[英]Cuda program for matrix batch multiplication
我是CUDA程序領域的新手,我正在嘗試重復cublasSgemmBatched
的功能,這意味着我要執行一批矩陣的矩陣乘法。 我嘗試將我的想法實現為以下代碼。
#include <stdio.h>
__global__ void BatchMulCUDA(float* array1, float* array2, int narray1, int dim, float* result)
{
int tx = blockIdx.x * blockDim.x + threadIdx.x;
if (tx < narray1 * dim)
{
float temp = 0;
int index = tx / dim;
#pragma
for (int i = 0; i < dim; i++)
{
temp += array1[tx * dim + i] * array2[index * dim + i];
}
result[tx] = temp;
}
}
void BatchMulGPU(float* array1, float* array2, int narray1, int dim, float* result)
{
dim3 threads(1024, 1);
dim3 grid(narray1 / 1024 + 1, 1);
int threadsPerBlock = threads.x * threads.y;
int blocksPerGrid = grid.x * grid.y;
printf("CUDA kernel launch with %d blocks of %d threads\n", blocksPerGrid, threadsPerBlock);
BatchMulCUDA<<<grid, threads>>>(array1, array2, narray1, dim, result);
}
但是,奇怪的是,我發現在索引19730之前可以獲得正確的輸出。在19730的元素之后,GPU的輸出始終為0。我不知道問題出在哪里。 我的代碼和測試功能的CPU版本如下。 我沒有意識到任何硬件限制嗎?
#include "kernel.h"
#include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <sys/time.h>
#include <math.h>
double cpuSecond()
{
struct timeval tp;
gettimeofday(&tp, NULL);
return ((double) tp.tv_sec + (double)tp.tv_usec*1e-6);
}
void BatchMulCPU(float* array1, float* array2, int narray1, int dim, float* result)
{
for (int i = 0; i < narray1 * dim; i++)
{
float temp = 0;
int index = i / dim;
for (int j = 0; j < dim; j++)
{
temp += array1[i * dim + j] * array2[index * dim + j];
}
result[i] = temp;
}
}
int main(int argc, char** argv)
{
int narray1 = 6980;
int dim = 4;
float* array1 = new float[narray1 * dim * dim];
float* array2 = new float[narray1 * dim];
float* resultGPU = new float[narray1 * dim];
float* resultCPU = new float[narray1 * dim];
float* d_array1;
float* d_array2;
float* d_result;
for (int i = 0; i < narray1 * dim * dim; i++)
{
array1[i] = static_cast<float> (rand() / (static_cast<float> (RAND_MAX / 10)));
}
for (int i = 0; i < narray1 * dim; i++)
{
array2[i] = static_cast<float> (rand() / (static_cast<float> (RAND_MAX / 10)));
}
cudaError_t err;
double iStart = cpuSecond();
err = cudaMalloc((void**)&d_array1, narray1 * dim * dim * sizeof(float));
err = cudaMalloc((void**)&d_array2, narray1 * dim * sizeof(float));
err = cudaMalloc((void**)&d_result, narray1 * dim * sizeof(float));
err = cudaMemcpy(d_array1, array1, narray1 * dim * dim * sizeof(float), cudaMemcpyHostToDevice);
err = cudaMemcpy(d_array2, array2, narray1 * dim * sizeof(float), cudaMemcpyHostToDevice);
BatchMulGPU(d_array1, d_array2, narray1, dim, d_result);
err = cudaMemcpy(resultGPU, d_result, narray1 * dim * sizeof(float), cudaMemcpyDeviceToHost);
double iElaps = cpuSecond() - iStart;
printf("Total GPU computation time is %lf \n" , iElaps);
iStart = cpuSecond();
BatchMulCPU(array1, array2, narray1, dim, resultCPU);
iElaps = cpuSecond() - iStart;
printf("Total CPU computation time is %lf \n" , iElaps);
float error = 0;
float temp = 0;
for (long i = 0; i < narray1 * dim; i++)
{
// temp = abs(resultCPU[i] - resultGPU[i]);
// if (temp > 0.5)
// {
// std::cout << i << std::endl;
// }
error += abs(resultCPU[i] - resultGPU[i]);
}
printf("Error is %f \n", error);
// for (int i = 19730; i < 19750; i++)
// {
// std::cout << "GPU " << resultGPU[i] << std::endl;
// std::cout << "CPU " << resultCPU[i] << std::endl;
// }
cudaFree(d_array1);
cudaFree(d_array2);
cudaFree(d_result);
return 0;
}
除了注釋中討論的WDDM TDR超時的可能性外,該代碼還有錯誤。
很明顯,內核設計期望啟動的網格總數(線程總數)等於或大於數組數乘以側面尺寸:
int tx = blockIdx.x * blockDim.x + threadIdx.x;
if (tx < narray1 * dim)
即narray1*dim
是所需的線程數
但是,正在啟動的數字僅為narray1
:
dim3 threads(1024, 1);
dim3 grid(narray1 / 1024 + 1, 1);
如果我們將上面的最后一行更改為:
dim3 grid((narray1*dim) / 1024 + 1, 1);
此代碼設計錯誤將得到解決。
該代碼對於少量矩陣(最多256個)正確工作的原因是由於網格中的舍入效果將最小narray1
1024個線程,即256 * 4( narray1
* dim
)。
cublasSgemmBatched
從我所看到的來看,這段代碼在功能上與cublasSgemmBatched
並不相似。 我不認為此代碼是我熟悉的任何矩陣乘法(矩陣點積)。
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