VS2010 CUDA8.0 工程配置

本文主要是介绍VS2010 CUDA8.0 工程配置，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

1.打开vs2010,创建win32控制台项目,命名为cuda_test,点确定
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2.勾选空项目
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3.右键”源文件”目录,选择 “添加–新建项”
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4.左侧选择NVIDIA CUDA8.0,在中间图标处选择CUDA/C file,命名为test
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5.右键cuda项目–生成自定义

6.勾选CUDA8.0
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7.右键刚才创建的test.cu文件.左侧选”配置属性–常规”,在右边选择项类型,下拉菜单中选CUDA
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8.在test.cu文件中添加cuda代码(见文末)

9.右键cuda项目——属性——配置属性——CUDA C/C++——常规——附加包含目录，增加一项:
C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0\common\inc
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该操作为是为了使工程找到所需的头文件

<helper_string.h>
<helper_cuda.h>
<helper_functions.h>

一般默认安装在路径
C:\ProgramData\NVIDIACorporation\CUDA Samples\v8.0\common\inc

10.在链接器–输入–附加依赖项,点击下拉编辑,添加一条cudart.lib,否则会报错无法生成项目
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11.开始运行,如图所示则创建成功
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附录:test.cu
(引用自cuda源码示例的vectorAdd)

/*** Copyright 1993-2015 NVIDIA Corporation.  All rights reserved.** Please refer to the NVIDIA end user license agreement (EULA) associated* with this source code for terms and conditions that govern your use of* this software. Any use, reproduction, disclosure, or distribution of* this software and related documentation outside the terms of the EULA* is strictly prohibited.**//*** Vector addition: C = A + B.** This sample is a very basic sample that implements element by element* vector addition. It is the same as the sample illustrating Chapter 2* of the programming guide with some additions like error checking.*/#include <stdio.h>// For the CUDA runtime routines (prefixed with "cuda_")
#include <cuda_runtime.h>#include <helper_cuda.h>
/*** CUDA Kernel Device code** Computes the vector addition of A and B into C. The 3 vectors have the same* number of elements numElements.*/
__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];}
}/*** Host main routine*/
int
main(void)
{// Error code to check return values for CUDA callscudaError_t err = cudaSuccess;// Print the vector length to be used, and compute its sizeint numElements = 50000;size_t size = numElements * sizeof(float);printf("[Vector addition of %d elements]\n", numElements);// Allocate the host input vector Afloat *h_A = (float *)malloc(size);// Allocate the host input vector Bfloat *h_B = (float *)malloc(size);// Allocate the host output vector Cfloat *h_C = (float *)malloc(size);// Verify that allocations succeededif (h_A == NULL || h_B == NULL || h_C == NULL){fprintf(stderr, "Failed to allocate host vectors!\n");exit(EXIT_FAILURE);}// Initialize the host input vectorsfor (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 Afloat *d_A = NULL;err = cudaMalloc((void **)&d_A, size);if (err != cudaSuccess){fprintf(stderr, "Failed to allocate device vector A (error code %s)!\n", cudaGetErrorString(err));exit(EXIT_FAILURE);}// Allocate the device input vector Bfloat *d_B = NULL;err = cudaMalloc((void **)&d_B, size);if (err != cudaSuccess){fprintf(stderr, "Failed to allocate device vector B (error code %s)!\n", cudaGetErrorString(err));exit(EXIT_FAILURE);}// Allocate the device output vector Cfloat *d_C = NULL;err = cudaMalloc((void **)&d_C, size);if (err != cudaSuccess){fprintf(stderr, "Failed to allocate device vector C (error code %s)!\n", cudaGetErrorString(err));exit(EXIT_FAILURE);}// Copy the host input vectors A and B in host memory to the device input vectors in// device memoryprintf("Copy input data from the host memory to the CUDA device\n");err = cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);if (err != cudaSuccess){fprintf(stderr, "Failed to copy vector A from host to device (error code %s)!\n", cudaGetErrorString(err));exit(EXIT_FAILURE);}err = cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);if (err != cudaSuccess){fprintf(stderr, "Failed to copy vector B from host to device (error code %s)!\n", cudaGetErrorString(err));exit(EXIT_FAILURE);}// Launch the Vector Add CUDA Kernelint 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();if (err != cudaSuccess){fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!\n", cudaGetErrorString(err));exit(EXIT_FAILURE);}// 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);if (err != cudaSuccess){fprintf(stderr, "Failed to copy vector C from device to host (error code %s)!\n", cudaGetErrorString(err));exit(EXIT_FAILURE);}// Verify that the result vector is correctfor (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 memoryerr = cudaFree(d_A);if (err != cudaSuccess){fprintf(stderr, "Failed to free device vector A (error code %s)!\n", cudaGetErrorString(err));exit(EXIT_FAILURE);}err = cudaFree(d_B);if (err != cudaSuccess){fprintf(stderr, "Failed to free device vector B (error code %s)!\n", cudaGetErrorString(err));exit(EXIT_FAILURE);}err = cudaFree(d_C);if (err != cudaSuccess){fprintf(stderr, "Failed to free device vector C (error code %s)!\n", cudaGetErrorString(err));exit(EXIT_FAILURE);}// Free host memoryfree(h_A);free(h_B);free(h_C);printf("Done\n");return 0;
}