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

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#include "convolution.h"
#include "util.h"
#include <mpi.h>
#include <stdio.h>
#include <cstdio>
#include <cuda_runtime.h>
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 input_line;
static int filter_size;
static int output_line;
#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;
// Array of device (GPU) pointers
static float *i_d[MAX_NUM_GPU];
static float *f_d[MAX_NUM_GPU];
static float *o_d[MAX_NUM_GPU];
static int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];
__global__ void conv4(float *input, float* filter, float* output,
int Nsplit, int C, int H, int W, int K, int R, int S, int OH, int OW,
int pad, int dilation, int stride)
{
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) { // # of input channel
for (int r = 0; r < R; ++r) { // filter height (=row)
for (int s = 0; s < S; s+=4) { // filter width (=column)
int h = oh * stride - pad + r * dilation;
int w = ow * stride - pad + s * dilation;
if (h < 0 || h >= H || w < 0 || w >= W) continue;
int idx_i = (n * C * H * W) + (c * H * W) + (h * W) + w;
int idx_f = (k * C * R * S) + (c * R * S) + (r * S) + s;
float4 i = make_float4(input[idx_i + 0], input[idx_i + 1], input[idx_i + 2], input[idx_i + 3]);
float4 f = reinterpret_cast<float4*>(filter)[idx_f/4];
o = o
+ i.x * f.x
+ i.y * f.y
+ i.z * f.z
+ i.w * f.w
;
}
}
}
output[(n * K * OH * OW) + (k * OH * OW) + (oh * OW) + ow] = o;
}
__global__ void conv(float *input, float* filter, float* output,
int Nsplit, int C, int H, int W, int K, int R, int S, int OH, int OW,
int pad, int dilation, int stride)
{
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) { // # of input channel
for (int r = 0; r < R; ++r) { // filter height (=row)
for (int s = 0; s < S; ++s) { // filter width (=column)
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 cuda_init(int ns, int ne)
{
int N_ = ne - ns;
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 GPU
for (int i = 0; i < num_devices; i++) {
Nbegin[i] = ns + (N_ / num_devices) * i;
Nend[i] = ns + (N_ / num_devices) * (i + 1);
/* debug */// printf("%d: Nbegin[%d] = %d, Nend[%d] = %d\n", mpi_rank, i, Nbegin[i], i, Nend[i]);
}
Nend[num_devices - 1] = ns + N_;
// Allocate device memory for each GPU
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaMalloc(&i_d[i], (Nend[i] - Nbegin[i]) * input_line * sizeof(float)) );
CUDA_CALL( cudaMalloc(&f_d[i], filter_size * sizeof(float)) );
CUDA_CALL( cudaMalloc(&o_d[i], (Nend[i] - Nbegin[i]) * output_line * sizeof(float)) );
}
// Upload A and B matrix to every GPU
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpy(i_d[i], input + Nbegin[i] * input_line,
(Nend[i] - Nbegin[i]) * input_line * sizeof(float),
cudaMemcpyHostToDevice) );
CUDA_CALL( cudaMemcpy(f_d[i], filter, filter_size * sizeof(float), cudaMemcpyHostToDevice) );
}
// DO NOT REMOVE; NEEDED FOR TIME NEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() );
}
}
void cuda_run()
{
// Launch kernel on every GPU
for (int i = 0; i < num_devices; i++) {
int Nsplit = Nend[i] - Nbegin[i];
dim3 blockDim(OW, 1);
dim3 gridDim(Nsplit, K, OH);
if (S==16 && R==16 && dilation==1) {
CUDA_CALL( cudaSetDevice(i) );
conv4<<<gridDim, blockDim>>>(i_d[i], f_d[i], o_d[i],
Nsplit, C, H, W, K, R, S, OH, OW,
pad, dilation, stride);
} else {
// printf("\nconv4!\n");
CUDA_CALL( cudaSetDevice(i) );
conv<<<gridDim, blockDim>>>(i_d[i], f_d[i], o_d[i],
Nsplit, C, H, W, K, R, S, OH, OW,
pad, dilation, stride);
}
}
// DO NOT REMOVE; NEEDED FOR TIME NEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() );
}
}
void cuda_final()
{
// Download C matrix from GPUs
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpy(output + Nbegin[i] * output_line, o_d[i],
(Nend[i] - Nbegin[i]) * output_line * sizeof(float),
cudaMemcpyDeviceToHost) );
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() );
}
}
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;
OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
input_line = C*H*W;
filter_size = K*C*R*S;
output_line = K*OH*OW;
// Allocate
if(mpi_rank != 0){
alloc_tensor(&input, N, C, H, W);
alloc_tensor(&output, N, K, OH, OW);
alloc_tensor(&filter, K, C, R, S);
}
// N split
int ns[mpi_world_size], ne[mpi_world_size];
for (int i = 0; i < mpi_world_size; i++) {
ns[i] = N / mpi_world_size * i;
ne[i] = N / mpi_world_size * (i + 1);
}
ne[mpi_world_size - 1] = N;
// Scatter Input
if (mpi_rank == 0) {
for (int i = 1; i < mpi_world_size; i++) {
MPI_Send(input + ns[i] * input_line, (ne[i] - ns[i]) * input_line, MPI_FLOAT, i, 0,
MPI_COMM_WORLD);
}
} else {
MPI_Recv(input + ns[mpi_rank] * input_line, (ne[mpi_rank] - ns[mpi_rank]) * input_line, MPI_FLOAT,
0, 0, MPI_COMM_WORLD, nullptr);
}
// Broadcast Filter
MPI_Bcast(filter, filter_size, MPI_FLOAT, 0, MPI_COMM_WORLD);
cuda_init(ns[mpi_rank], ne[mpi_rank]);
cuda_run();
cuda_final();
// Gather Output
if (mpi_rank == 0) {
for (int i = 1; i < mpi_world_size; i++) {
MPI_Recv(output + ns[i] * output_line, (ne[i] - ns[i]) * output_line, MPI_FLOAT, i, 0,
MPI_COMM_WORLD, nullptr);
}
} else {
MPI_Send(output + ns[mpi_rank] * output_line, (ne[mpi_rank] - ns[mpi_rank]) * output_line, 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);
}
void convolution_final(
int _N, int _C, int _H, int _W,
int _K, int _R, int _S,
int _pad, int _dilation, int _stride) {
}