146 lines
4.5 KiB
C++
146 lines
4.5 KiB
C++
#include "convolution.h"
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#include <mpi.h>
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#include <stdio.h>
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#include <omp.h>
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static float *input, *output, *filter;
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static int N, C, H, W;
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static int K, R, S;
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static int OH, OW;
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static int pad;
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static int dilation;
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static int stride;
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static int mpi_rank, mpi_world_size;
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void my_alloc_tensor(float **t, int D0, int D1, int D2, int D3) {
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*t = (float *) aligned_alloc(32, sizeof(float) * D0 * D1 * D2 * D3);
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if (*t == NULL) {
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printf("Failed to allocate memory for matrix.\n");
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exit(0);
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}
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}
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int min(int a, int b) { return a < b ? a : b; }
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void compute_conv(int is, int ie) {
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// int tNums = omp_get_num_threads();
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//#pragma omp parallel for num_threads(tNums)
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#pragma omp parallel for collapse(3) num_threads(100) schedule(dynamic)
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for (int n = is; n < ie; ++n) {
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for (int k = 0; k < K; ++k) {
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for (int oh = 0; oh < OH; ++oh) {
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for (int ow = 0; ow < OW; ++ow) {
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float o = 0.f;
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for (int c = 0; c < C; ++c) {
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for (int r = 0; r < R; ++r) {
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for (int s = 0; s < S; ++s) {
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int h = oh * stride - pad + r * dilation;
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int w = ow * stride - pad + s * dilation;
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if (h < 0 || h >= H || w < 0 || w >= W) continue;
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float i = input[n * C * H * W + c * H * W + h * W + w];
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float f = filter[k * C * R * S + c * R * S + r * S + s];
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o += i * f;
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}
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}
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}
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output[n * K * OH * OW + k * OH * OW + oh * OW + ow] = o;
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}
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}
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}
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}
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}
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void compute_conv_org() {
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// int tNums = omp_get_num_threads();
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//#pragma omp parallel for num_threads(tNums)
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#pragma omp parallel for collapse(3) num_threads(100) schedule(dynamic)
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for (int n = 0; n < N; ++n) {
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for (int k = 0; k < K; ++k) {
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for (int oh = 0; oh < OH; ++oh) {
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for (int ow = 0; ow < OW; ++ow) {
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float o = 0.f;
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for (int c = 0; c < C; ++c) {
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for (int r = 0; r < R; ++r) {
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for (int s = 0; s < S; ++s) {
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int h = oh * stride - pad + r * dilation;
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int w = ow * stride - pad + s * dilation;
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if (h < 0 || h >= H || w < 0 || w >= W) continue;
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float i = input[n * C * H * W + c * H * W + h * W + w];
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float f = filter[k * C * R * S + c * R * S + r * S + s];
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o += i * f;
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}
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}
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}
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output[n * K * OH * OW + k * OH * OW + oh * OW + ow] = o;
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}
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}
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}
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}
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}
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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) {
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input = _input;
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output = _output;
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filter = _filter;
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OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
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if (mpi_world_size > 1){
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// Calculate is and ie redundantly on every processes
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int is[mpi_world_size], ie[mpi_world_size];
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for (int i = 0; i < mpi_world_size; i++) {
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is[i] = N / mpi_world_size * i;
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ie[i] = N / mpi_world_size * (i + 1);
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}
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ie[mpi_world_size - 1] = N;
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if (mpi_rank != 0) {
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my_alloc_tensor(&input, N, C, H, W);
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my_alloc_tensor(&output, N, K, OH, OW);
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my_alloc_tensor(&filter, K, C, R, S);
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}
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// Scatter A
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if (mpi_rank == 0) {
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for (int i = 1; i < mpi_world_size; i++) {
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MPI_Send(input+is[i]*C*H*W, (ie[i]-is[i])*C*H*W, MPI_FLOAT, i, 0, MPI_COMM_WORLD);
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}
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}
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else {
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MPI_Recv(input+is[mpi_rank]*C*H*W, (ie[mpi_rank]-is[mpi_rank])*C*H*W, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, nullptr);
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}
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// Broadcast B
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MPI_Bcast(filter, K*C*R*S, MPI_FLOAT, 0, MPI_COMM_WORLD);
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compute_conv(is[mpi_rank], ie[mpi_rank]);
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// Gather C
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if (mpi_rank == 0) {
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for (int i = 1; i < mpi_world_size; i++) {
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MPI_Recv(output+is[i]*K*OH*OW, (ie[i]-is[i])*K*OH*OW, MPI_FLOAT, i, 0, MPI_COMM_WORLD, nullptr);
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}
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}
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else {
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MPI_Send(output+is[mpi_rank]*K*OH*OW, (ie[mpi_rank]-is[mpi_rank])*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD);
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}
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}
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else {
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compute_conv_org();
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}
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}
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void convolution_init(int _N, int _C, int _H, int _W, int _K, int _R, int _S, int _pad, int _dilation, int _stride) {
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N = _N; C = _C; H = _H; W = _W;
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K = _K; R = _R; S = _S;
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pad = _pad;
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dilation = _dilation;
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stride = _stride;
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MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
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MPI_Comm_size(MPI_COMM_WORLD, &mpi_world_size);
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}
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void convolution_final(int _N, int _C, int _H, int _W, int _K, int _R, int _S, int _pad, int _dilation, int _stride) {
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}
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