test_05.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",
    "        for jdx in range(d.shape[1]):\n",
    "            r[idx, jdx] = base_dummy(d[idx, jdx])\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 range(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 range(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": "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).reshape(size, 1).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<00:11,  1.39it/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,  1.29it/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,  1.22it/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,  1.17it/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",
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 6 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                     | 16/27 [00:11<00:09,  1.10it/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",
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 8 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                             | 17/27 [00:12<00:09,  1.01it/s]/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",
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 11 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                     | 18/27 [00:14<00:09,  1.09s/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",
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 15 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                              | 19/27 [00:15<00:10,  1.25s/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",
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 21 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                      | 20/27 [00:17<00:10,  1.48s/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",
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 29 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",
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 41 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 81%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                      | 22/27 [00:24<00:11,  2.37s/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",
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 57 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                               | 23/27 [00:29<00:12,  3.09s/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",
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 81 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      " 89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                       | 24/27 [00:35<00:12,  4.06s/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",
      "/home/ernst/Desktop/PROJEKTE/prj.TST2/github.poliastro/env310/lib/python3.10/site-packages/numba/cuda/dispatcher.py:488: NumbaPerformanceWarning: Grid size 114 will likely result in GPU under-utilization due to low occupancy.\n",
      "  warn(NumbaPerformanceWarning(msg))\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 27/27 [01:12<00:00,  2.69s/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').reshape(size, 1).copy()\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': [0.00047706200000000005,\n",
      "                  0.000647833,\n",
      "                  0.0009283440000000001,\n",
      "                  0.001298946,\n",
      "                  0.001828958,\n",
      "                  0.0025661110000000003,\n",
      "                  0.003637107,\n",
      "                  0.005129552,\n",
      "                  0.007231622,\n",
      "                  0.010210415,\n",
      "                  0.014433394,\n",
      "                  0.020368671,\n",
      "                  0.028930088000000003,\n",
      "                  0.040731931000000006,\n",
      "                  0.057597156000000004,\n",
      "                  0.081861604,\n",
      "                  0.11567413500000001,\n",
      "                  0.16322671500000002,\n",
      "                  0.231006608,\n",
      "                  0.326597793,\n",
      "                  0.462808359,\n",
      "                  0.655713147,\n",
      "                  0.924761475,\n",
      "                  1.3074934660000002,\n",
      "                  1.907413145,\n",
      "                  2.6688200010000003,\n",
      "                  3.642478291],\n",
      " 'guvectorized_cuda': [0.002926783,\n",
      "                       0.002918003,\n",
      "                       0.0029424330000000004,\n",
      "                       0.002974453,\n",
      "                       0.002975983,\n",
      "                       0.0032891440000000004,\n",
      "                       0.004073229,\n",
      "                       0.005907787,\n",
      "                       0.007025731,\n",
      "                       0.010147755000000001,\n",
      "                       0.013547310000000002,\n",
      "                       0.015592559,\n",
      "                       0.015518279000000001,\n",
      "                       0.015516599,\n",
      "                       0.015528400000000001,\n",
      "                       0.01569305,\n",
      "                       0.015563659,\n",
      "                       0.015556909,\n",
      "                       0.01574511,\n",
      "                       0.015607699000000001,\n",
      "                       0.015631479,\n",
      "                       0.01566796,\n",
      "                       0.015907241000000003,\n",
      "                       0.030271705000000003,\n",
      "                       0.030485556,\n",
