参考：http://blog.csdn.net/augusdi/article/details/12833235  第二节

新建NVIDIA项目：

 

新建项目及会生成一个简单的代码demo，计算矩阵的加法，如下（main中加了一些显示显卡性能的打印）：

#include "cuda_runtime.h"#include "device_launch_parameters.h"#include 
     
      cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size);__global__ void addKernel(int *c, const int *a, const int *b){    int i = threadIdx.x;    c[i] = a[i] + b[i];}int main(){    const int arraySize = 5;    const int a[arraySize] = { 1, 2, 3, 4, 5 };    const int b[arraySize] = { 10, 20, 30, 40, 50 };    int c[arraySize] = { 0 };    // Add vectors in parallel.    cudaError_t cudaStatus = addWithCuda(c, a, b, arraySize);    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "addWithCuda failed!");        return 1;    }    printf("{1,2,3,4,5} + {10,20,30,40,50} = {%d,%d,%d,%d,%d}\n",        c[0], c[1], c[2], c[3], c[4]);    // cudaDeviceReset must be called before exiting in order for profiling and    // tracing tools such as Nsight and Visual Profiler to show complete traces.    cudaStatus = cudaDeviceReset();    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "cudaDeviceReset failed!");        return 1;    }    int deviceCount;    cudaGetDeviceCount(&deviceCount);    int dev;    for (dev = 0; dev < deviceCount; dev++)    {        cudaDeviceProp deviceProp;        cudaGetDeviceProperties(&deviceProp, dev);        if (dev == 0)        {            if (/*deviceProp.major==9999 && */deviceProp.minor = 9999&&deviceProp.major==9999)                printf("\n");        }        printf("\nDevice%d:\"%s\"\n", dev, deviceProp.name);        printf("Total amount of global memory                   %u bytes\n", deviceProp.totalGlobalMem);        printf("Number of mltiprocessors                        %d\n", deviceProp.multiProcessorCount);        printf("Total amount of constant memory:                %u bytes\n", deviceProp.totalConstMem);        printf("Total amount of shared memory per block         %u bytes\n", deviceProp.sharedMemPerBlock);        printf("Total number of registers available per block:  %d\n", deviceProp.regsPerBlock);        printf("Warp size                                       %d\n", deviceProp.warpSize);        printf("Maximum number of threada per block:            %d\n", deviceProp.maxThreadsPerBlock);        printf("Maximum sizes of each dimension of a block:     %d x %d x %d\n", deviceProp.maxThreadsDim[0],            deviceProp.maxThreadsDim[1],            deviceProp.maxThreadsDim[2]);        printf("Maximum size of each dimension of a grid:       %d x %d x %d\n", deviceProp.maxGridSize[0], deviceProp.maxGridSize[1], deviceProp.maxGridSize[2]);        printf("Maximum memory pitch :                          %u bytes\n", deviceProp.memPitch);        printf("Texture alignmemt                               %u bytes\n", deviceProp.texturePitchAlignment);        printf("Clock rate                                      %.2f GHz\n", deviceProp.clockRate*1e-6f);    }    printf("\nTest PASSED\n");    getchar();    return 0;}// Helper function for using CUDA to add vectors in parallel.cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size){    int *dev_a = 0;    int *dev_b = 0;    int *dev_c = 0;    cudaError_t cudaStatus;    // Choose which GPU to run on, change this on a multi-GPU system.    cudaStatus = cudaSetDevice(0);    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "cudaSetDevice failed!  Do you have a CUDA-capable GPU installed?");        goto Error;    }    // Allocate GPU buffers for three vectors (two input, one output)    .    cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(int));    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "cudaMalloc failed!");        goto Error;    }    cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(int));    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "cudaMalloc failed!");        goto Error;    }    cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(int));    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "cudaMalloc failed!");        goto Error;    }    // Copy input vectors from host memory to GPU buffers.    cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "cudaMemcpy failed!");        goto Error;    }    cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(int), cudaMemcpyHostToDevice);    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "cudaMemcpy failed!");        goto Error;    }    // Launch a kernel on the GPU with one thread for each element.    addKernel<<<1, size>>>(dev_c, dev_a, dev_b);    // Check for any errors launching the kernel    cudaStatus = cudaGetLastError();    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "addKernel launch failed: %s\n", cudaGetErrorString(cudaStatus));        goto Error;    }        // cudaDeviceSynchronize waits for the kernel to finish, and returns    // any errors encountered during the launch.    cudaStatus = cudaDeviceSynchronize();    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!\n", cudaStatus);        goto Error;    }    // Copy output vector from GPU buffer to host memory.    cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost);    if (cudaStatus != cudaSuccess) {        fprintf(stderr, "cudaMemcpy failed!");        goto Error;    }Error:    cudaFree(dev_c);    cudaFree(dev_a);    cudaFree(dev_b);        return cudaStatus;}