chundoong-lab-ta/SamsungDS22/submissions/HW6/scsc.lee/mat_mul.cu

#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) {
	// Thread identifiers
	const int row = threadIdx.x;
	const int col = threadIdx.y;
	const int globalRow = (blockDim.x*WPT) * blockIdx.x + threadIdx.x;
	const int globalCol = blockDim.y * blockIdx.y + threadIdx.y;

  // Local memory to fit a tile of TS*TS elements of A and B
  __shared__ float Asub[TS][TS];
  __shared__ float Bsub[TS][TS];

  // Initialise the accumulation array
  float intermediateVal[WPT];
  for(int w = 0; w < WPT; w++){
     intermediateVal[w] = 0.0f;
  }

  // Loop over all tiles
	int numTiles;
	if(K % TS == 0){
		numTiles = K / TS;
	}
	else{
		numTiles = K / TS + 1;
	}
  
	for(int t = 0; t < numTiles; t++){
     for(int w = 0; w < WPT; w++) {
        const int tiledRow = TS * t + row;
        const int tiledCol = TS * t + col;
        
				if( (globalRow + w * RTS) < M && tiledCol < K ){
					Asub[row + w * RTS][col] = A[(globalRow + w * RTS) * K + tiledCol];
				}
				else{
					Asub[row + w * RTS][col] = 0.0f;
				}
				
				if( (tiledRow + w * RTS) < K && globalCol < N ){
        	Bsub[row + w * RTS][col] = B[(tiledRow + w * RTS) * N + globalCol];
				}
				else{
        	Bsub[row + w * RTS][col] = 0.0f;
				}
     }
     
     // Synchronise to make sure the tile is loaded
		 __syncthreads();

     // Perform the computation
     for(int k = 0; k < TS; k++){
        for(int w = 0; w < WPT; w++){
           intermediateVal[w] += Asub[row + w * RTS][k] * Bsub[k][col];
        }
     }

     // Synchronise before loading the next tile
		 __syncthreads();
  }

  for(int w = 0; w < WPT; w++){
		if( (globalRow + w * RTS) < M && globalCol < N){
    	C[(globalRow + w * RTS) * N + globalCol] = intermediateVal[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 split_M[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 gridDim(((Mend[i] - Mbegin[i]+TS-1)/TS), (N+TS-1)/TS, 1);
		dim3 blockDim(TS/WPT, TS, 1);

    CUDA_CALL( cudaSetDevice(i) );
    sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], split_M[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)) );
  }
	
	// Set GPU buffers
  for (int i = 0; i < num_devices; ++i) {
    split_M[i] = Mend[i] - Mbegin[i];
  }

  // 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() );
  }
}