chundoong-lab-ta/SamsungDS22/submissions/HW6/ktr.kim/mat_mul.cu

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2022-09-29 18:01:45 +09:00
#include "mat_mul.h"
#include "util.h"
#include <cstdio>
#include <cuda_runtime.h>
#define CUDA_CALL(f) \
{ \
cudaError_t err = (f); \
if (err != cudaSuccess) { \
fprintf(stderr, "CUDA error at [%s:%d] %d %s\n", __FILE__, __LINE__, \
err, cudaGetErrorString(err)); \
exit(1); \
} \
}
#define MAX_NUM_GPU 4
int num_devices = 0;
#define TS 32
#define WPT 16
#define RTS TS/WPT
// step 2: local tiling
/*
__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
const int row = threadIdx.x;
const int col = threadIdx.y;
const int global_row = blockDim.x * blockIdx.x + threadIdx.x;
const int global_col = blockDim.y * blockIdx.y + threadIdx.y;
__shared__ float Asub[TS*TS];
__shared__ float Bsub[TS*TS];
float acc = 0.0f;
// printf("(info) {row, col, global_row, global_col} = {%2d, %2d, %2d, %2d}\n",
// row, col, global_row, global_col);
const int numTiles = K / TS;
for(int t=0; t<numTiles; ++t)
{
// load one tile of A and B into local memory
const int tile_row = TS * t + row;
const int tile_col = TS * t + col;
// Asub[row*TS + col] = A[global_row * K + global_col];
// Bsub[row*TS + col] = B[global_row * N + global_col];
Asub[row*TS + col] = A[global_row * K + tile_col];
Bsub[row*TS + col] = B[tile_row * N + global_col];
// Asub[row*TS + col] = A[tile_row * K + global_col];
// Bsub[row*TS + col] = B[global_row * N + tile_col];
__syncthreads();
// printf("(info) global (%d, %d), Asub[%d, %d] = %+.3f\n",
// global_row, global_col, row, col, Asub[row*TS + col]);
// printf("(info) global (%d, %d), tile (%d, %d, %d) Asub[%d, %d] = %+.3f\n",
// global_row, global_col, t, tid_row, tid_col, row, col, Asub[row*TS + col]);
// printf("(info) global (%d, %d), tile (%d, %d, %d) Bsub[%d, %d] = %+.3f\n",
// global_row, global_col, t, tid_row, tid_col, row, col, Bsub[row*TS + col]);
for(int k=0; k<TS; ++k)
{
acc += Asub[row * TS + k] * Bsub[k * TS + col];
// printf("(info) Asub[%d, %d] = %+.3f Bsub[%d, %d] = %+.3f\n",
// row, k, Asub[row*TS + k],
// k, col, Bsub[k*TS + row]);
}
__syncthreads();
}
C[global_row * N + global_col] = acc;
// if( (t == 0) && (row == 0) && (col == 0) )
// {
// printf("A = \n");
// __print_mat(Asub, TS, TS);
// printf("B = \n");
// __print_mat(Bsub, TS, TS);
// }
}
*/
// step 3: tiling + more jobs to threads
__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
const int row = threadIdx.x;
const int col = threadIdx.y;
// const int blk_row = blockIdx.x;
// const int blk_col = blockIdx.y;
const int global_row = (blockDim.x * WPT) * blockIdx.x + threadIdx.x;
const int global_col = blockDim.y * blockIdx.y + threadIdx.y;
// printf("(info) blockDim: (%d, %d)\n", blockDim.x, blockDim.y);
// printf("(info) blockIdx: (%d, %d)\n", blockIdx.x, blockIdx.y);
// printf("(info) threadDim: (%d, %d)\n", threadIdx.x, threadIdx.y);
__shared__ float Asub[TS][TS];
__shared__ float Bsub[TS][TS];
float acc[WPT];
for(int w=0; w<WPT; ++w)
{
acc[w] = 0.0f;
}
const int num_tiles = (int) ((K + TS - 1)/TS);
for(int t=0; t<num_tiles; ++t)
{
for(int w=0; w<WPT; ++w)
{
const int t_row = TS * t + row;
