chundoong-lab-ta/SamsungDS22/submissions/final/h2.nam/B/convolution.cu

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#include "convolution.h"
#include "util.h"
#include <stdio.h>
#include <mpi.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 stride;
static int dilation;
static int mpi_rank, mpi_world_size;
static int num_devices = 1;
static int MPSize[2];
static int NUMOFN[MAX_NUM_GPU];
static float *in_d[MAX_NUM_GPU], *out_d[MAX_NUM_GPU], *filter_d[MAX_NUM_GPU];
__global__ void CudaConvolution(
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 grow = blockDim.x * blockIdx.x + threadIdx.x ;
const int gcolumn = blockDim.y * blockIdx.y + threadIdx.y;
int OH, OW;
int n, k, w;
OH = (_H + 2 * _pad - _dilation * (_R - 1) - 1) / _stride + 1;
OW = (_W + 2 * _pad - _dilation * (_S - 1) - 1) / _stride + 1;
w = gcolumn;
n = w / (_K * OW);
w = w - n *(_K * OW);
k = w / OW;
w = w - k * OW;
int col = w;
int row = grow;
if (grow >= OH || gcolumn >= _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;
}
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;
MPI_Request request;
MPI_Status status;
int offset = 0;
if (mpi_rank == 0 && mpi_world_size == 2 && MPSize[1] != 0) {
MPI_Isend(&input[MPSize[0]*C*H*W], MPSize[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 (MPSize[mpi_rank] < MAX_NUM_GPU) {
num_devices = MPSize[mpi_rank];
}
} else if (mpi_rank == 1 && MPSize[mpi_rank] != 0) {
alloc_tensor(&input, MPSize[1], C, H, W);
alloc_tensor(&output, MPSize[1], K, OH, OW);
alloc_tensor(&filter, _K, _C, _R, _S);
MPI_Recv(input, MPSize[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 (MPSize[mpi_rank] < MAX_NUM_GPU) {
num_devices = MPSize[mpi_rank];
}
}
offset = 0;
for (int i = 0 ; i < num_devices ; i++) {
CUDA_CALL( cudaMemcpy(in_d[i], input + offset, NUMOFN[i]*C*H*W*sizeof(float), cudaMemcpyHostToDevice) );
CUDA_CALL( cudaMemcpy(filter_d[i], filter, K*C*R*S*sizeof(float),cudaMemcpyHostToDevice) );
offset += NUMOFN[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, (NUMOFN[i]*K*OW + TS - 1)/TS, 1);
dim3 blockDim(TS, TS, 1);
CUDA_CALL( cudaSetDevice(i) );
CudaConvolution<<<gridDim, blockDim>>>(in_d[i], out_d[i], filter_d[i], NUMOFN[i], _C, _H, _W, _K, _R, _S, _pad, _dilation, _stride);
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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], NUMOFN[i]*K*OH*OW * sizeof(float), cudaMemcpyDeviceToHost) );
offset += NUMOFN[i]*K*OH*OW;
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() );
CUDA_CALL( cudaSetDevice(i) );
}
if (mpi_rank == 0 && mpi_world_size == 2 && MPSize[1] != 0) {
MPI_Recv(&output[MPSize[0]*K*OH*OW], MPSize[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
} else if(mpi_rank == 1 && MPSize[1] != 0){
MPI_Isend(output, MPSize[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_size(MPI_COMM_WORLD, &mpi_world_size);
MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
if (mpi_world_size == 2) {
MPSize[1] = _N / 2;
}
MPSize[0] = N - MPSize[1];
/* MP size of MPI MAX GPU */
if (MPSize[mpi_rank] < MAX_NUM_GPU) {
num_devices = MPSize[mpi_rank];
for (int i = 0 ; i < MPSize[mpi_rank] ; i++) {
NUMOFN[i] = 1;
}
} else {
num_devices = MAX_NUM_GPU;
int remain = MPSize[mpi_rank] % MAX_NUM_GPU;
int quot = MPSize[mpi_rank] / MAX_NUM_GPU;
for (int i = 0 ; i < MAX_NUM_GPU ; i++) {
NUMOFN[i] = quot;
if (i < remain) {
NUMOFN[i]++;
}
}
}
for (int i = 0 ; i < num_devices ; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaMalloc(&in_d[i], NUMOFN[i]*C*H*W*sizeof(float)) );
CUDA_CALL( cudaMalloc(&out_d[i], NUMOFN[i]*K*OH*OW*sizeof(float)) );
CUDA_CALL( cudaMalloc(&filter_d[i], K*C*R*S*sizeof(float)) );
}
}
void convolution_final(
int _N, int _C, int _H, int _W,
int _K, int _R, int _S,
int _pad, int _dilation, int _stride) {
}