要求
我写信请求有关优化我的解决方案/方法“CalculateConvolutionOutputTensor__im2col”的指导。我希望帮助确定超越我幼稚方法的最佳策略；提供有关任何相关 GPU 进程及其应用方式的直觉（例如，银行冲突）；并根据我可以调整的内容帮助解释上述配置文件。
使用 GeForce 2080 Ti 时，该方法的第一次运行需要 0.774 秒。我已经包含了我编写的唯一 CUDA C++ 内核的 Nsight Compute 配置文件的屏幕截图：im2col。
我能做的事情
我可以让每个 GPU 线程访问共享内存而不是全局内存。我可以将 GPU“堆”变量转移到内核“堆栈”，而不是取消对每个线程和内核内 for 循环迭代的引用。我可以将小参数放入 GPU 内存中的数组中，并将单个指针传递给这些数组。我可以使用更复杂的 im2col 版本。
我尝试过的事情
我宁愿不使用 cuDNN 7.6.5；当我使用 cuDNN 7.6.5 并编写语句“cudnnCreate(&cudnnHandle);”时，Nsight Compute 建议方法 cuModuleGetFunction 返回 CUDA_ERROR_NOT_FOUND。
重建解决方案
我用来创建这个项目的过程是使用 Visual Studio Community 2019 创建一个新的 CUDA 10.2 Runtime 项目，将默认源文件重命名为“main.cu”，将所有内容替换为下面的第一个代码块，添加“CalculateConvolutionOutputTensor__im2col.h ” 到我的项目中，在下面添加第二个代码块，在我的项目中添加“CalculateConvolutionOutputTensor__im2col.cu”，在下面添加第三个代码块，并添加“cublas.lib;” 到 Project Properties -> Linker -> Input -> Additional Dependencies。
主文件
// Allow use of cudaMalloc.
#include <cuda_runtime.h>
// Allow use of time(NULL) as a seed.
#include <ctime>
// Allow construction of a default_random_engine.
#include <random>
// Allow use of CalculateConvolutionOutputTensor__im2col.
#include "CalculateConvolutionOutputTensor__im2col.h"
int main()
{
// --------------------------------------------------------------------------
// Declare and define parameters of CalculateConvolutionOutputTensor__im2col.
// --------------------------------------------------------------------------
float* convolutionOutputTensor;
cudaMalloc(&convolutionOutputTensor, 6 * 3 * 19 * 19 * 4 * sizeof(float));
int elementsInFilter = 3 * 590 * 590;
int elementsInChannelOfOutputTensor = 19 * 19;
int imagesInSubdivision = 4;
int channelsInFilter_host = 3;
int* channelsInFilter_GPU;
cudaMalloc(&channelsInFilter_GPU, sizeof(int));
cudaMemcpy(channelsInFilter_GPU, &channelsInFilter_host, sizeof(int), cudaMemcpyHostToDevice);
int widthOfFilter_host = 590;
int* widthOfFilter_GPU;
cudaMalloc(&widthOfFilter_GPU, sizeof(int));
cudaMemcpy(widthOfFilter_GPU, &widthOfFilter_host, sizeof(int), cudaMemcpyHostToDevice);
int heightOfOutputTensor_host = 19;
int* heightOfOutputTensor_GPU;
cudaMalloc(&heightOfOutputTensor_GPU, sizeof(int));
cudaMemcpy(heightOfOutputTensor_GPU, &heightOfOutputTensor_host, sizeof(int), cudaMemcpyHostToDevice);
int widthOfOutputTensor_host = 19;
int* widthOfOutputTensor_GPU;
cudaMalloc(&widthOfOutputTensor_GPU, sizeof(int));
cudaMemcpy(widthOfOutputTensor_GPU, &widthOfOutputTensor_host, sizeof(int), cudaMemcpyHostToDevice);
int elementsInChannelOfOutputTensor_host = 19 * 19;
int* elementsInChannelOfOutputTensor_GPU;
cudaMalloc(&elementsInChannelOfOutputTensor_GPU, sizeof(int));
cudaMemcpy(
elementsInChannelOfOutputTensor_GPU,
&elementsInChannelOfOutputTensor_host,
sizeof(int),
cudaMemcpyHostToDevice);
int channelsInFilter_times_elementsInChannelOfOutputTensor_host = 3 * 19 * 19;
int* channelsInFilter_times_elementsInChannelOfOutputTensor_GPU;
cudaMalloc(&channelsInFilter_times_elementsInChannelOfOutputTensor_GPU, sizeof(int));
