n",
    "        r[idx] = guvectorized_dummy(d[idx])"
   ]
  },
  {
   "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[:])', '(n)->(n)', target = 'parallel')\n",
    "def guvectorized_parallel(d: float, r: float):\n",
    "    for idx in nb.prange(d.shape[0]):\n",
    "        r[idx] = guvectorized_parallel_dummy(d[idx])"
   ]
  },
  {
   "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[:])', '(n)->(n)', target = 'cuda')\n",
    "def guvectorized_cuda(d: float, r: float):\n",
    "    for idx in nb.prange(d.shape[0]):\n",
    "        r[idx] = guvectorized_cuda_dummy(d[idx])"
   ]
  },
  {
   "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": "07825b01-1970-4118-bc0d-5ca9d238bca0",
   "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\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": "a50682f6-1d47-468d-a01f-801833eaf427",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 44%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                                                                                                                                 | 11/25 [00:09<00:17,  1.22s/it]/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",
      " 52%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                                                                                                 | 13/25 [00:14<00:22,  1.90s/it]/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",
      " 56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                                                                                 | 14/25 [00:18<00:27,  2.46s/it]/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",
      " 60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                                                                | 15/25 [00:23<00:32,  3.26s/it]/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",
      " 64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                                                | 16/25 [00:30<00:39,  4.39s/it]/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",
      " 68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                                | 17/25 [00:40<00:47,  5.98s/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",
      " 72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                | 18/25 [00:53<00:57,  8.23s/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",
      " 76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                | 19/25 [01:12<01:08, 11.42s/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",
      " 80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                | 20/25 [01:39<01:19, 15.93s/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",
      " 84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                | 21/25 [02:16<01:29, 22.26s/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",
      " 88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                | 22/25 [03:08<01:33, 31.22s/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",
      " 92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                | 23/25 [04:21<01:27, 43.89s/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",
      " 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                | 24/25 [06:05<01:01, 61.79s/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%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [08:31<00:00, 20.45s/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 = 16, reps = 2):  # 16 or 23 ... 13 should do\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,   256,\n",
       "         362,   512,   724,  1024,  1448,  2048,  2896,  4096,  5792,\n",
       "        8192, 11585, 16384, 23170, 32768, 46340, 65536])"
      ]
     },
     "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': [0.000481822,\n",
      "                  0.000649533,\n",
      "                  0.000922184,\n",
      "                  0.001266826,\n",
      "                  0.0018272890000000002,\n",
      "                  0.002535821,\n",
      "                  0.003578837,\n",
      "                  0.005090454,\n",
      "                  0.007092293,\n",
      "                  0.010264958000000001,\n",
      "                  0.014521358000000002,\n",
      "                  0.020544026,\n",
      "                  0.029070047,\n",
      "                  0.040987261000000004,\n",
      "                  0.058053820000000006,\n",
      "                  0.081342579,\n",
      "                  0.114645254,\n",
      "                  0.163599343,\n",
      "                  0.232865335,\n",
      "                  0.328985491,\n",
      "                  0.4642121,\n",
      "                  0.653381867,\n",
      "                  0.9288389140000001,\n",
      "                  1.3001342200000001,\n",
      "                  1.8351700020000001],\n",
      " 'guvectorized_cuda': [0.016954919000000002,\n",
      "                       0.022913187,\n",
      "                       0.032971774,\n",
      "                       0.052537395,\n",
      "                       0.065298353,\n",
      "                       0.09153396700000001,\n",
      "                       0.129883476,\n",
      "                       0.18353033600000002,\n",
      "                       0.258966697,\n",
      "                       0.365897905,\n",
      "                       0.51725308,\n",
      "                       0.731172448,\n",
      "                       1.033806598,\n",
      "                       1.462373936,\n",
      "                       2.082971736,\n",
