chundoong-lab-ta/SamsungDS22/submissions/HW6/mssm.kim/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 TS 32
#define WPT 8
#define RTS (TS/WPT)

#define MAX_NUM_GPU 4
int num_devices = 0;

__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];
  }  
*/
  const int row = threadIdx.x;//get_local_id(0); // row index of C
  const int col = threadIdx.y;//get_local_id(1); // row index of C
  const int global_row = (blockDim.x*WPT)*blockIdx.x+threadIdx.x;//TS*get_group_id(0)+row;
  const int global_col = blockDim.y*blockIdx.y+threadIdx.y;//TS*get_group_id(1)+col;
//printf("row, col grow, gcol %d, %d, %d, %d \n", row,col, global_row, global_col);
  __shared__ float Asub[TS][TS];
  __shared__ float Bsub[TS][TS];

  float intermediate_val[WPT];
  for(int w=0;w<WPT;w++) {
	  intermediate_val[w] = 0.0f; 
  }

  //const int num_tiles = (K%TS)>0 ? K/TS+1 : K/TS;
  const int num_tiles = (K+TS-1)/TS;

  //printf("K, K %% TS, numtile %d %d %d\n", K,K%TS,num_tiles);
  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];
	 }
	 //barrier(CLK_LOCAL_MEM_FENCE);
	  __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];
	   }
	}

	 //barrier(CLK_LOCAL_MEM_FENCE);
	  __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];

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++) {
//	  printf("Here is num dev\n");
    //dim3 blockDim(1, 1, 1);
	  dim3 blockDim(TS/WPT,TS,1);
	  dim3 gridDim( ((Mend[i]-Mbegin[i]+TS-1)/TS), (N+TS-1)/TS, 1);
    //dim3 gridDim(Mend[i] - Mbegin[i], N, 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() );
  }
}