201 lines
6.2 KiB
Plaintext
201 lines
6.2 KiB
Plaintext
#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) {
|
|
}
|