CorrigeTD1ProgGPU.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lilaiguernelala/Programmation-GPU/blob/main/CorrigeTD1ProgGPU.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vjov-qc1v32O",
        "outputId": "805e5c2f-3994-4323-a90f-0774e44a28d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2023 NVIDIA Corporation\n",
            "Built on Tue_Aug_15_22:02:13_PDT_2023\n",
            "Cuda compilation tools, release 12.2, V12.2.140\n",
            "Build cuda_12.2.r12.2/compiler.33191640_0\n"
          ]
        }
      ],
      "source": [
        "!nvcc --version"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/andreinechaev/nvcc4jupyter.git\n",
        "%load_ext nvcc4jupyter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gNTbL4fywF2i",
        "outputId": "130f3af5-2a44-41b2-bad9-f90444629228"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/andreinechaev/nvcc4jupyter.git\n",
            "  Cloning https://github.com/andreinechaev/nvcc4jupyter.git to /tmp/pip-req-build-2krm4v3w\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/andreinechaev/nvcc4jupyter.git /tmp/pip-req-build-2krm4v3w\n",
            "  Resolved https://github.com/andreinechaev/nvcc4jupyter.git to commit 781ff5b76ba6c4c2d80dcbbec9983e147613cc71\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: nvcc4jupyter\n",
            "  Building wheel for nvcc4jupyter (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for nvcc4jupyter: filename=nvcc4jupyter-1.1.0-py3-none-any.whl size=8011 sha256=e2b2acbb79d726227ace2ffdeb18b901e4debb9cf9419b13a35a389a655ee4ac\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-iosnbexc/wheels/a8/b9/18/23f8ef71ceb0f63297dd1903aedd067e6243a68ea756d6feea\n",
            "Successfully built nvcc4jupyter\n",
            "Installing collected packages: nvcc4jupyter\n",
            "Successfully installed nvcc4jupyter-1.1.0\n",
            "Source files will be saved in \"/tmp/tmp_a71w10v\".\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise 3"
      ],
      "metadata": {
        "id": "vdZBR7NSxMLF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%cuda\n",
        "#include <iostream>\n",
        "#include <stdio.h>\n",
        "\n",
        "__global__ void linear_regression(float *inputs, float *targets, int size, float *weights, float *bias, float learning_rate, int epochs) {\n",
        "    int idx = blockIdx.x * blockDim.x + threadIdx.x;\n",
        "    float cost;\n",
        "    for (int epoch = 0; epoch < epochs; ++epoch) {\n",
        "        float predicted = inputs[idx] * weights[0] + bias[0];\n",
        "        float loss = predicted - targets[idx];\n",
        "        float d_weights = inputs[idx] * loss / (2 * size);\n",
        "        float d_bias = loss / (2 * size);\n",
        "        weights[0] -= learning_rate * d_weights;\n",
        "        bias[0] -= learning_rate * d_bias;\n",
        "        cost = sqrtf(powf(loss, 2) / (2 * size));\n",
        "        if (idx == 0) {\n",
        "            printf(\"Iteration: %d | Cost/Loss: %f | Weight: %f | Bias: %f\\n\", epoch + 1, cost, weights[0], bias[0]);\n",
        "        }\n",
        "    }\n",
        "}\n",
        "\n",
        "int main(void) {\n",
        "    int m = 300; // Number of training data points\n",
        "    int n = 50; // Number of testing data points\n",
        "    float learning_rate = 0.0001;\n",
        "    int epochs = 200000;\n",
