chundoong-lab-ta/SamsungDS22/submissions/HW6/dc.yang/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 TS 32                        // The square-root of the 2D tile-size (== work-group dims)

// Constants for kernels 3, 5
#define WPT 8                        // The amount of work-per-thread, i.e. the thread-coarsening factor
#define RTS (TS/WPT)                 // The reduced tile-size in one dimension
#define MAX_NUM_GPU 4
int num_devices = 0;

__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
    
    // Thread identifiers
//    const int row = get_local_id(0); // Local row ID (max: TS)
//    const int col = get_local_id(1); // Local col ID (max: TS/WPT == RTS)
//    const int globalRow = TS*get_group_id(0) + row; // Row ID of C (0..M)
//    const int globalCol = TS*get_group_id(1) + col; // Col ID of C (0..N)
   
    int row = threadIdx.x;
    int col = threadIdx.y;
    int globalRow = (blockDim.x*WPT) * blockIdx.x + threadIdx.x;
    int globalCol = blockDim.y * blockIdx.y + threadIdx.y;
    // Local memory to fit a tile of TS*TS elements of A and B
 //   __local float Asub[TS][TS];
 //   __local float Bsub[TS][TS];
    
    __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;
    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] = 0.0f;
			}
			else
			{
				Asub[row + w*RTS][col] = A[(globalRow + w*RTS)*K + tiledCol];
			}
			
			if(globalCol >= N  || tiledRow + w*RTS >= K)
			{
				Bsub[row + w*RTS][col] = 0.0f;
			}
			else
			{
				Bsub[row + w*RTS][col] = B[(tiledRow + w*RTS)*N + globalCol];
			}
        }
        
        // Synchronise 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];
            }
        }
 
        // Synchronise 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(globalRow + w*RTS >= M || globalCol >= N)
		{
			continue;
		}
		else
		{
			C[(globalRow + w*RTS)*N + globalCol] = 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(4, 4, 2);
    //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);
  }

  // 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() );
  }
}