【发布时间】：2017-04-06 12:07:48
【问题描述】：
我需要使用共享内存在 GPU 上实现矩阵转置函数。我以一种简单的方式完成了它，没有共享内存，它工作正常，也尝试使用 SM。但不幸的是，计算不正确，我不知道为什么。可以在here 和此问题的底部找到完整的工作示例。
编辑 1
我进一步知道，我有错误值的结果的第一个索引是索引 32（扁平矩阵的索引，所以matr[0][32] 以二维方式）。
如果需要更多信息，我很乐意提供。
下面列出了整个代码的简短摘录，类似于不工作的功能：
__global__ void notSoNaivaTransKernel(float *matrB, float *matrA, const int width,
const int height, const int nreps)
{
__shared__ float tile[TILE_DIM][TILE_DIM + 1];
int blockIdx_y = blockIdx.x;
int blockIdx_x = (blockIdx.x + blockIdx.y) % gridDim.x;
int xIndex = blockIdx_x * TILE_DIM + threadIdx.x;
int yIndex = blockIdx_y * TILE_DIM + threadIdx.y;
int index_in = xIndex + (yIndex)* width;
xIndex = blockIdx_y * TILE_DIM + threadIdx.x;
yIndex = blockIdx_x * TILE_DIM + threadIdx.y;
int index_out = xIndex + (yIndex)* height;
int r, i;
#pragma unroll
for (r = 0; r < nreps; r++)
{
#pragma unroll
for (i = 0; i < TILE_DIM; i += BLOCK_ROWS)
tile[threadIdx.y + i][threadIdx.x] = matrA[index_in + i * width];
__syncthreads();
#pragma unroll
for (i = 0; i < TILE_DIM; i += BLOCK_ROWS)
if (index_in + i * width < width * height)
matrB[index_out + i * height] = tile[threadIdx.x][threadIdx.y + i];
}
}
输出如下：
Avg. CPU Transpose Time: 0.106048 ms, Bandwidth: 3.771873 GB/s
Avg. GPU Naive Trans Time: 0.009871 ms, bandwidth: 40.520836 GB/s
Correct: 50000, Wrong: 0
Avg. GPU Trans with SM Time: 0.007598 ms, bandwidth: 52.643482 GB/s
Correct: 12352, Wrong: 37648
这是完整的工作示例。我从中剥离了大部分不必要的代码，因此它的填充更少：
#include "cuda_runtime.h"
#include "device_functions.h"
#include "device_launch_parameters.h"
#include <chrono>
#include <time.h>
#include <stdio.h>
#include <stdlib.h>
#define TILE_DIM 32
#define BLOCK_ROWS 8
#define BLOCK_COLS 32
cudaError_t matrMagicCuda(float *matrB, float *matrA, const int width, const int height, const int nreps, const int operation);
void cpuMatrTrans(float *matrB, float *matrA, const int width, const int height, const int nreps);
__global__ void naiveTransKernel(float *matrB, float *matrA, const int width, const int height, const int nreps);
__global__ void notSoNaivaTransKernel(float *matrB, float *matrA, const int width, const int height, const int nreps);
int main()
{
int i, width, height, nreps, size, wrong, correct;
double cpuTime, cpuBandwidth;
cudaError_t cudaStatus;
float *matrA, *matrATC, *matrATG, *matrAC;
srand(time(NULL));
nreps = 10000;
width = 500;
height = 100;
size = width * height;
matrA = (float*)malloc(size * sizeof(float)); // matrix A
matrAC = (float*)malloc(size * sizeof(float)); // matrix A copied
matrATC = (float*)malloc(size * sizeof(float)); // matrix A transposed by CPU
matrATG = (float*)malloc(size * sizeof(float)); // matrix A transposed by GPU
for (i = 0; i < size; i++)
{
matrA[i] = (float)i;
}
auto start = std::chrono::high_resolution_clock::now();
//CPU Transpose
cpuMatrTrans(matrATC, matrA, width, height, nreps);
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> diff = end - start;
cpuTime = (diff.count() * 1000) / nreps;
cpuBandwidth = (sizeof(float) * size * 2) / (cpuTime * 1000000);//scaling from ms to s and B to GB doen implicitly, shortened in fraction, times two for read and write