        "    float a = 0.5; // y = ax + b\n",
        "    float b = 2.0;\n",
        "    int size = m;\n",
        "    int dataSize = size * sizeof(float);\n",
        "    float *h_inputs, *h_targets, *h_weights, *h_bias;\n",
        "    float *d_inputs, *d_targets, *d_weights, *d_bias;\n",
        "\n",
        "    // Allocate host memory\n",
        "    h_inputs = (float*)malloc(dataSize);\n",
        "    h_targets = (float*)malloc(dataSize);\n",
        "    h_weights = (float*)malloc(sizeof(float));\n",
        "    h_bias = (float*)malloc(sizeof(float));\n",
        "\n",
        "    // Generate data\n",
        "    for (int i = 0; i < m; ++i) {\n",
        "        h_inputs[i] = i + 1;\n",
        "        h_targets[i] = (i + 1) * a + b;\n",
        "    }\n",
        "\n",
        "    // Allocate device memory\n",
        "    cudaMalloc((void**)&d_inputs, dataSize);\n",
        "    cudaMalloc((void**)&d_targets, dataSize);\n",
        "    cudaMalloc((void**)&d_weights, sizeof(float));\n",
        "    cudaMalloc((void**)&d_bias, sizeof(float));\n",
        "\n",
        "    // Copy data to device memory\n",
        "    cudaMemcpy(d_inputs, h_inputs, dataSize, cudaMemcpyHostToDevice);\n",
        "    cudaMemcpy(d_targets, h_targets, dataSize, cudaMemcpyHostToDevice);\n",
        "\n",
        "    // Initialize weights and bias\n",
        "    h_weights[0] = 0;\n",
        "    h_bias[0] = 0;\n",
        "\n",
        "    // Copy initial weights and bias to device memory\n",
        "    cudaMemcpy(d_weights, h_weights, sizeof(float), cudaMemcpyHostToDevice);\n",
        "    cudaMemcpy(d_bias, h_bias, sizeof(float), cudaMemcpyHostToDevice);\n",
        "\n",
        "    // Launch kernel\n",
        "    linear_regression<<<1, m>>>(d_inputs, d_targets, size, d_weights, d_bias, learning_rate, epochs);\n",
        "\n",
        "    // Copy back weights and bias\n",
        "    cudaMemcpy(h_weights, d_weights, sizeof(float), cudaMemcpyDeviceToHost);\n",
        "    cudaMemcpy(h_bias, d_bias, sizeof(float), cudaMemcpyDeviceToHost);\n",
        "\n",
        "    printf(\"Final Weights: %f\\n\", h_weights[0]);\n",
        "    printf(\"Final Bias: %f\\n\", h_bias[0]);\n",
        "\n",
        "    // Free device memory\n",
        "    cudaFree(d_inputs);\n",
        "    cudaFree(d_targets);\n",
        "    cudaFree(d_weights);\n",
        "    cudaFree(d_bias);\n",
        "\n",
        "    // Free host memory\n",
        "    free(h_inputs);\n",
        "    free(h_targets);\n",
        "    free(h_weights);\n",
        "    free(h_bias);\n",
        "\n",
        "    return 0;\n",
        "}\n"
      ],
      "metadata": {
        "id": "KssMuanhxSgC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be174c7f-efcd-41f6-c8c0-44c2a9cd6b75"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mLe flux de sortie a été tronqué et ne contient que les 5000 dernières lignes.\u001b[0m\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "IOPub data rate exceeded.\n",
            "The notebook server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--NotebookApp.iopub_data_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
            "NotebookApp.rate_limit_window=3.0 (secs)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mLe flux de sortie a été tronqué et ne contient que les 5000 dernières lignes.\u001b[0m\n",
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            "Iteration: 185249 | Cost/Loss: 0.074182 | Weight: 0.570023 | Bias: 0.112887\n",
