chundoong-lab-ta/SamsungDS22/submissions/final/ym.tai/tmp-B/convolution.cu

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#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 MAX_NUM_NODES 2
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 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 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) {
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;
}
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 == 0) {
// Upload input and filter to every GPU
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpy(input_d[i], input + (Nbegin[i] * C * H * W),
(Nend[i] - Nbegin[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( cudaSetDevice(i) );
CUDA_CALL( cudaDeviceSynchronize() );
}
// Launch kernel on every GPU
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_kernel<<<gridDim, blockDim>>>(input_d[i], output_d[i], filter_d[i], (Nend[i] - Nbegin[i]), C, H, W, K, R, S, OH, OW, pad, dilation, stride);
}
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaDeviceSynchronize() );
}
}
}
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;
OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
MPI_Comm_size(MPI_COMM_WORLD, &mpi_world_size);
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 problem size for each GPU
if (N >= num_devices) {
for (int i = 0; i < num_devices; i++) {
Nbegin[i] = (N / num_devices) * i;
Nend[i] = (N / num_devices) * (i + 1);
}
Nend[num_devices - 1] = N;
}
else {
for (int i = 0; i < N; i++) {
Nbegin[i] = i;
Nend[i] = (i + 1);
}
for (int i = N; i < num_devices; i++) {
Nbegin[i] = 0;
Nend[i] = 0;
}
}
// 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(&filter_d[i], K * C * R * S * sizeof(float)) );
CUDA_CALL( cudaMalloc(&output_d[i], (Nend[i] - Nbegin[i]) * K * OH * OW * sizeof(float)) );
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaDeviceSynchronize() );
}
}
}
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 == 0) {
// Download output from GPUs
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpy(output + (Nbegin[i] * K * OH * OW), output_d[i],
(Nend[i] - Nbegin[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() );
}
}
}