Final6.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Final6.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/4en1x/diploma/blob/master/Final6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "KY1ZYpoI90PK",
        "colab_type": "code",
        "outputId": "3d59c7d4-b12d-402c-fd5b-1ffcba09c80d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install git+git://github.com/andreinechaev/nvcc4jupyter.git\n",
        "%load_ext nvcc_plugin\n",
        "!cd /usr/src\n",
        "!ln -sfn linux-headers-2.6.35-28-generic linux"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+git://github.com/andreinechaev/nvcc4jupyter.git\n",
            "  Cloning git://github.com/andreinechaev/nvcc4jupyter.git to /tmp/pip-req-build-fnmqvoq_\n",
            "Building wheels for collected packages: NVCCPlugin\n",
            "  Building wheel for NVCCPlugin (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Stored in directory: /tmp/pip-ephem-wheel-cache-f4qvqn7p/wheels/10/c2/05/ca241da37bff77d60d31a9174f988109c61ba989e4d4650516\n",
            "Successfully built NVCCPlugin\n",
            "Installing collected packages: NVCCPlugin\n",
            "Successfully installed NVCCPlugin-0.0.2\n",
            "created output directory at /content/src\n",
            "Out bin /content/result.out\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bVieFH4T969C",
        "colab_type": "code",
        "outputId": "1e5390f2-82a8-4722-ff16-31bd2138d85f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "%%cu\n",
        "#include <cuda_runtime.h>\n",
        "#include \"device_launch_parameters.h\"\n",
        "#include <stdlib.h>\n",
        "#include <stdio.h>\n",
        "#include <float.h>\n",
        "#include \"linux/kernel.h\"\n",
        "#include <iostream>\n",
        "#include <ctime>\n",
        "#include <iomanip>\n",
        "#include <fstream>\n",
        "#include <math.h>\n",
        "#include <cmath>\n",
        "\n",
        "using namespace std;\n",
        "\n",
        "#define ITERATIONS 10000\n",
        "#define N 256\n",
        "\n",
        "#define e 2.7182818\n",
        "\n",
        "#define blockZSize 2\n",
        "\n",
        "#define blockXSize 64\n",
        "#define blockYSize 64\n",
        "\n",
        "#define tileXSize 2\n",
        "#define tileYSize 2\n",
        "\n",
        "#define extN (N + 2)\n",
        "#define Dx 1\n",
        "#define h ((float) Dx / (N + 1))\n",
        "#define h2 powf(h, 2.0)\n",
        "\n",
        "__device__\n",
        "float f(int i, int j) {\n",
        "\treturn 2 * powf(e, i * h + j * h);\n",
        "}\n",
        "\n",
        "float u_t(float x) {\n",
        "\treturn powf(e, x);\n",
        "}\n",
        "\n",
        "float u_l(float x) {\n",
        "\treturn powf(e, x);\n",
        "}\n",
        "\n",
        "float u_r(float x) {\n",
        "\treturn powf(e, x + 1);\n",
        "}\n",
        "\n",
        "float u_d(float x) {\n",
        "\treturn powf(e, x + 1);\n",
        "}\n",
        "\n",
        "\n",
        "__device__\n",
        "void tile(float* matrix, int iGl, int jGl, bool isBlack) {\n",
        "\tint iBegin = iGl * blockXSize + threadIdx.x * tileXSize + 1;\n",
        "\tint jBegin = jGl * blockYSize + threadIdx.y * tileYSize + 1;\n",
        "\n",
        "  int N1 = tileXSize + blockZSize * 2;\n",
        "  int N2 = tileYSize + blockZSize * 2;\n",
        "  \n",
        "\tfloat helper[6 * 6];\n",
        "\n",
        "  for(int i = 0; i < N1; i++) {\n",
        "\t\tfor(int j = 0; j < N2; j++) {\n",
        "      int dAI = iBegin - blockZSize + i;\n",
        "      int dAJ = jBegin - blockZSize + j;\n",
        "      if(dAI < 0 || dAI >= extN || dAJ < 0 || dAJ >= extN) {\n",
        "          helper[i * N1 + j] = FLT_MAX;\n",
        "      }\n",
        "      else {\n",
        "          helper[i * N1 + j] = matrix[extN * dAI + dAJ];\n",
        "      }\n",
        "\t\t}\n",
        "\t}\n",
        "  \n",
        "  int iSplit = iBegin - blockZSize;\n",
        "  int jSplit = jBegin - blockZSize;\n",
        "    \n",
        "  for(int k = 1; k < blockZSize; k++) {\n",
        "\t\tfor (int i = k; i < N1 - k; i++) {\n",