            "Iteration: 185250 | Cost/Loss: 0.074182 | Weight: 0.570023 | Bias: 0.112887\n",
            "Iteration: 185251 | Cost/Loss: 0.074182 | Weight: 0.570023 | Bias: 0.112888\n",
            "Iteration: 185252 | Cost/Loss: 0.074182 | Weight: 0.570024 | Bias: 0.112888\n",
            "Iteration: 185253 | Cost/Loss: 0.074182 | Weight: 0.570024 | Bias: 0.112888\n",
            "Iteration: 185254 | Cost/Loss: 0.074182 | Weight: 0.570024 | Bias: 0.112889\n",
            "Iteration: 185255 | Cost/Loss: 0.074182 | Weight: 0.570025 | Bias: 0.112889\n",
            "Iteration: 185256 | Cost/Loss: 0.074182 | Weight: 0.570025 | Bias: 0.112889\n",
            "Iteration: 185257 | Cost/Loss: 0.074182 | Weight: 0.570025 | Bias: 0.112890\n",
            "Iteration: 185258 | Cost/Loss: 0.074182 | Weight: 0.570026 | Bias: 0.112890\n",
            "Iteration: 185259 | Cost/Loss: 0.074182 | Weight: 0.570026 | Bias: 0.112890\n",
            "Iteration: 185260 | Cost/Loss: 0.074182 | Weight: 0.570026 | Bias: 0.112891\n",
            "Iteration: 185261 | Cost/Loss: 0.074182 | Weight: 0.570026 | Bias: 0.112891\n",
            "Iteration: 185262 | Cost/Loss: 0.074182 | Weight: 0.570027 | Bias: 0.112891\n",
            "Iteration: 185263 | Cost/Loss: 0.074182 | Weight: 0.570027 | Bias: 0.112891\n",
            "Iteration: 185264 | Cost/Loss: 0.074182 | Weight: 0.570027 | Bias: 0.112892\n",
            "Iteration: 185265 | Cost/Loss: 0.074182 | Weight: 0.570028 | Bias: 0.112892\n",
            "Iteration: 185266 | Cost/Loss: 0.074182 | Weight: 0.570028 | Bias: 0.112892\n",
            "Iteration: 185267 | Cost/Loss: 0.074182 | Weight: 0.570028 | Bias: 0.112893\n",
            "Iteration: 185268 | Cost/Loss: 0.074182 | Weight: 0.570029 | Bias: 0.112893\n",
            "Iteration: 185269 | Cost/Loss: 0.074182 | Weight: 0.570029 | Bias: 0.112893\n",
            "Iteration: 185270 | Cost/Loss: 0.074182 | Weight: 0.570029 | Bias: 0.112894\n",
            "Iteration: 185271 | Cost/Loss: 0.074182 | Weight: 0.570029 | Bias: 0.112894\n",
            "Iteration: 185272 | Cost/Loss: 0.074182 | Weight: 0.570030 | Bias: 0.112894\n",
            "Iteration: 185273 | Cost/Loss: 0.074182 | Weight: 0.570030 | Bias: 0.112895\n",
            "Iteration: 185274 | Cost/Loss: 0.074182 | Weight: 0.570030 | Bias: 0.112895\n",
            "Iteration: 185275 | Cost/Loss: 0.074182 | Weight: 0.570031 | Bias: 0.112895\n",
            "Iteration: 185276 | Cost/Loss: 0.074182 | Weight: 0.570031 | Bias: 0.112895\n",
            "Iteration: 185277 | Cost/Loss: 0.074182 | Weight: 0.570031 | Bias: 0.112896\n",
            "Iteration: 185278 | Cost/Loss: 0.074182 | Weight: 0.570032 | Bias: 0.112896\n",
            "Iteration: 185279 | Cost/Loss: 0.074182 | Weight: 0.570032 | Bias: 0.112896\n",
            "Iteration: 185280 | Cost/Loss: 0.074182 | Weight: 0.570032 | Bias: 0.112897\n",
            "Iteration: 185281 | Cost/Loss: 0.074182 | Weight: 0.570032 | Bias: 0.112897\n",
            "Iteration: 185282 | Cost/Loss: 0.074182 | Weight: 0.570033 | Bias: 0.112897\n",
            "Iteration: 185283 | Cost/Loss: 0.074182 | Weight: 0.570033 | Bias: 0.112898\n",
            "Iteration: 185284 | Cost/Loss: 0.074182 | Weight: 0.570033 | Bias: 0.112898\n",
            "Iteration: 185285 | Cost/Loss: 0.074182 | Weight: 0.570034 | Bias: 0.112898\n",
            "Iteration: 185286 | Cost/Loss: 0.074181 | Weight: 0.570034 | Bias: 0.112898\n",
