chundoong-lab-ta/SamsungDS22/submissions/HW6/hkyoo.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 MAX_NUM_GPU 4
#define WPT 8
#define TS 32
int num_devices = 0;

__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
    int row = threadIdx.x;
    int col = threadIdx.y;

    int global_row =   TS * ( blockIdx.x ) + threadIdx.x;
    int global_col =   TS * ( blockIdx.y ) + threadIdx.y;

    __shared__  float Asub[TS][TS];
   __shared__  float Bsub[TS][TS];
   float ABsub[WPT];
    for(int w = 0; w < WPT; w++){
       ABsub[w] = 0.0f;
    }
    const int RTS = TS/WPT;
    const int num_tiles = (K % TS == 0)? K / TS : K / TS +1;
    
    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( t_col >= K ) Asub[row + w*RTS][col] = 0;
                else Asub[row + w*RTS][col] = A[(global_row + w*RTS) * K + t_col];

                if( (t_row >= K) || (global_col >= N) ) Bsub[row + w*RTS][col] = 0;
                else Bsub[row + w*RTS][col] = B[(t_row + w*RTS)*N + global_col];
       }

 __syncthreads();
    for(int k = 0; k < TS; k++){
      for(int w = 0; w < WPT; w++){
        ABsub[w] += Asub[row + w*RTS][k] * Bsub[k][col];
      }
    }
   __syncthreads();
 }

    for(int w = 0; w < WPT; w++){
            if(global_col < N) C[(global_row + w*RTS)*N + global_col] = ABsub[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) {
    
    M = _M, N = _N, K= _K;
    int m = (M/TS)*TS;
    // Launch kernel on every GPU
  for (int i = 0; i < num_devices; i++) {
    dim3 gridDim( ( (Mend[i] - Mbegin[i])/WPT + TS/WPT -1) / (TS/WPT) , (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], m, N, K);

  }

// DO NOT REMOVE; NEEDED FOR TIME MEASURE
  for (int i = 0; i < num_devices; i++) {
    CUDA_CALL( cudaSetDevice(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
  int m = (M/TS)*TS;
  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) {
  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( cudaSetDevice(i) );
    CUDA_CALL( cudaDeviceSynchronize() );
  }
  for(int i = Mend[num_devices-1]; i < M; i++){
    for(int k = 0; k < K; k++){
      float aik = A[i*K + k];
      for(int j = 0; j < N; j++){
        C[i*N + j] += aik * B[k*N + j];
      }
    }
  }


}