pycudaexampleslocal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PYCUDA: \n",
    "Needs a GPU and pycuda install"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/dtrad/geoml_course/blob/master/pycudaexamples.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KCI-5gw8o5eC",
    "outputId": "066f9199-a4ae-4940-b289-7e973f76bd46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "GNOME_DESKTOP_SESSION_ID=this-is-deprecated\n",
      "CONDA_EXE=/home/dtrad/anaconda3/bin/conda\n",
      "_CE_M=\n",
      "APPLICATION_INSIGHTS_NO_DIAGNOSTIC_CHANNEL=true\n",
      "SNAP_REAL_HOME=/home/dtrad\n",
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      "PYTHONPATH=/home/dtrad/madagascar/lib/python3.9/site-packages:/home/dtrad/anaconda3/bin:/home/dtrad/madagascar/src-master/framework/rsf/:/media/dtrad/disk2/pythonexamples/MLprojects/lib\n",
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     ]
    }
   ],
   "source": [
    "!env "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Environment needs ipykernel to be installed outside the notebook (in a terminal)\n",
    "# conda install -n pycuda ipykernel --update-deps --force-reinstall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p6bZ5nlZo7TF",
    "outputId": "9b1bb2d2-7081-4cf2-d31e-8bc83f6851c6"
   },
   "outputs": [],
   "source": [
    "# If not installed, for example in Collab, needs this \n",
    "#!pip install pycuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v9_dptFopnbX",
    "outputId": "30f5f7a1-07db-4751-e62c-028f8e953a75"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/dtrad\n"
     ]
    }
   ],
   "source": [
    "!echo $HOME\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oJIWjmESqnNV",
    "outputId": "5a65acf9-1ab1-43b7-f280-7373baba7898"
   },
   "outputs": [],
   "source": [
    "!pip config list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "nTCzBbGnuWl5"
   },
   "outputs": [],
   "source": [
    "import pycuda\n",
    "import pycuda.driver as drv\n",
    "drv.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x1Q7FTc12qDV",
    "outputId": "8cf816f8-a71a-4a80-9815-bf824161c615"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA device query (PyCUDA version) \n",
      "\n",
      "Detected 2 CUDA Capable device(s) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('CUDA device query (PyCUDA version) \\n')\n",
    "print('Detected {} CUDA Capable device(s) \\n'.format(drv.Device.count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0lW5r_po2uje",
    "outputId": "c985ce2d-b95c-4000-ffe5-9888a7df3766"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pycuda._driver.Device object at 0x7fbf9a029940>\n",
      "Device 0: NVIDIA GeForce RTX 2060 SUPER\n",
      "\t Compute Capability: 7.5\n",
      "\t Total Memory: 7982 megabytes\n",
      "<pycuda._driver.Device object at 0x7fbf9a029b70>\n",
      "Device 1: NVIDIA GeForce GTX 960\n",
      "\t Compute Capability: 5.2\n",
      "\t Total Memory: 1988 megabytes\n"
     ]
    }
   ],
   "source": [
    "for i in range(drv.Device.count()):\n",
    "    gpu_device = drv.Device(i)\n",
    "    print(gpu_device)\n",
    "    print( 'Device {}: {}'.format( i, gpu_device.name() ) )\n",
    "    compute_capability = float( '%d.%d' % gpu_device.compute_capability() )\n",
    "    print( '\\t Compute Capability: {}'.format(compute_capability))\n",
    "    print( '\\t Total Memory: {} megabytes'.format(gpu_device.total_memory()//(1024**2)))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "WihXA51A2yq_"
   },
   "outputs": [],
   "source": [
    "import pycuda.autoinit\n",
    "from pycuda import gpuarray\n",
    "from time import time\n",
    "from pycuda.elementwise import ElementwiseKernel \n",
    "import numpy as np  \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "LuHfbtDz3S8g"
   },
   "outputs": [],
   "source": [
    "host_data = np.float32( np.random.random(50000000) )\n",
    "\n",
    "gpu_2x_ker = ElementwiseKernel(\n",
    "\"float *in, float *out\",\n",
    "\"out[i] = 2*in[i];\",\n",
    "\"gpu_2x_ker\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "xySlvYLZ4DP2"
   },
   "outputs": [],
   "source": [
    "def speedcomparison():\n",
    "    t1 = time()\n",
    "    host_data_2x =  host_data * np.float32(2)\n",
    "    t2 = time()\n",
    "    print('total time to compute on CPU: %f' % (t2 - t1))\n",
    "    device_data = gpuarray.to_gpu(host_data)\n",
    "    # allocate memory for output\n",
    "    device_data_2x = gpuarray.empty_like(device_data)\n",
    "    t1 = time()\n",
    "    gpu_2x_ker(device_data, device_data_2x)\n",
    "    t2 = time()\n",
    "    from_device = device_data_2x.get()\n",
    "    print('total time to compute on GPU: %f' % (t2 - t1))\n",
    "    print('Is the host computation the same as the GPU computation? : {}'.format(np.allclose(from_device, host_data_2x) ))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bPS-7sUt4Io2",
    "outputId": "e43ebb8b-d428-465b-b433-94e5c6b37c46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total time to compute on CPU: 0.064909\n",
      "total time to compute on GPU: 0.077960\n",
      "Is the host computation the same as the GPU computation? : True\n"
     ]
    }
   ],
   "source": [
    "# First time GPU is slow because it needs to compile the code.\n",
    "speedcomparison()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kUvz3QJa4SPq",
    "outputId": "582e5633-6af4-4fc3-a5fb-67e3439c02ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total time to compute on CPU: 0.061355\n",
      "total time to compute on GPU: 0.000088\n",
      "Is the host computation the same as the GPU computation? : True\n"
     ]
    }
   ],
   "source": [
    "speedcomparison()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UMHE0FBR9ZS4"
   },
   "outputs": [],
   "source": []
  }
 ],
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