            "Iteration: 185287 | Cost/Loss: 0.074181 | Weight: 0.570034 | Bias: 0.112899\n",
            "Iteration: 185288 | Cost/Loss: 0.074181 | Weight: 0.570035 | Bias: 0.112899\n",
            "Iteration: 185289 | Cost/Loss: 0.074181 | Weight: 0.570035 | Bias: 0.112899\n",
            "Iteration: 185290 | Cost/Loss: 0.074181 | Weight: 0.570035 | Bias: 0.112900\n",
            "Iteration: 185291 | Cost/Loss: 0.074181 | Weight: 0.570035 | Bias: 0.112900\n",
            "Iteration: 185292 | Cost/Loss: 0.074181 | Weight: 0.570036 | Bias: 0.112900\n",
            "Iteration: 185293 | Cost/Loss: 0.074181 | Weight: 0.570036 | Bias: 0.112901\n",
            "Iteration: 185294 | Cost/Loss: 0.074181 | Weight: 0.570036 | Bias: 0.112901\n",
            "Iteration: 185295 | Cost/Loss: 0.074181 | Weight: 0.570037 | Bias: 0.112901\n",
            "Iteration: 185296 | Cost/Loss: 0.074181 | Weight: 0.570037 | Bias: 0.112902\n",
            "Iteration: 185297 | Cost/Loss: 0.074181 | Weight: 0.570037 | Bias: 0.112902\n",
            "Iteration: 185298 | Cost/Loss: 0.074181 | Weight: 0.570037 | Bias: 0.112902\n",
            "Iteration: 185299 | Cost/Loss: 0.074181 | Weight: 0.570038 | Bias: 0.112902\n",
            "Iteration: 185300 | Cost/Loss: 0.074181 | Weight: 0.570038 | Bias: 0.112903\n",
            "Iteration: 185301 | Cost/Loss: 0.074181 | Weight: 0.570038 | Bias: 0.112903\n",
            "Iteration: 185302 | Cost/Loss: 0.074181 | Weight: 0.570039 | Bias: 0.112903\n",
            "Iteration: 185303 | Cost/Loss: 0.074181 | Weight: 0.570039 | Bias: 0.112904\n",
            "Iteration: 185304 | Cost/Loss: 0.074181 | Weight: 0.570039 | Bias: 0.112904\n",
            "Iteration: 185305 | Cost/Loss: 0.074181 | Weight: 0.570040 | Bias: 0.112904\n",
            "Iteration: 185306 | Cost/Loss: 0.074181 | Weight: 0.570040 | Bias: 0.112905\n",
            "Iteration: 185307 | Cost/Loss: 0.074181 | Weight: 0.570040 | Bias: 0.112905\n",
            "Iteration: 185308 | Cost/Loss: 0.074181 | Weight: 0.570040 | Bias: 0.112905\n",
            "Iteration: 185309 | Cost/Loss: 0.074181 | Weight: 0.570041 | Bias: 0.112906\n",
            "Iteration: 185310 | Cost/Loss: 0.074181 | Weight: 0.570041 | Bias: 0.112906\n",
            "Iteration: 185311 | Cost/Loss: 0.074181 | Weight: 0.570041 | Bias: 0.112906\n",
            "Iteration: 185312 | Cost/Loss: 0.074181 | Weight: 0.570042 | Bias: 0.112906\n",
            "Iteration: 185313 | Cost/Loss: 0.074181 | Weight: 0.570042 | Bias: 0.112907\n",
            "Iteration: 185314 | Cost/Loss: 0.074181 | Weight: 0.570042 | Bias: 0.112907\n",
            "Iteration: 185315 | Cost/Loss: 0.074181 | Weight: 0.570043 | Bias: 0.112907\n",
            "Iteration: 185316 | Cost/Loss: 0.074181 | Weight: 0.570043 | Bias: 0.112908\n",
            "Iteration: 185317 | Cost/Loss: 0.074181 | Weight: 0.570043 | Bias: 0.112908\n",
            "Iteration: 185318 | Cost/Loss: 0.074181 | Weight: 0.570043 | Bias: 0.112908\n",
            "Iteration: 185319 | Cost/Loss: 0.074181 | Weight: 0.570044 | Bias: 0.112909\n",
            "Iteration: 185320 | Cost/Loss: 0.074181 | Weight: 0.570044 | Bias: 0.112909\n",
            "Iteration: 185321 | Cost/Loss: 0.074181 | Weight: 0.570044 | Bias: 0.112909\n",
            "Iteration: 185322 | Cost/Loss: 0.074181 | Weight: 0.570045 | Bias: 0.112909\n",
