1.使用nsys分析GPU应用程序

痛定思痛。 2022-12-23 03:54 282阅读 0赞

## 例子 ##

GPU 跨步 矩阵加法  
vector-add.cu

#include <stdio.h>
    
    /* * Refactor host function to run as CUDA kernel */
    
    __global__
    void initWith(float num, float *a, int N)
    { 
      int index = threadIdx.x + blockIdx.x * blockDim.x;
      int stride = blockDim.x * gridDim.x;
    
      for(int i = index; i < N; i += stride)
      { 
        a[i] = num;
      }
    }
    
    __global__
    void addArraysInto(float *result, float *a, float *b, int N)
    { 
      int index = threadIdx.x + blockIdx.x * blockDim.x;
      int stride = blockDim.x * gridDim.x;
    
      for(int i = index; i < N; i += stride)
      { 
        result[i] = a[i] + b[i];
      }
    }
    
    void checkElementsAre(float target, float *array, int N)
    { 
      for(int i = 0; i < N; i++)
      { 
        if(array[i] != target)
        { 
          printf("FAIL: array[%d] - %0.0f does not equal %0.0f\n", i, array[i], target);
          exit(1);
        }
      }
      printf("Success! All values calculated correctly.\n");
    }
    
    int main()
    { 
      int deviceId;
      int numberOfSMs;
    
      cudaGetDevice(&deviceId);
      cudaDeviceGetAttribute(&numberOfSMs, cudaDevAttrMultiProcessorCount, deviceId);
      printf("Device ID: %d\tNumber of SMs: %d\n", deviceId, numberOfSMs);
    
      const int N = 2<<24;
      size_t size = N * sizeof(float);
    
      float *a;
      float *b;
      float *c;
    
      cudaMallocManaged(&a, size);
      cudaMallocManaged(&b, size);
      cudaMallocManaged(&c, size);
    
      size_t threadsPerBlock;
      size_t numberOfBlocks;
    
      threadsPerBlock = 256;
      numberOfBlocks = 32 * numberOfSMs;
    
      cudaError_t addArraysErr;
      cudaError_t asyncErr;
    
      /* * Launch kernels. */
    
      initWith<<<numberOfBlocks, threadsPerBlock>>>(3, a, N);
      initWith<<<numberOfBlocks, threadsPerBlock>>>(4, b, N);
      initWith<<<numberOfBlocks, threadsPerBlock>>>(0, c, N);
    
      /* * Now that initialization is happening on a GPU, host code * must be synchronized to wait for its completion. */
    
      cudaDeviceSynchronize();
    
      addArraysInto<<<numberOfBlocks, threadsPerBlock>>>(c, a, b, N);
    
      addArraysErr = cudaGetLastError();
      if(addArraysErr != cudaSuccess) printf("Error: %s\n", cudaGetErrorString(addArraysErr));
    
      asyncErr = cudaDeviceSynchronize();
      if(asyncErr != cudaSuccess) printf("Error: %s\n", cudaGetErrorString(asyncErr));
    
      checkElementsAre(7, c, N);
    
      cudaFree(a);
      cudaFree(b);
      cudaFree(c);
    }

编译：

nvcc -o single-thread-vector-add vector-add.cu -run
    # nvcc  -arch=sm_70 -O3 -Xcompiler="-march=native -fopenmp" vector-add.cu  -o baseline

## 1. nsys ##

nsys 之前的名称叫做 nvprof

### nsys 命令行使用方法 ###

nsys profile --stats=true ./single-thread-vector-add
    
    # nsys profile --stats=true --force-overwrite=true  -o baseline-report ./single-thread-vector-add

nsys profile将生成一个qdrep报告文件，该文件可以以多种方式使用。 我们在这里使用–stats = true标志表示我们希望打印输出摘要统计信息。 输出的信息有很多，包括：

*  配置文件配置详细信息
 *  报告文件的生成详细信息
 *  **CUDA API统计信息**
 *  **CUDA核函数的统计信息**
 *  **CUDA内存操作统计信息（时间和大小）**
 *  操作系统内核调用接口的统计信息

要使用上面黑体字的3个部分。 可以将使用生成的报告文件将其提供给Nsight Systems 进行可视化分析。

默认情况下，nsys profile不会覆盖现有的报告文件。 这样做是为了防止在进行概要分析时意外丢失工作。 如果出于某种原因，宁愿覆盖现有的报告文件，例如在快速迭代期间，可以向nsys profile提供-f标志以允许覆盖现有的报告文件。

nsys 输出结果：

Warning: LBR backtrace method is not supported on this platform. DWARF backtrace method will be used.
    Collecting data...
    Device ID: 0	Number of SMs: 80
    Success! All values calculated correctly.
    Processing events...
    Capturing symbol files...
    Saving temporary "/tmp/nsys-report-591f-683b-4f10-63c8.qdstrm" file to disk...
    Creating final output files...
    
