使用libnvidia-ml.so动态库采集：
#include <stdio.h>
#include <string.h>
#include <sstream>
#include <unistd.h>
#include <stdint.h>
#include <string.h>
#include <stdlib.h>
#include <unistd.h>
#include <time.h>
#include <dlfcn.h>
#define CUDAAPI
#define LOAD_FUNC(l, s) dlsym(l, s)
#define DL_CLOSE_FUNC(l) dlclose(l)
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;
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;
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 *NVMLDEVICEGETMEMORYINFO)(nvmlDevice_t device, nvmlMemory_t *memory); // nvmlDeviceGetMemoryInfo
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
#define GPU_MAX_SIZE 128
#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)
void getdata() {
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;
NVMLDEVICEGETMEMORYINFO nvml_device_get_memory_info;
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;
nvml_lib = dlopen("libnvidia-ml.so", RTLD_NOW);
if(nvml_lib == NULL){
return;
}
unsigned int device_count = 0;
nvmlMemory_t memory_info;
nvmlUtilization_t gpu_utilization;
int i = 0;
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(NVMLDEVICEGETMEMORYINFO, nvml_device_get_memory_info, "nvmlDeviceGetMemoryInfo");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETUTILIZATIONRATES, nvml_device_get_utilization_rates, "nvmlDeviceGetUtilizationRates");
CHECK_LOAD_NVML_FUNC(NVMLDEVICEGETTEMPERATURE, nvml_device_get_temperature, "nvmlDeviceGetTemperature");
check_nvml_errors(nvml_init());
check_nvml_errors(nvml_device_get_count(&device_count));
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_memory_info(device_handel, &memory_info));
check_nvml_errors(nvml_device_get_utilization_rates(device_handel, &gpu_utilization));
printf("GPU:%d\t, Utilization:[gpu:%u, memory:%u], Memory:[total:%llu, free:%llu, used:%llu]\n ", gpu_utilization.gpu, gpu_utilization.memory, memory_info.total, memory_info.free, memory_info.used);
}
gpu_fail:
nvml_shutdown();
//关闭动态库
dlclose(nvml_lib);
}
int main(int argc, char **argv)
{
getdata();
return 0;
}
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