chundoong-lab-ta/SamsungDS22/submissions/HW6/cheolkyoo.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
#define TS	32
#define WPT	8
#define RTS	4
int num_devices = 0;

__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {

    // Thread identifiers
    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
    __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(tiledRow + w * RTS >= K || globalCol >= N) {
	        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
        __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
	__syncthreads();
    }

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

}

/*
__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];
  }
}
*/

// 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( 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() );
  }
}