CUDA模板使用

编译环境：

VS 2010    

CUDA 7.5

好处：核函数不需重载，可适应任意输入的数据类型

#include "cuda_runtime.h"

#include "device_launch_parameters.h"

#include <stdio.h>

#include <iostream>

using namespace std;

template <class T>

cudaError_t addWithCuda(T *c, const T *a, const T *b, unsigned int size);

template <class T>

__global__ void addKernel(T *c, const T *a, const T *b)

{

    int i = threadIdx.x;

    c[i] = a[i] + b[i];

}

//template <class T>

int main()

{

    const int arraySize = 5;

    const float a[arraySize] = { 1, 2, 3, 4, 5 };

    const float b[arraySize] = { 10, 20, 30, 40, 50 };

    float 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} = {%f,%f,%f,%f,%f}\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;

    }

    return 0;

}

// Helper function for using CUDA to add vectors in parallel.

template <class T>

cudaError_t addWithCuda(T *c, const T *a, const T *b, unsigned int size)

{

    T *dev_a = 0;

    T *dev_b = 0;

    T *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(T));

    if (cudaStatus != cudaSuccess) {

        fprintf(stderr, "cudaMalloc failed!");

        goto Error;

    }

    cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(T));

    if (cudaStatus != cudaSuccess) {

        fprintf(stderr, "cudaMalloc failed!");

        goto Error;

    }

    cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(T));

    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(T), cudaMemcpyHostToDevice);

    if (cudaStatus != cudaSuccess) {

        fprintf(stderr, "cudaMemcpy failed!");

        goto Error;

    }

    cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(T), 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(T), cudaMemcpyDeviceToHost);

    if (cudaStatus != cudaSuccess) {

        fprintf(stderr, "cudaMemcpy failed!");

        goto Error;

    }

Error:

    cudaFree(dev_c);

    cudaFree(dev_a);

    cudaFree(dev_b);

    return cudaStatus;

}