            "Iteration: 185323 | Cost/Loss: 0.074181 | Weight: 0.570045 | Bias: 0.112910"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "IOPub data rate exceeded.\n",
            "The notebook server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--NotebookApp.iopub_data_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
            "NotebookApp.rate_limit_window=3.0 (secs)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#include <stdio.h>\n",
        "#include <stdlib.h>\n",
        "#include <math.h>\n",
        "\n",
        "__global__ void linear_regression(float *inputs, float *targets, int size, float *weights, float *bias, float learning_rate, int epochs) {\n",
        "    int idx = blockIdx.x * blockDim.x + threadIdx.x;\n",
        "    float cost;\n",
        "    for (int epoch = 0; epoch < epochs; ++epoch) {\n",
        "        float predicted = inputs[idx] * weights[0] + bias[0];\n",
        "        float loss = predicted - targets[idx];\n",
        "        float d_weights = inputs[idx] * loss / (2 * size);\n",
        "        float d_bias = loss / (2 * size);\n",
        "        weights[0] -= learning_rate * d_weights;\n",
        "        bias[0] -= learning_rate * d_bias;\n",
        "        cost = sqrtf(powf(loss, 2) / (2 * size));\n",
        "        if (idx == 0) {\n",
        "            printf(\"Iteration: %d | Cost/Loss: %f | Weight: %f | Bias: %f\\n\", epoch + 1, cost, weights[0], bias[0]);\n",
        "        }\n",
        "    }\n",
        "}\n",
        "\n",
        "int main(void) {\n",
        "    int m = 300; // Number of training data points\n",
        "    int n = 50; // Number of testing data points\n",
        "    float learning_rate = 0.0001;\n",
        "    int epochs = 200000;\n",
        "    float a = 0.5; // y = ax + b\n",
        "    float b = 2.0;\n",
        "    int size = m;\n",
        "    int dataSize = size * sizeof(float);\n",
        "    float *h_inputs, *h_targets, *h_weights, *h_bias;\n",
        "    float *d_inputs, *d_targets, *d_weights, *d_bias;\n",
        "\n",
        "    // Allocate host memory\n",
        "    h_inputs = (float*)malloc(dataSize);\n",
        "    h_targets = (float*)malloc(dataSize);\n",
        "    h_weights = (float*)malloc(sizeof(float));\n",
        "    h_bias = (float*)malloc(sizeof(float));\n",
        "\n",
        "    // Generate data\n",
        "    for (int i = 0; i < m; ++i) {\n",
        "        h_inputs[i] = i + 1;\n",
        "        h_targets[i] = (i + 1) * a + b;\n",
        "    }\n",
        "\n",
        "    // Allocate device memory\n",
        "    cudaMalloc((void**)&d_inputs, dataSize);\n",
        "    cudaMalloc((void**)&d_targets, dataSize);\n",
        "    cudaMalloc((void**)&d_weights, sizeof(float));\n",
        "    cudaMalloc((void**)&d_bias, sizeof(float));\n",
        "\n",
        "    // Copy data to device memory\n",
        "    cudaMemcpy(d_inputs, h_inputs, dataSize, cudaMemcpyHostToDevice);\n",
        "    cudaMemcpy(d_targets, h_targets, dataSize, cudaMemcpyHostToDevice);\n",
        "\n",
        "    // Initialize weights and bias\n",
        "    h_weights[0] = 0;\n",
        "    h_bias[0] = 0;\n",
        "\n",
        "    // Copy initial weights and bias to device memory\n",
        "    cudaMemcpy(d_weights, h_weights, sizeof(float), cudaMemcpyHostToDevice);\n",
        "    cudaMemcpy(d_bias, h_bias, sizeof(float), cudaMemcpyHostToDevice);\n",
        "\n",
        "    // Launch kernel\n",
