深度学习operation: matmul之NV shader实现
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2022-03-10 16:51:13
最近工作需要写shader,决定开个新坑,我写过的shader都会在后续文章公开。写shader还是有必要了解一下NV GPU的硬件建构,至少知道device代码运行时的物理地址映射,CPU程序运行时有堆和栈的概念,而NV GPU 运行时是不同的,例如CPU c++ 函数内局部变量放在栈里,但NV GPU c++函数局部变量放在相应的SP寄存器里,特别是要写出高性能shader,往往需要对针对特定硬件架构做优化。此实现利用了share memory做加速。下面是工程结构:build.shnvcc...
最近工作需要写shader,决定开个新坑,我写过的shader都会在后续文章公开。
写shader还是有必要了解一下NV GPU的硬件建构,至少知道device代码运行时的物理地址映射,CPU程序运行时有堆和栈的概念,而NV GPU 运行时是不同的,例如CPU c++ 函数内局部变量放在栈里,但NV GPU c++函数局部变量放在相应的SP寄存器里,特别是要写出高性能shader,往往需要对针对特定硬件架构做优化。
此实现利用了share memory做加速。
下面是工程结构:
build.sh
nvcc -x cu matmul.cu -o matmul
kernels.h
#ifndef KERNELS_H
#define KERNELS_H
#include "kernels.cu"
#endif
kernels.cu
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#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)
{
//row = 1, col = 2;
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;
}
// Thread block size
// Forward declaration of the matrix multiplication kernel
__global__ void MatMulKernel(const Matrix, const Matrix, Matrix);
// Matrix multiplication kernel called by MatMul()
__global__ void MatMulKernel(const Matrix A, const 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);
}
matmul.cu
#include <cstdio>
#include <cstdlib>
#include <iostream>
#include <ctime>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include "kernels.h"
#define BLOCK_SIZE 16
int GenerateRandInt(int min, int max, int counter)
{
srand(((int)time(0)) + counter);
return (rand()%(max - min + 1) + min);
}
// 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);
MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
// 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(int argc, char** argv)
{
int size_A = BLOCK_SIZE*2*BLOCK_SIZE*3;
float matrix_A[size_A];
std::cout<<"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"<<std::endl;
for(int i=0; i<size_A; i++)
{
matrix_A[i] = (float)GenerateRandInt(1,100,i);
std::cout<<matrix_A[i]<<", ";
}
std::cout<<std::endl;
Matrix A;
A.height = BLOCK_SIZE*2;
A.width = BLOCK_SIZE*3;
A.elements = matrix_A;
int size_B = BLOCK_SIZE*3*BLOCK_SIZE*4;
float matrix_B[size_B];
std::cout<<"BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB"<<std::endl;
for(int i=0; i<size_B; i++)
{
matrix_B[i] = (float)GenerateRandInt(1,100,i);
std::cout<<matrix_B[i]<<", ";
}
std::cout<<std::endl;
Matrix B;
B.height = BLOCK_SIZE*3;
B.width = BLOCK_SIZE*4;
B.elements = matrix_B;
int size_C = BLOCK_SIZE*2*BLOCK_SIZE*4;
float matrix_C[size_C];
Matrix C;
C.height = BLOCK_SIZE*3;
C.width = BLOCK_SIZE*4;
C.elements = matrix_C;
MatMul(A,B,C);
std::cout<<"CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC"<<std::endl;
for(int i=0; i<size_C; i++)
{
std::cout<<C.elements[i]<<", ";
}
std::cout<<std::endl;
return 0;
}
本文地址:https://blog.csdn.net/qq_33345917/article/details/107578989