chundoong-lab-ta/SamsungDS22/submissions/final/won-seok.lee/B/convolution.cu

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2022-09-29 18:01:45 +09:00
#include "convolution.h"
#include <mpi.h>
#include <stdio.h>
#include <cuda_runtime.h>
#include "util.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 4
static float *input, *output, *filter;
static float *in_d[MAX_NUM_GPU], *out_d[MAX_NUM_GPU], *fil_d[MAX_NUM_GPU];
static int N, C, H, W;
static int K, R, S;
static int pad;
static int dilation;
static int stride;
static int mpi_rank, mpi_world_size;
static int size[2];
static int NN[MAX_NUM_GPU];
static int OH, OW;
int num_devices = 1;
__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, int OH, int OW) {
const int globalRow = blockDim.x * blockIdx.x + threadIdx.x ;
const int globalCol = blockDim.y * blockIdx.y + threadIdx.y;
int KK = blockDim.z * blockIdx.z +threadIdx.z;
//int OH, OW;
//OH = (_H + 2 * _pad - _dilation * (_R - 1) - 1) / _stride + 1;
//OW = (_W + 2 * _pad - _dilation * (_S - 1) - 1) / _stride + 1;
if (KK >= _K) return;
int n, w;
w = globalCol;
n = w / OW;
w = w - n * OW;
int col = w;
int row = globalRow;
if (globalRow >= OH || globalCol >= _N*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[KK*_C*_R*_S + c*_R*_S + i*_S + j];
o += in * fil;
}
}
}
_output[n*_K*OH*OW + KK*OH*OW + row*OW + col] = 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) {
int offset = 0;
MPI_Request request;
MPI_Status status;
input = _input;
output = _output;
filter = _filter;
if (mpi_rank == 0 && mpi_world_size == 2 && size[1] != 0) {
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);
if(size[mpi_rank] < MAX_NUM_GPU) {
num_devices = size[mpi_rank];
}
}
else if (mpi_rank == 1 && size[mpi_rank] != 0) {
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);
if(size[mpi_rank] < MAX_NUM_GPU) {
num_devices = size[mpi_rank];
}
}
offset = 0;
for(int i = 0; i < num_devices; i++) {
CUDA_CALL (cudaMemcpy( in_d[i], input + offset, NN[i]*C*H*W*sizeof(float), cudaMemcpyHostToDevice) );
CUDA_CALL (cudaMemcpy( fil_d[i], filter, K*C*R*S*sizeof(float), cudaMemcpyHostToDevice) );
offset += NN[i] * C * H * W;
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL ( cudaDeviceSynchronize() );
}
for (int i = 0; i < num_devices; i++) {
//dim3 gridDim( (OH+TS-1)/TS, (NN[i]*K*OW + TS - 1)/TS, 1);
//dim3 blockDim( TS, TS, 1);
dim3 gridDim( (OH+TS-1)/TS, (NN[i]*K*OW + TS - 1)/TS, (K+TS-1)/TS);
dim3 blockDim( TS, TS, TS);
CUDA_CALL ( cudaSetDevice(i) );
conv <<<gridDim, blockDim>>> (in_d[i], out_d[i], fil_d[i], NN[i], _C, _H, _W, _K, _R, _S, _pad, _dilation, _stride, OH, OW);
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL ( cudaSetDevice(i) );
CUDA_CALL ( cudaDeviceSynchronize() );
}
offset = 0;
for (int i = 0; i < num_devices; i++) {
CUDA_CALL ( cudaSetDevice(i) );
CUDA_CALL ( cudaMemcpy(output + offset, out_d[i], NN[i]*K*OH*OW * sizeof(float), cudaMemcpyDeviceToHost) );
offset += NN[i]*K*OH*OW;
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL ( cudaSetDevice(i) );
CUDA_CALL ( cudaDeviceSynchronize() );
}
if (mpi_rank == 0 && mpi_world_size == 2 && size[1] != 0) {
MPI_Recv(&output[size[0]*K*OH*OW], size[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
}
else if (mpi_rank == 1 && size[1] != 0) {
MPI_Isend(output, size[1]*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
}
}
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);
OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
//printf("debug: mpi_world_size=%d\n",mpi_world_size);
//printf("debug: _N=%d\n",_N);
if(mpi_world_size == 2 && _N > 4) size[1] = _N / 2;
else size[1] = 0;
size[0] = N - size[1];
//printf("debug: size[0]=%d\n",size[0]);
//printf("debug: size[1]=%d\n",size[1]);
if(size[mpi_rank] < MAX_NUM_GPU) {
num_devices = size[mpi_rank];
for(int i = 0; i<size[mpi_rank]; i++)
NN[i] = 1;
}
else {
//num_devices = size[mpi_rank];
num_devices = 4;
int quotient = size[mpi_rank] / MAX_NUM_GPU;
int remain = size[mpi_rank] % MAX_NUM_GPU;
for (int i=0; i<MAX_NUM_GPU; i++) {
NN[i] = quotient;
if(i < remain) NN[i]++;
}
}
//printf("debug: num_devices=%d\n",num_devices);
for(int i = 0; i < num_devices; i++) {
// if(i<4) {
CUDA_CALL ( cudaSetDevice(i) );
CUDA_CALL ( cudaMalloc(&in_d[i], NN[i]*C*H*W*sizeof(float)) );
CUDA_CALL ( cudaMalloc(&out_d[i], NN[i]*K*OH*OW*sizeof(float)) );
CUDA_CALL ( cudaMalloc(&fil_d[i], K*C*R*S*sizeof(float)) );
// }
// else {
// printf("error: i num=%d, num_devices=%d\n",i,num_devices);
// }
}
for (int i = 0; i < num_devices; 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) {
}