chundoong-lab-ta/SamsungDS22/submissions/HW6/g.kwak/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
int num_devices = 0;
#define TS 32
#define WPT 8
#define RTS 4
//////////////////////////////////////////////////////////////////////
__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];
}
*/
// Thread identifiers
const int row = threadIdx.x; // local row ID (TS/WPT IDs)
const int col = threadIdx.y; // local col ID (0~31)
const int global_row = TS * blockIdx.x + row; // row ID of C (0~M)
const int global_col = TS * blockIdx.y + col; // col ID of C (0~N)
__shared__ float Asub[TS][TS];
__shared__ float Bsub[TS][TS];
float acc[WPT];
for (int w=0; w<WPT; w++) {
acc[w] = 0.0f;
}
const int num_tiles = (K+TS-1)/ TS; // TODO
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();
// barrier(CLK_LOCAL_MEM_FENCE);
for (int k = 0; k < TS; k++) {
for (int w = 0; w < WPT; w++) {
acc[w] += Asub[row + w*RTS][k] * Bsub[k][col];
}
}
__syncthreads();
// barrier(CLK_LOCAL_MEM_FENCE);
}
// store in C
for (int w=0; w<WPT; w++) {
if (global_row + w*RTS >= M || global_col >= N) continue; // TODO
C[(global_row + w*RTS)*N + global_col] = acc[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 Msize[MAX_NUM_GPU], 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++) {
Msize[i] = Mend[i] - Mbegin[i];
//dim3 blockDim(1, 1, 1);
//dim3 gridDim(Mend[i] - Mbegin[i], N, 1);
dim3 blockDim(TS/WPT, TS, 1);
dim3 gridDim(((Msize[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], Msize[i], 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() );
}
}