chundoong-lab-ta/SamsungDS22/submissions/HW6/dk2003.lim/mat_mul.cu

228 lines
6.1 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
//int num_devices = 0; // ORG
int num_devices = 4; // DEBUG
/*
// ORIGINAL VERSION
__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i >= M || j >= N)
return;
C[i * N + j] = 0;
for (int k = 0; k < K; ++k) {
C[i * N + j] += A[i * K + k] * B[k * N + j];
}
}
*/
// 41490 GFLOPS: TS(64), WPT(8)
#define TS 64
#define WPT 8
// 35000 GFLOPS: TS(72), WPT(8)
//#define TS 72
//#define WPT 8
// 30490 GFLOPS: TS(64), WPT(16)
//#define TS 64
//#define WPT 16
// 30000 GFLOPS: TS(64), WPT(4)
//#define TS 64
//#define WPT 4
// 28000 GFLOPS: TS(72), WPT(16)
//#define TS 72
//#define WPT 16
// 17000 GFLOPS: TS(16), WPT(8)
//#define TS 16
//#define WPT 8
#define RTS TS/WPT
// VERSION: PARTITIONED FOR M
__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = threadIdx.y;
int col = threadIdx.x;
//int global_row = (blockDim.y*WPT)*blockIdx.y + threadIdx.y;
//int global_col = (blockDim.x)*blockIdx.x + threadIdx.x;
int global_row = (blockDim.y*WPT)*by + ty;
int global_col = (blockDim.x)*bx + tx;
__shared__ float Asub[TS][TS];
__shared__ float Bsub[TS][TS];
float intermediate_val[WPT];
// printf("DEBUG: (bx,by) = (%d, %d), (tx,ty)=(%d, %d), (bsx,bsy)=(%d,%d), (global_row, global_col)= (%d,%d) \n",bx, by, tx,ty, blockDim.x, blockDim.y, global_row, global_col);
for(int w=0; w < WPT; w++) {
intermediate_val[w] = 0.0f;
}
const int num_tiles = (K % TS) ? K/TS +1 : K/TS; // aligned for TS(tile size)
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;
// for zero padding for aligned area
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];
// for zero padding for aligned area
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];
}
}
// 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];
//static int Mi[MAX_NUM_GPU];
static int Msz;
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++) {
int bsx, bsy;
bsx = TS;
bsy = TS/WPT;
dim3 blockDim(bsx, bsy, 1);
Msz = (Mend[i] - Mbegin[i]);
int gx, gy;
gx = N;
gy = (Msz+WPT-1)/WPT;
// aligned for block
gx = (gx + bsx -1)/bsx;
gy = (gy + bsy -1)/bsy;
dim3 gridDim(gx, gy, 1);
CUDA_CALL( cudaSetDevice(i) );
sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], Msz, 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() );
}
}