如何查看Nvidia的GPU運行狀態
如何查看Nvidia的GPU運行狀態
在使用nvidia的GPU進行運算的時候，一般會有須要瞭解GPU運行狀態需求。在下面的文章中，將會介紹我在實際使用中到的方法。 git
1.使用"nvidia-smi"查看GPU狀態信息
一般安裝好nvidia驅動程序後會自動安裝好nvidia-smi，使用nvidia-smi是最簡單直接的查看GPU狀態信息的方法。github
直接運行"nvidia-smi"，就能夠查看當前的運行狀態。能夠查看到GPU的各個核心的工做狀態、溫度、內存信息、及使用GPU的進程信息等。windows
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.27 Driver Version: 367.27 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GRID K1 Off | 0000:03:00.0 Off | N/A |
| N/A 63C P0 18W / 31W | 545MiB / 4036MiB | 28% Default |
+-------------------------------+----------------------+----------------------+
| 1 GRID K1 Off | 0000:04:00.0 Off | N/A |
| N/A 58C P0 14W / 31W | 195MiB / 4036MiB | 6% Default |
+-------------------------------+----------------------+----------------------+
| 2 GRID K1 Off | 0000:05:00.0 Off | N/A |
| N/A 40C P0 16W / 31W | 99MiB / 4036MiB | 18% Default |
+-------------------------------+----------------------+----------------------+
| 3 GRID K1 Off | 0000:06:00.0 Off | N/A |
| N/A 60C P0 16W / 31W | 200MiB / 4036MiB | 30% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 9133 C ...e/ffmpeg_nvenc_decode/_release/bin/ffmpeg 48MiB |
| 0 9136 C ...e/ffmpeg_nvenc_decode/_release/bin/ffmpeg 48MiB |
| 1 9132 C ...e/ffmpeg_nvenc_decode/_release/bin/ffmpeg 38MiB |
| 1 9133 C ...e/ffmpeg_nvenc_decode/_release/bin/ffmpeg 38MiB |
| 1 9134 C ...e/ffmpeg_nvenc_decode/_release/bin/ffmpeg 38MiB |
| 1 9135 C ...e/ffmpeg_nvenc_decode/_release/bin/ffmpeg 38MiB |
| 1 9136 C ...e/ffmpeg_nvenc_decode/_release/bin/ffmpeg 38MiB |
| 2 9132 C ...e/ffmpeg_nvenc_decode/_release/bin/ffmpeg 48MiB |
| 2 9134 C ...e/ffmpeg_nvenc_decode/_release/bin/ffmpeg 48MiB |
| 3 9135 C ...e/ffmpeg_nvenc_decode/_release/bin/ffmpeg 48MiB |
+-----------------------------------------------------------------------------+
若是想了解更多信息，運行"nvidia-smi -a"或"nvidia-smi -q"，能夠查看到詳盡的GPU信息。好比我我的比較關心使用GPU進行視頻編解碼的時候的GPU使用狀況，就能夠經過"Utilization"字段下的信息查看到各類使用率的狀況。bash
Utilization
Gpu : 5 %
Memory : 6 %
Encoder : 40 %
Decoder : 0 %
2.使用"NVML API"獲取GPU狀態信息
使用"nvidia-smi"雖然簡單方便，但由於它是個應用程序，不是很方便在代碼中進行使用。因此就嘗試找找官方是否提供了獲取GPU信息的方法，果真"NVML API"就是所需。在以後的時間裏，經過參考API文檔，比較快速的整理出我但願獲取到的GPU運行狀態信息。ide
"NVML API"主要須要庫libnvidia-ml，我用它主要是獲取GPU的基本信息和各類使用率。具體使用了哪些API這裏不作贅述，其實直接看API文檔和個人測試源碼更容易理解。測試
API文檔：http://docs.nvidia.com/deploy/pdf/NVML_API_Reference_Guide.pdfui
最後，簡單暴力的附上代碼：https://github.com/shenhailuanma/selfTestCode/blob/master/getNvidiaInfo/vn_info.clua
#include <stdio.h>
#include <stdint.h>
#include <string.h>
#include <stdlib.h>
#include <unistd.h>
#include <time.h>
#if defined(_WIN32)
#include <windows.h>
#else
#include <dlfcn.h>
#endif
#if defined(_WIN32)
#define CUDAAPI __stdcall
#else
#define CUDAAPI
#endif
#if defined(_WIN32)
#define LOAD_FUNC(l, s) GetProcAddress(l, s)
#define DL_CLOSE_FUNC(l) FreeLibrary(l)
#else
#define LOAD_FUNC(l, s) dlsym(l, s)
#define DL_CLOSE_FUNC(l) dlclose(l)
#endif
/**
* Return values for NVML API calls.
