Code Monkey home page Code Monkey logo

pca-matrix-summation-with-a-2d-grid-and-2d-blocks.-adapt-it-to-integer-matrix-addition.-'s Introduction

PCA-Matrix-summation-with-a-2D-grid-and-2D-blocks.-Adapt-it-to-integer-matrix-addition.-

Aim:

To implement Matrix summation with 2D grids and blocks.

Procedure:

  1. Initialize matrix sizes (nx and ny)
  2. Allocate memory on the host and initialize data
  3. Allocate memory on the device and transfer data from the host to the device
  4. Configure grid and block dimensions for the GPU kernel
  5. Launch the GPU kernel (sumMatrixOnGPU2D) to perform matrix addition
  6. Copy the GPU results back to the host
  7. Verify and compare the results between the host and GPU
  8. Free allocated memory
  9. Reset the GPU device

Program:

Float Matrix Addition

#include "../common/common.h"
#include <cuda_runtime.h>
#include <stdio.h>

/*
 * This example demonstrates a simple vector sum on the GPU and on the host.
 * sumArraysOnGPU splits the work of the vector sum across CUDA threads on the
 * GPU. A 2D thread block and 2D grid are used. sumArraysOnHost sequentially
 * iterates through vector elements on the host.
 */

void initialData(float *ip, const int size)
{
    int i;

    for(i = 0; i < size; i++)
    {
        ip[i] = (float)(rand() & 0xFF) / 10.0f;
    }

    return;
}

void sumMatrixOnHost(float *A, float *B, float *C, const int nx,
                     const int ny)
{
    float *ia = A;
    float *ib = B;
    float *ic = C;

    for (int iy = 0; iy < ny; iy++)
    {
        for (int ix = 0; ix < nx; ix++)
        {
            ic[ix] = ia[ix] + ib[ix];

        }

        ia += nx;
        ib += nx;
        ic += nx;
    }

    return;
}


void checkResult(float *hostRef, float *gpuRef, const int N)
{
    double epsilon = 1.0E-8;
    bool match = 1;

    for (int i = 0; i < N; i++)
    {
        if (abs(hostRef[i] - gpuRef[i]) > epsilon)
        {
            match = 0;
            printf("host %f gpu %f\n", hostRef[i], gpuRef[i]);
            break;
        }
    }

    if (match)
        printf("Arrays match.\n\n");
    else
        printf("Arrays do not match.\n\n");
}

// grid 2D block 2D
__global__ void sumMatrixOnGPU2D(float *MatA, float *MatB, float *MatC, int nx,
                                 int ny)
{
    unsigned int ix = threadIdx.x + blockIdx.x * blockDim.x;
    unsigned int iy = threadIdx.y + blockIdx.y * blockDim.y;
    unsigned int idx = iy * nx + ix;

    if (ix < nx && iy < ny)
        MatC[idx] = MatA[idx] + MatB[idx];
}

int main(int argc, char **argv)
{
    printf("%s Starting...\n", argv[0]);

    // set up device
    int dev = 0;
    cudaDeviceProp deviceProp;
    CHECK(cudaGetDeviceProperties(&deviceProp, dev));
    printf("Using Device %d: %s\n", dev, deviceProp.name);
    CHECK(cudaSetDevice(dev));

    // set up data size of matrix
    int nx = 1 << 14;
    int ny = 1 << 14;

    int nxy = nx * ny;
    int nBytes = nxy * sizeof(float);
    printf("Matrix size: nx %d ny %d\n", nx, ny);

    // malloc host memory
    float *h_A, *h_B, *hostRef, *gpuRef;
    h_A = (float *)malloc(nBytes);
    h_B = (float *)malloc(nBytes);
    hostRef = (float *)malloc(nBytes);
    gpuRef = (float *)malloc(nBytes);

    // initialize data at host side
    double iStart = seconds();
    initialData(h_A, nxy);
    initialData(h_B, nxy);
    double iElaps = seconds() - iStart;
    printf("Matrix initialization elapsed %f sec\n", iElaps);

    memset(hostRef, 0, nBytes);
    memset(gpuRef, 0, nBytes);

