230 lines
6.9 KiB
Plaintext
230 lines
6.9 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 NUM_WORK_ITEM (32)
|
||
|
|
#define VECTOR_WIDTH (16)
|
||
|
|
#define RTS (NUM_WORK_ITEM / VECTOR_WIDTH)
|
||
|
|
#define ALIGN_UP(_X, _Y) (((_X) + (_Y) - 1) & ~((_Y) - 1))
|
||
|
|
int num_devices = 0;
|
||
|
|
|
||
|
|
__global__ void sgemm(float *__restrict A, float *__restrict B, float *__restrict C, int M, int N, int K, int NON_OPTIMAL)
|
||
|
|
{
|
||
|
|
// blockDim.x -> NUM_WORK_ITEM
|
||
|
|
const int i = threadIdx.x; // row index of C
|
||
|
|
const int j = threadIdx.y; // column index of C
|
||
|
|
const int global_row = NUM_WORK_ITEM * blockIdx.x + i;
|
||
|
|
const int global_col = NUM_WORK_ITEM * blockIdx.y + j;
|
||
|
|
float intermediate_val[16] = {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f};
|
||
|
|
|
||
|
|
__shared__ float tileA[NUM_WORK_ITEM][NUM_WORK_ITEM];
|
||
|
|
__shared__ float tileB[NUM_WORK_ITEM][NUM_WORK_ITEM];
|
||
|
|
|
||
|
|
if (NON_OPTIMAL == 0)
|
||
|
|
{
|
||
|
|
const int num_tiles = K / NUM_WORK_ITEM;
|
||
|
|
|
||
|
|
// printf("i : %d, j : %d, global_row : %d, global_col : %d\n", i, j, global_row, global_col);
|
||
|
|
for (int t = 0; t < num_tiles; t++)
|
||
|
|
{
|
||
|
|
for (int w = 0; w < VECTOR_WIDTH; w++)
|
||
|
|
{
|
||
|
|
const int t_row = NUM_WORK_ITEM * t + i;
|
||
|
|
const int t_col = NUM_WORK_ITEM * t + j;
|
||
|
|
|
||
|
|
tileA[i + w * RTS][j] = A[((global_row + w * RTS)) * K + t_col];
|
||
|
|
tileB[i + w * RTS][j] = B[((t_row + w * RTS)) * N + global_col];
|
||
|
|
}
|
||
|
|
|
||
|
|
__syncthreads();
|
||
|
|
|
||
|
|
for (int k = 0; k < NUM_WORK_ITEM; k++)
|
||
|
|
{
|
||
|
|
for (int w = 0; w < VECTOR_WIDTH; w++)
|
||
|
|
{
|
||
|
|
intermediate_val[w] += tileA[i + w * RTS][k] * tileB[k][j];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
__syncthreads();
|
||
|
|
}
|
||
|
|
|
||
|
|
for (int w = 0; w < VECTOR_WIDTH; w++)
|
||
|
|
{
|
||
|
|
C[(global_row + w * RTS) * N + global_col] = intermediate_val[w];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
const int num_tiles = (K + NUM_WORK_ITEM - 1) / NUM_WORK_ITEM;
|
||
|
|
|
||
|
|
// printf("i : %d, j : %d, global_row : %d, global_col : %d\n", i, j, global_row, global_col);
|
||
|
|
for (int t = 0; t < num_tiles; t++)
|
||
|
|
{
|
||
|
|
for (int w = 0; w < VECTOR_WIDTH; w++)
|
||
|
|
{
|
||
|
|
const int t_row = NUM_WORK_ITEM * t + i;
|
||
|
|
const int t_col = NUM_WORK_ITEM * t + j;
|
||
|
|
if (global_row + w * RTS >= M || t_col >= K)
|
||
|
|
{
|
||
|
|
tileA[i + w * RTS][j] = 0.0f;
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
tileA[i + w * RTS][j] = A[((global_row + w * RTS)) * K + t_col];
|
||
|
|
}
|
||
|
|
|
||
|
|
if (t_row + w * RTS >= K || global_col >= N)
|
||
|
|
{
|
||
|
|
tileB[i + w * RTS][j] = 0.0f;
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
tileB[i + w * RTS][j] = B[((t_row + w * RTS)) * N + global_col];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
__syncthreads();
|
||
|
|
|
||
|
|
for (int k = 0; k < NUM_WORK_ITEM; k++)
|
||
|
|
{
|
||
|
|
for (int w = 0; w < VECTOR_WIDTH; w++)
|
||
|
|
{
|
||
|
|
intermediate_val[w] += tileA[i + w * RTS][k] * tileB[k][j];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
__syncthreads();
|
||
|
|
}
|
||
|
|
|
||
|
|
for (int w = 0; w < VECTOR_WIDTH; w++)
|
||
|
|
{
|
||
|
|
if(global_row + w * RTS >= M || global_col >= N)
|
||
|
|
{
|
||
|
|
break;
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
C[(global_row + w * RTS) * N + global_col] = intermediate_val[w];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
// printf("C[%d] = , %+.3f, %+.3f, %+.3f, %+.3f,...\n", (global_row) * N + global_col, intermediate_val[0],intermediate_val[1],intermediate_val[2],intermediate_val[3]);
|
||
|
|
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
// 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], Msize[MAX_NUM_GPU];
|
||
|
|
static int NON_OPTIMAL;
|
||
|
|
|
||
|
|
void mat_mul(float *_A, float *_B, float *_C, int _M, int _N, int _K) {
|
||
|
|
|
||
|
|
if (_M % (NUM_WORK_ITEM * num_devices) != 0
|
||
|
|
|| _N % NUM_WORK_ITEM != 0
|
||
|
|
|| _K % NUM_WORK_ITEM != 0)
|
||
|
|
{
|
||
|
|
NON_OPTIMAL = 1;
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
NON_OPTIMAL = 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
// Launch kernel on every GPU
|
||
|
|
for (int i = 0; i < num_devices; i++) {
|
||
|
|
dim3 blockDim(RTS, NUM_WORK_ITEM, 1);
|
||
|
|
dim3 gridDim(ALIGN_UP(Msize[i], NUM_WORK_ITEM) / NUM_WORK_ITEM , \
|
||
|
|
ALIGN_UP(N, NUM_WORK_ITEM) / NUM_WORK_ITEM, 1);
|
||
|
|
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], Msize[i], N, K, NON_OPTIMAL);
|
||
|
|
}
|
||
|
|
|
||
|
|
// 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);
|
||
|
|
Msize[i] = Mend[i] - Mbegin[i];
|
||
|
|
}
|
||
|
|
Mend[num_devices - 1] = M;
|
||
|
|
Msize[num_devices - 1] = Mend[num_devices - 1] - Mbegin[num_devices - 1];
|
||
|
|
|
||
|
|
// Allocate device memory for each GPU
|
||
|
|
for (int i = 0; i < num_devices; i++) {
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
CUDA_CALL( cudaMalloc(&a_d[i], (Msize[i]) * K * sizeof(float)) );
|
||
|
|
CUDA_CALL( cudaMalloc(&b_d[i], K * N * sizeof(float)) );
|
||
|
|
CUDA_CALL( cudaMalloc(&c_d[i], (Msize[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,
|
||
|
|
(Msize[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],
|
||
|
|
(Msize[i]) * N * sizeof(float),
|
||
|
|
cudaMemcpyDeviceToHost) );
|
||
|
|
}
|
||
|
|
|
||
|
|
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
|
||
|
|
for (int i = 0; i < num_devices; i++) {
|
||
|
|
CUDA_CALL( cudaDeviceSynchronize() );
|
||
|
|
}
|
||
|
|
}
|