      "                       2.9451551830000002,\n",
      "                       4.165205782,\n",
      "                       5.890758665000001,\n",
      "                       8.389444063000001,\n",
      "                       11.858659274,\n",
      "                       16.789724993,\n",
      "                       23.743292586000003,\n",
      "                       33.578744714,\n",
      "                       47.486094772,\n",
      "                       67.156372007],\n",
      " 'guvectorized_parallel': [0.0007651740000000001,\n",
      "                           0.0033767360000000004,\n",
      "                           0.001446837,\n",
      "                           0.003625047,\n",
      "                           0.002796493,\n",
      "                           0.003902318,\n",
      "                           0.005458565,\n",
      "                           0.007039942,\n",
      "                           0.009132933000000001,\n",
      "                           0.012150426,\n",
      "                           0.016396496,\n",
      "                           0.022374344,\n",
      "                           0.030998305,\n",
      "                           0.04297766,\n",
      "                           0.059825049000000005,\n",
      "                           0.085264167,\n",
      "                           0.11762804900000001,\n",
      "                           0.166262605,\n",
      "                           0.234333691,\n",
      "                           0.32942982400000004,\n",
      "                           0.46545481400000005,\n",
      "                           0.656491971,\n",
      "                           0.9193939000000001,\n",
      "                           1.293963671,\n",
      "                           1.83927312],\n",
      " 'vectorized': [0.00046808200000000004,\n",
      "                0.0006362130000000001,\n",
      "                0.0009181250000000001,\n",
      "                0.001281276,\n",
      "                0.0018210880000000002,\n",
      "                0.002558861,\n",
      "                0.0036164870000000003,\n",
      "                0.005128793,\n",
      "                0.007234743,\n",
      "                0.010260658,\n",
      "                0.014460327,\n",
      "                0.020217524,\n",
      "                0.028916575000000003,\n",
      "                0.041018521,\n",
      "                0.058072091000000006,\n",
      "                0.08169594000000001,\n",
      "                0.115629908,\n",
      "                0.16425135500000002,\n",
      "                0.23330652600000001,\n",
      "                0.32815705700000003,\n",
      "                0.462217831,\n",
      "                0.6557020990000001,\n",
      "                0.9280995910000001,\n",
      "                1.296603245,\n",
      "                1.8260158990000002],\n",
      " 'vectorized_cuda': [0.0027532330000000003,\n",
      "                     0.0028247330000000003,\n",
      "                     0.002873323,\n",
      "                     0.0029996840000000003,\n",
      "                     0.002690763,\n",
      "                     0.003037414,\n",
      "                     0.003581387,\n",
      "                     0.005023203,\n",
      "                     0.006337779,\n",
      "                     0.008987632,\n",
      "                     0.012227997,\n",
      "                     0.015105990000000001,\n",
      "                     0.01510147,\n",
      "                     0.015297771000000002,\n",
      "                     0.015253231,\n",
      "                     0.01529688,\n",
      "                     0.015320472000000002,\n",
      "                     0.015383471000000001,\n",
      "                     0.015449032000000001,\n",
      "                     0.015419042,\n",
      "                     0.015403482000000001,\n",
      "                     0.015637873,\n",
      "                     0.017390371,\n",
      "                     0.033783526,\n",
      "                     0.034038018],\n",
      " 'vectorized_parallel': [0.000155751,\n",
      "                         0.0017843980000000002,\n",
      "                         0.00015640100000000002,\n",
      "                         0.000223221,\n",
      "                         0.00017876,\n",
      "                         0.000310551,\n",
      "                         0.000432883,\n",
      "                         0.00033078100000000004,\n",
      "                         0.001543878,\n",
      "                         0.0018121790000000001,\n",
      "                         0.001006554,\n",
      "                         0.001457147,\n",
      "                         0.0014229870000000001,\n",
      "                         0.0017942190000000001,\n",
      "                         0.002430471,\n",
      "                         0.010385939,\n",
      "                         0.004846912,\n",
      "                         0.006391320000000001,\n",
      "                         0.018266655,\n",
      "                         0.022103733,\n",
      "                         0.019217529,\n",
      "                         0.030530662000000004,\n",
      "                         0.038257258,\n",
      "                         0.053391667000000004,\n",
      "                         0.07556486000000001]}\n"
     ]
    }
   ],
   "source": [
    "pprint(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab330785-b87b-43bc-9f93-a8d735ebb475",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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