chundoong-lab-ta/SamsungDS22/submissions/final/ym.tai/tmp-B/convolution.cu

#include "convolution.h"
#include <mpi.h>
#include <stdio.h>
#include "util.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 MAX_NUM_NODES 2
int num_devices = 0;

static float *input, *output, *filter;
static int N, C, H, W;
static int K, R, S;
static int OH, OW;
static int pad;
static int dilation;
static int stride;
static int mpi_rank, mpi_world_size;

// Array of device (GPU) pointers
static float *input_d[MAX_NUM_GPU];
static float *filter_d[MAX_NUM_GPU];
static float *output_d[MAX_NUM_GPU];
static int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];


__global__ void conv_kernel(
    float *input_, float *output_, float *filter_, 
    int N, int C, int H, int W,
    int K, int R, int S,
    int OH, int OW,
    int pad, int dilation, int stride) {

  int n = blockIdx.x;
  int k = blockIdx.y;
  int oh = blockIdx.z;
  int ow = threadIdx.x;

  float o = 0.f;
  for (int c = 0; c < C; ++c) {
    for (int r = 0; r < R; ++r) {
      for (int s = 0; s < S; ++s) {
        int h = oh * stride - pad + r * dilation;
        int w = ow * stride - pad + s * dilation;
        if (h < 0 || h >= H || w < 0 || w >= W) continue;
        float i = input_[n * C * H * W + c * H * W + h * W + w];
        float f = filter_[k * C * R * S + c * R * S + r * S + s];
        o += i * f;
      }
    }
  }
  output_[n * K * OH * OW + k * OH * OW + oh * OW + ow] = o;

}

void convolution (float *_input, float *_output, float *_filter, 
  int _N, int _C, int _H, int _W,
  int _K, int _R, int _S,
  int _pad, int _dilation, int _stride) {
  
  input  = _input;
  output = _output;
  filter = _filter;

  if (mpi_rank == 0) {
    // Upload input and filter to every GPU
    for (int i = 0; i < num_devices; i++) {
      CUDA_CALL( cudaMemcpy(input_d[i], input + (Nbegin[i] * C * H * W),
                  (Nend[i] - Nbegin[i]) * C * H * W * sizeof(float),
                  cudaMemcpyHostToDevice) );
      CUDA_CALL( cudaMemcpy(filter_d[i], filter, K * C * R * S * 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() );
    }

    // Launch kernel on every GPU
    for (int i = 0; i < num_devices; i++) {      
      dim3 blockDim(OW, 1);
      dim3 gridDim((Nend[i] - Nbegin[i]), K, OH);

      CUDA_CALL( cudaSetDevice(i) );
      conv_kernel<<<gridDim, blockDim>>>(input_d[i], output_d[i], filter_d[i], (Nend[i] - Nbegin[i]), C, H, W, K, R, S, OH, OW, pad, dilation, stride);
    }

    for (int i = 0; i < num_devices; i++) {
      CUDA_CALL( cudaSetDevice(i) );
      CUDA_CALL( cudaDeviceSynchronize() );
    }
  }

}


void convolution_init(
    int _N, int _C, int _H, int _W,
    int _K, int _R, int _S,
    int _pad, int _dilation, int _stride) {
  N = _N; C = _C; H = _H; W = _W;
  K = _K; R = _R; S = _S;
  pad = _pad;
  dilation = _dilation;
  stride = _stride;

  OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
  OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;

  MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
  MPI_Comm_size(MPI_COMM_WORLD, &mpi_world_size);
  
  if (mpi_rank == 0) {
    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
    if (N >= num_devices) {
      for (int i = 0; i < num_devices; i++) {
        Nbegin[i] = (N / num_devices) * i;
        Nend[i] = (N / num_devices) * (i + 1);
      }
      Nend[num_devices - 1] = N;
    }
    else {
      for (int i = 0; i < N; i++) {
        Nbegin[i] = i;
        Nend[i] = (i + 1);
      }
      for (int i = N; i < num_devices; i++) {
        Nbegin[i] = 0;
        Nend[i] = 0;
      }
    } 
    
    // Allocate device memory for each GPU
    for (int i = 0; i < num_devices; i++) {
      CUDA_CALL( cudaSetDevice(i) );
      CUDA_CALL( cudaMalloc(&input_d[i], (Nend[i] - Nbegin[i]) * C * H * W * sizeof(float)) );
      CUDA_CALL( cudaMalloc(&filter_d[i], K * C * R * S * sizeof(float)) );
      CUDA_CALL( cudaMalloc(&output_d[i], (Nend[i] - Nbegin[i]) * K * OH * OW * sizeof(float)) );
    }

    // DO NOT REMOVE; NEEDED FOR TIME MEASURE
    for (int i = 0; i < num_devices; i++) {
      CUDA_CALL( cudaSetDevice(i) );
      CUDA_CALL( cudaDeviceSynchronize() );
    }

  }
}

void convolution_final(
    int _N, int _C, int _H, int _W,
    int _K, int _R, int _S,
    int _pad, int _dilation, int _stride) {
  if (mpi_rank == 0) {
    // Download output from GPUs
    for (int i = 0; i < num_devices; i++) {
      CUDA_CALL( cudaMemcpy(output  + (Nbegin[i] * K * OH * OW), output_d[i],
                  (Nend[i] - Nbegin[i]) * K * OH * OW * sizeof(float),
                  cudaMemcpyDeviceToHost) );
    }

    // DO NOT REMOVE; NEEDED FOR TIME MEASURE
    for (int i = 0; i < num_devices; i++) {
      CUDA_CALL( cudaDeviceSynchronize() );
    }
  }
}