03_numba_vectorize.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "03_numba_vectorize.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jeonggunlee/CUDATeaching/blob/master/03_numba_vectorize.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SacPvzX7FPKP",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGep1QbsEtpu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "659afbc3-7361-4dc6-ad87-136e84e8abbc"
      },
      "source": [
        "!pip install numba"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numba in /usr/local/lib/python3.6/dist-packages (0.40.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from numba) (1.16.4)\n",
            "Requirement already satisfied: llvmlite>=0.25.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba) (0.29.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRxVJynzEwv6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d5e49b69-e543-4926-f1f0-45e189df3953"
      },
      "source": [
        "from numba import jit\n",
        "from numpy import arange\n",
        "\n",
        "# jit decorator tells Numba to compile this function.\n",
        "# The argument types will be inferred by Numba when function is called.\n",
        "@jit\n",
        "def sum2d(arr):\n",
        "    M, N = arr.shape\n",
        "    result = 0.0\n",
        "    for i in range(M):\n",
        "        for j in range(N):\n",
        "            result += arr[i,j]\n",
        "    return result\n",
        "\n",
        "a = arange(9).reshape(3,3)\n",
        "print(sum2d(a))\n",
        "\n",
        "# [출력 결과]\n",
        "# 36.0"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "36.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3Lwds4VE_JL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "e1723204-5a9c-4156-9d9c-b1bc8e623ee3"
      },
      "source": [
        "from __future__ import print_function, division, absolute_import\n",
        "\n",
        "from timeit import default_timer as timer\n",
        "from matplotlib.pylab import imshow, jet, show, ion\n",
        "import numpy as np\n",
        "\n",
        "from numba import jit\n",
        "\n",
        "\n",
        "@jit\n",
        "def mandel(x, y, max_iters):\n",
        "    \"\"\"\n",
        "    Given the real and imaginary parts of a complex number,\n",
        "    determine if it is a candidate for membership in the Mandelbrot\n",
        "    set given a fixed number of iterations.\n",
        "    \"\"\"\n",
        "    i = 0\n",
        "    c = complex(x,y)\n",
        "    z = 0.0j\n",
        "    for i in range(max_iters):\n",
        "        z = z*z + c\n",
        "        if (z.real*z.real + z.imag*z.imag) >= 4:\n",
        "            return i\n",
        "\n",
        "    return 255\n",
        "\n",
        "@jit\n",
        "def create_fractal(min_x, max_x, min_y, max_y, image, iters):\n",
        "    height = image.shape[0]\n",
        "    width = image.shape[1]\n",
        "\n",
        "    pixel_size_x = (max_x - min_x) / width\n",
        "    pixel_size_y = (max_y - min_y) / height\n",
        "    for x in range(width):\n",
        "        real = min_x + x * pixel_size_x\n",
        "        for y in range(height):\n",
        "            imag = min_y + y * pixel_size_y\n",
        "            color = mandel(real, imag, iters)\n",
        "            image[y, x] = color\n",
        "\n",
        "    return image\n",
        "\n",
        "image = np.zeros((500 * 2, 750 * 2), dtype=np.uint8)\n",
        "s = timer()\n",
        "create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20)\n",
        "e = timer()\n",
        "print(e - s)\n",
        "imshow(image)\n",
        "# jet()\n",
        "# ion()\n",
        "show()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.22704317800003082\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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y+9Pyf8j5J6jk3dnmA7+QIuKuqjZpVnvib7VImia7OMhxpM/VmvXDyyRpi8I8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\nwBsMBkNHMQJvMBgMHeX/B+tnZb92NFSSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zI3fvBhXHN2L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!export NUMBAPRO_NVVM=/usr/local/cuda-10.1/nvvm/lib64/libnvvm.so\n",
        "!export NUMBAPRO_LIBDEVICE=/usr/local/cuda-10.1/nvvm/libdevice/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02Es33sSFVza",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "d0588ffd-13ad-4007-9643-95594c189585"
      },
      "source": [
        "import numpy as np\n",
        "from timeit import default_timer as timer\n",
        "from numba import vectorize\n",
        " \n",
        "# This should be a substantially high value. On my test machine, this took\n",
        "# 33 seconds to run via the CPU and just over 3 seconds on the GPU.\n",
        "NUM_ELEMENTS = 100000000\n",
        " \n",
        "# This is the CPU version.\n",
        "def vector_add_cpu(a, b):\n",
        "  c = np.zeros(NUM_ELEMENTS, dtype=np.float32)\n",
        "  for i in range(NUM_ELEMENTS):\n",
        "    c[i] = a[i] + b[i]\n",
        "  return c\n",
        " \n",
        "# This is the GPU version. Note the @vectorize decorator. This tells\n",
        "# numba to turn this into a GPU vectorized function.\n",
        "@vectorize([\"float32(float32, float32)\"], target='cuda')\n",
        "def vector_add_gpu(a, b):\n",
        "  return a + b;\n",
        " \n",
        "def main():\n",
        "  a_source = np.ones(NUM_ELEMENTS, dtype=np.float32)\n",
        "  b_source = np.ones(NUM_ELEMENTS, dtype=np.float32)\n",
        " \n",
        "  # Time the CPU function\n",
        "  start = timer()\n",
        "  vector_add_cpu(a_source, b_source)\n",
        "  vector_add_cpu_time = timer() - start\n",
        " \n",
        "  # Time the GPU function\n",
        "  start = timer()\n",
        "  vector_add_gpu(a_source, b_source)\n",
        "  vector_add_gpu_time = timer() - start\n",
        " \n",
        "  # Report times\n",
        "  print(\"CPU function took %f seconds.\" % vector_add_cpu_time)\n",
        "  print(\"GPU function took %f seconds.\" % vector_add_gpu_time)\n",
        " \n",
        "  return 0\n",
        " \n",
        "if __name__ == \"__main__\":\n",