const int t_col = TS * t + col;
if( (global_row + w*RTS) >= M || (t_col >= K) )
{
Asub[row + w*RTS][col] = 0.0f;
}
else{
Asub[row + w*RTS][col] = A[(global_row + w*RTS) * K + t_col];
}
if( (t_row + w*RTS >= K) || (global_col >= N) )
{
Bsub[row + w*RTS][col] = 0.0f;
}
else
{
Bsub[row + w*RTS][col] = B[(t_row + w*RTS) * N + global_col];
}
}
__syncthreads();
for(int k=0; k<TS; ++k)
{
for(int w=0; w<WPT; ++w)
{
acc[w] += Asub[row + w*RTS][k] * Bsub[k][col];
}
}
__syncthreads();
}
for(int w=0; w<WPT; ++w)
{
if( (global_row + w*RTS >= M) || (global_col >= N))
continue;
C[(global_row + w*RTS) * N + global_col] = acc[w];
}
}
// Array of device (GPU) pointers
static float *a_d[MAX_NUM_GPU];
static float *b_d[MAX_NUM_GPU];
static float *c_d[MAX_NUM_GPU];
static int M, N, K;
static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
void mat_mul(float *_A, float *_B, float *_C, int _M, int _N, int _K) {
// printf("\n");
// printf("(info) A = \n");
// print_mat(_A, _M, _K);
// printf("(info) B = \n");
// print_mat(_B, _K, _N);
// Launch kernel on every GPU
for (int i = 0; i < num_devices; i++) {
// dim3 blockDim(TS, TS, 1); //
// dim3 gridDim(((Mend[i] - Mbegin[i])/TS), (N)/TS, 1);
dim3 blockDim(TS/WPT, TS, 1); //
dim3 gridDim(((Mend[i] - Mbegin[i]+TS-1)/TS), (N+TS-1)/TS, 1);
CUDA_CALL( cudaSetDevice(i) );
sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], M, N, K);
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaDeviceSynchronize() );
}
}
void mat_mul_init(float *A, float *B, float *C, int _M, int _N, int _K) {
M = _M, N = _N, K = _K;
CUDA_CALL( cudaGetDeviceCount(&num_devices) );
// num_devices = 1; // TODO: step-by-step
printf("Using %d devices\n", num_devices);
for (int i = 0; i < num_devices; i++) {
cudaDeviceProp prop;
CUDA_CALL( cudaGetDeviceProperties(&prop, i) );
// Try printing more detailed information here
printf("[GPU %d] %s\n", i, prop.name);
}
if (num_devices <= 0) {
printf("No CUDA device found. Aborting\n");
exit(1);
}
// Setup problem size for each GPU
for (int i = 0; i < num_devices; i++) {
Mbegin[i] = (M / num_devices) * i;
Mend[i] = (M / num_devices) * (i + 1);
}
Mend[num_devices - 1] = M;
// Allocate device memory for each GPU
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaMalloc(&a_d[i], (Mend[i] - Mbegin[i]) * K * sizeof(float)) );
CUDA_CALL( cudaMalloc(&b_d[i], K * N * sizeof(float)) );
CUDA_CALL( cudaMalloc(&c_d[i], (Mend[i] - Mbegin[i]) * N * sizeof(float)) );
}
// Upload A and B matrix to every GPU
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpy(a_d[i], A + Mbegin[i] * K,
(Mend[i] - Mbegin[i]) * K * sizeof(float),
cudaMemcpyHostToDevice) );
CUDA_CALL( cudaMemcpy(b_d[i], B, K * N * sizeof(float), cudaMemcpyHostToDevice) );
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaDeviceSynchronize() );
}
}
void mat_mul_final(float *A, float *B, float *C, int M, int N, int K) {
// Do any post-matmul cleanup work here.
// Download C matrix from GPUs
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpy(C + Mbegin[i] * N, c_d[i],
(Mend[i] - Mbegin[i]) * N * sizeof(float),
cudaMemcpyDeviceToHost) );
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaDeviceSynchronize() );
}
}