chundoong-lab-ta/SamsungDS22/submissions/HW6/taekyung.yeo/mat_mul.cu

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#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 TS 32
#define WPT 8
#define RTS (TS/WPT)
int num_devices = 0;
__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*WPT)*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 temp_val[WPT];
for(int w=0; w<WPT; w++) {
temp_val[w] = 0.0f;
}
const int tiles = (K+TS-1)/TS;
for(int t=0; t<tiles; t++){
for(int w=0; w<WPT; w++){
const int temp_row = TS*t+row;
const int temp_col = TS*t+col;
if(global_row+w*RTS>=M || temp_col >= K) {Asub[row+w*RTS][col]=0.0f;}
else Asub[row+w*RTS][col]=A[(global_row+w*RTS)*K+temp_col];
if(temp_row+w*RTS>=K||global_col>=N) {Bsub[row+w*RTS][col]=0.0f;}
else Bsub[row+w*RTS][col]=B[(temp_row+w*RTS)*N+global_col];
}
__syncthreads();
for (int k=0; k<TS; k++) {
for(int w=0; w<WPT; w++) {
temp_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] = temp_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(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( 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() );
}
}