      "                       0.045286721,\n",
      "                       0.060015767000000005],\n",
      " 'guvectorized_parallel': [0.00017334000000000002,\n",
      "                           0.001138595,\n",
      "                           0.000166471,\n",
      "                           0.000185531,\n",
      "                           0.000223221,\n",
      "                           0.00033877200000000004,\n",
      "                           0.000645333,\n",
      "                           0.00037038200000000005,\n",
      "                           0.00045056200000000005,\n",
      "                           0.000588822,\n",
      "                           0.001596177,\n",
      "                           0.001155725,\n",
      "                           0.0013820260000000002,\n",
      "                           0.0018065980000000002,\n",
      "                           0.00233992,\n",
      "                           0.005131743,\n",
      "                           0.010453257,\n",
      "                           0.006416869,\n",
      "                           0.009580623,\n",
      "                           0.013445080000000002,\n",
      "                           0.020773873,\n",
      "                           0.032686436,\n",
      "                           0.043014451,\n",
      "                           0.055254796,\n",
      "                           0.08103120100000001,\n",
      "                           0.11780924400000001,\n",
      "                           0.15954149],\n",
      " 'vectorized': [0.00046643300000000004,\n",
      "                0.000639853,\n",
      "                0.000915704,\n",
      "                0.001279566,\n",
      "                0.0018171180000000002,\n",
      "                0.0025578420000000003,\n",
      "                0.0036138560000000004,\n",
      "                0.0051210430000000005,\n",
      "                0.007228751,\n",
      "                0.010225946000000001,\n",
      "                0.014419814000000001,\n",
      "                0.02040932,\n",
      "                0.028809627,\n",
      "                0.040766451,\n",
      "                0.057631987,\n",
      "                0.081465253,\n",
      "                0.11548941400000001,\n",
      "                0.16274796400000002,\n",
      "                0.230710936,\n",
      "                0.327068745,\n",
      "                0.46369614200000003,\n",
      "                0.654313961,\n",
      "                0.9540470050000001,\n",
      "                1.308482621,\n",
      "                1.8524344110000002,\n",
      "                2.698722425,\n",
      "                3.7001044180000005],\n",
      " 'vectorized_cuda': [0.003025044,\n",
      "                     0.002980144,\n",
      "                     0.003143934,\n",
      "                     0.003060563,\n",
      "                     0.003036224,\n",
      "                     0.0034664440000000004,\n",
      "                     0.004043067,\n",
      "                     0.005720806,\n",
      "                     0.007210772000000001,\n",
      "                     0.010252786,\n",
      "                     0.013905262000000002,\n",
      "                     0.017371027,\n",
      "                     0.017233797000000002,\n",
      "                     0.017466128,\n",
      "                     0.017350398,\n",
      "                     0.017252336,\n",
      "                     0.017358967,\n",
      "                     0.017335887,\n",
      "                     0.017299437,\n",
      "                     0.017398638,\n",
      "                     0.017388897,\n",
      "                     0.017602838000000003,\n",
      "                     0.017450247000000002,\n",
      "                     0.033953491,\n",
      "                     0.034296202000000005,\n",
      "                     0.050575615000000004,\n",
      "                     0.06707676900000001],\n",
      " 'vectorized_parallel': [0.0012497860000000001,\n",
      "                         0.0011706750000000001,\n",
      "                         0.0019834180000000002,\n",
      "                         0.00015835100000000002,\n",
      "                         0.00028822200000000003,\n",
      "                         0.000509222,\n",
      "                         0.000409702,\n",
      "                         0.000518393,\n",
      "                         0.00219571,\n",
      "                         0.00234066,\n",
      "                         0.000990495,\n",
      "                         0.002685762,\n",
      "                         0.00238069,\n",
      "                         0.0024556110000000003,\n",
      "                         0.002651402,\n",
      "                         0.008745269,\n",
      "                         0.00459101,\n",
      "                         0.008659119,\n",
      "                         0.009398442,\n",
      "                         0.012962728000000001,\n",
      "                         0.022004549,\n",
      "                         0.028908959,\n",
      "                         0.041996287,\n",
      "                         0.057461705,\n",
      "                         0.082381996,\n",
      "                         0.12053564500000001,\n",
      "                         0.169011982]}\n"
     ]
    }
   ],
   "source": [
    "pprint(r)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "ab330785-b87b-43bc-9f93-a8d735ebb475",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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