    // add matrix at host side for result checks
    iStart = seconds();
    sumMatrixOnHost(h_A, h_B, hostRef, nx, ny);
    iElaps = seconds() - iStart;
    printf("sumMatrixOnHost elapsed %f sec\n", iElaps);

    // malloc device global memory
    float *d_MatA, *d_MatB, *d_MatC;
    CHECK(cudaMalloc((void **)&d_MatA, nBytes));
    CHECK(cudaMalloc((void **)&d_MatB, nBytes));
    CHECK(cudaMalloc((void **)&d_MatC, nBytes));

    // transfer data from host to device
    CHECK(cudaMemcpy(d_MatA, h_A, nBytes, cudaMemcpyHostToDevice));
    CHECK(cudaMemcpy(d_MatB, h_B, nBytes, cudaMemcpyHostToDevice));

    // invoke kernel at host side
    int dimx = 32;
    int dimy = 32;
    dim3 block(dimx, dimy);
    dim3 grid((nx + block.x - 1) / block.x, (ny + block.y - 1) / block.y);

    iStart = seconds();
    sumMatrixOnGPU2D<<<grid, block>>>(d_MatA, d_MatB, d_MatC, nx, ny);
    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    printf("sumMatrixOnGPU2D <<<(%d,%d), (%d,%d)>>> elapsed %f sec\n", grid.x,
           grid.y,
           block.x, block.y, iElaps);
    // check kernel error
    CHECK(cudaGetLastError());

    // copy kernel result back to host side
    CHECK(cudaMemcpy(gpuRef, d_MatC, nBytes, cudaMemcpyDeviceToHost));

    // check device results
    checkResult(hostRef, gpuRef, nxy);

    // free device global memory
    CHECK(cudaFree(d_MatA));
    CHECK(cudaFree(d_MatB));
    CHECK(cudaFree(d_MatC));

    // free host memory
    free(h_A);
    free(h_B);
    free(hostRef);
    free(gpuRef);

    // reset device
    CHECK(cudaDeviceReset());

    return (0);
}

Integer Matrix Addition

#include "common.h"
#include <cuda_runtime.h>
#include <stdio.h>

/*
 * This example demonstrates a simple vector sum on the GPU and on the host.
 * sumArraysOnGPU splits the work of the vector sum across CUDA threads on the
 * GPU. A 2D thread block and 2D grid are used. sumArraysOnHost sequentially
 * iterates through vector elements on the host.
 */

void initialData(int *ip, const int size)
{
    int i;

    for(i = 0; i < size; i++)
    {
        ip[i] = (int)(rand() & 0xFF) / 10.0f;
    }

    return;
}

void sumMatrixOnHost(int *A, int *B, int *C, const int nx,
                     const int ny)
{
    int *ia = A;
    int *ib = B;
    int *ic = C;

    for (int iy = 0; iy < ny; iy++)
    {
        for (int ix = 0; ix < nx; ix++)
        {
            ic[ix] = ia[ix] + ib[ix];

        }

        ia += nx;
        ib += nx;
        ic += nx;
    }

    return;
}


void checkResult(int *hostRef, int *gpuRef, const int N)
{
    double epsilon = 1.0E-8;
    bool match = 1;

    for (int i = 0; i < N; i++)
    {
        if (abs(hostRef[i] - gpuRef[i]) > epsilon)
        {
            match = 0;
            printf("host %d gpu %d\n", hostRef[i], gpuRef[i]);
            break;
        }
    }

    if (match)
        printf("Arrays match.\n\n");
    else
        printf("Arrays do not match.\n\n");
}

