chundoong-lab-ta/SamsungDS22/submissions/HW6/c.m.lee/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 BLOCK_SIZE 16

__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
    // Block index
    int bx = blockIdx.x;
    int by = blockIdx.y;

    // Thread index
    int tx = threadIdx.x;
    int ty = threadIdx.y;

    // Index of the first sub-matrix of A processed by the block
    int aBegin = K * BLOCK_SIZE * by;

    // Index of the last sub-matrix of A processed by the block
    int aEnd   = aBegin + K - 1;

    // Step size used to iterate through the sub-matrices of A
    int aStep  = BLOCK_SIZE;

    // Index of the first sub-matrix of B processed by the block
    int bBegin = BLOCK_SIZE * bx;

    // Step size used to iterate through the sub-matrices of B
    int bStep  = BLOCK_SIZE * N;

    // Csub is used to store the element of the block sub-matrix
    // that is computed by the thread
    float Csub = 0;

    // Loop over all the sub-matrices of A and B
    // required to compute the block sub-matrix
    for (int a = aBegin, b = bBegin;
         a <= aEnd;
         a += aStep, b += bStep)
    {

        // Declaration of the shared memory array As used to
        // store the sub-matrix of A
        __shared__ float As[BLOCK_SIZE][BLOCK_SIZE];

        // Declaration of the shared memory array Bs used to
        // store the sub-matrix of B
        __shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];

        // Load the matrices from device memory
        // to shared memory; each thread loads
        // one element of each matrix
        As[ty][tx] = A[a + K * ty + tx];
        Bs[ty][tx] = B[b + N * ty + tx];

        // Synchronize to make sure the matrices are loaded
        __syncthreads();

        // Multiply the two matrices together;
        // each thread computes one element
        // of the block sub-matrix
#pragma unroll
        for (int k = 0; k < BLOCK_SIZE; ++k)
        {
            Csub += As[ty][k] * Bs[k][tx];
        }

        // Synchronize to make sure that the preceding
        // computation is done before loading two new
        // sub-matrices of A and B in the next iteration
        __syncthreads();
    }

    // Write the block sub-matrix to device memory;
    // each thread writes one element
    int c = N * BLOCK_SIZE * by + BLOCK_SIZE * bx;
    C[c + N * ty + tx] = Csub;
}

// 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++) {
    int rounded_N = (N + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE;
    int rounded_M = ((Mend[i] - Mbegin[i]) + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE;

    dim3 blockDim(BLOCK_SIZE, BLOCK_SIZE);
    dim3 gridDim(rounded_N/BLOCK_SIZE, rounded_M/BLOCK_SIZE);

    CUDA_CALL( cudaSetDevice(i) );
    sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], Mend[i] - Mbegin[i], rounded_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
  int rounded_N = (N + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE;
  for (int i = 0; i < num_devices; i++) {
    int rounded_M = ((Mend[i] - Mbegin[i]) + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE;
    CUDA_CALL( cudaSetDevice(i) );
    CUDA_CALL( cudaMalloc(&a_d[i], rounded_M * K * sizeof(float)) );
    CUDA_CALL( cudaMalloc(&b_d[i], K * rounded_N * sizeof(float)) );
    CUDA_CALL( cudaMalloc(&c_d[i], rounded_M * rounded_N * sizeof(float)) );
    CUDA_CALL( cudaMemset(a_d[i], 0, rounded_M * K * sizeof(float)) );
    CUDA_CALL( cudaMemset(b_d[i], 0, K * rounded_N * sizeof(float)) );
    CUDA_CALL( cudaMemset(c_d[i], 0, rounded_M * rounded_N * sizeof(float)) );
  }

  // Upload A and B matrix to every GPU
  for (int i = 0; i < num_devices; i++) {
    CUDA_CALL( cudaSetDevice(i) );
    CUDA_CALL( cudaMemcpy(a_d[i], A + Mbegin[i] * K,
               (Mend[i] - Mbegin[i]) * K * sizeof(float),
               cudaMemcpyHostToDevice) );
    for (int j = 0; j < K; j++) {
      CUDA_CALL( cudaMemcpy(b_d[i] + j * rounded_N, B + j * N, 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.
  int rounded_N = (N + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE;

  // Download C matrix from GPUs
  for (int i = 0; i < num_devices; i++) {
    CUDA_CALL( cudaSetDevice(i) );
    for (int j = 0; j < (Mend[i] - Mbegin[i]); j++) {
      CUDA_CALL( cudaMemcpy(C + Mbegin[i] * N + j * N, c_d[i] + j * rounded_N, 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() );
  }
}