CUDA ---- device管理
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 / 5.5 CUDA Capability Major/Minor version number: 2.0 Total amount of global memory: 5.25 MBytes (5636554752 bytes) GPU Clock rate: 1147 MHz (1.15 GHz) Memory Clock rate: 1566 Mhz Memory Bus Width: 384-bit L2 Cache Size: 786432 bytes Max 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 2048 Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 32768 Warp size: 32 Maximum number of threads per multiprocessor: 1536 Maximum number of threads per block: 1024 Maximum sizes of each dimension of a block: 1024 x 1024 x 64 Maximum sizes of each dimension of a grid: 65535 x 65535 x 65535 Maximum 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 -L GPU 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 5 Memory Usage Total : 5375 MB Used : 9 MB Free : 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.
代码下载：CodeSamples.zip
