CUDA 4，CUDA

CUDA 4，CUDA

device管理

NVIDIA提供了集中凡是來查詢和管理GPU device，掌握GPU信息查詢很重要，因為這可以幫助你設置kernel的執行配置。

本博文將主要介紹下面兩方面內容：

• CUDA runtime API function
• NVIDIA系統管理命令行

使用runtime API來查詢GPU信息

你可以使用下面的function來查詢所有關於GPU device 的信息：

cudaError_t cudaGetDeviceProperties(cudaDeviceProp *prop, int device);

GPU的信息放在cudaDeviceProp這個結構體中。

代碼

#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);
}                

編譯運行：

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

輸出：

./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

決定最佳GPU

對於支持多GPU的系統，是需要從中選擇一個來作為我們的device的，抉擇出最佳計算性能GPU的一種方法就是由其擁有的處理器數量決定，可以用下面的代碼來選擇最佳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來查詢GPU信息

nvidia-smi是一個命令行工具，可以幫助你管理操作GPU device，並且允許你查詢和更改device狀態。

nvidia-smi用處很多，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)

然後可以使用下面的命令來查詢GPU 0 的詳細信息：

$nvidia-smi –q –i 0

下面是該命令的一些參數，可以精簡nvidia-smi的顯示信息：

MEMORY

UTILIZATION

ECC

TEMPERATURE

POWER

CLOCK

COMPUTE

PIDS

PERFORMANCE

SUPPORTED_CLOCKS

PAGE_RETIREMENT

ACCOUNTING

比如，顯示只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

設置device

對於多GPU系統，使用nvidia-smi可以查看各GPU屬性，每個GPU從0開始依次標注，使用環境變量CUDA_VISIBLE_DEVICES可以指定GPU而不用修改application。

可以設置環境變量CUDA_VISIBLE_DEVICES-2來屏蔽其他GPU，這樣只有GPU2能被使用。當然也可以使用CUDA_VISIBLE_DEVICES-2,3來設置多個GPU，他們的device ID分別為0和1.

代碼下載：CodeSamples.zip