*/
typedef enum nvmlReturn_enum
{
NVML_SUCCESS = 0, //!< The operation was successful
NVML_ERROR_UNINITIALIZED = 1, //!< NVML was not first initialized with nvmlInit()
NVML_ERROR_INVALID_ARGUMENT = 2, //!< A supplied argument is invalid
NVML_ERROR_NOT_SUPPORTED = 3, //!< The requested operation is not available on target device
NVML_ERROR_NO_PERMISSION = 4, //!< The current user does not have permission for operation
NVML_ERROR_ALREADY_INITIALIZED = 5, //!< Deprecated: Multiple initializations are now allowed through ref counting
NVML_ERROR_NOT_FOUND = 6, //!< A query to find an object was unsuccessful
NVML_ERROR_INSUFFICIENT_SIZE = 7, //!< An input argument is not large enough
NVML_ERROR_INSUFFICIENT_POWER = 8, //!< A device's external power cables are not properly attached
NVML_ERROR_DRIVER_NOT_LOADED = 9, //!< NVIDIA driver is not loaded
NVML_ERROR_TIMEOUT = 10, //!< User provided timeout passed
NVML_ERROR_UNKNOWN = 999 //!< An internal driver error occurred
} nvmlReturn_t;
typedef void * nvmlDevice_t;
/* Memory allocation information for a device. */
typedef struct nvmlMemory_st
{
unsigned long long total; //!< Total installed FB memory (in bytes)
unsigned long long free; //!< Unallocated FB memory (in bytes)
unsigned long long used; //!< Allocated FB memory (in bytes). Note that the driver/GPU always sets aside a small amount of memory for bookkeeping
} nvmlMemory_t;
/* Information about running compute processes on the GPU */
typedef struct nvmlProcessInfo_st
{
unsigned int pid; //!< Process ID
unsigned long long usedGpuMemory; //!< Amount of used GPU memory in bytes.
//!< Under WDDM, \ref NVML_VALUE_NOT_AVAILABLE is always reported
//!< because Windows KMD manages all the memory and not the NVIDIA driver
} nvmlProcessInfo_t;
/* Utilization information for a device. */
typedef struct nvmlUtilization_st
{
unsigned int gpu; //!< Percent of time over the past second during which one or more kernels was executing on the GPU
unsigned int memory; //!< Percent of time over the past second during which global (device) memory was being read or written
} nvmlUtilization_t;
typedef nvmlReturn_t(CUDAAPI *NVMLINIT)(void); // nvmlInit
typedef nvmlReturn_t(CUDAAPI *NVMLSHUTDOWN)(void); // nvmlShutdown
typedef nvmlReturn_t(CUDAAPI *NVMLDEVICEGETCOUNT)(unsigned int *deviceCount); // nvmlDeviceGetCount
typedef nvmlReturn_t(CUDAAPI *NVMLDEVICEGETHANDLEBYINDEX)(unsigned int index, nvmlDevice_t *device); // nvmlDeviceGetHandleByIndex
typedef nvmlReturn_t(CUDAAPI *NVMLDEVICEGETDECODERUTILIZATION)(nvmlDevice_t device, unsigned int *utilization,unsigned int *samplingPeriodUs); // nvmlDeviceGetDecoderUtilization
typedef nvmlReturn_t(CUDAAPI *NVMLDEVICEGETENCODERUTILIZATION)(nvmlDevice_t device, unsigned int *utilization,unsigned int *samplingPeriodUs); // nvmlDeviceGetEncoderUtilization
typedef nvmlReturn_t(CUDAAPI *NVMLDEVICEGETMEMORYINFO)(nvmlDevice_t device, nvmlMemory_t *memory); // nvmlDeviceGetMemoryInfo
typedef nvmlReturn_t(CUDAAPI *NVMLDEVICEGETRUNNINGPROCESSES)(nvmlDevice_t device, unsigned int *infoCount,nvmlProcessInfo_t *infos);// nvmlDeviceGetComputeRunningProcesses
