Question

如何使用给定的计算gpu内存带宽：

数据样本大小（Gb）。
内核执行时间（nvprof输出）。

GPU：gtx 1050 ti
Cuda：8.0
操作系统：Windows 10
IDE：Visual studio 2015

通常我会使用这个公式：bandwidth [Gb/s] = data_size [Gb] / average_time [s]。

但是当我对get_mem_kernel()内核使用上面的公式时，我得到了错误的结果：441,93 [Gb/s]。

我认为这个结果是错误的，因为在gtx 1050 ti的技术规范中，全局内存带宽为112 [Gb\s]。

我在哪里犯了错误，或者还有其他我不理解的内容？

示例代码：

// cpp libs:
#include <iostream>
#include <sstream>
#include <fstream>
#include <iomanip>
#include <stdexcept>

// cuda libs:
#include <cuda_runtime.h>
#include <device_launch_parameters.h>

#define ERROR_CHECK(CHECK_) if (CHECK_ != cudaError_t::cudaSuccess) { std::cout << "cuda error" << std::endl; throw std::runtime_error("cuda error"); }

using data_type = double;

template <typename T> constexpr __forceinline__
T div_s(T dividend, T divisor)
{
    using P = double;
    return static_cast <T> (static_cast <P> (dividend + divisor - 1) / static_cast <P> (divisor));
}

__global__
void set_mem_kernel(const unsigned int size, data_type * const in_data)
{
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < size)
    {
        in_data[idx] = static_cast <data_type> (idx);
    }
}

__global__
void get_mem_kernel(const unsigned int size, data_type * const in_data)
{
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    data_type val = 0;
    if (idx < size)
    {
        val = in_data[idx];
    }
}

struct quit_program
{
public:
    ~quit_program()
    {
        try
        {
            ERROR_CHECK(cudaDeviceReset());
        }
        catch (...) {}
    }
} quit;

int main()
{
    unsigned int size = 12500000; // 100 mb;
    size_t       byte = size * sizeof(data_type);

    dim3 threads (256, 1, 1);
    dim3 blocks  (div_s(size, threads.x), 1, 1);

    std::cout << size << std::endl;
    std::cout << byte << std::endl;
    std::cout << std::endl;

    std::cout << threads.x << std::endl;
    std::cout << blocks.x  << std::endl;
    std::cout << std::endl;

    // data:
    data_type * d_data = nullptr;
    ERROR_CHECK(cudaMalloc(&d_data, byte));

    for (int i = 0; i < 20000; i++)
    {
        set_mem_kernel <<<blocks, threads>>> (size, d_data);
        ERROR_CHECK(cudaDeviceSynchronize());
        ERROR_CHECK(cudaGetLastError());

        get_mem_kernel <<<blocks, threads>>> (size, d_data);
        ERROR_CHECK(cudaDeviceSynchronize());
        ERROR_CHECK(cudaGetLastError());
    }

    // Exit:
    ERROR_CHECK(cudaFree(d_data));
    ERROR_CHECK(cudaDeviceReset());
    return EXIT_SUCCESS;
}

nvproof结果：

D:\Dev\visual_studio\nevada_test_site\x64\Release>nvprof ./cuda_test.exe
12500000
100000000

256
48829

==10508== NVPROF is profiling process 10508, command: ./cuda_test.exe
==10508== Warning: Unified Memory Profiling is not supported on the current configuration because a pair of devices without peer-to-peer support is detected on this multi-GPU setup. When peer mappings are not available, system falls back to using zero-copy memory. It can cause kernels, which access unified memory, to run slower. More details can be found at: http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#um-managed-memory
==10508== Profiling application: ./cuda_test.exe
==10508== Profiling result:
Time(%)      Time     Calls       Avg       Min       Max  Name
 81.12%  19.4508s     20000  972.54us  971.22us  978.32us  set_mem_kernel(unsigned int, double*)
 18.88%  4.52568s     20000  226.28us  224.45us  271.14us  get_mem_kernel(unsigned int, double*)

==10508== API calls:
Time(%)      Time     Calls       Avg       Min       Max  Name
 97.53%  26.8907s     40000  672.27us  247.98us  1.7566ms  cudaDeviceSynchronize
  1.61%  443.32ms     40000  11.082us  5.8340us  183.43us  cudaLaunch
  0.51%  141.10ms         1  141.10ms  141.10ms  141.10ms  cudaMalloc
  0.16%  43.648ms         1  43.648ms  43.648ms  43.648ms  cudaDeviceReset
  0.08%  22.182ms     80000     277ns       0ns  121.07us  cudaSetupArgument
  0.06%  15.437ms     40000     385ns       0ns  24.433us  cudaGetLastError
  0.05%  12.929ms     40000     323ns       0ns  57.253us  cudaConfigureCall
  0.00%  1.1932ms        91  13.112us       0ns  734.09us  cuDeviceGetAttribute
  0.00%  762.17us         1  762.17us  762.17us  762.17us  cudaFree
  0.00%  359.93us         1  359.93us  359.93us  359.93us  cuDeviceGetName
  0.00%  8.3880us         1  8.3880us  8.3880us  8.3880us  cuDeviceTotalMem
  0.00%  2.5520us         3     850ns     364ns  1.8230us  cuDeviceGetCount
  0.00%  1.8240us         3     608ns     365ns  1.0940us  cuDeviceGet

CUDA Samples\v8.0\1_Utilities\bandwidthTest结果：

[CUDA Bandwidth Test] - Starting...
Running on...

 Device 0: GeForce GTX 1050 Ti
 Quick Mode

 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     11038.4

 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     11469.6

 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     95214.0

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

Answer 1

编译器正在优化内存读取。 Robert Crovella指出了这一点。谢谢你的帮助 - 我永远不会猜到它。

详细：
我的编译器正在优化远离val变量和扩展内存读取。

从nvprof输出计算内存带宽的奇怪结果

1 个答案: