215 lines
6.8 KiB
Plaintext
215 lines
6.8 KiB
Plaintext
#define TS 32 // tile size
|
|
#define WPT 8 // work per thread
|
|
#define RTS (TS/WPT)
|
|
#define WPT_NR 16 // work per thread in case no remainder
|
|
#define RTS_NR (TS/WPT_NR)
|
|
|
|
#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
|
|
int num_devices = 0;
|
|
|
|
// single precision matrix multiplication
|
|
__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
|
|
|
|
// thread index
|
|
const int row = threadIdx.x;; // local row index of C
|
|
const int col = threadIdx.y;; // local column index of C
|
|
const int global_row = (blockDim.x * WPT) * blockIdx.x + threadIdx.x; // global row index of C
|
|
const int global_col = blockDim.y * blockIdx.y + threadIdx.y; // global column index of C
|
|
__shared__ float Asub[TS][TS];
|
|
__shared__ float Bsub[TS][TS];
|
|
|
|
float intermediate_val[WPT];
|
|
|
|
//printf("row, col: %d, %d // global row, global col: %d, %d \n", row, col, global_row, global_col);
|
|
|
|
for(int w = 0; w < WPT; w++) {
|
|
intermediate_val[w] = 0.0f;
|
|
}
|
|
|
|
// remainder
|
|
const int num_tiles = (K + TS - 1) / TS;
|
|
//printf("K = %d, K % TS = %d, numtile = %d\n", K, (K % TS), num_tiles);
|
|
|
|
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++) {
|
|
intermediate_val[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;
|
|
else
|
|
C[(global_row + w * RTS) * N + global_col] = intermediate_val[w];
|
|
}
|
|
}
|
|
|
|
__global__ void sgemmNoRemainder(float *A, float *B, float *C, int M, int N, int K) {
|
|
|
|
// thread index
|
|
const int row = threadIdx.x;; // local row index of C
|
|
const int col = threadIdx.y;; // local column index of C
|
|
const int global_row = (blockDim.x * WPT) * blockIdx.x + threadIdx.x; // global row index of C
|
|
const int global_col = blockDim.y * blockIdx.y + threadIdx.y; // global column index of C
|
|
__shared__ float Asub[TS][TS];
|
|
__shared__ float Bsub[TS][TS];
|
|
|
|
float intermediate_val[WPT_NR];
|
|
|
|
for(int w = 0; w < WPT_NR; w++) {
|
|
intermediate_val[w] = 0.0f;
|
|
}
|
|
|
|
// No remainder
|
|
const int num_tiles = K / TS;
|
|
|
|
for(int t = 0; t < num_tiles; t++){
|
|
for(int w = 0; w < WPT_NR; w++){
|
|
const int t_row = TS * t + row;
|
|
const int t_col = TS * t + col;
|
|
|
|
Asub[row + w * RTS_NR][col] = A[(global_row + w * RTS_NR) * K + t_col];
|
|
Bsub[row + w * RTS_NR][col] = B[(t_row + w * RTS_NR) * N + global_col];
|
|
}
|
|
__syncthreads();
|
|
|
|
for (int k = 0; k < TS; k++) {
|
|
for(int w = 0; w < WPT_NR; w++) {
|
|
intermediate_val[w] += Asub[row + w * RTS_NR][k] * Bsub[k][col];
|
|
}
|
|
}
|
|
__syncthreads();
|
|
}
|
|
|
|
for(int w = 0; w < WPT_NR; w++) {
|
|
C[(global_row + w * RTS_NR) * N + global_col] = intermediate_val[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) {
|
|
|
|
// Launch kernel on every GPU
|
|
for (int i = 0; i < num_devices; i++) {
|
|
//dim3 blockDim(1, 1, 1);
|
|
//dim3 gridDim(Mend[i] - Mbegin[i], N, 1);
|
|
|
|
dim3 blockDim(RTS, 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( 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
|
|
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) {
|
|
// 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( cudaDeviceSynchronize() );
|
|
}
|
|
}
|