chundoong-lab-ta/SamsungDS22/submissions/HW6/hs5006.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
int num_devices = 0;

#define TS 32
#define WPT 8
#define RTS (TS/WPT)

__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
  //const int row = get_local_id(0); // local row ID (TS/WPT IDs) // local row ID (0~31) // row index of C
  //const int col = get_local_id(1); // local col ID (0~31) // column index of C
	//const int global_row = TS * get_group_id(0) + row; // row ID of C (0~M)
	//const int global_col = TS * get_group_id(1) + col; // col ID of C (0~N)
  int row = threadIdx.x; // local row ID (TS/WPT IDs) // local row ID (0~31) // row index of C
  int col = threadIdx.y; // local col ID (0~31) // column index of C
	int global_row = (blockDim.x*WPT)*blockIdx.x+threadIdx.x; // row ID of C (0~M)
	int global_col = blockDim.y * blockIdx.y + threadIdx.y; // col ID of C (0~N)

	//__local float Asub[TS][TS];
	//__local float Bsub[TS][TS];
	__shared__ float Asub[TS][TS];
	__shared__ float Bsub[TS][TS];

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

	//const int num_tiles = K / TS;
	const int num_tiles = (K + TS - 1) / TS;

	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];
			}
		}

    __syncthreads();

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

    __syncthreads();

	}

	for (int w = 0; w < WPT; w++) {
		if(((global_row + w*RTS) < M) && (global_col < N)) {
			C[(global_row + w*RTS) * N + global_col] = acc[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++) {
    //dim3 blockDim(1, 1, 1);
    //dim3 gridDim(Mend[i] - Mbegin[i], N, 1);
    dim3 blockDim(TS/WPT, TS, 1);
    dim3 gridDim((Mend[i] - Mbegin[i] + TS - 1)/TS, (N+TS-1)/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( 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
  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( cudaSetDevice(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( cudaSetDevice(i) );
    CUDA_CALL( cudaDeviceSynchronize() );
  }
}