        "    linear_regression<<<1, m>>>(d_inputs, d_targets, size, d_weights, d_bias, learning_rate, epochs);\n",
        "\n",
        "    // Copy back weights and bias\n",
        "    cudaMemcpy(h_weights, d_weights, sizeof(float), cudaMemcpyDeviceToHost);\n",
        "    cudaMemcpy(h_bias, d_bias, sizeof(float), cudaMemcpyDeviceToHost);\n",
        "\n",
        "    printf(\"Final Weights: %f\\n\", h_weights[0]);\n",
        "    printf(\"Final Bias: %f\\n\", h_bias[0]);\n",
        "\n",
        "    // Free device memory\n",
        "    cudaFree(d_inputs);\n",
        "    cudaFree(d_targets);\n",
        "    cudaFree(d_weights);\n",
        "    cudaFree(d_bias);\n",
        "\n",
        "    // Free host memory\n",
        "    free(h_inputs);\n",
        "    free(h_targets);\n",
        "    free(h_weights);\n",
        "    free(h_bias);\n",
        "\n",
        "    return 0;\n",
        "}\n"
      ],
      "metadata": {
        "id": "pHPP4_oK8Sqn",
        "outputId": "3ab96912-3b93-4e23-c70b-4ea7c617740d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<ipython-input-5-a504d8acd278>, line 5)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-a504d8acd278>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    __global__ void linear_regression(float *inputs, float *targets, int size, float *weights, float *bias, float learning_rate, int epochs) {\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise 4"
      ],
      "metadata": {
        "id": "4TUi2GvA35r7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%cuda\n",
        "#include <iostream>\n",
        "#include <chrono>\n",
        "\n",
        "#define N 512\n",
        "#define Niter 1000\n",
        "\n",
        "void addCPU(int *a, int *b, int *c) {\n",
        "    for (int i = 0; i < N*N ; i++) {\n",
        "        c[i] = a[i] + b[i];\n",
        "    }\n",
        "}\n",
        "\n",
        "__global__ void addKernel1D (int *a, int *b, int *c) {\n",
        "    int tid = threadIdx.x + blockIdx.x * blockDim.x;\n",
        "    while (tid < N*N) {\n",
        "        c[tid] = a[tid] + b[tid];\n",
        "        tid += blockDim.x * gridDim.x;\n",
        "    }\n",
        "}\n",
        "\n",
        "__global__ void addKernel2D (int *a, int *b, int *c) {\n",
        "    int tidx = threadIdx.x + blockIdx.x * blockDim.x;\n",
        "    int tidy = threadIdx.y + blockIdx.y * blockDim.y;\n",
        "    int id = tidx + tidy * N;\n",
        "    while (tidx < N && tidy < N) {\n",
        "        c[id] = a[id] + b[id];\n",
        "        tidx += blockDim.x * gridDim.x;\n",
        "        tidy += blockDim.y * gridDim.y;\n",
        "        id = tidx + tidy * N;\n",
        "    }\n",
        "}\n",
        "\n",
        "int main(void) {\n",
        "    int A[N*N], B[N*N], C[N*N], D[N*N];\n",
        "    int *d_A, *d_B, *d_C;\n",
        "    cudaEvent_t start, stop;\n",
        "    cudaEventCreate(&start);\n",
        "    cudaEventCreate(&stop);\n",
        "\n",
        "    cudaMalloc(&d_A, N*N*sizeof(int));\n",
        "    cudaMalloc(&d_B, N*N*sizeof(int));\n",
        "    cudaMalloc(&d_C, N*N*sizeof(int));\n",
        "\n",
        "    for (int i = 0 ; i < N*N ; i++) {\n",
        "            A[i] = i;\n",
        "            B[i] = i+1;\n",
        "    }\n",
        "    cudaMemcpy(d_A, A, N*N*sizeof(int), cudaMemcpyHostToDevice);\n",
        "    cudaMemcpy(d_B, B, N*N*sizeof(int), cudaMemcpyHostToDevice);\n",
