chundoong-lab-ta/SamsungDS22/submissions/HW6/jh8846.kim/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 K_MAX 8192
int num_devices = 0;
#define TILE_WIDTH 16
__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K)
{
__shared__ float ds_M[TILE_WIDTH][TILE_WIDTH];
__shared__ float ds_N[TILE_WIDTH][TILE_WIDTH];
int bx = blockIdx.x, by = blockIdx.y;
int tx = threadIdx.x, ty = threadIdx.y;
int Row = by * TILE_WIDTH + ty;
int Col = bx * TILE_WIDTH + tx;
if(K >= K_MAX)
{
if (Row > M || Col > N) return;
}
float Pvalue = 0;
int end_m = (K-1)/TILE_WIDTH+1;
for (int m = 0; m < end_m; ++m)
{
if (Row < M && m*TILE_WIDTH+tx < K)
ds_M[ty][tx] = A[Row*K + m*TILE_WIDTH+tx];
else
ds_M[ty][tx] = 0;
if (Col < N && m*TILE_WIDTH+ty < K)
ds_N[ty][tx] = B[(m*TILE_WIDTH+ty)*N+Col];
else
ds_N[ty][tx] = 0;
__syncthreads();
for (int k = 0; k < TILE_WIDTH; ++k)
Pvalue += ds_M[ty][k] * ds_N[k][tx];
__syncthreads();
}
if (Row < M && Col < N)
C[Row*N+Col] = Pvalue;
}
/*
int ROW = blockDim.x * blockIdx.x + threadIdx.x;
int COL = blockDim.y * blockIdx.y + threadIdx.y;
if (ROW >= M || COL >= N)
return;
if(ROW<M && COL<N)
{
float value = 0.0f;
for(int i=0; i<N; i++)
{
value += A[ROW*K + i] * B[i*N + COL];
}
C[ROW*N + COL] = value;
}
*/
/*
float sum = 0.0f;
for(int k=0; k<M; ++k)
{
float a = A[i*K + k];
float b = B[k*N + j];
sum += a*b;
}
C[i*N + j] = sum;
*/
/*
C[i * N + j] = 0;
for (int k = 0; k < K; ++k) {
C[i * N + j] += A[i * K + k] * B[k * N + j];
}
*/
// 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 dimGrid((N-1)/TILE_WIDTH+1, (M-1)/TILE_WIDTH+1, 1);
dim3 dimBlock(TILE_WIDTH, TILE_WIDTH, 1);
//dim3 blockDim(16, 16, 1);
//dim3 gridDim(ceil(K/16.0), ceil(M/16.0), 1);
CUDA_CALL( cudaSetDevice(i) );
sgemm<<<dimGrid, dimBlock>>>(a_d[i], b_d[i], c_d[i], (Mend[i] - Mbegin[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) );
CUDA_CALL(cudaFree(a_d[i]));
CUDA_CALL(cudaFree(b_d[i]));
CUDA_CALL(cudaFree(c_d[i]));
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() );
}
}