216 lines
6.8 KiB
Plaintext
216 lines
6.8 KiB
Plaintext
#include "convolution.h"
|
|
#include <mpi.h>
|
|
#include <stdio.h>
|
|
#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); \
|
|
} \
|
|
}
|
|
|
|
__global__ void sgemm(float *_input, float *_filter, float *_output,
|
|
int _N, int _C, int _H, int _W, int _K, int _R, int _S,
|
|
int _pad, int _dilation, int _stride) {
|
|
int n = blockIdx.x;
|
|
int k = blockIdx.y;
|
|
int oh = blockIdx.z;
|
|
int ow = threadIdx.x;
|
|
|
|
const int OH = (_H + 2 * _pad - _dilation * (_R - 1) - 1) / _stride + 1;
|
|
const int OW = (_W + 2 * _pad - _dilation * (_S - 1) - 1) / _stride + 1;
|
|
|
|
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 = 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;
|
|
}
|
|
|
|
#define MAX_NUM_NODE 2
|
|
#define MAX_NUM_GPU 4
|
|
|
|
static int num_devices = 0;
|
|
|
|
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;
|
|
|
|
// Array of device (GPU) pointers
|
|
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, K, R, S, pad, dilation, stride;
|
|
static int Nbegin[MAX_NUM_NODE], Nend[MAX_NUM_NODE];
|
|
static int Gbegin[MAX_NUM_GPU], Gend[MAX_NUM_GPU];
|
|
|
|
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) {
|
|
input = _input;
|
|
output = _output;
|
|
filter = _filter;
|
|
|
|
if (mpi_rank >= mpi_world_size) return;
|
|
|
|
//OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
|
|
//OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
|
|
|
|
//if (mpi_rank == 0) {
|
|
|
|
/*---------- seperating for nodes ----------*/
|
|
if (mpi_rank != 0){
|
|
input = (float *) aligned_alloc(32, sizeof(float) * N*C*H*W);
|
|
filter = (float *) aligned_alloc(32, sizeof(float) * K*C*R*S);
|
|
output = (float *) aligned_alloc(32, sizeof(float) * N*K*OH*OW);
|
|
}
|
|
|
|
if (mpi_rank == 0){
|
|
for (int i=1; i<mpi_world_size; ++i){
|
|
MPI_Send(&input[ Nbegin[i] * C*H*W ], (Nend[i] - Nbegin[i]) * C*H*W, MPI_FLOAT, i, 0, MPI_COMM_WORLD);
|
|
}
|
|
} else {
|
|
MPI_Recv (&input[ Nbegin[mpi_rank] * C*H*W], (Nend[mpi_rank] - Nbegin[mpi_rank]) * C*H*W, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, nullptr);
|
|
}
|
|
MPI_Bcast(filter, K*C*R*S, MPI_FLOAT, 0, MPI_COMM_WORLD);
|
|
|
|
|
|
/*---------- calculating ----------*/
|
|
// Upload A and B matrix to every GPU
|
|
for (int i = 0; i < num_devices; i++) {
|
|
CUDA_CALL( cudaMemcpy(input_d[i], input + Gbegin[i] * C*H*W, (Gend[i] - Gbegin[i]) * C*H*W * sizeof(float), cudaMemcpyHostToDevice) );
|
|
CUDA_CALL( cudaMemcpy(filter_d[i], filter, K*C*R*S * sizeof(float), cudaMemcpyHostToDevice) );
|
|
}
|
|
|
|
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
|
|
for (int i = 0; i < num_devices; i++) {
|
|
CUDA_CALL( cudaDeviceSynchronize() );
|
|
}
|
|
|
|
// Launch kernel on every GPU
|
|
for (int i = 0; i < num_devices; i++) {
|
|
dim3 blockDim( OW, 1, 1);
|
|
dim3 gridDim(Gend[i]-Gbegin[i], K, OH);
|
|
|
|
CUDA_CALL( cudaSetDevice(i) );
|
|
sgemm<<<gridDim, blockDim>>>(input_d[i], filter_d[i], output_d[i], Gend[i]-Gbegin[i], C,H,W,K,R,S,pad,dilation,stride);
|
|
}
|
|
|
|
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
|
|
for (int i = 0; i < num_devices; i++) {
|
|
CUDA_CALL( cudaDeviceSynchronize() );
|
|
}
|
|
|
|
// Download C matrix from GPUs
|
|
for (int i = 0; i < num_devices; i++) {
|
|
CUDA_CALL( cudaMemcpy(output + Gbegin[i] * K*OH*OW, output_d[i], (Gend[i] - Gbegin[i]) * K*OH*OW * sizeof(float), cudaMemcpyDeviceToHost) );
|
|
}
|
|
|
|
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
|
|
for (int i = 0; i < num_devices; i++) {
|
|
CUDA_CALL( cudaDeviceSynchronize() );
|
|
}
|
|
|
|
/*---------- merging output ----------*/
|
|
if (mpi_rank == 0){
|
|
for(int i=1; i<mpi_world_size; ++i){
|
|
MPI_Recv (&output[Nbegin[i] * K*OH*OW], (Nend[i] - Nbegin[i]) * K*OH*OW, MPI_FLOAT, i, 0, MPI_COMM_WORLD, nullptr);
|
|
}
|
|
} else {
|
|
MPI_Send (&output[Nbegin[mpi_rank] * K*OH*OW], (Nend[mpi_rank] - Nbegin[mpi_rank]) * K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD);
|
|
}
|
|
|
|
|
|
//}
|
|
}
|
|
|
|
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_rank >= mpi_world_size) return;
|
|
|
|
//if (mpi_rank == 0) {
|
|
|
|
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) );
|
|
|
|
// Try printing more detailed information here
|
|
printf("[GPU %d] %s\n", i, prop.name);
|
|
}
|
|
|
|
if (num_devices <= 0) {
|
|
printf("No CUDA device found. Aborting\n");
|
|
exit(1);
|
|
}
|
|
|
|
// Setup size for each Node
|
|
for (int i=0; i < mpi_world_size; ++i){
|
|
Nbegin[i] = N / mpi_world_size * i;
|
|
Nend[i] = N / mpi_world_size * (i+1);
|
|
}
|
|
Nend[mpi_world_size-1] = N;
|
|
|
|
// Setup problem size for each GPU
|
|
for (int i = 0; i < num_devices; i++) {
|
|
Gbegin[i] = Nbegin[mpi_rank] + (Nend[mpi_rank] - Nbegin[mpi_rank]) / num_devices * i;
|
|
Gend[i] = Nbegin[mpi_rank] + (Nend[mpi_rank] - Nbegin[mpi_rank]) / num_devices * (i + 1);
|
|
}
|
|
Gend[num_devices - 1] = Nend[mpi_rank];
|
|
|
|
// Allocate device memory for each GPU
|
|
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(&input_d[i], (Gend[i] - Gbegin[i]) * C*H*W * sizeof(float)) );
|
|
CUDA_CALL( cudaMalloc(&filter_d[i], K*C*R*S * sizeof(float)) );
|
|
CUDA_CALL( cudaMalloc(&output_d[i], (Gend[i] - Gbegin[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) {
|
|
|
|
if (mpi_rank >= mpi_world_size) return;
|
|
//if (mpi_rank == 0){
|
|
//}
|
|
}
|
|
|