CUDA学习笔记三



device管理

NVIDIA提供了集中凡是来查询和管理GPU device，掌握GPU信息查询很重要，因为这可以帮助你设置kernel的执行配置。

本博文将主要介绍下面两方面内容：

• CUDA runtime API function
• NVIDIA系统管理命令行

使用runtime API来查询GPU信息

你可以使用下面的function来查询所有关于GPU device 的信息：

cudaError_t cudaGetDeviceProperties(cudaDeviceProp *prop, int device);

GPU的信息放在cudaDeviceProp这个结构体中。

代码

#include <cuda_runtime.h>#include <stdio.h>int main(int argc, char **argv) {    　　printf("%s Starting...\n", argv[0]);    int deviceCount = 0;    cudaError_t error_id = cudaGetDeviceCount(&deviceCount);    if (error_id != cudaSuccess) {        printf("cudaGetDeviceCount returned %d\n-> %s\n",        (int)error_id, cudaGetErrorString(error_id));        printf("Result = FAIL\n");        exit(EXIT_FAILURE);    }    if (deviceCount == 0) {        printf("There are no available device(s) that support CUDA\n");    } else {        printf("Detected %d CUDA Capable device(s)\n", deviceCount);    }    int dev, driverVersion = 0, runtimeVersion = 0;    dev =0;    cudaSetDevice(dev);    cudaDeviceProp deviceProp;    cudaGetDeviceProperties(&deviceProp, dev);    printf("Device %d: \"%s\"\n", dev, deviceProp.name);    cudaDriverGetVersion(&driverVersion);    cudaRuntimeGetVersion(&runtimeVersion);    printf(" CUDA Driver Version / Runtime Version %d.%d / %d.%d\n",driverVersion/1000, (driverVersion%100)/10,runtimeVersion/1000, (runtimeVersion%100)/10);    printf(" CUDA Capability Major/Minor version number: %d.%d\n",deviceProp.major, deviceProp.minor);    printf(" Total amount of global memory: %.2f MBytes (%llu bytes)\n",(float)deviceProp.totalGlobalMem/(pow(1024.0,3)),(unsigned long long) deviceProp.totalGlobalMem);    printf(" GPU Clock rate: %.0f MHz (%0.2f GHz)\n",deviceProp.clockRate * 1e-3f, deviceProp.clockRate * 1e-6f);    printf(" Memory Clock rate: %.0f Mhz\n",deviceProp.memoryClockRate * 1e-3f);    printf(" Memory Bus Width: %d-bit\n",deviceProp.memoryBusWidth);    if (deviceProp.l2CacheSize) {        printf(" L2 Cache Size: %d bytes\n",        deviceProp.l2CacheSize);    }    printf(" Max Texture Dimension Size (x,y,z) 1D=(%d), 2D=(%d,%d), 3D=(%d,%d,%d)\n",    deviceProp.maxTexture1D , deviceProp.maxTexture2D[0],    deviceProp.maxTexture2D[1],    deviceProp.maxTexture3D[0], deviceProp.maxTexture3D[1],    deviceProp.maxTexture3D[2]);    printf(" Max Layered Texture Size (dim) x layers 1D=(%d) x %d, 2D=(%d,%d) x %d\n",    deviceProp.maxTexture1DLayered[0], deviceProp.maxTexture1DLayered[1],    deviceProp.maxTexture2DLayered[0], deviceProp.maxTexture2DLayered[1],    deviceProp.maxTexture2DLayered[2]);    printf(" Total amount of constant memory: %lu bytes\n",deviceProp.totalConstMem);    printf(" Total amount of shared memory per block: %lu bytes\n",deviceProp.sharedMemPerBlock);    printf(" Total number of registers available per block: %d\n",deviceProp.regsPerBlock);    printf(" Warp size: %d\n", deviceProp.warpSize);    printf(" Maximum number of threads per multiprocessor: %d\n",deviceProp.maxThreadsPerMultiProcessor);    printf(" Maximum number of threads per block: %d\n",deviceProp.maxThreadsPerBlock);    printf(" Maximum sizes of each dimension of a block: %d x %d x %d\n",    deviceProp.maxThreadsDim[0],    deviceProp.maxThreadsDim[1],    deviceProp.maxThreadsDim[2]);    printf(" Maximum sizes of each dimension of a grid: %d x %d x %d\n",    deviceProp.maxGridSize[0],    deviceProp.maxGridSize[1],    deviceProp.maxGridSize[2]);    printf(" Maximum memory pitch: %lu bytes\n", deviceProp.memPitch);    exit(EXIT_SUCCESS);}