        "\t\t  for (int j = k; j < N2 - k; j += 1) {\n",
        "        int b = N1 * i + j;\n",
        "        if(helper[b + N1] != FLT_MAX && helper[b - N1] != FLT_MAX && helper[b + 1] != FLT_MAX && helper[b - 1] != FLT_MAX) {\n",
        "            helper[b] = 0.25 * (\n",
        "                helper[b + N1]\n",
        "              + helper[b - N1]\n",
        "              + helper[b + 1]\n",
        "              + helper[b - 1] \n",
        "              - h2 * f(iSplit + i, jSplit + j)\n",
        "            );\n",
        "        }\n",
        "\t\t  }\n",
        "\t  }\n",
        "\t}\n",
        "\n",
        "  //printf(\"iGl : %d, %d, %d, %d, %d, %d, %d \\n\", iGl, jGl, threadIdx.x, threadIdx.y, isBlack, iBegin, jBegin);\n",
        "    \n",
        "  for (int i = iBegin; i < iBegin + tileXSize; i++) {\n",
        "\t\tfor (int j = jBegin; j < jBegin + tileYSize; j += 1) {\n",
        "      int helperIndex = N1 * (i - iBegin + blockZSize) + j - jBegin + blockZSize;\n",
        "\t\t\tmatrix[extN * i + j] = 0.25 * (\n",
        "          helper[helperIndex + N1]\n",
        "        + helper[helperIndex - N1]\n",
        "        + helper[helperIndex + 1]\n",
        "\t\t\t\t+ helper[helperIndex - 1] \n",
        "        - h2 * f(i - blockZSize + 1, j - blockZSize + 1)\n",
        "      );\n",
        "      helper[helperIndex] =\tmatrix[extN * i + j];\n",
        "\t\t}\n",
        "\t}\n",
        "    \n",
        "}\n",
        "\n",
        "__global__\n",
        "void calculateBlack(float* matrix) {\n",
        "\ttile(matrix, blockIdx.x, blockIdx.y, true);\n",
        "}\n",
        "\n",
        "__global__\n",
        "void calculateRed(float* matrix) {\n",
        "\ttile(matrix, blockIdx.x, blockIdx.y, false);\n",
        "}\n",
        "\n",
        "float func(int i, int j) {\n",
        "\treturn powf(e, i * h + j * h);\n",
        "}\n",
        "\n",
        "float* createMatrix(int row, int col) {\n",
        "\tfloat* matrix = new float[row * col];\n",
        "\n",
        "\tfor(int i = 0; i < row; i++) {\n",
        "\t\tfor(int j = 0; j < col; j++) {\n",
        "\t\t\tmatrix[i * row + j] = 0;\n",
        "\t\t}\n",
        "\t}\n",
        "\n",
        "  for(int i = 0; i < row; i++) {\n",
        "      matrix[0 * row + i] = u_t((i) * h);\n",
        "      matrix[(row - 1) * row + i] = u_d((i) * h);\n",
        "  }\n",
        "\n",
        "  for(int j = 0; j < col; j++) {\n",
        "      matrix[j * col + 0] = u_l((j) * h);\n",
        "      matrix[j * col + row - 1] = u_r((j) * h);\n",
        "\t}\n",
        "    \n",
        "\treturn matrix;\n",
        "}\n",
        "\n",
        "float calculateDifference(float* matrix1) {\n",
        "\tfloat tolerance = 0.0f;\n",
        "\n",
        "  for (int i = 1; i < extN - 1; i++) {\n",
        "\t\tfor (int j = 1; j < extN - 1; j++) {\n",
        "      float fCurrent = func(i, j);\n",
        "      if(abs(matrix1[i * extN + j] - fCurrent) > tolerance) {\n",
        "          tolerance = abs(matrix1[i * extN + j] - fCurrent);\n",
        "      } \n",
        "\t\t}\n",
        "\t}\n",
        "\n",
        "\treturn tolerance;\n",
        "}\n",
        "\n",
        "int main() {\n",
        "  float g = 0.0;\n",
        "  for(int y=0; y < 5; y +=1) {\n",
        "\tfloat* matrixHost = createMatrix(extN, extN);\n",
        "\tfloat* matrixDevice;\n",
        "\n",
        "\tint matrixSize = sizeof(float) * extN * extN;\n",
        "\n",
        "\tcudaMalloc((void**)&matrixDevice, matrixSize);\n",
        "\tcudaMemcpy(matrixDevice, matrixHost, matrixSize, cudaMemcpyHostToDevice);\n",
        "\n",
        "\tlong startTime = clock();\n",
        "\tdim3 blocks(N / blockXSize, N / blockYSize);\n",
        "\tdim3 threads(blockXSize / tileXSize, blockYSize / tileYSize);\n",
        "\n",
        "\tfor (size_t i = 0; i < ITERATIONS; i++) {\n",
        "\t\tcalculateBlack <<< blocks, threads >>>(matrixDevice);\n",
        "\t\tcudaDeviceSynchronize();\n",
        "\t\tcudaPeekAtLastError();\n",
        "\n",
        "    if(i%100 == 0) {\n",
