CUDA从组成到编程(二) Hello World!

首先是官网给的向量加法的例子。
我的环境是ubuntu16, cuda7.5
先是常用命令：

这个的作用是使用自带的工具每0.5s刷新一下GPU的状态，可以作监控用

$ watch -n 0.5 nvida-smi

检查cuda编译器是否安装正确

$ which nvcc

检查GPU型号

$ ls  -l /dev/nv*

编译与运行

$ nvcc hello.cu -o hello
$ ./hello

#include 

__global__ void vector_add(int *a, int *b, int *c)
{
    /* insert code to calculate the index properly using blockIdx.x, blockDim.x, threadIdx.x */
    int index = blockIdx.x * blockDim.x + threadIdx.x;
    c[index] = a[index] + b[index];
}

/* experiment with N */
/* how large can it be? */
#define N (2048*2048)
#define THREADS_PER_BLOCK 512

int main()
{
    int *a, *b, *c;
    int *d_a, *d_b, *d_c;
    int size = N * sizeof( int );

    /* allocate space for device copies of a, b, c */

    cudaMalloc( (void **) &d_a, size );
    cudaMalloc( (void **) &d_b, size );
    cudaMalloc( (void **) &d_c, size );

    /* allocate space for host copies of a, b, c and setup input values */

    a = (int *)malloc( size );
    b = (int *)malloc( size );
    c = (int *)malloc( size );

    for( int i = 0; i < N; i++ )
    {
        a[i] = b[i] = i;
        c[i] = 0;
    }

    /* copy inputs to device */
    /* fix the parameters needed to copy data to the device */
    cudaMemcpy( d_a, a, size, cudaMemcpyHostToDevice );
    cudaMemcpy( d_b, b, size, cudaMemcpyHostToDevice );

    /* launch the kernel on the GPU */
    /* insert the launch parameters to launch the kernel properly using blocks and threads */ 
    vector_add<<< (N + (THREADS_PER_BLOCK-1)) / THREADS_PER_BLOCK, THREADS_PER_BLOCK >>>( d_a, d_b, d_c );

    /* copy result back to host */
    /* fix the parameters needed to copy data back to the host */
    cudaMemcpy( c, d_c, size, cudaMemcpyDeviceToHost );


    printf( "c[0] = %d\n",c[0] );
    printf( "c[%d] = %d\n",N-1, c[N-1] );

    /* clean up */

    free(a);
    free(b);
    free(c);
    cudaFree( d_a );
    cudaFree( d_b );
    cudaFree( d_c );

    return 0;
} /* end main */