编译运行：

$ nvcc checkDeviceInfor.cu -o checkDeviceInfor$ ./checkDeviceInfor

输出：

./checkDeviceInfor Starting...Detected 2 CUDA Capable device(s)Device 0: "Tesla M2070"CUDA Driver Version / Runtime Version 5.5 / 5.5CUDA Capability Major/Minor version number: 2.0Total amount of global memory: 5.25 MBytes (5636554752 bytes)GPU Clock rate: 1147 MHz (1.15 GHz)Memory Clock rate: 1566 MhzMemory Bus Width: 384-bitL2 Cache Size: 786432 bytesMax Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536,65535), 3D=(2048,2048,2048)Max Layered Texture Size (dim) x layers 1D=(16384) x 2048, 2D=(16384,16384) x 2048Total amount of constant memory: 65536 bytesTotal amount of shared memory per block: 49152 bytesTotal number of registers available per block: 32768Warp size: 32Maximum number of threads per multiprocessor: 1536Maximum number of threads per block: 1024Maximum sizes of each dimension of a block: 1024 x 1024 x 64Maximum sizes of each dimension of a grid: 65535 x 65535 x 65535Maximum memory pitch: 2147483647 bytes

决定最佳GPU

对于支持多GPU的系统，是需要从中选择一个来作为我们的device的，抉择出最佳计算性能GPU的一种方法就是由其拥有的处理器数量决定，可以用下面的代码来选择最佳GPU。

int numDevices = 0;cudaGetDeviceCount(&numDevices);if (numDevices > 1) {    int maxMultiprocessors = 0, maxDevice = 0;    for (int device=0; device<numDevices; device++) {        cudaDeviceProp props;        cudaGetDeviceProperties(&props, device);        if (maxMultiprocessors < props.multiProcessorCount) {            maxMultiprocessors = props.multiProcessorCount;            maxDevice = device;        }    }    cudaSetDevice(maxDevice);}    

使用nvidia-smi来查询GPU信息

nvidia-smi是一个命令行工具，可以帮助你管理操作GPU device，并且允许你查询和更改device状态。

nvidia-smi用处很多，比如，下面的指令：

$ nvidia-smi -LGPU 0: Tesla M2070 (UUID: GPU-68df8aec-e85c-9934-2b81-0c9e689a43a7)GPU 1: Tesla M2070 (UUID: GPU-382f23c1-5160-01e2-3291-ff9628930b70)

然后可以使用下面的命令来查询GPU 0 的详细信息：

$nvidia-smi –q –i 0

下面是该命令的一些参数，可以精简nvidia-smi的显示信息：

MEMORY

UTILIZATION

ECC

TEMPERATURE

POWER

CLOCK

COMPUTE

PIDS

PERFORMANCE

SUPPORTED_CLOCKS

PAGE_RETIREMENT

ACCOUNTING

比如，显示只device memory的信息：

$nvidia-smi –q –i 0 –d    MEMORY | tail –n 5Memory UsageTotal : 5375 MBUsed : 9 MBFree : 5366 MB

设置device

对于多GPU系统，使用nvidia-smi可以查看各GPU属性，每个GPU从0开始依次标注，使用环境变量CUDA_VISIBLE_DEVICES可以指定GPU而不用修改application。

可以设置环境变量CUDA_VISIBLE_DEVICES-2来屏蔽其他GPU，这样只有GPU2能被使用。当然也可以使用CUDA_VISIBLE_DEVICES-2,3来设置多个GPU，他们的device ID分别为0和1.