chundoong-lab-ta/SamsungDS22/submissions/final/hs5006.kim/B/convolution.cu

204 lines
6.1 KiB
Plaintext
Raw Normal View History

2022-09-29 18:01:45 +09:00
#include "convolution.h"
#include "util.h"
#include <mpi.h>
#include <stdio.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
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;
static int size[2];
__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, OW;
OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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;
} // s
} // r
} // c
output[n * K * OH * OW + k * OH * OW + oh * OW + ow] = o;
}
static float *input_d[MAX_NUM_GPU];
static float *output_d[MAX_NUM_GPU];
static float *filter_d[MAX_NUM_GPU];
static int Nbegin[MAX_NUM_GPU], Nend[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) {
MPI_Request request;
MPI_Status status;
input = _input;
output = _output;
filter = _filter;
if(mpi_rank == 0 && mpi_world_size == 2) {
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 if (mpi_world_size == 2) {
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);
}
// Upload input and filter to every GPU
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpyAsync(input_d[i], input + Nbegin[i]*C*H*W,
(Nend[i] - Nbegin[i])*C*H*W*sizeof(float),cudaMemcpyHostToDevice) );
CUDA_CALL( cudaMemcpyAsync(filter_d[i], filter, K*C*R*S*sizeof(float), cudaMemcpyHostToDevice) );
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaDeviceSynchronize() );
}
for (int i = 0; i < num_devices; i++) {
dim3 blockDim(OW,1);
dim3 gridDim(Nend[i]-Nbegin[i],K,OH);
CUDA_CALL ( cudaSetDevice(i) );
conv<<<gridDim, blockDim>>>(input_d[i], output_d[i], filter_d[i], Nend[i]-Nbegin[i], C, H, W, K, R, S, pad, dilation, stride);
}
// Download output from GPUs
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpyAsync(output + Nbegin[i] * K * OH * OW , output_d[i],
(Nend[i] - Nbegin[i]) * K * OH * OW * sizeof(float),
cudaMemcpyDeviceToHost) );
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaDeviceSynchronize() );
}
if (mpi_rank == 0 && mpi_world_size == 2) {
MPI_Recv(&output[size[0]*K*OH*OW], size[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
}
else if (mpi_world_size == 2) {
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);
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 problem size for each Node
if (mpi_world_size == 2)
size[1] = N / 2;
else
size[1] = 0;
size[0] = N - size[1];
// Setup problem size for each GPU per Node
if (mpi_rank == 1 && mpi_world_size == 2) {
for (int i = 0; i < num_devices; i++) {
Nbegin[i] = (size[1] / num_devices) * i;
Nend[i] = (size[1] / num_devices) * (i + 1);
}
Nend[num_devices - 1] = size[1];
}
else { // mpi_rank == 0
for (int i = 0; i < num_devices; i++) {
Nbegin[i] = (size[0] / num_devices) * i;
Nend[i] = (size[0] / num_devices) * (i + 1);
}
Nend[num_devices - 1] = size[0];
}
OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 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], (Nend[i] - Nbegin[i])*C*H*W*sizeof(float)) );
CUDA_CALL( cudaMalloc(&output_d[i], (Nend[i] - Nbegin[i])*K*OH*OW*sizeof(float)) );
CUDA_CALL( cudaMalloc(&filter_d[i], K*C*R*S*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) {
}