printf("Avg. CPU Transpose Time: %f ms, Bandwidth: %f GB/s\n\n", cpuTime, cpuBandwidth);
correct = 0;
wrong = 0;
//Naive transpose
matrMagicCuda(matrATG, matrA, width, height, nreps, 1);
//Check if calc was correct
for (i = 0; i < size; i++)
{
if (matrATC[i] != matrATG[i])
{
/*printf("ERROR - %d - ATC:%f - ATG:%f\n\n", i, matrATC[i], matrATG[i]);
return;*/
wrong++;
}
else
{
correct++;
}
}
printf("\tCorrect: %d, Wrong: %d\n\n", correct, wrong);
correct = 0;
wrong = 0;
//Transpose with shared memory
matrMagicCuda(matrATG, matrA, width, height, nreps, 2);
//Check if calc was correct
for (i = 0; i < size; i++)
{
if (matrATC[i] != matrATG[i])
{
/*printf("ERROR - %d - ATC:%f - ATG:%f\n\n", i, matrATC[i], matrATG[i]);
return;*/
wrong++;
}
else
{
correct++;
}
}
//printf("\tTranspose with SM on GPU was executed correctly.\n\n");
printf("\tCorrect: %d, Wrong: %d\n\n", correct, wrong);
correct = 0;
wrong = 0;
// cudaDeviceReset must be called before exiting in order for profiling and
// tracing tools such as Nsight and Visual Profiler to show complete traces.
cudaStatus = cudaDeviceReset();
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaDeviceReset failed!\n");
return 1;
}
return 0;
}
cudaError_t matrMagicCuda(float *matrB, float *matrA, const int width, const int height, const int nreps, const int operation)
{
float elapsed = 0;
float *dev_matrA = 0;
float *dev_matrB = 0;
cudaError_t cudaStatus;
dim3 dim_grid, dim_block;
double gpuBandwidth;
int size = width * height;
dim_block.x = TILE_DIM;
dim_block.y = BLOCK_ROWS;
dim_block.z = 1;
dim_grid.x = (width + TILE_DIM - 1) / TILE_DIM;
dim_grid.y = (height + TILE_DIM - 1) / TILE_DIM;
dim_grid.z = 1;
// Choose which GPU to run on, change this on a multi-GPU system.
cudaStatus = cudaSetDevice(0);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaSetDevice failed! Do you have a CUDA-capable GPU installed?");
goto Error;
}
// Allocate GPU buffers for three matrix
cudaStatus = cudaMalloc((void**)&dev_matrA, size * sizeof(float));
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_matrB, size * sizeof(float));
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
// Copy input matrix from host memory to GPU buffers.
cudaStatus = cudaMemcpy(dev_matrA, matrA, size * sizeof(float), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
switch (operation)
{
case(1):
{
cudaEventRecord(start);
// Launch a kernel on the GPU with one thread for each element.
naiveTransKernel << <dim_grid, dim_block >> >(dev_matrB, dev_matrA, width, height, nreps);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
elapsed /= nreps;
gpuBandwidth = (sizeof(float) * size * 2) / (elapsed * 1000000);//scaling from ms to s and B to GB doen implicitly, shortened in fraction, times two for read and write
printf("Avg. GPU Naive Trans Time: %f ms, bandwidth: %f GB/s\n", elapsed, gpuBandwidth);
break;
}
case(2):
{
cudaEventRecord(start);
// Launch a kernel on the GPU with one thread for each element.