cudaMemcpy(
channelsInFilter_times_elementsInChannelOfOutputTensor_GPU,
&channelsInFilter_times_elementsInChannelOfOutputTensor_host,
sizeof(int),
cudaMemcpyHostToDevice);
int elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor_host = 3 * 590 * 19 * 19;
int* elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor_GPU;
cudaMalloc(&elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor_GPU, sizeof(int));
cudaMemcpy(
elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor_GPU,
&elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor_host,
sizeof(int),
cudaMemcpyHostToDevice);
int elementsInInputTensor = 3 * 608 * 608 * 4;
float* inputTensor_host = new float[elementsInInputTensor];
for (int i = 0; i < elementsInInputTensor; ++i) {
inputTensor_host[i] = ((float)(i % 255)) / 255.0;
}
float* inputTensor_GPU;
cudaMalloc(&inputTensor_GPU, elementsInInputTensor * sizeof(float));
cudaMemcpy(
inputTensor_GPU,
inputTensor_host,
elementsInInputTensor * sizeof(float),
cudaMemcpyHostToDevice);
delete[] inputTensor_host;
int horizontalFilterStride_host = 1;
int* horizontalFilterStride_GPU;
cudaMalloc(&horizontalFilterStride_GPU, sizeof(int));
cudaMemcpy(
horizontalFilterStride_GPU,
&horizontalFilterStride_host,
sizeof(int),
cudaMemcpyHostToDevice);
int channelsInImage_host = 3;
int* channelsInImage_GPU;
cudaMalloc(&channelsInImage_GPU, sizeof(int));
cudaMemcpy(channelsInImage_GPU, &channelsInImage_host, sizeof(int), cudaMemcpyHostToDevice);
int verticalFilterStride_host = 1;
int* verticalFilterStride_GPU;
cudaMalloc(&verticalFilterStride_GPU, sizeof(int));
cudaMemcpy(
verticalFilterStride_GPU,
&verticalFilterStride_host,
sizeof(int),
cudaMemcpyHostToDevice);
int elementsInCrossSectionOfImage_host = 3 * 608;
int* elementsInCrossSectionOfImage_GPU;
cudaMalloc(&elementsInCrossSectionOfImage_GPU, sizeof(int));
cudaMemcpy(
elementsInCrossSectionOfImage_GPU,
&elementsInCrossSectionOfImage_host,
sizeof(int),
cudaMemcpyHostToDevice);
int elementsInImage_host = 3 * 608 * 608;
int* elementsInImage_GPU;
cudaMalloc(&elementsInImage_GPU, sizeof(int));
cudaMemcpy(elementsInImage_GPU, &elementsInImage_host, sizeof(int), cudaMemcpyHostToDevice);
int filters = 6 * 3;
int elementsInFilterTensor = 6 * 3 * 3 * 590 * 590;
float* filterTensor_host = new float[elementsInFilterTensor];
std::default_random_engine randomNumberGenerator(time(NULL));
std::normal_distribution<float> normalDistribution(0.0, 1.0);
for (int i = 0; i < elementsInFilterTensor; ++i) {
filterTensor_host[i] = normalDistribution(randomNumberGenerator) / sqrt((float)elementsInFilterTensor);
}
float* filterTensor_GPU;
cudaMalloc(&filterTensor_GPU, elementsInFilterTensor * sizeof(float));
cudaMemcpy(
filterTensor_GPU,
filterTensor_host,
elementsInFilterTensor * sizeof(float),
cudaMemcpyHostToDevice);
delete[] filterTensor_host;
int elementsInOutputSubtensor = 6 * 3 * 19 * 19;
// -------------------------------------------------
// Execute CalculateConvolutionOutputTensor__im2col.