        "        cudaMemcpy(matrixHost, matrixDevice, matrixSize, cudaMemcpyDeviceToHost);\n",
        "        float diff = calculateDifference(matrixHost);\n",
        "        if(diff < 0.01) break;\n",
        "    }  \n",
        "\t}\n",
        "  \n",
        "\tstd::cout << \"Calculation take time: \" << clock() - startTime << std::endl;\n",
        "  g += clock() - startTime;\n",
        "  cout << \" Difference: \" << calculateDifference(matrixHost);\n",
        "\tcudaError_t cudaResult = cudaMemcpy(matrixHost, matrixDevice, matrixSize, cudaMemcpyDeviceToHost);\n",
        "\n",
        "\tif (cudaResult != cudaSuccess) {\n",
        "\t\tstd::cout << \"Cuda error: \" << cudaResult << std::endl;\n",
        "\t}\n",
        "  }\n",
        "  cout << \"      \" << g / 5;\n",
        "\treturn 0;\n",
        "}"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Calculation take time: 2805183\\n Difference: 0.556795Calculation take time: 2806384\\n Difference: 0.556797Calculation take time: 2806255\\n Difference: 0.556785Calculation take time: 2808330\\n Difference: 0.556784Calculation take time: 2806362\\n Difference: 0.556796      2.80654e+06'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "1pnMK-cKHhl8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "128 4  8.555\n",
        "\n",
        "128 8 6.64975\n",
        "\n",
        "128 16 15.7738\n",
        "\n",
        "64 2 6.44552\n",
        "\n",
        "64 4 3.83954\n",
        "\n",
        "64 8 4.95606\n",
        "\n",
        "64 16 13.0364\n",
        "\n",
        "32 2 4.2347\n",
        "\n",
        "32 4 3.60903\n",
        "\n",
        "32 8 4.55861\n",
        "\n",
        "32 16 11.5468\n",
        "\n",
        "16 2 3.65424\n",
        "\n",
        "16 4 3.53603\n",
        "\n",
        "16 8 7.73568\n",
        "\n",
        "8 2 4.14145\n",
        "\n",
        "8 4 5.19636\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "Ah3mgcLEBk3Z",
        "colab_type": "code",
        "outputId": "6c6a05b6-10e4-43f8-a948-ae5def308de3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "objects = (\n",
        "    'blockSize = 128, tileSize = 4', \n",
        "    'blockSize = 128, tileSize = 8', \n",
        "    'blockSize = 128, tileSize = 16', \n",
        "    'blockSize = 64, tileSize = 2', \n",
        "    'blockSize = 64, tileSize = 4', \n",
        "    'blockSize = 64, tileSize = 8',\n",
        "    'blockSize = 64, tileSize = 16',\n",
        "    'blockSize = 32, tileSize = 2',\n",
        "    'blockSize = 32, tileSize = 4',\n",
        "    'blockSize = 32, tileSize = 8',\n",
        "    'blockSize = 32, tileSize = 16',\n",
        "    'blockSize = 16, tileSize = 2',\n",
        "    'blockSize = 16, tileSize = 4',\n",
        "    'blockSize = 16, tileSize = 5',\n",
        "    'blockSize = 8, tileSize = 2',\n",
        "    'blockSize = 8, tileSize = 4'\n",
        "    \n",
        ")\n",
        "y_pos = np.arange(len(objects))\n",
        "performance = [8.555,6.64975,15.7738, 6.44552, 3.83954, 4.95606, 13.0364, 4.2347, 3.60903, 4.55861, 11.5468, 3.65424, 3.53603, 7.73568, 4.14145, 5.19636]\n",
        "\n",
        "plt.style.use('seaborn-darkgrid')\n",
        "plt.barh(y_pos, performance, align='center', alpha=0.5)\n",
        "plt.yticks(y_pos, objects)\n",
        "plt.title(\"Время работы в зависимоти от размера блоков/тайлов, размер матрицы = 216\", loc='left', fontsize=12, fontweight=0, color='orange')\n",
        "plt.ylabel('Разбиение на блоки и тайлы')\n",
        "plt.xlabel('Время')\n",
        "\n",
        "for i, v in enumerate(performance):\n",
        "    plt.text(performance[i] + 0.1, \n",
        "             i-.25,\n",
        "              performance[i], \n",
        "              fontsize=11, \n",
        "              color='red')\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wWFljqF//wQcrSZJ0n6p0jd4TTzzBpEmTSE5OZvLkySY1ehMnTmTmzJlERUVh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\nCABXr16lpKQEBwcH0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25ER0dTWFjItGnTjHr0oqKimDNnDhkZGSiVShYuXIi9vT0+Pj4E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\ngg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}