c++ 和cuda混合编程 VS2015 C++ 调用 cuda

1 新建一个C++ 项目

2 右键添加一个cuda C/C++ file

3 添加下面 lib 库

右键项目->属性->链接器->输入->附加依赖项目：

cudart_static.lib
kernel32.lib
user32.lib
gdi32.lib
winspool.lib
comdlg32.lib
advapi32.lib
shell32.lib
ole32.lib
oleaut32.lib
uuid.lib
odbc32.lib
odbccp32.lib

4 关键一步 添加自定义生成

右键项目->属性->生成依赖项->自定义生成：

5. cuda.cu文件内容

#include <stdio.h>
#include <random>


#include <cuda_runtime.h>
#include <device_launch_parameters.h>

__global__ void
vectorAdd(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];
	}
}


int mainCuda(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);

	
	// 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);

	// Allocate the device input vector B
	float *d_B = NULL;
	err = cudaMalloc((void **)&d_B, size);

	// Allocate the device output vector C
	float *d_C = NULL;
	err = cudaMalloc((void **)&d_C, size);


	// 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);

	err = cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);

	// 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();

	// 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);

	// 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);

	err = cudaFree(d_B);

	err = cudaFree(d_C);


	// Free host memory
	free(h_A);
	free(h_B);
	free(h_C);

	printf("Done\n");
	return 0;
}

6. Main.cpp 文件内容

#include <stdio.h>
#include <stdlib.h>



extern int mainCuda(void);

void main()
{
	mainCuda();
	system("pause");
}