// -------------------------------------------------
CalculateConvolutionOutputTensor__im2col(
convolutionOutputTensor,
elementsInFilter,
elementsInChannelOfOutputTensor_host,
imagesInSubdivision,
channelsInFilter_GPU,
widthOfFilter_GPU,
heightOfOutputTensor_GPU,
widthOfOutputTensor_GPU,
elementsInChannelOfOutputTensor_GPU,
channelsInFilter_times_elementsInChannelOfOutputTensor_GPU,
elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor_GPU,
inputTensor_GPU,
horizontalFilterStride_GPU,
channelsInImage_GPU,
verticalFilterStride_GPU,
elementsInCrossSectionOfImage_GPU,
elementsInImage_GPU,
filters,
filterTensor_GPU,
elementsInOutputSubtensor);
cudaFree(channelsInFilter_GPU);
cudaFree(widthOfFilter_GPU);
cudaFree(heightOfOutputTensor_GPU);
cudaFree(widthOfOutputTensor_GPU);
cudaFree(elementsInChannelOfOutputTensor_GPU);
cudaFree(channelsInFilter_times_elementsInChannelOfOutputTensor_GPU);
cudaFree(elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor_GPU);
cudaFree(inputTensor_GPU);
cudaFree(horizontalFilterStride_GPU);
cudaFree(channelsInImage_GPU);
cudaFree(verticalFilterStride_GPU);
cudaFree(elementsInCrossSectionOfImage_GPU);
cudaFree(elementsInImage_GPU);
cudaFree(filterTensor_GPU);
// --------------------------------------------------
// Make sure that convolutionOutputTensor is correct.
// --------------------------------------------------
float* convolutionOutputTensor_test = new float[6 * 3 * 19 * 19 * 4];
cudaMemcpy(
convolutionOutputTensor_test,
convolutionOutputTensor,
6 * 3 * 19 * 19 * 4 * sizeof(float),
cudaMemcpyDeviceToHost);
printf("convolutionOutputTensor_test: {");
for (int i = 0; i < 18; ++i) {
printf("%f, ", convolutionOutputTensor_test[i]);
}
printf("...}\n");
delete[] convolutionOutputTensor_test;
cudaFree(convolutionOutputTensor);
return 0;
}
CalculateConvolutionOutputTensor__im2col.h
void CalculateConvolutionOutputTensor__im2col(
float* convolutionOutputTensor_child,
int elementsInFilter_child,
int elementsInChannelOfOutputTensor_host_child,
int imagesInSubdivision_child,
int* channelsInFilter,
int* widthOfFilter,
int* heightOfOutputTensor,
int* widthOfOutputTensor,
int* elementsInChannelOfOutputTensor_GPU_child,
int* channelsInFilter_times_elementsInChannelOfOutputTensor,
int* elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor,
float* inputTensor_child,
int* horizontalFilterStride,
int* channelsInImage,
int* verticalFilterStride,
int* elementsInCrossSectionOfImage,
int* elementsInImage,
int filters_child,
float* filterTensor,
int elementsInOutputSubtensor_child);
CalculateConvolutionOutputTensor__im2col.cu
// Allow use of __global__.
#include <cuda_runtime.h>
// Allow declaration of cublasHandle.
#include "cublas_v2.h"
// Allow use of blockIdx.x, blockDim.x, and threadIdx.x.
#include <device_launch_parameters.h>
__global__
void im2col(
float* col_child,
int* channelsInFilter_child,
int* widthOfFilter_child,
int* heightOfOutputTensor_child,
int* widthOfOutputTensor_child,
int* elementsInChannelOfOutputTensor_child,
int* channelsInFilter_times_elementsInChannelOfOutputTensor_child,
int* elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor_child,
float* inputTensor_child_child,
int* horizontalFilterStride_child,
int* channelsInImage_child,
int* verticalFilterStride_child,
int* elementsInCrossSectionOfImage_child,
int* image_child,
int* elementsInImage_child);
void CalculateConvolutionOutputTensor__im2col(
float* convolutionOutputTensor_child,
int elementsInFilter_child,
int elementsInChannelOfOutputTensor_host_child,
int imagesInSubdivision_child,
int* channelsInFilter,
int* widthOfFilter,
int* heightOfOutputTensor,
int* widthOfOutputTensor,
int* elementsInChannelOfOutputTensor_GPU_child,
int* channelsInFilter_times_elementsInChannelOfOutputTensor,
int* elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor,
float* inputTensor_child,
int* horizontalFilterStride,
int* channelsInImage,
int* verticalFilterStride,
int* elementsInCrossSectionOfImage,
int* elementsInImage,
int filters_child,
float* filterTensor,
int elementsInOutputSubtensor_child)
{
// -----------------------------------------
// Define and declare parameters for im2col.
