本篇博文转载于https://www.cnblogs.com/1024incn/tag/CUDA/,仅用于学习。
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
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.