Linus Baumberger
344 lines
9.9 KiB
C++
344 lines
9.9 KiB
C++
// # This file uses avx2
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// # This is the first variant and should serve as a base for further avx2 optimizations
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// Okay our first step is to use a SoA instead of an AoS
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// We only do SoA for the pedestrian struct, there we wanna optimize such that we access for the ped-ped forces adjacent pedestrians (in the arrays)
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#include "../headers/board.hpp"
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#include "../headers/constants.hpp"
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#include "../headers/pedestrian_reduced.hpp"
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#include <algorithm>
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#include <cmath>
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#include <fstream>
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#include <iostream>
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#include <math.h>
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#include <random>
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#include <tuple>
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#include <vector>
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#define SEED 42
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#define BOARD_WIDTH 10.0
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#define BOARD_LENGTH 50.0
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#define WALL_EPS 1e-3
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#define BOARD_WIDTH_MINUS_WALL_EPS 9.999
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#ifndef RANDOM_SEED
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#define RANDOM_SEED 0
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#endif
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#ifndef BENCHMARK
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#define BENCHMARK 0
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#endif
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using namespace std;
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// we wanna use arrays so we can implement SOA, we create arrays for all the data we need and access
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vector<double> pos_x, pos_y;
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vector<double> sb_sq;
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vector<double> sb_e_x, sb_e_y;
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vector<double> e_x, e_y;
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vector<double> curr_vel_x, curr_vel_y;
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vector<double> w_x, w_y;
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vector<double> desired_v0;
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vector<double> v_max;
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vector<double> destination_x, destination_y;
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vector<int> color;
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Pedestrian *init_pedestrians(int n_p) {
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#if RANDOM_SEED
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random_device rd;
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mt19937 gen(rd());
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#else
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mt19937 gen(SEED);
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#endif
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normal_distribution<double> velocity_gen(v0_mean, v0_std_dev);
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uniform_real_distribution<double> y_gen(0.3, BOARD_WIDTH - 0.3);
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uniform_real_distribution<double> x_gen_left(2.0, BOARD_LENGTH / 2.0);
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uniform_real_distribution<double> x_gen_right(BOARD_LENGTH / 2.0, BOARD_LENGTH - 2.0);
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// resize all our arrays for this run
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pos_x.resize(n_p);
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pos_y.resize(n_p);
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sb_sq.resize(n_p);
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sb_e_x.resize(n_p);
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sb_e_y.resize(n_p);
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e_x.resize(n_p);
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e_y.resize(n_p);
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curr_vel_x.resize(n_p);
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curr_vel_y.resize(n_p);
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w_x.resize(n_p);
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w_y.resize(n_p);
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desired_v0.resize(n_p);
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v_max.resize(n_p);
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destination_x.resize(n_p);
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destination_y.resize(n_p);
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color.resize(n_p);
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int n_p_half = n_p / 2;
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for (int i = 0; i < n_p_half; i++) {
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double x = x_gen_left(gen);
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double y = y_gen(gen);
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double dest_x = BOARD_LENGTH + 30.0; // renamed to avoid shadowing destination_x[]
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double dest_y = y; // same idea for destination_y[]
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double init_e_x = dest_x - x;
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double init_e_y = dest_y - y;
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double init_e_norm = sqrt(init_e_x * init_e_x + init_e_y * init_e_y);
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double init_e_unit_x = init_e_x / init_e_norm;
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double init_e_unit_y = init_e_y / init_e_norm;
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double d_v0;
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do {
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d_v0 = velocity_gen(gen);
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} while (d_v0 <= 0.1);
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double v_m = 1.3 * d_v0;
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color[i] = 0;
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pos_x[i] = x;
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pos_y[i] = y;
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e_x[i] = init_e_unit_x;
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e_y[i] = init_e_unit_y;
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v_max[i] = v_m;
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desired_v0[i] = d_v0;
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destination_x[i] = dest_x;
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destination_y[i] = dest_y;
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// Fields the Pedestrian constructor would have zero-initialized:
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w_x[i] = 0.0;
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w_y[i] = 0.0;
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curr_vel_x[i] = 0.0;
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curr_vel_y[i] = 0.0;
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sb_sq[i] = 0.0;
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sb_e_x[i] = 0.0;
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sb_e_y[i] = 0.0;
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}
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for (int i = n_p_half; i < n_p; i++) {
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double x = x_gen_right(gen);
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double y = y_gen(gen);
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double dest_x = -30;
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double dest_y = y;
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double init_e_x = dest_x - x;
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double init_e_y = dest_y - y;
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double init_e_norm = sqrt(init_e_x * init_e_x + init_e_y * init_e_y);
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double init_e_unit_x = init_e_x / init_e_norm;
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double init_e_unit_y = init_e_y / init_e_norm;
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double d_v0;
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do {
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d_v0 = velocity_gen(gen);
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} while (d_v0 <= 0.1);
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double v_m = 1.3 * d_v0;
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color[i] = 1;
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pos_x[i] = x;
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pos_y[i] = y;
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e_x[i] = init_e_unit_x;
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e_y[i] = init_e_unit_y;
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v_max[i] = v_m;
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desired_v0[i] = d_v0;
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destination_x[i] = dest_x;
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destination_y[i] = dest_y;
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// Fields the Pedestrian constructor would have zero-initialized:
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w_x[i] = 0.0;
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w_y[i] = 0.0;
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curr_vel_x[i] = 0.0;
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curr_vel_y[i] = 0.0;
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sb_sq[i] = 0.0;
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sb_e_x[i] = 0.0;
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sb_e_y[i] = 0.0;
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}
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return nullptr;
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}
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Board init_board_without_obstacles() { return Board(BOARD_LENGTH, BOARD_WIDTH, 0, nullptr); }
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int run_simulation(Pedestrian *pedestrians, Board &board, int n_p, int n_iter, const char *dump_path) {
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#if BENCHMARK
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(void)dump_path;
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#endif
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#if !BENCHMARK
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const int dump_interval = 1;
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vector<tuple<int, int, double, double, double, double, int>> samples;
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if (dump_path) {
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samples.reserve(static_cast<size_t>(n_iter / dump_interval + 1) * n_p);
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for (int i = 0; i < n_p; i++) {
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// Pedestrian &p = pedestrians[i];
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samples.emplace_back(0, i, pos_x[i], pos_y[i], e_x[i], e_y[i], color[i]);
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}
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}
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#endif
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for (int t = 0; t < n_iter; t++) {
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for (int i = 0; i < n_p; i++) {
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double desired_velocity_x = e_x[i] * desired_v0[i];
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double desired_velocity_y = e_y[i] * desired_v0[i];
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double f_alpha_dt_x = (desired_velocity_x - curr_vel_x[i]) * dt_times_tau_alpha_inv;
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double f_alpha_dt_y = (desired_velocity_y - curr_vel_y[i]) * dt_times_tau_alpha_inv;
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for (int j = 0; j < n_p; j++) {
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if (i == j)
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continue;
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double r_ab_x = pos_x[i] - pos_x[j];
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double r_ab_y = pos_y[i] - pos_y[j];
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double r_ab_norm = sqrt(r_ab_x * r_ab_x + r_ab_y * r_ab_y);
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double r_ab2_x = r_ab_x - sb_e_x[j];
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double r_ab2_y = r_ab_y - sb_e_y[j];
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double r_ab2_norm = sqrt(r_ab2_x * r_ab2_x + r_ab2_y * r_ab2_y);
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double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
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double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - sb_sq[j];
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double two_b = sqrt(two_b_sq);
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double scalar_mult_common = (r_ab_norm_sum / (two_b * r_ab_norm * r_ab2_norm)) * (NEG_V0_AB_div_TWO_SIGMA * exp(two_b * NEG_TWO_SIGMA_INV));
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double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
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double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
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double f_alpha_beta_neg_x = r_ab_x * scalar_mult_r_ab + r_ab2_x * scalar_mult_r_ab2;
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double f_alpha_beta_neg_y = r_ab_y * scalar_mult_r_ab + r_ab2_y * scalar_mult_r_ab2;
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double w_val;
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double a = (e_x[i] * f_alpha_beta_neg_x + e_y[i] * f_alpha_beta_neg_y) * cos_phi_inv;
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double b_sq = f_alpha_beta_neg_x * f_alpha_beta_neg_x + f_alpha_beta_neg_y * f_alpha_beta_neg_y;
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w_val = dt_neg;
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if (a >= 0 && (a * a > b_sq)) {