    Processing [==============================================================100%]
    Saved report file to "/tmp/nsys-report-591f-683b-4f10-63c8.qdrep"
    Exporting 1815 events: [==================================================100%]
    
    Exported successfully to
    /tmp/nsys-report-591f-683b-4f10-63c8.sqlite
    
    Generating CUDA API Statistics...
    CUDA API Statistics (nanoseconds)
    
    Time(%)      Total Time       Calls         Average         Minimum         Maximum  Name                                                                            
    -------  --------------  ----------  --------------  --------------  --------------  --------------------------------------------------------------------------------
       82.0       230346229           3      76782076.3           27983       230270146  cudaMallocManaged                                                               
       10.7        29967501           2      14983750.5          508540        29458961  cudaDeviceSynchronize                                                           
        7.2        20316135           3       6772045.0         5613359         9006679  cudaFree                                                                        
        0.1          143177           4         35794.3            7600           76464  cudaLaunchKernel                                                                
    
    
    
    
    Generating CUDA Kernel Statistics...
    CUDA Kernel Statistics (nanoseconds)
    
    Time(%)      Total Time   Instances         Average         Minimum         Maximum  Name                                                                                                                                                                                                                                                                                                                                         
    -------  --------------  ----------  --------------  --------------  --------------  --------------------------------------------------------------------------------------------------------------------                                                                                                                                                                                                                         
       98.3        29535995           3       9845331.7         9744129         9932220  initWith(float, float*, int)                                                                                                                                                                                                                                                                                                                 
        1.7          506802           1        506802.0          506802          506802  addArraysInto(float*, float*, float*, int)                                                                                                                                                                                                                                                                                                   
    
    
    
    Generating CUDA Memory Operation Statistics...
    CUDA Memory Operation Statistics (nanoseconds)
    
    Time(%)      Total Time  Operations         Average         Minimum         Maximum  Name                                                                            
    -------  --------------  ----------  --------------  --------------  --------------  --------------------------------------------------------------------------------
      100.0        11401344         768         14845.5            1919           82688  [CUDA Unified Memory memcpy DtoH]                                               
    
    
    CUDA Memory Operation Statistics (KiB)
    
                  Total      Operations              Average            Minimum              Maximum  Name                                                                            
    -------------------  --------------  -------------------  -----------------  -------------------  --------------------------------------------------------------------------------
             131072.000             768              170.667              4.000             1020.000  [CUDA Unified Memory memcpy DtoH]                                               
    
    
    
    
    Generating Operating System Runtime API Statistics...
    Operating System Runtime API Statistics (nanoseconds)
    
    Time(%)      Total Time       Calls         Average         Minimum         Maximum  Name                                                                            
    -------  --------------  ----------  --------------  --------------  --------------  --------------------------------------------------------------------------------
       48.0       582535369          32      18204230.3           44658       100178751  poll                                                                            
       43.5       527276305          31      17008913.1           25004       100129329  sem_timedwait                                                                   
        6.5        78624695         575        136738.6            1048        16511688  ioctl                                                                           
        1.9        22856000          87        262712.6            1702         8953591  mmap                                                                            
        0.1          749890          73         10272.5            3658           23110  open64                                                                          
        0.0          154491           4         38622.8           38189           38997  pthread_create                                                                  
        0.0          128939          23          5606.0            1642           15319  fopen                                                                           
        0.0          112944          10         11294.4            8110           15695  write                                                                           
        0.0           93073           3         31024.3           23805           42251  fgets                                                                           
        0.0           78912          69          1143.7            1006            4990  fcntl                                                                           
        0.0           52553          16          3284.6            1718            5436  munmap                                                                          
        0.0           44010          16          2750.6            1605            4228  fclose                                                                          
        0.0           35526           5          7105.2            3976           10642  open                                                                            
        0.0           26782          12          2231.8            1268            4230  read                                                                            
        0.0           14721           3          4907.0            4662            5059  pipe2                                                                           
        0.0           10488           3          3496.0            2115            4987  fread                                                                           
        0.0            9801           4          2450.2            2192            2745  mprotect                                                                        
        0.0            9542           2          4771.0            4501            5041  socket                                                                          
        0.0            6078           1          6078.0            6078            6078  connect                                                                         
        0.0            2548           1          2548.0            2548            2548  bind                                                                            
        0.0            1983           1          1983.0            1983            1983  listen                                                                          
    
    
    
    
    Generating NVTX Push-Pop Range Statistics...
    NVTX Push-Pop Range Statistics (nanoseconds)
    
    
    
    
    Report file moved to "report13.qdrep"
    Report file moved to "report13.sqlite"

应用程序分析完毕后，请使用分析输出中显示的信息回答下列问题：

此应用程序中唯一调用的 CUDA 核函数的名称是什么？

Generating CUDA Kernel Statistics...
    CUDA Kernel Statistics (nanoseconds)
    
    Time(%)      Total Time   Instances         Average         Minimum         Maximum  Name                                                                                                                                                                                                                                                                                                                                         
    -------  --------------  ----------  --------------  --------------  --------------  --------------------------------------------------------------------------------------------------------------------                                                                                                                                                                                                                         
      100.0      2184487204           1    2184487204.0      2184487204      2184487204  addVectorsInto(float*, float*, float*, int)

此核函数运行了多少次？  
1 次。

此核函数的运行时间为多少？请记录下此时间：您将继续优化此应用程序，再和此时间做对比。  
2184487204 ns = 2.1 s