        "  main()\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NvvmSupportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/cudadrv/nvvm.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m    110\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m                     \u001b[0minst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdriver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_cudalib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'nvvm'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mccc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/cudadrv/libs.py\u001b[0m in \u001b[0;36mopen_cudalib\u001b[0;34m(lib, ccc)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'library %s not found'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mccc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: library nvvm not found",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mNvvmSupportError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-45ade93195d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m   \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-22-45ade93195d1>\u001b[0m in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m   \u001b[0;31m# Time the GPU function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m   \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m   \u001b[0mvector_add_gpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_source\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb_source\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m   \u001b[0mvector_add_gpu_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/dispatcher.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m     86\u001b[0m                       \u001b[0mthe\u001b[0m \u001b[0minput\u001b[0m \u001b[0marguments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \"\"\"\n\u001b[0;32m---> 88\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mCUDAUFuncMechanism\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/npyufunc/deviceufunc.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(cls, typemap, args, kws)\u001b[0m\n\u001b[1;32m    302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m             \u001b[0mdevarys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdevout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m             \u001b[0mcr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevarys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0many_device\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/dispatcher.py\u001b[0m in \u001b[0;36mlaunch\u001b[0;34m(self, func, count, stream, args)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mis_device_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAutoJitCUDAKernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m             \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m             \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mspecialize\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    762\u001b[0m         argtypes = tuple(\n\u001b[1;32m    763\u001b[0m             [self.typingctx.resolve_argument_type(a) for a in args])\n\u001b[0;32m--> 764\u001b[0;31m         \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    765\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mcompile\u001b[0;34m(self, sig)\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefinitions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m                 \u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    784\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mbind\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    498\u001b[0m         \u001b[0mForce\u001b[0m \u001b[0mbinding\u001b[0m \u001b[0mto\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mCUDA\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    499\u001b[0m         \"\"\"\n\u001b[0;32m--> 500\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    376\u001b[0m         \u001b[0mcufunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcufunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m             \u001b[0mptx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mptx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m             \u001b[0;31m# Link\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0march\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnvvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_arch_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m             ptx = nvvm.llvm_to_ptx(self.llvmir, opt=3, arch=arch,\n\u001b[0;32m--> 350\u001b[0;31m                                    **self._extra_options)\n\u001b[0m\u001b[1;32m    351\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mptx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDUMP_ASSEMBLY\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/cudadrv/nvvm.py\u001b[0m in \u001b[0;36mllvm_to_ptx\u001b[0;34m(llvmir, **opts)\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mllvm_to_ptx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mllvmir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m     \u001b[0mcu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCompilationUnit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m     \u001b[0mlibdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLibDevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0march\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'arch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'compute_20'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m     \u001b[0;31m# New LLVM generate a shorthand for datalayout that NVVM does not know\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/cudadrv/nvvm.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mCompilationUnit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdriver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNVVM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnvvm_program\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnvvmCreateProgram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbyref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/cudadrv/nvvm.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m    114\u001b[0m                     errmsg = (\"libNVVM cannot be found. Do `conda install \"\n\u001b[1;32m    115\u001b[0m                               \"cudatoolkit`:\\n%s\")\n\u001b[0;32m--> 116\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mNvvmSupportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m                 \u001b[0;31m# Find & populate functions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNvvmSupportError\u001b[0m: libNVVM cannot be found. Do `conda install cudatoolkit`:\nlibrary nvvm not found",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLlYtqe3Ga_M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "9a7d2dd9-db72-458d-f82b-0db94a6bd35d"
      },
      "source": [
        "!LANG=C apt search cuda-toolkit"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sorting... Done\n",
            "Full Text Search... Done\n",
            "\u001b[32mcuda-toolkit-10-0\u001b[0m/unknown,now 10.0.130-1 amd64 [installed,automatic]\n",
            "  CUDA Toolkit 10.0 meta-package\n",
            "\n",
            "\u001b[32mcuda-toolkit-10-1\u001b[0m/unknown 10.1.168-1 amd64\n",
            "  CUDA Toolkit 10.1 meta-package\n",
            "\n",
            "\u001b[32mnvidia-cuda-toolkit\u001b[0m/bionic 9.1.85-3ubuntu1 amd64\n",
            "  NVIDIA CUDA development toolkit\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJuJRgfTGdKE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}