test_hash_cuda.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN7Sje281qYQjiKuMAjwlV3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tonystz/SimpleGPUHashTable/blob/master/test_hash_cuda.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wN8fU7TUKdGc"
      },
      "outputs": [],
      "source": [
        "!git clone https://github.com/nosferalatu/SimpleGPUHashTable.git\n",
        "!nvcc -ptx -o hash.ptx SimpleGPUHashTable/src/linearprobing.cu && ls *.ptx\n",
        "!pip install pycuda # install cuda\n",
        "import pycuda.driver as cuda\n",
        "import pycuda.autoinit\n",
        "from pycuda.compiler import SourceModule"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "a = np.full((4, 4), 1, dtype=np.float32)\n",
        "\n",
        "a_gpu = cuda.mem_alloc(a.nbytes)\n",
        "cuda.memcpy_htod(a_gpu, a)\n",
        "\n",
        "\n",
        "mod = SourceModule(\"\"\"\n",
        "  __global__ void doublify(float *a)\n",
        "  {\n",
        "    int idx = threadIdx.x + threadIdx.y*4;\n",
        "    a[idx] *= 2;\n",
        "  }\n",
        "  \"\"\")\n",
        "\n",
        "func = mod.get_function(\"doublify\")\n",
        "func(a_gpu, block=(4,4,1))\n",
        "\n",
        "a_doubled = np.empty_like(a)\n",
        "cuda.memcpy_dtoh(a_doubled, a_gpu)\n",
        "print(a_doubled)\n",
        "print(a)"
      ],
      "metadata": {
        "id": "wzOPHVXOLYAv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}