chundoong-lab-ta/SamsungDS22/submissions/final/yoojin73.kim/B/convolution.cu

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2022-09-29 18:01:45 +09:00
#include "util.h"
#include <cstdio>
#include "convolution.h"
#include <mpi.h>
#include <stdio.h>
#include <cuda_runtime.h>
#define TS 8
#define MAX_NUM_GPU 4
#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); \
} \
}
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;
int num_devices = 0;
__global__ void conv(
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) {
const int globalRow = blockDim.x * blockIdx.x + threadIdx.x;
const int globalCol = blockDim.y * blockIdx.y + threadIdx.y;
int OH, OW;
OH = (_H + 2 * _pad - _dilation * (_R - 1) - 1) / _stride + 1;
OW = (_W + 2 * _pad - _dilation * (_S - 1) - 1) / _stride + 1;
int n, k, w;
w = globalCol;
n = w / (_K*OW);
w = w - n * (_K*OW);
k = w / OW;
w = w - k*OW;
int col = w;
int row = globalRow;
if (globalRow >= OH || globalCol >= _N*_K*OW) return;
int start_row = row * _stride - _pad;
int start_col = col * _stride - _pad;
float o = 0.0f;
for (int c = 0; c < _C; c++) {
for (int i = 0; i < _R; i++) {
for (int j = 0; j < _S; j++) {
int h = start_row + i * _dilation;
int w = start_col + j * _dilation;
if (h <0 || w <0 || h >=_H || w >=_W) continue;
float in = _input[n*_C*_W*_H + c*_W*_H + h*_W + w];
float fil = _filter[k*_C*_R*_S + c*_R*_S+ i*_S +j];
o += in * fil;
}
}
}
_output[n*_K*OH*OW + k*OH*OW + row*OW + col] = o;
}
static float *in_d[MAX_NUM_GPU];
static float *out_d[MAX_NUM_GPU];
static float *fil_d[MAX_NUM_GPU];
static int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];
int size[2];
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) {
//CUDA_CALL( cudaGetDeviceCount(&num_devices) );
MPI_Request request;
MPI_Status status;
input = _input;
output = _output;
filter = _filter;
if (mpi_world_size == 2) size[1] = _N/2;
else size[1] = 0;
size[0] = _N-size[1];
OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
if (mpi_rank == 0 && mpi_world_size == 2) {
MPI_Isend(&input[size[0]*C*H*W], size[1]*C*H*W, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request);
MPI_Isend(filter, K*C*R*S, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request);
} else if (mpi_world_size == 2) {
alloc_tensor(&input, size[1], C, H, W);
alloc_tensor(&output, size[1], K, OH, OW);
alloc_tensor(&filter, K, C, R, S);
MPI_Recv(input, size[1]*C*H*W, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &status);
MPI_Recv(filter, K*C*R*S, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &status);
}
int n = size[mpi_rank];
CUDA_CALL( cudaGetDeviceCount(&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;
for (int i = 0; i < num_devices; i++) {
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaMalloc(&in_d[i], (Nend[i] - Nbegin[i]) * _C * _H * _W * sizeof(float)) );
CUDA_CALL( cudaMalloc(&out_d[i], (Nend[i] - Nbegin[i]) * _K * OH * OW * sizeof(float)) );
CUDA_CALL( cudaMalloc(&fil_d[i], _K * _C * _R * _S * sizeof(float)) );
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpy(in_d[i], input + Nbegin[i] * _C*_H*_W,
(Nend[i] - Nbegin[i]) * _C*_H*_W * sizeof(float),
cudaMemcpyHostToDevice) );
CUDA_CALL( cudaMemcpy(fil_d[i], filter, _K * _C*_R*_S * sizeof(float), cudaMemcpyHostToDevice) );
}
for (int i = 0; i < num_devices; i++) {
dim3 gridDim((OH+TS-1)/TS, ((Nend[i] - Nbegin[i])*_K*OW+TS-1)/TS,1);
dim3 blockDim(TS,TS,1);
CUDA_CALL( cudaSetDevice(i) );
conv<<<gridDim, blockDim>>>(in_d[i], out_d[i], fil_d[i], Nend[i] - Nbegin[i], _C,_H,_W,_K,_R,_S,_pad,_dilation,_stride);
}
}
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;
MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
MPI_Comm_size(MPI_COMM_WORLD, &mpi_world_size);
}
void convolution_final(
int _N, int _C, int _H, int _W,
int _K, int _R, int _S,
int _pad, int _dilation, int _stride) {
MPI_Request request;
MPI_Status status;
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpy(output + (Nbegin[i] * _K*OH*OW), out_d[i],
(Nend[i] - Nbegin[i]) * _K*OH*OW * sizeof(float),
cudaMemcpyDeviceToHost) );
}
if (mpi_rank == 0 && mpi_world_size == 2) {
MPI_Recv(&output[size[0]*K*OH*OW], size[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
} else if (mpi_world_size == 2) {
MPI_Isend(output, size[1]*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
}
}