265 lines
7.2 KiB
Plaintext
265 lines
7.2 KiB
Plaintext
|
#include "convolution.h"
|
||
|
|
#include <mpi.h>
|
||
|
|
#include <stdio.h>
|
||
|
|
#include "util.h"
|
||
|
|
#include <cstdio>
|
||
|
|
#include <cuda_runtime.h>
|
||
|
|
#define MASTER 0
|
||
|
|
#define SLAVE 1
|
||
|
|
#define TS 8
|
||
|
|
#define MAX_NUM_GPU 4
|
||
|
|
|
||
|
|
#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 int N_Size[MAX_NUM_GPU];
|
||
|
|
int num_devices = 1;
|
||
|
|
|
||
|
|
// Array of device (GPU) pointers
|
||
|
|
static float *in_d[MAX_NUM_GPU];
|
||
|
|
static float *out_d[MAX_NUM_GPU];
|
||
|
|
static float *fil_d[MAX_NUM_GPU];
|
||
|
|
//static int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];
|
||
|
|
|
||
|
|
__global__ void conv_kernel(
|
||
|
|
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;
|
||
|
|
|
||
|
|
|
||
|
|
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.f;
|
||
|
|
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;
|
||
|
|
}
|
||
|
|
|
||
|
|
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) {
|
||
|
|
|
||
|
|
N = _N; C = _C; H = _H; W = _W;
|
||
|
|
K = _K; R = _R; S = _S;
|
||
|
|
pad = _pad;
|
||
|
|
dilation = _dilation;
|
||
|
|
stride = _stride;
|
||
|
|
input = _input;
|
||
|
|
output = _output;
|
||
|
|
filter = _filter;
|
||
|
|
|
||
|
|
MPI_Status status;
|
||
|
|
MPI_Request request;
|
||
|
|
int size[2];
|
||
|
|
int offset;
|
||
|
|
|
||
|
|
if (mpi_world_size == 2 && N > 4) size[1] = N / 2;
|
||
|
|
else size[1] = 0;
|
||
|
|
size[0] = N - size[1];
|
||
|
|
|
||
|
|
OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
|
||
|
|
OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
|
||
|
|
|
||
|
|
|
||
|
|
if(size[mpi_rank] < MAX_NUM_GPU)
|
||
|
|
{
|
||
|
|
num_devices = size[mpi_rank];
|
||
|
|
for (int i = 0; i <size[mpi_rank]; i++)
|
||
|
|
{
|
||
|
|
N_Size[i]=1;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
num_devices = MAX_NUM_GPU;
|
||
|
|
int qq = size[mpi_rank] / MAX_NUM_GPU;
|
||
|
|
int rr = size[mpi_rank] % MAX_NUM_GPU;
|
||
|
|
for (int i = 0; i < MAX_NUM_GPU; i++)
|
||
|
|
{
|
||
|
|
N_Size[i] = qq;
|
||
|
|
if (i < rr) N_Size[i]++;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
for (int i = 0; i <num_devices; i++)
|
||
|
|
{
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
CUDA_CALL( cudaMalloc(&in_d[i], N_Size[i]*C*H*W*sizeof(float)));
|
||
|
|
CUDA_CALL( cudaMalloc(&out_d[i], N_Size[i]*K*OH*OW*sizeof(float)));
|
||
|
|
CUDA_CALL( cudaMalloc(&fil_d[i], K*C*R*S*sizeof(float)));
|
||
|
|
}
|
||
|
|
/*
|
||
|
|
// Setup problem size for each GPU
|
||
|
|
for (int i = 0; i < num_devices; i++) {
|
||
|
|
Nbegin[i] = (size[mpi_rank] / num_devices) * i;
|
||
|
|
Nend[i] = (size[mpi_rank] / num_devices) * (i + 1);
|
||
|
|
}
|
||
|
|
Nend[num_devices - 1] = size[mpi_rank];
|
||
|
|
*/
|
||
|
|
|
||
|
|
|
||
|
|
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, SLAVE, 0, MPI_COMM_WORLD, &request); // to SLAVE
|
||
|
|
MPI_Isend(filter, K*C*R*S, MPI_FLOAT, SLAVE, 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, MASTER, 0, MPI_COMM_WORLD, &status); // from Master
|
||
|
|
MPI_Recv(filter, K*C*R*S, MPI_FLOAT, MASTER, 0, MPI_COMM_WORLD, &status);
|
||
|
|
if(size[mpi_rank] < MAX_NUM_GPU)
|
||
|
|
{
|
||
|
|
num_devices = size[mpi_rank];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
// Upload A and B matrix to every GPU
|
||
|
|
offset =0;
|
||
|
|
for (int i = 0; i < num_devices; i++)
|
||
|
|
{
|
||
|
|
CUDA_CALL( cudaMemcpy(in_d[i], input + offset, N_Size[i]*C*H*W * sizeof(float), cudaMemcpyHostToDevice));
|
||
|
|
CUDA_CALL( cudaMemcpy(fil_d[i], filter, K*C*R*S*sizeof(float), cudaMemcpyHostToDevice) );
|
||
|
|
offset += N_Size[i]*C*H*W;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
for (int i = 0; i < num_devices; i++)
|
||
|
|
{
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
CUDA_CALL( cudaDeviceSynchronize() );
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
for (int i = 0; i <num_devices; i++)
|
||
|
|
{
|
||
|
|
dim3 gridDim((OH+TS-1)/TS, (N_Size[i]*K*OW + TS - 1)/TS, 1);
|
||
|
|
dim3 blockDim(TS,TS,1);
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
conv_kernel<<<gridDim, blockDim>>>(in_d[i], out_d[i], fil_d[i], N_Size[i], C, H, W, K, R, S, pad, dilation, stride);
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
for (int i = 0; i < num_devices; i++)
|
||
|
|
{
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
CUDA_CALL( cudaDeviceSynchronize() );
|
||
|
|
}
|
||
|
|
|
||
|
|
// Download C matrix from GPUs
|
||
|
|
offset = 0;
|
||
|
|
for (int i = 0; i < num_devices; i++)
|
||
|
|
{
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
CUDA_CALL( cudaMemcpy(output + offset, out_d[i],N_Size[i]*K*OH*OW*sizeof(float),cudaMemcpyDeviceToHost));
|
||
|
|
offset += N_Size[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, SLAVE, 0, MPI_COMM_WORLD, &status); // from SLAVE
|
||
|
|
}
|
||
|
|
else if(mpi_rank == 1 && size[1] !=0)
|
||
|
|
{
|
||
|
|
MPI_Isend(output, size[1]*K*OH*OW, MPI_FLOAT, MASTER, 0, MPI_COMM_WORLD, &request); // to MASTER
|
||
|
|
}
|
||
|
|
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void convolution_init(
|
||
|
|
int _N, int _C, int _H, int _W,
|
||
|
|
int _K, int _R, int _S,
|
||
|
|
int _pad, int _dilation, int _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);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
void convolution_final(
|
||
|
|
int _N, int _C, int _H, int _W,
|
||
|
|
int _K, int _R, int _S,
|
||
|
|
int _pad, int _dilation, int _stride)
|
||
|
|
{
|
||
|
|
|
||
|
|
}
|
||
|
|
|