typedef nvmlReturn_t(CUDAAPI *NVMLDEVICEGETPPROCESSNAME)(unsigned int pid, char *name, unsigned int length); // nvmlSystemGetProcessName
typedef nvmlReturn_t(CUDAAPI *NVMLDEVICEGETUTILIZATIONRATES)(nvmlDevice_t device, nvmlUtilization_t *utilization); // nvmlDeviceGetUtilizationRates
typedef nvmlReturn_t(CUDAAPI *NVMLDEVICEGETTEMPERATURE)(nvmlDevice_t device, int sensorType, unsigned int *temp); // nvmlDeviceGetTemperature
typedef struct nvGpuUnitInfo_st
{
unsigned int decoder_utilization;
unsigned int encoder_utilization;
unsigned int gpu_utilization;
unsigned int memory_utilization;
unsigned int temperature;
unsigned long long memory_total;
unsigned long long memory_free;
unsigned long long memory_used;
}nvGpuUnitInfo_t;
#define GPU_MAX_SIZE 64
typedef struct nvGpuInfo_st
{
unsigned int device_count;
nvGpuUnitInfo_t devices[GPU_MAX_SIZE];
}nvGpuInfo_t;
#define RETURN_SUCCESS 0
#define RETURN_ERROR_LOAD_LIB (-1)
#define RETURN_ERROR_LOAD_FUNC (-2)
#define RETURN_ERROR_LIB_FUNC (-3)
#define RETURN_ERROR_NULL_POINTER (-4)
#define CHECK_LOAD_NVML_FUNC(t, f, s) \
do { \
(f) = (t)LOAD_FUNC(nvml_lib, s); \
if (!(f)) { \
printf("Failed loading %s from NVML library\n", s); \
retCode = RETURN_ERROR_LOAD_FUNC; \
goto gpu_fail; \
} \
} while (0)
static int check_nvml_error(int err, const char *func)
{
if (err != NVML_SUCCESS) {
printf(" %s - failed with error code:%d\n", func, err);
return 0;
}
return 1;
}
#define check_nvml_errors(f) \
do{ \
if (!check_nvml_error(f, #f)) { \
retCode = RETURN_ERROR_LIB_FUNC; \
goto gpu_fail;\
}\
}while(0)
static int get_gpu_info(nvGpuInfo_t *infos)
{
if(infos == NULL){
return RETURN_ERROR_NULL_POINTER;
}
int retCode = RETURN_SUCCESS;
void* nvml_lib;
NVMLINIT nvml_init;
NVMLSHUTDOWN nvml_shutdown;
NVMLDEVICEGETCOUNT nvml_device_get_count;
NVMLDEVICEGETHANDLEBYINDEX nvml_device_get_handle_by_index;
NVMLDEVICEGETDECODERUTILIZATION nvml_device_get_decoder_utilization;
NVMLDEVICEGETENCODERUTILIZATION nvml_device_get_encoder_utilization;
NVMLDEVICEGETMEMORYINFO nvml_device_get_memory_info;
NVMLDEVICEGETRUNNINGPROCESSES nvml_device_get_running_processes;
NVMLDEVICEGETPPROCESSNAME nvml_device_get_process_name;
NVMLDEVICEGETUTILIZATIONRATES nvml_device_get_utilization_rates;
NVMLDEVICEGETTEMPERATURE nvml_device_get_temperature;
nvmlDevice_t device_handel;
unsigned int utilization_value = 0;
unsigned int utilization_sample = 0;
int best_gpu = 0;
unsigned int decoder_used = 100;
// open the libnvidia-ml.so
nvml_lib = NULL;
#if defined(_WIN32)
if (sizeof(void*) == 8) {
nvml_lib = LoadLibrary(TEXT("nvidia-ml.dll"));
} else {
nvml_lib = LoadLibrary(TEXT("nvidia-ml.dll"));
}
#else
nvml_lib = dlopen("libnvidia-ml.so", RTLD_LAZY);
#endif
if(nvml_lib == NULL){
return RETURN_ERROR_LOAD_LIB;
}
CHECK_LOAD_NVML_FUNC(NVMLINIT, nvml_init, "nvmlInit");
