chundoong-lab-ta/SamsungDS22/submissions/HW6/jicheol.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 TILE_WIDTH 16

int num_devices = 0;
__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,
      tx = threadIdx.x, ty = threadIdx.y,
      Row = by * TILE_WIDTH + ty,
      Col = bx * TILE_WIDTH + tx;
  float Pvalue = 0;

  for(int m = 0; m < (K-1)/TILE_WIDTH + 1; ++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;
}  

// 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++) {
    int size = (i == num_devices - 1) ? (M- (M/num_devices*(num_devices -1))): M/num_devices;
    dim3 blockDim(TILE_WIDTH, TILE_WIDTH, 1);
    //dim3 gridDim((N-1)/TILE_WIDTH+1, (M-1)/TILE_WIDTH+1, 1);
    dim3 gridDim((N-1)/TILE_WIDTH+1, (size-1)/TILE_WIDTH+1, 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) );
  
  //num_devices = 1; // 
  
  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() );
  }
}