获取amd的GPU使用率，使用C++代码

你可以使用 AMD GPU Profiler 工具来获取 AMD GPU 的使用率。以下是获取 AMD GPU 使用率的示例 C 代码：

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

// AMD GPU Profiler的头文件
#include "CL/CL.h"
#include "CPerfCounter.h"

int main()
{
    int deviceCount = 0;
    cl_device_id* devices = NULL;
    cl_ulong maxClockFrequency = 0;
    cl_uint numOfComputeUnits = 0;

    cl_platform_id platformId = 0;
    clGetPlatformIDs(1, &platformId, NULL);

    // 获取所有的AMD GPU设备
    clGetDeviceIDs(platformId, CL_DEVICE_TYPE_GPU, 0, NULL, &deviceCount);

    devices = (cl_device_id*)malloc(sizeof(cl_device_id) * deviceCount);
    clGetDeviceIDs(platformId, CL_DEVICE_TYPE_GPU, deviceCount, devices, NULL);

    for (int i = 0; i < deviceCount; ++i)
    {
        cl_device_id device = devices[i];

        // 获取设备的运行包络信息
        cl_ulong maxClockFrequency = 0;
        clGetDeviceInfo(device, CL_DEVICE_MAX_CLOCK_FREQUENCY, sizeof(cl_ulong), &maxClockFrequency, NULL);

        cl_uint numOfComputeUnits = 0;
        clGetDeviceInfo(device, CL_DEVICE_MAX_COMPUTE_UNITS, sizeof(cl_uint), &numOfComputeUnits, NULL);

        // 创建 OpenCL 上下文
        cl_context_properties cps[3] = { CL_CONTEXT_PLATFORM, (cl_context_properties)platformId, 0 };
        cl_context context = clCreateContext(cps, 1, &device, NULL, NULL, NULL);

        // 创建命令队列
        cl_command_queue commandQueue = clCreateCommandQueue(context, device, CL_QUEUE_PROFILING_ENABLE, NULL);

        // 创建 Profiling 对象
        CPerfCounter timer(numOfComputeUnits, commandQueue);

        // 创建程序对象和内核对象
        const char* kernelSource = "__kernel void gpuUtil(__global float* result)\n"
                                   "{\n"
                                   "    int tid = get_global_id(0);\n"
                                   "    float a = 0.0f;\n"
                                   "    while (true)\n"
                                   "    {\n"
                                   "        a += tid * 0.1f;\n"
                                   "        result[tid] = a;\n"
                                   "    }\n"
                                   "}\n";

        cl_program program = clCreateProgramWithSource(context, 1, &kernelSource, NULL, NULL);
        clBuildProgram(program, 1, &device, NULL, NULL, NULL);

        cl_kernel kernel = clCreateKernel(program, "gpuUtil", NULL);

        // 创建输出数据缓冲区
        cl_mem output = clCreateBuffer(context, CL_MEM_WRITE_ONLY | CL_MEM_ALLOC_HOST_PTR, sizeof(float) * numOfComputeUnits, NULL, NULL);

        // 设置内核参数
        clSetKernelArg(kernel, 0, sizeof(cl_mem), &output);

        // 启动内核
        size_t global_work_size[] = { numOfComputeUnits };
        clEnqueueNDRangeKernel(commandQueue, kernel, 1, NULL, global_work_size, NULL, 0, NULL, NULL);

        // 读取结果
        float* result = (float*)clEnqueueMapBuffer(commandQueue, output, CL_TRUE, CL_MAP_READ, 0, sizeof(float) * numOfComputeUnits, 0, NULL, NULL, NULL);

        // 计算 GPU 使用率
        double gpuUtilization = timer.GetUtilization(result);

        printf("AMD GPU %d - Utilization: %.2f%%\n", i + 1, gpuUtilization);

        // 清理资源
        clEnqueueUnmapMemObject(commandQueue, output, result, 0, NULL, NULL);
        clReleaseMemObject(output);

        clReleaseKernel(kernel);
        clReleaseProgram(program);

        clReleaseCommandQueue(commandQueue);
        clReleaseContext(context);
    }

    free(devices);

    return 0;
}

在上面的示例代码中，我们通过 AMD GPU Profiler 工具来获取GPU使用率。它利用了AMD GPU Profiler的函数库和CPerfCounter类库来计算GPU使用率，并利用OpenCL技术进行核操作。需要注意的是，该示例代码只是一个简单的演示，仅供参考，需要针对具体的GPU进行优化和调整。