CUDA内核函数的连续执行

以下是两个连续的CUDA核函数衔接的一种思路：

要完成的功能：1. 向量的计算computer（暂时以两向量求和为例）；2. 对结果向量求和SUM。

思路1：写1个计算的内核函数，中间结果保留，求和函数调用cublas。

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "cublas_v2.h"

#include 
#include 

#pragma comment(lib, "cublas.lib")

cudaError_t addWithCuda(const int *a, const int *b, unsigned int size, float& sum);

__global__ void addKernel(const int *a, const int *b, float *c)
{
    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 };

    // Add vectors in parallel.
	float sum = 0.0;
    cudaError_t cudaStatus = addWithCuda(a, b, arraySize, sum);
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "addWithCuda failed!");
        return 1;
    }
    printf("sum: %f\n", sum);

    // 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.
cudaError_t addWithCuda(const int *a, const int *b, unsigned int size, float& sum)
{
    int *dev_a = 0;
    int *dev_b = 0;
    float *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(float));
    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_a, dev_b, dev_c);


    // 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;
    }

	cublasHandle_t handle;
	cublasStatus_t stat;
	
	stat = cublasCreate(&handle);
	if (stat != CUBLAS_STATUS_SUCCESS) {
		printf("CUBLAS initialization failed\n");
		exit(-1);
	}
	
	cublasSasum(handle, size, dev_c, 1, &sum);
	if (stat != CUBLAS_STATUS_SUCCESS) {
		printf("data execution failed");
		cublasDestroy(handle);
		exit(-1);
	}	
	cublasDestroy(handle);

Error:
    cudaFree(dev_c);
    cudaFree(dev_a);
    cudaFree(dev_b);
    
    return cudaStatus;
}

思路2：写两个内核函数，1个调用另外一个（待求证）。

__device__ void sum(const float *c, float* sum){
}

__global__ void calculate(const int *a, const int *b, float *c, float *sum)
{
	//calculate....
	int i = threadIdx.x;
	c[i] = a[i] + b[i];
	__syncthreads();
	sum(c, sum)

}

思路3：写两个内核函数，顺序执行，即自己动手实现sum函数。

__global__ void calculate(const int *a, const int *b, float *c)
{
	//calculate....
	int i = threadIdx.x;
	c[i] = a[i] + b[i];
}

__global__ void sum(const float *c, float* sum){
}

推荐思路1，因为尽量调用库函数（不自己手写），开发效率会提高。