GPU-CUDA编程实践（一）_gpu cuda 样例_大师鲁的博客

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1. __host__ cudaError_t cudaMalloc( void **devPtr, size_t size)

该函数主要用来分配设备上的内存（即显存中的内存）。该函数被声明为了__host__，即表示被host所调用，即在cpu中执行的代码所调用。返回值：为cudaError_t类型，实质为cudaError的枚举类型，其中定义了一系列的错误代码。如果函数调用成功，则返回cudaSuccess。第一个参数，void ** 类型，devPtr：用于接受该函数所分配的内存地址第二个参数，size_t类型，size：指定分配内存的大小，单位为字

enum __device_builtin__ cudaMemcpyKind
{
cudaMemcpyHostToHost = 0, /**< Host -> Host */
cudaMemcpyHostToDevice = 1, /**< Host -> Device */
cudaMemcpyDeviceToHost = 2, /**< Device -> Host */
cudaMemcpyDeviceToDevice = 3, /**< Device -> Device */
cudaMemcpyDefault = 4 /**< Default based unified virtual address space */
};

cudaMemcpyKind决定了拷贝的方向，即是从主机的内存拷贝至设备内存，还是将设备内存拷贝值主机内存等。cudaMemcpy内部根据拷贝的类型（kind）来决定调用以下的某个函数：

4. __host__ cudaError_t cudaDeviceReset( void )

该函数销毁当前进程中当前设备上所有的内存分配和重置所有状态，调用该函数达到重新初始该设备的作用。应该注意，在调用该函数时，应该确保该进程中其他host线程不能访问该设备！

下面是一个简单的向量相加的程序

* 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 main(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); // Verify that allocations succeeded if (h_A == NULL || h_B == NULL || h_C == NULL) fprintf(stderr , "Failed to allocate host vectors! \n " ); exit (EXIT_FAILURE); // 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); 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 B float *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 C float *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 memory printf( "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 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(); 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 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); 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 memory free(h_A); free(h_B); free(h_C); printf( "Done \n " ); return 0 ;

目前CUDA和OpenCL是最主流的两个GPU编程库，CUDA和OpenCL都是原生支持C/C++的，其它语言想要访问还有些麻烦，比如Java，需要通过JNI来访问CUDA或者OpenCL。基于JNI，现今有各种Java版本的GPU编程库，比如JCUDA等。另一种思路就是语言还是由java来编写，通过一种工具将java转换成C。

图2 GPU编程库

检查电脑是否有c++编译环境,没有则需要安装 . 检查电脑NVIDIA的 Cuda 驱动版本：控制面板->NVIDIA控制面板,驱动下载地址 https://www.nvidia.com/download/index.aspx?lang=en-us 下载 Cuda ：https://developer.nvidia.com/ cuda -toolkit-archive 下载J cuda ：10.... J Cuda 可以将 CUDA runtime 和driver api与java相连接，从而实现java程序调用 GPU 资源，进行并行加速的目的具体介绍可参考http://www.j cuda .org/j cuda /J Cuda .html 一。安装J CUDA 1. 下载J CUDA libraries（注意此前电脑上应该已经安装 CUDA 的相应文件，并本文是看小破站某 cuda 入门教程留下来的笔记，多上PPT上内容，夹杂一点自己的理解，和代码注释教程地址:https://www.bilibili.com/video/av74148375 git地址（PPT和源码）：https://github.com/huiscliu/tutorials 主要目的是为Gstreamer打点基础，不然基本抓瞎什么是 GPU 计算为什么要使用 GPU 计算 CPU与 GPU 分工与协作 GPU 计算架构...