#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
#define RTS 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];
//  }
  
    int row = threadIdx.x; // Local row ID (max: TS)
    int col = threadIdx.y; // Local col ID (max: TS/WPT == RTS)
    int globalRow =  (blockDim.x*WPT) * blockIdx.x + threadIdx.x; // Row ID of C (0..M)
    int globalCol =  blockDim.y * blockIdx.y + threadIdx.y; // Col ID of C (0..N)
 
    //if (globalRow >= M || globalCol >= N)
    //return;
 
    //printf("%d %d %d %d \n",globalRow,globalCol, M, N);   
    
    //if(globalRow >= M || globalCol >=N) {
 
      //return;
    //}

    // Local memory to fit a tile of TS*TS elements of A and B
    __shared__ float Asub[TS][TS];
    __shared__ float Bsub[TS][TS];

    // Initialise the accumulation registers
    float acc[WPT];
    for (int w=0; w<WPT; w++) {
        acc[w] = 0.0f;
    }
    
    // Loop over all tiles
    const int numTiles = (K+TS-1)/TS;
    
    //printf("%d %d %d %d %d\n",globalRow,globalCol, M, N,numTiles);
    for (int t=0; t<numTiles; t++) {
        
        // Load one tile of A and B into local memory
        for (int w=0; w<WPT; w++) {
            const int tiledRow = TS*t + row;
            const int tiledCol = TS*t + col;
            
            if((globalRow+w*RTS) < M && (tiledCol < K)) {
              Asub[row+w*RTS][col] = A[(globalRow+w*RTS)*K+tiledCol];
            }else{
              Asub[row+w*RTS][col] = 0.0f;
            }
            
            if((tiledRow+w*RTS < K) && (globalCol < N)){
              Bsub[row+w*RTS][col] = B[(tiledRow+w*RTS)*N+globalCol];
            }else{
              Bsub[row+w*RTS][col] = 0.0f;
            }
        }

        // Synchronise to make sure the tile is loaded
        __syncthreads();
        //barrier(CLK_LOCAL_MEM_FENCE);

        // 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];                
            }
        }

        // Synchronise before loading the next tile
        __syncthreads();
    }

    // Store the final results in C
    for (int w=0; w<WPT; w++) {
        if((globalRow+ w*RTS) < M && globalCol < N){
           C[(globalRow+ w*RTS)*N + globalCol] = acc[w];
        }//else{
           //C[(globalRow+ w*RTS)*N + globalCol] = 0.0f;
        //}                 
    }
}

// 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]+TS-1)/TS), (N+TS-1)/TS, 1);

    CUDA_CALL( cudaSetDevice(i) );
    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) );
  
  //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() );
  }
}