CUDA学习笔记二

标签：cuda

简单的向量加

/**
 * Vector addition: C = A + B.
 *
 * This sample is a very basic sample that implements element by element
 * vector addition. It is the same as the sample illustrating Chapter 2
 * of the programming guide with some additions like error checking.
 */


#include <stdio.h>


// For the CUDA runtime routines (prefixed with "cuda_")
#include <cuda_runtime.h>


/**
 * CUDA Kernel Device code
 *
 * Computes the vector addition of A and B into C. The 3 vectors have the same
 * number of elements numElements.
 */
__global__ void	vectorAdd(const float *A, const float *B, float *C, int numElements)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;//计算线程index

    //printf("thread index:%d\n",i);//CUDA 2.0以上支持核函数打印，当然当线程数很多的时候容易引起一些问<span style="white-space:pre">				</span>    //题。所以我通常调试的时候会将整个grid的线程数设为1，然后调试。
    if (i < numElements)
    {
        C[i] = A[i] + B[i];
    }
}


/**
 * Host main routine
 */
int
main(void)
{
    // Error code to check return values for CUDA calls
    cudaError_t err = cudaSuccess;


    // Print the vector length to be used, and compute its size
    int numElements = 50000;
    size_t size = numElements * sizeof(float);
    printf("[Vector addition of %d elements]\n", numElements);


    // Allocate the host input vector A
    float *h_A = (float *)malloc(size);


    // Allocate the host input vector B
    float *h_B = (float *)malloc(size);


    // Allocate the host output vector C
    float *h_C = (float *)malloc(size);


    // Verify that allocations succeeded
    if (h_A == NULL || h_B == NULL || h_C == NULL)
    {
        fprintf(stderr, "Failed to allocate host vectors!\n");
        exit(EXIT_FAILURE);
    }


    // Initialize the host input vectors
    for (int i = 0; i < numElements; ++i)
    {
        h_A[i] = rand()/(float)RAND_MAX;
        h_B[i] = rand()/(float)RAND_MAX;
    }


    // Allocate the device input vector A
    float *d_A = NULL;
    err = cudaMalloc((void **)&d_A, size);


    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to allocate device vector A (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    // Allocate the device input vector B
    float *d_B = NULL;
    err = cudaMalloc((void **)&d_B, size);


    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to allocate device vector B (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    // Allocate the device output vector C
    float *d_C = NULL;
    err = cudaMalloc((void **)&d_C, size);


    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to allocate device vector C (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    // Copy the host input vectors A and B in host memory to the device input vectors in
    // device memory
    printf("Copy input data from the host memory to the CUDA device\n");
    err = cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);


    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to copy vector A from host to device (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    err = cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);


    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to copy vector B from host to device (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    // Launch the Vector Add CUDA Kernel
    int threadsPerBlock = 256;
    int blocksPerGrid =(numElements + threadsPerBlock - 1) / threadsPerBlock;//根据数据规模分配block的处理方式
    printf("CUDA kernel launch with %d blocks of %d threads\n", blocksPerGrid, threadsPerBlock);
    vectorAdd<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_C, numElements);
    err = cudaGetLastError();


    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    // Copy the device result vector in device memory to the host result vector
    // in host memory.
    printf("Copy output data from the CUDA device to the host memory\n");
    err = cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);


    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to copy vector C from device to host (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    // Verify that the result vector is correct
    for (int i = 0; i < numElements; ++i)
    {
        if (fabs(h_A[i] + h_B[i] - h_C[i]) > 1e-5)
        {
            fprintf(stderr, "Result verification failed at element %d!\n", i);
            exit(EXIT_FAILURE);
        }
    }


    printf("Test PASSED\n");


    // Free device global memory
    err = cudaFree(d_A);


    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to free device vector A (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    err = cudaFree(d_B);


    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to free device vector B (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    err = cudaFree(d_C);


    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to free device vector C (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    // Free host memory
    free(h_A);
    free(h_B);
    free(h_C);


    // Reset the device and exit
    // cudaDeviceReset causes the driver to clean up all state. While
    // not mandatory in normal operation, it is good practice.  It is also
    // needed to ensure correct operation when the application is being
    // profiled. Calling cudaDeviceReset causes all profile data to be
    // flushed before the application exits
    err = cudaDeviceReset();//着重想记录和介绍的一个地方，之前在做Muti-GPU的时候都是free掉内存之后就不管了，后来发现了这个函数，很好的习惯！
							//记得刚开始写CUDA程序的时候并行图像金字塔，没写对，显示的结果总是可以看到之前GPU处理过的图像，让我百思不得其解，哈哈
							//对于菜鸟来说，刚开始编写并行程序会遇到很多莫名其妙的问题……
    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to deinitialize the device! error=%s\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }


    printf("Done\n");
    return 0;
}

CUDA学习笔记二

标签：cuda

原文地址：http://blog.csdn.net/lucky_greenegg/article/details/47204293