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

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#include "convolution.h"
#include <mpi.h>
#include <stdio.h>
#include <cuda_runtime.h>
#include "util.h"
#define TS 8
#define MAX_NODE 2
#define MAX_NUM_GPU 8
#define TILE_WIDTH 32
#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;
static float *in_d[MAX_NUM_GPU];
static float *out_d[MAX_NUM_GPU];
static float *fil_d[MAX_NUM_GPU];
static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
static int MM[MAX_NUM_GPU];
int num_devices;
__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;
// __shared__ float ds_i[TILE_WIDTH][TILE_WIDTH];
// __shared__ float ds_f[TILE_WIDTH][TILE_WIDTH];
int n = blockIdx.x;
int k = blockIdx.y;
int oh = blockIdx.z;
int ow = threadIdx.x;
float o = 0;
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) {
int size[MAX_NODE];
input = _input;
output = _output;
filter = _filter;
MPI_Request request;
MPI_Status status;
// if(mpi_world_size == 2)
// size[1] = _N/2;
// else
size[1] = 0;
size[0] = _N - size[1];
/* if(mpi_world_size == 2){
for(int i=0; i< num_devices; i++){
Mbegin[i] = (N/2) /num_devices * i;
Mend[i] = (N/2) /num_devices*(i+1);
}
for(int i=0; i< num_devices; i++){
Mbegin[i+4] = (N/2)/num_devices * i;
Mend[i+4] = (N/2)/num_devices*(i+1);
}
Mend[num_devices*2-1] = N;
for(int i=0; i<num_devices*2; i++){
MM[i] = Mend[i] - Mbegin[i];
}
}else{
*/
for(int i=0; i< num_devices; i++){
Mbegin[i] = (N/num_devices) * i;
Mend[i] = (N/num_devices)*(i+1);
}
Mend[num_devices-1] = N;
for(int i=0; i<num_devices; i++){
MM[i] = Mend[i] - Mbegin[i];
}
// }
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);
}
*/
if(mpi_rank == 0){
for(int i=0; i< num_devices ; i++){
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaMalloc(&in_d[i], MM[i]*_C*_H*_W*sizeof(float)));
CUDA_CALL( cudaMalloc(&out_d[i], MM[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 + Mbegin[i]*_C*_H*_W, MM[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++){
CUDA_CALL( cudaDeviceSynchronize() );
}
//printf("check1\n");
for(int i=0; i<num_devices; i++){
dim3 blockDim(OW, 1);
dim3 gridDim(MM[i],_K,OH);
CUDA_CALL( cudaSetDevice(i) );
conv<<<gridDim, blockDim>>>(in_d[i],out_d[i],fil_d[i],MM[i],_C,_H,_W,_K,_R,_S,_pad,_dilation,_stride);
}
// printf("check2\n");
for(int i=0; i< num_devices; i++){
CUDA_CALL( cudaDeviceSynchronize());
}
}
else{
/* printf("no 1\n");
for(int i=0; i< num_devices; i++){
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaMalloc(&in_d[i+4], MM[i+4]*_C*_H*_W*sizeof(float)));
CUDA_CALL( cudaMalloc(&out_d[i+4], MM[i+4]*_K*OH*OW*sizeof(float)));
CUDA_CALL( cudaMalloc(&fil_d[i+4], _K*_C*_R*_S*sizeof(float)));
}
printf("no 2\n");
for(int i=0; i< num_devices; i++){
CUDA_CALL( cudaMemcpy(in_d[i+4], _input + Mbegin[i+4]*_C*_H*_W, MM[i+4]*_C*_H*_W*sizeof(float), cudaMemcpyHostToDevice));
CUDA_CALL( cudaMemcpy(fil_d[i+4], _filter, _K*_C*_R*_S*sizeof(float), cudaMemcpyHostToDevice));
}
printf("no 3\n");
for(int i=0; i <num_devices; i++){
CUDA_CALL( cudaDeviceSynchronize() );
}
printf("check1\n");
for(int i=0; i<num_devices; i++){
dim3 blockDim(OW, 1);
dim3 gridDim(MM[i+4],_K,OH);
CUDA_CALL( cudaSetDevice(i) );
conv<<<gridDim, blockDim>>>(in_d[i+4],out_d[i+4],fil_d[i+4],MM[i+4],_C,_H,_W,_K,_R,_S,_pad,_dilation,_stride);
}
printf("check2\n");
for(int i=0; i< num_devices; i++){
CUDA_CALL( cudaDeviceSynchronize());
*/// }
}
// printf("check3\n");
}
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);
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) );
printf("[GPU %d] %s\n", i, prop.name);
}
if(num_devices <= 0){
printf("No CUDA device found. Aborting\n");
exit(1);
}
/*
if(mpi_world_size == 2){
size[1] = _N/2;
node_num = 2;
}
else {
size[1] = 0;
node_num = 1;
}
size[0] = N - size[1];
for(int i=0; i< num_devices; i++){
Mbegin[i] = (N/num_devices) * i;
Mend[i] = (N/num_devices)*(i+1);
}
Mend[num_devices-1] = N;
for(int i=0; i<num_devices; i++){
MM[i] = Mend[i] - Mbegin[i];
}
*/
/*
OH = (H + 2*pad - dilation*(R -1) -1)/stride +1;
OW = (W + 2*pad - dilation*(S -1) -1)/stride +1;
for(int i=0; i< num_devices ; i++){
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaMalloc(&in_d[i], MM[i]*_C*_H*_W*sizeof(float)));
CUDA_CALL( cudaMalloc(&out_d[i], MM[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 + Mbegin[i]*_C*_H*_W, MM[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++){
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 && mpi_world_size == 2){
printf("final 1\n");
for(int i=0; i<num_devices*2; i++){
CUDA_CALL( cudaMemcpy(output+Mbegin[i]*K*OH*OW, out_d[i], MM[i]*K*OH*OW*sizeof(float), cudaMemcpyDeviceToHost));
}
printf("final 2\n");
for(int i = 0; i < num_devices*2;i++){
CUDA_CALL( cudaDeviceSynchronize() );
}
}else if(mpi_rank == 0 && mpi_world_size == 1){
*/
if(mpi_rank == 0){
for(int i=0; i<num_devices; i++){
CUDA_CALL( cudaMemcpy(output+Mbegin[i]*K*OH*OW, out_d[i], MM[i]*K*OH*OW*sizeof(float), cudaMemcpyDeviceToHost));
}
for(int i = 0; i < num_devices;i++){
CUDA_CALL( cudaDeviceSynchronize() );
}}
// }
//printf("Done\n");
}
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