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w_val = c_neg_times_dt;
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}
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f_alpha_dt_x = f_alpha_dt_x + f_alpha_beta_neg_x * w_val;
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f_alpha_dt_y = f_alpha_dt_y + f_alpha_beta_neg_y * w_val;
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}
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for (int k = 0; k < board.n_obstacles; k++) {
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double v1_x = pos_x[i] - board.obstacles[k].start.x;
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double v1_y = pos_y[i] - board.obstacles[k].start.y;
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double projection = (v1_x * board.obstacles[k].obstacle_v.x + v1_y * board.obstacles[k].obstacle_v.y) * board.obstacles[k].length_sq_inv;
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if (projection < 0.0)
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projection = 0.0;
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else if (projection > 1.0)
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projection = 1.0;
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double r_aB_x = pos_x[i] - (board.obstacles[k].start.x + board.obstacles[k].obstacle_v.x * projection);
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double r_aB_y = pos_y[i] - (board.obstacles[k].start.y + board.obstacles[k].obstacle_v.y * projection);
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double r_aB_norm = sqrt(r_aB_x * r_aB_x + r_aB_y * r_aB_y);
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double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
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double f_alpha_B_neg_x = r_aB_x * scalar_mult;
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double f_alpha_B_neg_y = r_aB_y * scalar_mult;
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double w_val;
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double a = (e_x[i] * f_alpha_B_neg_x + e_y[i] * f_alpha_B_neg_y) * cos_phi_inv;
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double b_sq = f_alpha_B_neg_x * f_alpha_B_neg_x + f_alpha_B_neg_y * f_alpha_B_neg_y;
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w_val = dt_neg;
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if (a >= 0 && (a * a > b_sq)) {
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w_val = c_neg_times_dt;
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}
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double f_alpha_B_dt_x = f_alpha_B_neg_x * w_val;
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double f_alpha_B_dt_y = f_alpha_B_neg_y * w_val;
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f_alpha_dt_x += f_alpha_B_dt_x;
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f_alpha_dt_y += f_alpha_B_dt_y;
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}
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w_x[i] += f_alpha_dt_x;
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w_y[i] += f_alpha_dt_y;
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}
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for (int i = 0; i < n_p; i++) {
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double w_norm = sqrt(w_x[i] * w_x[i] + w_y[i] * w_y[i]);
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double correction_factor = min(1.0, v_max[i] / w_norm);
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curr_vel_x[i] = w_x[i] * correction_factor;
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curr_vel_y[i] = w_y[i] * correction_factor;
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pos_x[i] = pos_x[i] + curr_vel_x[i] * dt;
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pos_y[i] = pos_y[i] + curr_vel_y[i] * dt;
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if (color[i] == 0 && pos_x[i] > (BOARD_LENGTH - 1.5)) {
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pos_x[i] = 1.0;
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destination_y[i] = pos_y[i];
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w_x[i] = desired_v0[i];
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w_y[i] = 0.0;
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curr_vel_x[i] = w_x[i];
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curr_vel_y[i] = w_y[i];
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}
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if (color[i] == 1 && pos_x[i] < 1.5) {
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pos_x[i] = BOARD_LENGTH - 1.0;
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destination_y[i] = pos_y[i];
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w_x[i] = -desired_v0[i];
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w_y[i] = 0.0;
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curr_vel_x[i] = w_x[i];
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curr_vel_y[i] = w_y[i];
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}
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pos_y[i] = clamp(pos_y[i], WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
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double ex = destination_x[i] - pos_x[i];
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double ey = destination_y[i] - pos_y[i];
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double e_norm_inv = 1.0 / sqrt(ex * ex + ey * ey);
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e_x[i] = ex * e_norm_inv;
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e_y[i] = ey * e_norm_inv;
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double velocity_norm_sq = curr_vel_x[i] * curr_vel_x[i] + curr_vel_y[i] * curr_vel_y[i];
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double velocity_norm = sqrt(velocity_norm_sq);
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sb_sq[i] = velocity_norm_sq * delta_t_sq;
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sb_e_x[i] = e_x[i] * (velocity_norm * delta_t);
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sb_e_y[i] = e_y[i] * (velocity_norm * delta_t);
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#if !BENCHMARK
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if (dump_path && (t % dump_interval == 0)) {
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samples.emplace_back(t + 1, i, pos_x[i], pos_y[i], e_x[i], e_y[i], color[i]);
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}
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#endif
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}
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}
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#if !BENCHMARK
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if (dump_path) {
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ofstream out(dump_path);
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out << "time,id,x,y,p.e.x,p.e.y,color\n";
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for (auto &[t, id, x, y, e_x, e_y, c] : samples) {
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out << t << ',' << id << ',' << x << ',' << y << ',' << e_x << ',' << e_y << ',' << c << '\n';
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}
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}
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#endif
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return 0;
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}
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