CHECK_LOAD_NVML_FUNC(NVMLSHUTDOWN, nvml_shutdown, "nvmlShutdown");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETCOUNT, nvml_device_get_count, "nvmlDeviceGetCount");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETHANDLEBYINDEX, nvml_device_get_handle_by_index, "nvmlDeviceGetHandleByIndex");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETDECODERUTILIZATION, nvml_device_get_decoder_utilization, "nvmlDeviceGetDecoderUtilization");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETENCODERUTILIZATION, nvml_device_get_encoder_utilization, "nvmlDeviceGetEncoderUtilization");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETMEMORYINFO, nvml_device_get_memory_info, "nvmlDeviceGetMemoryInfo");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETRUNNINGPROCESSES, nvml_device_get_running_processes, "nvmlDeviceGetComputeRunningProcesses");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETPPROCESSNAME, nvml_device_get_process_name, "nvmlSystemGetProcessName");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETUTILIZATIONRATES, nvml_device_get_utilization_rates, "nvmlDeviceGetUtilizationRates");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETTEMPERATURE, nvml_device_get_temperature, "nvmlDeviceGetTemperature");
// get gpu info
check_nvml_errors(nvml_init());
unsigned int device_count = 0;
check_nvml_errors(nvml_device_get_count(&device_count));
infos->device_count = device_count;
nvmlMemory_t memory_info;
nvmlUtilization_t gpu_utilization;
int i = 0;
for(i = 0; i < device_count; i++){
check_nvml_errors(nvml_device_get_handle_by_index(i, &device_handel));
check_nvml_errors(nvml_device_get_decoder_utilization(device_handel, &infos->devices[i].decoder_utilization, &utilization_sample));
check_nvml_errors(nvml_device_get_encoder_utilization(device_handel, &infos->devices[i].encoder_utilization, &utilization_sample));
check_nvml_errors(nvml_device_get_memory_info(device_handel, &memory_info));
infos->devices[i].memory_total = memory_info.total;
infos->devices[i].memory_free = memory_info.free;
infos->devices[i].memory_used = memory_info.used;
check_nvml_errors(nvml_device_get_utilization_rates(device_handel, &gpu_utilization));
infos->devices[i].gpu_utilization = gpu_utilization.gpu;
infos->devices[i].memory_utilization = gpu_utilization.memory;
check_nvml_errors(nvml_device_get_temperature(device_handel, 0, &infos->devices[i].temperature));
}
gpu_fail:
nvml_shutdown();
return retCode;
}
static void print_gpu_info(nvGpuInfo_t * infos)
{
printf("device count:%u\n", infos->device_count);
int i = 0;
for(i = 0; i < infos->device_count; i++){
printf("GPU:%d\t, Utilization:[decoder:%u, encoder:%u, gpu:%u, memory:%u], Temperature:%uC, Memory:[total:%llu, free:%llu, used:%llu]\n ",
i, infos->devices[i].decoder_utilization, infos->devices[i].encoder_utilization, infos->devices[i].gpu_utilization, infos->devices[i].memory_utilization,
infos->devices[i].temperature, infos->devices[i].memory_total, infos->devices[i].memory_free, infos->devices[i].memory_used);
}
}
int main(void)
{
nvGpuInfo_t gpu_buf;
int ret = get_gpu_info(&gpu_buf);
if(!ret)
print_gpu_info(&gpu_buf);
return ret;
}
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