202 lines
6.4 KiB
Plaintext
202 lines
6.4 KiB
Plaintext
|
#include "convolution.h"
|
||
|
|
#include <mpi.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
|
||
|
|
|
||
|
|
__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, int OW) {
|
||
|
|
|
||
|
|
const int globalRow = blockDim.x * blockIdx.x + threadIdx.x;
|
||
|
|
const int globalCol = blockDim.y * blockIdx.y + threadIdx.y;
|
||
|
|
int n, k;
|
||
|
|
|
||
|
|
n = globalCol/(_K*OW);
|
||
|
|
k = (globalCol-n*(_K*OW))/OW;
|
||
|
|
int col = (globalCol-n*(_K*OW))-k*OW;
|
||
|
|
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 r = 0; r<_R; r++) {
|
||
|
|
for (int s = 0; s < _S; ++s) {
|
||
|
|
int h = start_row + r * _dilation;
|
||
|
|
int w = start_col + 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 + row * OW + col] = o;
|
||
|
|
}
|
||
|
|
|
||
|
|
static float *input, *output, *filter;
|
||
|
|
static float *input_d[MAX_NUM_GPU];
|
||
|
|
static float *filter_d[MAX_NUM_GPU];
|
||
|
|
static float *output_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;
|
||
|
|
int num_devices = 0;
|
||
|
|
static int size[2];
|
||
|
|
static int Mbegin[MAX_NUM_GPU], NN[MAX_NUM_GPU];
|
||
|
|
static int OH, OW;
|
||
|
|
|
||
|
|
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) {
|
||
|
|
MPI_Request request;
|
||
|
|
MPI_Status status;
|
||
|
|
|
||
|
|
input = _input;
|
||
|
|
output = _output;
|
||
|
|
filter = _filter;
|
||
|
|
|
||
|
|
if (size[1] != 0) {
|
||
|
|
if (mpi_rank == 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);
|
||
|
|
} else {
|
||
|
|
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] != 0) {
|
||
|
|
for (int i = 0; i < num_devices; i++) {
|
||
|
|
CUDA_CALL( cudaMemcpy(input_d[i], input + Mbegin[i]*C*H*W,
|
||
|
|
NN[i]*C*H*W * sizeof(float),
|
||
|
|
cudaMemcpyHostToDevice) );
|
||
|
|
CUDA_CALL( cudaMemcpy(filter_d[i], filter, K*C*R*S * sizeof(float), cudaMemcpyHostToDevice) );
|
||
|
|
}
|
||
|
|
|
||
|
|
// Launch kernel on every GPU
|
||
|
|
for (int i = 0; i < num_devices; i++) {
|
||
|
|
dim3 gridDim((OH+TS-1)/TS, (NN[i]*K*OW + TS - 1)/TS, 1);
|
||
|
|
dim3 blockDim(TS, TS, 1);
|
||
|
|
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
conv<<<gridDim, blockDim>>>(input_d[i], output_d[i], filter_d[i], NN[i], _C, _H, _W, _K, _R, _S, _pad, _dilation, _stride, OH, OW);
|
||
|
|
}
|
||
|
|
|
||
|
|
// Download C matrix from GPUs
|
||
|
|
for (int i = 0; i < num_devices; i++) {
|
||
|
|
CUDA_CALL( cudaMemcpy(output + Mbegin[i] * K*OH*OW, output_d[i],
|
||
|
|
NN[i] * K*OH*OW * sizeof(float),
|
||
|
|
cudaMemcpyDeviceToHost) );
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
if(size[1] != 0) {
|
||
|
|
if (mpi_rank == 0) {
|
||
|
|
MPI_Irecv(&output[size[0]*K*OH*OW], size[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request);
|
||
|
|
} else {
|
||
|
|
MPI_Isend(output, size[1]*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
|
||
|
|
}
|
||
|
|
|
||
|
|
MPI_Wait(&request, &status);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
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);
|
||
|
|
|
||
|
|
if (mpi_world_size == 2 && _N > 4) size[1] = _N / 2;
|
||
|
|
else size[1] = 0;
|
||
|
|
size[0] = N - size[1];
|
||
|
|
|
||
|
|
if (size[mpi_rank] < MAX_NUM_GPU) {
|
||
|
|
num_devices = size[mpi_rank];
|
||
|
|
for (int i = 0 ; i < size[mpi_rank] ; i++)
|
||
|
|
{
|
||
|
|
NN[i] = 1;
|
||
|
|
Mbegin[i] = i;
|
||
|
|
}
|
||
|
|
} else {
|
||
|
|
num_devices = MAX_NUM_GPU;
|
||
|
|
int q = size[mpi_rank] / MAX_NUM_GPU;
|
||
|
|
int r = size[mpi_rank] % MAX_NUM_GPU;
|
||
|
|
int sum = 0;
|
||
|
|
for (int i = 0 ; i < MAX_NUM_GPU ; i++) {
|
||
|
|
NN[i] = q;
|
||
|
|
Mbegin[i] = sum;
|
||
|
|
if (i == MAX_NUM_GPU - 1) {
|
||
|
|
NN[i] += r;
|
||
|
|
}
|
||
|
|
sum += NN[i];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
|
||
|
|
OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
|
||
|
|
|
||
|
|
// possigle to MPI
|
||
|
|
CUDA_CALL( cudaGetDeviceCount(&num_devices) );
|
||
|
|
|
||
|
|
for (int i = 0; i < num_devices; i++) {
|
||
|
|
cudaDeviceProp prop;
|
||
|
|
CUDA_CALL( cudaGetDeviceProperties(&prop, i) );
|
||
|
|
}
|
||
|
|
if (num_devices <= 0) {
|
||
|
|
exit(1);
|
||
|
|
}
|
||
|
|
|
||
|
|
// Allocate device memory for each GPU
|
||
|
|
for (int i = 0; i < num_devices; i++) {
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
CUDA_CALL( cudaMalloc(&input_d[i], NN[i]*C*H*W * sizeof(float)) );
|
||
|
|
CUDA_CALL( cudaMalloc(&filter_d[i], K*C*R*S * sizeof(float)) );
|
||
|
|
CUDA_CALL( cudaMalloc(&output_d[i], NN[i]*K*OH*OW * 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) {
|
||
|
|
}
|