chundoong-lab-ta/SamsungDS22/submissions/final/jihye65.park/tmp-B/convolution_edit.cu

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#include "mat_mul.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 TS 8
#define WPT 8
#define RTS (TS/WPT)
// Array of device (GPU) pointers
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, dilation, stride;
static int mpi_rank, mpi_world_size;
static int num_devices =1;
static int size[2];
static int MM[MAX_NUM_GPU];
static int OH, OW;
__global__ 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){
const int globalRow = blockDim.x * blockIdx.x + threadIdx.x;
const int glocalCol = 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] = 0;
}
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;
intput = _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;
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() ); //memcpy를 async로 할때 필요
}
// Launch kernel on every GPU
for (int i = 0; i < num_devices; i++) { //최적화 필요
dim3 blockDim(TS, TS, 1);
dim3 gridDim((OH+TS-1)/TS, (NN[i]*K*OW+TS-1)/TS,1);
CUDA_CALL( cudaSetDevice(i) );
convolution<<<gridDim, blockDim>>>(in_d[i], out_d[i], fil_d[i], NN[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],
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_WLROD, &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(
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){
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;
if(mpi_world_size ==2 && _N>4) size[1] = _N/2;
else size[1]=0;
size[0] = N-size[0];
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 = MAX_NUM_GPU;
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]++;
}
}
for(int i=0; i<num_devices; i++){
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)) );
}
}
void mat_mul_final(
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){
}