// grid 2D block 2D
__global__ void sumMatrixOnGPU2D(int *MatA, int *MatB, int *MatC, int nx,
                                 int ny)
{
    unsigned int ix = threadIdx.x + blockIdx.x * blockDim.x;
    unsigned int iy = threadIdx.y + blockIdx.y * blockDim.y;
    unsigned int idx = iy * nx + ix;

    if (ix < nx && iy < ny)
        MatC[idx] = MatA[idx] + MatB[idx];
}

int main(int argc, char **argv)
{
    printf("%s Starting...\n", argv[0]);

    // set up device
    int dev = 0;
    cudaDeviceProp deviceProp;
    CHECK(cudaGetDeviceProperties(&deviceProp, dev));
    printf("Using Device %d: %s\n", dev, deviceProp.name);
    CHECK(cudaSetDevice(dev));

    // set up data size of matrix
    int nx = 1 << 3;
    int ny = 1 << 3;

    int nxy = nx * ny;
    int nBytes = nxy * sizeof(int);
    printf("Matrix size: nx %d ny %d\n", nx, ny);

    // malloc host memory
    int *h_A, *h_B, *hostRef, *gpuRef;
    h_A = (int *)malloc(nBytes);
    h_B = (int *)malloc(nBytes);
    hostRef = (int *)malloc(nBytes);
    gpuRef = (int *)malloc(nBytes);

    // initialize data at host side
    double iStart = seconds();
    initialData(h_A, nxy);
    initialData(h_B, nxy);
    double iElaps = seconds() - iStart;
    printf("Matrix initialization elapsed %f sec\n", iElaps);

    memset(hostRef, 0, nBytes);
    memset(gpuRef, 0, nBytes);

    // add matrix at host side for result checks
    iStart = seconds();
    sumMatrixOnHost(h_A, h_B, hostRef, nx, ny);
    iElaps = seconds() - iStart;
    printf("sumMatrixOnHost elapsed %f sec\n", iElaps);

    // malloc device global memory
    int *d_MatA, *d_MatB, *d_MatC;
    CHECK(cudaMalloc((void **)&d_MatA, nBytes));
    CHECK(cudaMalloc((void **)&d_MatB, nBytes));
    CHECK(cudaMalloc((void **)&d_MatC, nBytes));

    // transfer data from host to device
    CHECK(cudaMemcpy(d_MatA, h_A, nBytes, cudaMemcpyHostToDevice));
    CHECK(cudaMemcpy(d_MatB, h_B, nBytes, cudaMemcpyHostToDevice));

    // invoke kernel at host side
    int dimx = 32;
    int dimy = 32;
    dim3 block(dimx, dimy);
    dim3 grid((nx + block.x - 1) / block.x, (ny + block.y - 1) / block.y);

    iStart = seconds();
    sumMatrixOnGPU2D<<<grid, block>>>(d_MatA, d_MatB, d_MatC, nx, ny);
    CHECK(cudaDeviceSynchronize());
    iElaps = seconds() - iStart;
    printf("sumMatrixOnGPU2D <<<(%d,%d), (%d,%d)>>> elapsed %f sec\n", grid.x,
           grid.y,
           block.x, block.y, iElaps);
    // check kernel error
    CHECK(cudaGetLastError());

    // copy kernel result back to host side
    CHECK(cudaMemcpy(gpuRef, d_MatC, nBytes, cudaMemcpyDeviceToHost));

    // check device results
    checkResult(hostRef, gpuRef, nxy);

    // free device global memory
    CHECK(cudaFree(d_MatA));
    CHECK(cudaFree(d_MatB));
    CHECK(cudaFree(d_MatC));

    // free host memory
    free(h_A);
    free(h_B);
    free(hostRef);
    free(gpuRef);

    // reset device
    CHECK(cudaDeviceReset());

    return (0);
}

Output:

Float Matrix Addition

image

Integer Matrix Addition

image

Result:

Thus, matrix summation using 2D grids and 2D blocks has been performed successfully.

pca-matrix-summation-with-a-2d-grid-and-2d-blocks.-adapt-it-to-integer-matrix-addition.-'s People

Contributors

aswini-j avatar gowri4622 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.