        "\n",
        "    auto t_start = std::chrono::high_resolution_clock::now();\n",
        "    for (int i = 0; i <  Niter ; i++) // We do Niter to have an averaged execution time\n",
        "      addCPU(A, B, C);\n",
        "    auto t_end = std::chrono::high_resolution_clock::now();\n",
        "\n",
        "    cudaDeviceProp properties;\n",
        "    cudaGetDeviceProperties(&properties, 0);\n",
        "    int nThreads = properties.maxThreadsPerBlock; // Get maximum number of threads per block\n",
        "    int nBlocks = (N*N + nThreads - 1) / nThreads; // Get right number of blocks to cover our problem\n",
        "\n",
        "    dim3 gridDim(8,nBlocks/8+1);\n",
        "    dim3 blockDim(2,nThreads/2);\n",
        "\n",
        "    addKernel1D<<<nBlocks,nThreads>>>(d_A, d_B, d_C); // WARM-UP\n",
        "\n",
        "    cudaEventRecord(start);\n",
        "    for (int i = 0; i <  Niter ; i++) // We do Niter to have an averaged execution time\n",
        "      addKernel1D<<<nBlocks,nThreads>>>(d_A, d_B, d_C);\n",
        "    cudaEventRecord(stop);\n",
        "    cudaEventSynchronize(stop);\n",
        "    float ms;\n",
        "    cudaEventElapsedTime(&ms, start, stop);\n",
        "\n",
        "    cudaMemcpy(D, d_C, N*N*sizeof(int), cudaMemcpyDeviceToHost);\n",
        "    cudaMemcpy(B, d_B, N*N*sizeof(int), cudaMemcpyDeviceToHost);\n",
        "\n",
        "    int diff = 0;\n",
        "    for (int i = 0 ; i < N ; i++) {\n",
        "        if(D[i] != C[i]) diff = D[i] - C[i];\n",
        "    }\n",
        "    if(diff != 0) {\n",
        "        printf(\"Wrong computation : diff = %d\", diff);\n",
        "        return 0;\n",
        "    }\n",
        "    printf(\"CPU execution time = %f ms\\n\", std::chrono::duration<double, std::milli>(t_end-t_start).count()/Niter);\n",
        "    printf(\"GPU execution time 1D = %f ms\\n\", ms/Niter);\n",
        "    addKernel2D<<<gridDim,blockDim>>>(d_A, d_B, d_C); // WARM-UP\n",
        "\n",
        "    cudaEventRecord(start);\n",
        "    for (int i = 0; i <  Niter ; i++) // We do Niter to have an averaged execution time\n",
        "      addKernel2D<<<gridDim,blockDim>>>(d_A, d_B, d_C);\n",
        "    cudaEventRecord(stop);\n",
        "    cudaEventSynchronize(stop);\n",
        "    cudaEventElapsedTime(&ms, start, stop);\n",
        "\n",
        "    cudaMemcpy(D, d_C, N*N*sizeof(int), cudaMemcpyDeviceToHost);\n",
        "    cudaMemcpy(B, d_B, N*N*sizeof(int), cudaMemcpyDeviceToHost);\n",
        "\n",
        "    diff = 0;\n",
        "    for (int i = 0 ; i < N ; i++) {\n",
        "        if(D[i] != C[i]) diff = D[i] - C[i];\n",
        "    }\n",
        "    if(diff != 0) {\n",
        "        printf(\"Wrong computation : diff = %d\", diff);\n",
        "        return 0;\n",
        "    }\n",
        "    printf(\"GPU execution time 2D = %f ms\\n\", ms/Niter);\n",
        "\n",
        "  cudaFree(d_A);\n",
        "  cudaFree(d_B);\n",
        "  cudaFree(d_C);\n",
        "\n",
        "}"
      ],
      "metadata": {
        "id": "QYT9qRDa37rS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2e0d48e-8755-498e-bb15-3066b3b4fd3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU execution time = 0.754999 ms\n",
            "GPU execution time 1D = 0.011890 ms\n",
            "GPU execution time 2D = 0.007938 ms\n",
            "\n"
          ]
        }
      ]
    }
  ]
}