chundoong-lab-ta/SamsungDS22/submissions/final/jj15.kim/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
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 num_devices = 1;
static int size[2];
static int Nsize[MAX_NUM_GPU];
static int OH, OW;
static MPI_Request request;
static MPI_Status status;
__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 = (_H + 2 * _pad - _dilation * (_R - 1) - 1) / _stride + 1;
int OW = (_W + 2 * _pad - _dilation * (_S - 1) - 1) / _stride + 1;
int n = blockIdx.x;
int k = blockIdx.y;
int oh = blockIdx.z;
int ow = threadIdx.x;
float o = 0.f;
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 offset = 0;
if (mpi_rank == 0) {
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) {
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, Nsize[i] * C * H * W * sizeof(float),
cudaMemcpyHostToDevice) );
CUDA_CALL( cudaMemcpy(fil_d[i], filter, K * C * R * S * sizeof(float),
cudaMemcpyHostToDevice) );
offset += Nsize[i] * C * H * W;
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() );
}
for (int i = 0; i < num_devices; i++) {
dim3 blockDim(OW, 1, 1);
dim3 gridDim(Nsize[i], K, OH);
CUDA_CALL( cudaSetDevice(i) );
conv<<<gridDim, blockDim>>>(in_d[i], out_d[i], fil_d[i], Nsize[i],
_C, _H, _W, _K, _R, _S, _pad, _dilation, _stride);
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() );
}
// Download output from GPUs
offset = 0;
for (int i=0; i<num_devices ; ++i) {
CUDA_CALL( cudaMemcpy(output + offset, out_d[i], Nsize[i] * K * OH * OW * sizeof(float),
cudaMemcpyDeviceToHost) );
offset += Nsize[i] * K * OH * OW;
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; 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;
#if 0
size[0] = (mpi_world_size == 2 && N > MAX_NUM_GPU)? N / 2 + 1 : N;
size[1] = N - size[0];
#else
size[0] = (mpi_world_size == 2 && N > MAX_NUM_GPU)? (int)(N * 0.60) : N;
size[1] = N - size[0];
#endif
if (size[mpi_rank] < MAX_NUM_GPU) {
num_devices = size[mpi_rank];
for (int i = 0; i < size[mpi_rank]; i++) {
Nsize[i] = 1;
}
}
else {
num_devices = MAX_NUM_GPU;
for (int i = 0; i < MAX_NUM_GPU; i++) {
Nsize[i] = size[mpi_rank] / MAX_NUM_GPU;
if(i < size[mpi_rank] % MAX_NUM_GPU) {
Nsize[i]++;
}
}
}
for (int i = 0 ; i < num_devices ; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaMalloc(&in_d[i], Nsize[i] * C * H * W * sizeof(float)) );
CUDA_CALL( cudaMalloc(&out_d[i], Nsize[i] * K * OH * OW * sizeof(float)) );
CUDA_CALL( cudaMalloc(&fil_d[i], K * C * R * S * sizeof(float)) );
}
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);
}
}
void convolution_final(
int _N, int _C, int _H, int _W,
int _K, int _R, int _S,
int _pad, int _dilation, int _stride) {
}