chundoong-lab-ta/SamsungDS22/submissions/HW6/dk.han/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
int num_devices = 0;

//#define TS 8
#define TS 16
//#define TS 32 //about 9000

__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];
  }
*/


__shared__ float ds_M[TS][TS];
__shared__ float ds_N[TS][TS];
  int bx = blockIdx.x, by = blockIdx.y,
     tx = threadIdx.x, ty = threadIdx.y,
     Row = by * TS + ty,
     Col = bx * TS + tx;
  float Pvalue = 0;
  
  int min = (K%TS!=0 && K<TS) ? K : TS;

  for (int m = 0; m < (K-1)/TS+1; m++) {
     if (Row < M && m*TS+tx < K)
        ds_M[ty][tx] = A[Row*K + m*TS+tx];
     else
        ds_M[ty][tx] = 0;
     if (Col < N && m*TS+ty < K)
        ds_N[ty][tx] = B[(m*TS+ty)*N+Col];
     else
        ds_N[ty][tx] = 0;
  
     __syncthreads();
     //for (int k = 0; k < (K-1)/TS+1; ++k)
     for (int k = 0; k < min; ++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++) {
//    dim3 blockDim(1, 1, 1);
//    dim3 gridDim(Mend[i] - Mbegin[i], N, 1);
//    dim3 blockDim(TS, TS, 1);
//    dim3 gridDim(Mend[i] - Mbegin[i], N, 1);
//    dim3 dimGrid((numCColumns-1)/TILE_WIDTH+1, (numCRows-1)/TILE_WIDTH+1, 1);
//    dim3 gridDim((N-1)/TS+1, ((M-1)/4-1)/TS+1, 1);
//    dim3 gridDim((Mend[i] - Mbegin[i] -1)/TS+1, (N-1)/TS+1,  1);
    dim3 gridDim((N-1)/TS+1, (Mend[i] - Mbegin[i]-1)/TS+1,  1);
    dim3 blockDim(TS, TS, 1);


    CUDA_CALL( cudaSetDevice(i) );
    //sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], M, N, K);
    sgemm<<<gridDim, blockDim>>>(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) );
  }

  // DO NOT REMOVE; NEEDED FOR TIME MEASURE
  for (int i = 0; i < num_devices; i++) {
    CUDA_CALL( cudaDeviceSynchronize() );
  }
}