#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;

//__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];
//  }
//}

#define TILE_ROW 32
#define TILE_COL (TILE_ROW/WIDTH)

#define TS  32
#define WPT 8
#define RTS 4


__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
	// Thread identifiers
	int row = threadIdx.x; // local row ID(TS/WPT IDs)
	int col = threadIdx.y; // local col ID(0~31)
	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)

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

	float acc[WPT];
	for (int w=0; w<WPT; w++) {
		acc[w] = 0.0f;
	}
	// Loop over all tiles
	const int num_tiles = (K+TS-1) / TS;
	for (int t = 0; t<num_tiles; t++) {

		// Load one tile of A and B into local memory
		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];
		}
	}

	// Synchronize to make sure the tile is loaded
//	barrier(CLK_LOCAL_MEM_FENCE);
	__syncthreads();
	// Perform the computation for a single tile 
	for (int k=0; k<TS; k++) {
		for (int w=0; w<WPT; w++) {
			acc[w] += Asub[row + w*RTS][k] * Bsub[k][col];
		}
	}
	
	// Synchronize before loading the next tile
//	barrier(CLK_LOCAL_MEM_FENCE);
	__syncthreads();
}

// Store the final results in C
for (int w=0; w<WPT; w++) {
	if (global_row + w*RTS >= M || global_col >= N) continue;
	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(TS/WPT, TS, 1);
//    dim3 gridDim(Mend[i] - Mbegin[i], N, 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( 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() );
  }
}