// -----------------------------------------
// Define parameters for the execution configuration of im2col.
int threads_per_block_for_im2col = 885;
int blocks_for_im2col =
(elementsInFilter_child + threads_per_block_for_im2col - 1) / threads_per_block_for_im2col;
// Declare col.
float* col;
// The following statement is required to
// prevent automatic casting of a product to an eight-byte integer.
int elementsInFilter_times_elementsInChannelOfOutputTensor =
elementsInFilter_child * elementsInChannelOfOutputTensor_host_child;
cudaMalloc(&col, elementsInFilter_times_elementsInChannelOfOutputTensor * sizeof(float));
// -----------------------------------------------------------------------------
// Define parameters for calculating the matrix product of filterTensor and col.
// -----------------------------------------------------------------------------
// Define a cublasHandle_t object called cublasHandle.
// Declaring cublasHandle requires '#include "cublas_v2.h"'.
// Defining cublasHandle requires adding "cublas.lib" to
// Properties -> Linker -> Input -> Additional Dependencies.
cublasHandle_t cublasHandle;
cublasCreate(&cublasHandle);
// Define parameters for (not) including
// a portion of a third matrix in product_filterTensor_and_col.
float one = 1.0;
float zero = 0.0;
// ------------------------------------------------------------
// For each image in subdivision,
// sculpt image into matrix col.
// Calculate the matrix product of filterTensor and col and
// store the product as a subtensor of convolutionOutputTensor.
// ------------------------------------------------------------
// The following statement is required to
// prevent automatic casting of a product to an eight-byte integer.
int image_times_elementsInOutputSubtensor;
int* image_GPU;
cudaMalloc(&image_GPU, sizeof(int));
for (int image_host = 0; image_host < imagesInSubdivision_child; ++image_host) {
cudaMemcpy(image_GPU, &image_host, sizeof(int), cudaMemcpyHostToDevice);
im2col<<<blocks_for_im2col, threads_per_block_for_im2col>>>
(col,
channelsInFilter,
widthOfFilter,
heightOfOutputTensor,
widthOfOutputTensor,
elementsInChannelOfOutputTensor_GPU_child,
channelsInFilter_times_elementsInChannelOfOutputTensor,
elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor,
inputTensor_child,
horizontalFilterStride,
channelsInImage,
verticalFilterStride,
elementsInCrossSectionOfImage,
image_GPU,
elementsInImage);
cudaDeviceSynchronize();
// The following statement is required to
// prevent automatic casting of a product to an eight-byte integer.
image_times_elementsInOutputSubtensor = image_host * elementsInOutputSubtensor_child;
cublasSgemm(
cublasHandle,
CUBLAS_OP_N,
CUBLAS_OP_N,
elementsInChannelOfOutputTensor_host_child,
filters_child,
elementsInFilter_child,
&one,
col,
elementsInChannelOfOutputTensor_host_child,
filterTensor,
elementsInFilter_child,
&zero,
convolutionOutputTensor_child + image_times_elementsInOutputSubtensor,
elementsInChannelOfOutputTensor_host_child);
}
cudaFree(col);
cudaFree(image_GPU);
}
__global__
void im2col(
float* col_child,
int* channelsInFilter_child,
int* widthOfFilter_child,
int* heightOfOutputTensor_child,
int* widthOfOutputTensor_child,
int* elementsInChannelOfOutputTensor_child,
int* channelsInFilter_times_elementsInChannelOfOutputTensor_child,
int* elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor_child,
float* inputTensor_child_child,
int* horizontalFilterStride_child,
int* channelsInImage_child,
int* verticalFilterStride_child,
int* elementsInCrossSectionOfImage_child,
int* image,
int* elementsInImage_child)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
int c_prime = index % (*channelsInFilter_child);
int temp = (index - c_prime) / (*channelsInFilter_child);
int w_prime = temp % (*widthOfFilter_child);
int h_prime = temp / (*widthOfFilter_child);
for (int h = 0; h < (*heightOfOutputTensor_child); ++h) {
for (int w = 0; w < (*widthOfOutputTensor_child); ++w) {
col_child[
w +
h * (*widthOfOutputTensor_child) +
c_prime * (*elementsInChannelOfOutputTensor_child) +
w_prime * (*channelsInFilter_times_elementsInChannelOfOutputTensor_child) +
h_prime * (*elementsInCrossSectionOfFilter_times_elementsInChannelOfOutputTensor_child)] =
inputTensor_child_child[
c_prime +
(w * (*horizontalFilterStride_child) + w_prime) * (*channelsInImage_child) +
(h * (*verticalFilterStride_child) + h_prime) * (*elementsInCrossSectionOfImage_child) +
(*image) * (*elementsInImage_child)];
}
}
}
