CUDA基础

主要的第一个参数。为什么是两个星星呢？用个例子来说明下。

      float *device_data=NULL;      size_t size = 1024*sizeof(float);      cudaMalloc((void**)&device_data, size); 

上面这个例子中我在显存中申请了一个包含1024个单精度浮点数的一维数组。而device_data这个指针是存储在主存上的。之所以取device_data的地址，是为了将cudaMalloc在显存上获得的数组首地址赋值给device_data。在函数中为形参赋值是不会在实参中繁盛变化的，但是指针传递的是地址，我们操作了某个地址的数据，实际上是真的改变了指定地址的数据。像这个申请显存的函数，第一个参数传递的是device_data这个指针的地址，然后改变这个地址的内容就会带给实参真正的改变。

/** * 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>#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__ voidvectorAdd(const float *A, const float *B, float *C, int numElements){    int i = blockDim.x * blockIdx.x + threadIdx.x;    if (i < numElements)    {        C[i] = A[i] + B[i];    }}/** * Host main routine */intmain(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;    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();    if (err != cudaSuccess)    { fprintf(stderr, "Failed to deinitialize the device! error=%s\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    printf("Done\n");    return 0;}