notSoNaivaTransKernel << <dim_grid, dim_block >> >(dev_matrB, dev_matrA, width, height, nreps);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
elapsed /= nreps;
gpuBandwidth = (sizeof(float) * size * 2) / (elapsed * 1000000);//scaling from ms to s and B to GB doen implicitly, shortened in fraction, times two for read and write
printf("Avg. GPU Trans with SM Time: %f ms, bandwidth: %f GB/s\n", elapsed, gpuBandwidth);
break;
}
default:
printf("No matching opcode was found.\n");
}
// Check for any errors launching the kernel
cudaStatus = cudaGetLastError();
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "Kernel launch failed: %s\n", cudaGetErrorString(cudaStatus));
goto Error;
}
// cudaDeviceSynchronize waits for the kernel to finish, and returns
// any errors encountered during the launch.
cudaStatus = cudaDeviceSynchronize();
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching Kernel!\n", cudaStatus);
goto Error;
}
// Copy output matrix from GPU buffer to host memory.
cudaStatus = cudaMemcpy(matrB, dev_matrB, size * sizeof(float), cudaMemcpyDeviceToHost);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
Error:
cudaFree(dev_matrB);
cudaFree(dev_matrA);
return cudaStatus;
}
void cpuMatrTrans(float *matrB, float *matrA, const int width, const int height, const int nreps)
{
int i, j, r;
#pragma unroll
for (r = 0; r < nreps; r++)
#pragma unroll
for (i = 0; i < height; i++)
#pragma unroll
for (j = 0; j < width; j++)
matrB[j * height + i] = matrA[i * width + j];
}
__global__ void naiveTransKernel(float *matrB, float *matrA, const int width, const int height, const int nreps)
{
int i, r;
int row = blockIdx.x * TILE_DIM + threadIdx.x;
int col = blockIdx.y * TILE_DIM + threadIdx.y;
int index_in = row + width * col;
int index_out = col + height * row;
#pragma unroll
for (r = 0; r < nreps; r++)
#pragma unroll
for (i = 0; i < TILE_DIM; i += BLOCK_ROWS)
if (index_in + i * width < width * height)
matrB[index_out + i] = matrA[index_in + i * width];
}
__global__ void notSoNaivaTransKernel(float *matrB, float *matrA, const int width, const int height, const int nreps)
{
__shared__ float tile[TILE_DIM][TILE_DIM + 1];
int blockIdx_y = blockIdx.x;
int blockIdx_x = (blockIdx.x + blockIdx.y) % gridDim.x;
int xIndex = blockIdx_x * TILE_DIM + threadIdx.x;
int yIndex = blockIdx_y * TILE_DIM + threadIdx.y;
int index_in = xIndex + (yIndex)* width;
xIndex = blockIdx_y * TILE_DIM + threadIdx.x;
yIndex = blockIdx_x * TILE_DIM + threadIdx.y;
int index_out = xIndex + (yIndex)* height;
int r, i;
#pragma unroll
for (r = 0; r < nreps; r++)
{
#pragma unroll
for (i = 0; i < TILE_DIM; i += BLOCK_ROWS)
tile[threadIdx.y + i][threadIdx.x] = matrA[index_in + i * width];
__syncthreads();
#pragma unroll
for (i = 0; i < TILE_DIM; i += BLOCK_ROWS)
if (index_in + i * width < width * height)
matrB[index_out + i * height] = tile[threadIdx.x][threadIdx.y + i];
}
}
【问题讨论】：
-
显然你希望能够做非方阵转置？还是只有方阵转置？您应该在在您的问题中包含minimal reproducible example，而不是在外部链接中。
-
是的，我也想对非方阵进行转置。为了便于阅读，我将其放在外面，但如您所愿，我也会将其添加到问题中。
-
您的代码正在进行越界访问。任何时候您在使用 CUDA 代码时遇到问题，您都应该使用 proper cuda error checking 并使用
cuda-memcheck运行您的代码。即使您不理解错误输出，它也会对其他试图帮助您的人有用。另请注意，虽然我知道这可能是一个学习练习，但对于认真的工作，建议您使用像 cublas geam 这样的库例程。