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我需要在使用共享內存的GPU上實現矩陣轉置功能。我以一種簡單的方式完成了這項工作，沒有共享內存，工作正常，而且還嘗試使用SM。但不幸的是，計算不正確，我不明白爲什麼。一個完整的實例可以在here找到，並在這個問題的底部。具有共享內存的CUDA矩陣轉置
EDIT 1
我還知道，結果，其中我有一個錯誤的值的第一個指數是指數32（扁平型矩陣，所以matr[0][32]在二維的方式）。
如果還有更多的信息，我會很樂意爲他們提供幫助。
從近似於不工作函數整個代碼中的一個片段被列舉如下：
__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];
}
}
輸出看起來像這樣：
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
以下是完整的工作示例。我從它剝去了大部分不必要的代碼，所以它的填充較少：
#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];
}
}
顯然您希望能夠進行非方形矩陣轉置？還是隻有方矩陣轉置？你應該在你的問題中包含[mcve] *，而不是在外部鏈接中。 –
是的，我也想在非方矩陣上進行轉置。爲了便於閱讀，我將它放在了外面，但如您所願，我還會將其添加到該問題中。 – JRsz
您的代碼正在進行超出邊界的訪問。無論何時，如果您在使用CUDA代碼時遇到問題，都應該使用[適當的cuda錯誤檢查]（http://stackoverflow.com/questions/14038589）並使用'cuda-memcheck'運行您的代碼。即使你不明白錯誤輸出，它也會對其他試圖幫助你的人有用。另外請注意，雖然我明白這可能是一個學習練習，但對於認真的工作，建議您使用cublas [geam]這樣的庫例程（http://docs.nvidia.com/cuda/cublas/index.html#cublas- LT-T-GT-GEAM）。 –