CUDA学习笔记04：向量之和

参考资料

CUDA编程模型系列二(向量操作)_哔哩哔哩_bilibili （非常好的学习资料！）

vs2019

随意新建一个空项目，按照之前的环境配置配好项目依赖：

CUDA学习笔记02：测试程序hello world-CSDN博客

代码结构如下：

代码片段，demo_gpu.cu：

#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <stdio.h>
#include <math.h>__global__ void vecAdd(const double* x, const double* y, double* z, int count)
{const int index = blockDim.x * blockIdx.x + threadIdx.x;// t00 t01 t02 t10 t11 t12 t20 t21 t22if (index < count){z[index] = x[index] + y[index];}
}void vecAdd_cpu(const double* x, const double* y, double* z, int count)
{for (int i = 0; i < count; ++i){z[i] = x[i] + y[i];}
}void test()
{const int N = 1000;const int M = sizeof(double) * N;//cpu mem allocdouble* h_x = (double*)malloc(M);double* h_y = (double*)malloc(M);double* h_z = (double*)malloc(M);double* result_cpu = (double*)malloc(M);for (int i = 0; i < N; ++i){h_x[i] = 1;h_y[i] = 2;}double* d_x, * d_y, * d_z;cudaMalloc((void**)&d_x, M);cudaMalloc((void**)&d_y, M);cudaMalloc((void**)&d_z, M);cudaMemcpy(d_x, h_x, M, cudaMemcpyHostToDevice);cudaMemcpy(d_y, h_y, M, cudaMemcpyHostToDevice);const int block_size = 128;const int grid_size = (N + block_size - 1) / block_size;vecAdd << <grid_size, block_size >> > (d_x, d_y, d_z, N);cudaMemcpy(h_z, d_z, M, cudaMemcpyDeviceToHost);vecAdd_cpu(h_x, h_y, result_cpu, N);bool error = false;for (int i = 0; i < N; ++i){if (fabs(result_cpu[i] - h_z[i]) > (1.0e-10)){error = true;}}printf("Result: %s\n", error ? "Errors" : "Pass");free(h_x);free(h_y);free(h_z);free(result_cpu);cudaFree(d_x);cudaFree(d_y);cudaFree(d_z);
}

主函数：

#include <iostream>void test();int main()
{test();std::cout << "Finished! \n";
}

运行结果：

ok，结果通过。

Linux（CMake）

如果是使用CMake配置环境就更简单了，CMakeLists.txt这样写即可：

cmake_minimum_required(VERSION 3.10)project(vector_add LANGUAGES CXX CUDA)add_definitions(-std=c++11)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CUDA_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/build)
set(CMAKE_CXX_FLAGS  "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -O0 -Wfatal-errors -pthread -w -g")find_package(CUDA REQUIRED)cuda_add_executable(vector_add add.cu)

代码结构：

add.cu和前边的代码没有区别：

#include <stdio.h>
#include <math.h>__global__ void vecAdd(const double *x, const double *y, double *z, int count)
{const int index = blockDim.x * blockIdx.x + threadIdx.x;// t00 t01 t02 t10 t11 t12 t20 t21 t22if( index < count){z[index] = x[index] + y[index];}
}void vecAdd_cpu(const double *x, const double *y, double *z, int count)
{for(int i = 0; i<count; ++i){z[i] = x[i] + y[i];}
}int main()
{const int N = 1000;const int M = sizeof(double) * N;//cpu mem allocdouble *h_x = (double*) malloc(M);double *h_y = (double*) malloc(M);double *h_z = (double*) malloc(M);double *result_cpu = (double*) malloc(M);for( int i = 0; i<N; ++i){h_x[i] = 1;h_y[i] = 2;}double *d_x, *d_y, *d_z;cudaMalloc((void**) &d_x, M );cudaMalloc((void**) &d_y, M );cudaMalloc((void**) &d_z, M );cudaMemcpy(d_x ,h_x ,M , cudaMemcpyHostToDevice);cudaMemcpy(d_y ,h_y ,M , cudaMemcpyHostToDevice);const int block_size = 128;const int grid_size  = (N + block_size -1)/block_size;vecAdd<<<grid_size, block_size>>>(d_x, d_y, d_z, N);cudaMemcpy( h_z, d_z, M, cudaMemcpyDeviceToHost);vecAdd_cpu(h_x, h_y, result_cpu, N);bool error = false;for(int i=0; i<N; ++i){if(fabs(result_cpu[i] - h_z[i]) > (1.0e-10)){error = true;}}printf("Result: %s\n", error?"Errors" : "Pass");free(h_x);free(h_y);free(h_z);free(result_cpu);cudaFree(d_x);cudaFree(d_y);cudaFree(d_z);}

当前目录下建立build路径并且进入，用cmake构建: