164 lines
5.0 KiB
Plaintext
164 lines
5.0 KiB
Plaintext
|
#include "mat_mul.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
|
||
|
#define WPT 8
|
||
|
#define TS 32
|
||
|
int num_devices = 0;
|
||
|
|
||
|
__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
|
||
|
int row = threadIdx.x;
|
||
|
int col = threadIdx.y;
|
||
|
|
||
|
int global_row = TS * ( blockIdx.x ) + threadIdx.x;
|
||
|
int global_col = TS * ( blockIdx.y ) + threadIdx.y;
|
||
|
|
||
|
__shared__ float Asub[TS][TS];
|
||
|
__shared__ float Bsub[TS][TS];
|
||
|
float ABsub[WPT];
|
||
|
for(int w = 0; w < WPT; w++){
|
||
|
ABsub[w] = 0.0f;
|
||
|
}
|
||
|
const int RTS = TS/WPT;
|
||
|
const int num_tiles = (K % TS == 0)? K / TS : K / TS +1;
|
||
|
|
||
|
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( t_col >= K ) Asub[row + w*RTS][col] = 0;
|
||
|
else Asub[row + w*RTS][col] = A[(global_row + w*RTS) * K + t_col];
|
||
|
|
||
|
if( (t_row >= K) || (global_col >= N) ) Bsub[row + w*RTS][col] = 0;
|
||
|
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++){
|
||
|
ABsub[w] += Asub[row + w*RTS][k] * Bsub[k][col];
|
||
|
}
|
||
|
}
|
||
|
__syncthreads();
|
||
|
}
|
||
|
|
||
|
for(int w = 0; w < WPT; w++){
|
||
|
if(global_col < N) C[(global_row + w*RTS)*N + global_col] = ABsub[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) {
|
||
|
|
||
|
M = _M, N = _N, K= _K;
|
||
|
int m = (M/TS)*TS;
|
||
|
// Launch kernel on every GPU
|
||
|
for (int i = 0; i < num_devices; i++) {
|
||
|
dim3 gridDim( ( (Mend[i] - Mbegin[i])/WPT + TS/WPT -1) / (TS/WPT) , (N + TS-1)/TS , 1);
|
||
|
dim3 blockDim(TS/WPT, 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) );
|
||
|
|
||
|
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
|
||
|
int m = (M/TS)*TS;
|
||
|
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( cudaDeviceSynchronize() );
|
||
|
}
|
||
|
}
|
||
|
|
||
|
void mat_mul_final(float *A, float *B, float *C, int M, int N, int K) {
|
||
|
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() );
|
||
|
}
|
||
|
for(int i = Mend[num_devices-1]; i < M; i++){
|
||
|
for(int k = 0; k < K; k++){
|
||
|
float aik = A[i*K + k];
|
||
|
for(int j = 0; j < N; j++){
|
||
|
C[i*N + j] += aik * B[k*N + j];
|
||
|
}
|
||
|
}
|
||
|
}
|
||
|
|
||
|
|
||
|
}
|