I want to use shared memory to speed up gemm, I got code from
but it is slower than gemm with global memory, I want to know what’s wrong with my code:
I compare naiveKernel (global memory), MatMulKernel(shared memory) and cublasgemm,Matrix A B C size 4096*4096,the result is :
shared mem kernel time 1631.566650ms
cublas time 41.416702ms
naive time 1208.498047ms
12800HX 4070 laptop 32GBmemory
nvcc -std=c++17 -arch=sm_89 -g -lcublas -lcudart -G -O3 -o test test.cu
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <iostream>
#include <stdio.h>
#include <cublas_v2.h>
// Thread block size
#define BLOCK_SIZE 16
// Matrices are stored in row-major order:
// M(row, col) = *(M.elements + row * M.stride + col)
typedef struct {
int width;
int height;
int stride;
float* elements;
} Matrix;
// Get a matrix element
__device__ float GetElement(const Matrix A, int row, int col)
{
return A.elements[row * A.stride + col];
}
// Set a matrix element
__device__ void SetElement(Matrix A, int row, int col,
float value)
{
A.elements[row * A.stride + col] = value;
}
// Get the BLOCK_SIZExBLOCK_SIZE sub-matrix Asub of A that is
// located col sub-matrices to the right and row sub-matrices down
// from the upper-left corner of A
__device__ Matrix GetSubMatrix(Matrix A, int row, int col)
{
Matrix Asub;
Asub.width = BLOCK_SIZE;
Asub.height = BLOCK_SIZE;
Asub.stride = A.stride;
Asub.elements = &A.elements[A.stride * BLOCK_SIZE * row
+ BLOCK_SIZE * col];
return Asub;
}
// Forward declaration of the matrix multiplication kernel
__global__ void MatMulKernel(const Matrix, const Matrix, Matrix);
__global__ void naiveKernel(const Matrix, const Matrix, Matrix);
// Matrix multiplication - Host code
// Matrix dimensions are assumed to be multiples of BLOCK_SIZE
void MatMul(const Matrix A, const Matrix B, Matrix C)
{
// Load A and B to device memory
Matrix d_A;
d_A.width = d_A.stride = A.width; d_A.height = A.height;
size_t size = A.width * A.height * sizeof(float);
cudaMalloc(&d_A.elements, size);
cudaMemcpy(d_A.elements, A.elements, size,
cudaMemcpyHostToDevice);
Matrix d_B;
d_B.width = d_B.stride = B.width; d_B.height = B.height;
size = B.width * B.height * sizeof(float);
cudaMalloc(&d_B.elements, size);
cudaMemcpy(d_B.elements, B.elements, size,
cudaMemcpyHostToDevice);
// Allocate C in device memory
Matrix d_C;
d_C.width = d_C.stride = C.width; d_C.height = C.height;
size = C.width * C.height * sizeof(float);
cudaMalloc(&d_C.elements, size);
// Invoke kernel
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y);
cudaEvent_t start,end;
cudaEventCreate(&start);
cudaEventCreate(&end);
cudaEventRecord(start);
// cudaEventSynchronize(start);
MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
cudaEventRecord(end);
cudaEventSynchronize(end);
float msec;
cudaEventElapsedTime(&msec, start, end);
printf("shared mem kernel time %.6f\n",msec);
cublasHandle_t cublas_handle;
cublasCreate(&cublas_handle);
float cublas_alpha = 1.0;
float cublas_beta = 0;
cudaEvent_t start2,end2;
cudaEventCreate(&start2);
cudaEventCreate(&end2);
cudaEventRecord(start2);
cudaEventSynchronize(start2);
cublasSgemm_v2(cublas_handle, CUBLAS_OP_N, CUBLAS_OP_N, 4096,4096, 4096,
&cublas_alpha, d_A.elements, 4096, d_B.elements, 4096, &cublas_beta, d_C.elements, 4096);
cudaEventRecord(end2);
cudaEventSynchronize(end2);
float msec2;
cudaEventElapsedTime(&msec2, start2, end2);
printf("cublas time %.6fms\n",msec2);
cudaEvent_t start3,end3;
cudaEventCreate(&start3);
cudaEventCreate(&end3);
cudaEventRecord(start3);
cudaEventSynchronize(start3);
naiveKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
cudaEventRecord(end3);
cudaEventSynchronize(end3);
float msec3;
cudaEventElapsedTime(&msec3, start3, end3);
printf("naive time %.6fms\n",msec3);
// Read C from device memory
cudaMemcpy(C.elements, d_C.elements, size,
cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(d_A.elements);
cudaFree(d_B.elements);
cudaFree(d_C.elements);
}
int main(void) {
Matrix A, B, C;
A.width = B.width = C.width = 4096;
A.height = B.height = C.height = 4096;
A.stride = B.stride = C.stride = 128;
int sizeA = A.width * A.height * sizeof(float);
int sizeB = B.width * B.height * sizeof(float);
int sizeC = C.width * C.height * sizeof(float);
A.elements = (float *)malloc(sizeA);
B.elements = (float *)malloc(sizeB);
C.elements = (float *)malloc(sizeC);
for (int i = 0; i < A.width * A.height; i++)
{A.elements[i] = 1;}
for (int i = 0; i < B.width * B.height; i++)
{B.elements[i] = 2;}
MatMul(A, B, C);
return 0;
}
// Matrix multiplication kernel called by MatMul()
__global__ void MatMulKernel(Matrix A, Matrix B, Matrix C)
{
// Block row and column
int blockRow = blockIdx.y;
int blockCol = blockIdx.x;
// Each thread block computes one sub-matrix Csub of C
Matrix Csub = GetSubMatrix(C, blockRow, blockCol);
// Each thread computes one element of Csub
// by accumulating results into Cvalue
float Cvalue = 0;
// Thread row and column within Csub
int row = threadIdx.y;
int col = threadIdx.x;
// Loop over all the sub-matrices of A and B that are
// required to compute Csub
// Multiply each pair of sub-matrices together
// and accumulate the results
for (int m = 0; m < (A.width / BLOCK_SIZE); ++m) {
// Get sub-matrix Asub of A
Matrix Asub = GetSubMatrix(A, blockRow, m);
// Get sub-matrix Bsub of B
Matrix Bsub = GetSubMatrix(B, m, blockCol);
// Shared memory used to store Asub and Bsub respectively
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
// Load Asub and Bsub from device memory to shared memory
// Each thread loads one element of each sub-matrix
As[row][col] = GetElement(Asub, row, col);
Bs[row][col] = GetElement(Bsub, row, col);
// Synchronize to make sure the sub-matrices are loaded
// before starting the computation
__syncthreads();
// Multiply Asub and Bsub together
for (int e = 0; e < BLOCK_SIZE; ++e)
Cvalue += As[row][e] * Bs[e][col];
// Synchronize to make sure that the preceding
// computation is done before loading two new
// sub-matrices of A and B in the next iteration
__syncthreads();
}
// Write Csub to device memory
// Each thread writes one element
SetElement(Csub, row, col, Cvalue);
}
__global__ void naiveKernel(Matrix A, Matrix B, Matrix C)
{
int n = blockIdx.x * blockDim.x + threadIdx.x;
int m = blockIdx.y * blockDim.y + threadIdx.y;
int N = A.width;
int M = B.height;
int K = A.height;
if (n < N && m < M){
float sum = 0;
for (int k = 0; k < K; k++){
//sum += A[m*K+k] * B[k*N+n];
sum = fmaf(A.elements[m*K+k], B.elements[k*N+n], sum);
}
C.elements[m*N+n] = sum;
}
}