lbaumberger
147 lines
4.3 KiB
C++
147 lines
4.3 KiB
C++
#include "board.hpp"
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#include "forces.hpp"
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#include "math.h"
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#include "pedestrian.hpp"
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#include "vector2d.hpp"
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double g(double v_max_alpha, double w_alpha_norm) {
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if (w_alpha_norm <= v_max_alpha) {
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return 1;
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}
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return v_max_alpha / w_alpha_norm;
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}
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using namespace std;
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// Numer of pedestrians
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// $n_p$
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// const int n_p = 100;
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// Number of iterations
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// $n_{iter}$
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// const int n_iter = 100;
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Pedestrian *init_pedestrians(int n_p) {
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Pedestrian *P = new Pedestrian[n_p];
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for (int i = 0; i < n_p; i++) {
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P[i] = Pedestrian();
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}
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return P;
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}
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Board init_board(int width, int length) {
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Vector2d obs[] = {Vector2d(2.0, 1.0), Vector2d(4.0, 1.0), Vector2d(6.0, 2.0), Vector2d(6.0, 4.0)};
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return Board(width, length, 4, obs);
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}
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int main(int argc, const char *argv[]) {
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Board B = init_board(10, 15);
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int n_p = atoi(argv[1]);
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int n_iter = atoi(argv[2]);
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// Logic to get/dump the simulation data
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const int dump_interval = 50; // dump every 5 iterations -> 20 fps (since dt = 0.01)
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const char *dump_path = (argc >= 4) ? argv[3] : nullptr;
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std::vector<std::tuple<int, int, double, double, int>> samples; // t, id, x, y, color
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if (dump_path)
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samples.reserve((size_t)n_iter / dump_interval * n_p);
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Pedestrian *pedestrians = init_pedestrians(n_p);
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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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Pedestrian &p = pedestrians[i];
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p.F_i.reset();
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// # 8 flops
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//
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// $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) \vec{e}_\alpha(t) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
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//
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compute_acceleration_term(&p);
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// # 55N - 55 flops
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//
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// $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta})$
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//
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compute_repulsive_effects_from_pedestrians(pedestrians, &p, n_p);
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// # 192 flops
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//
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// $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
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//
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compute_repulsive_effects_from_borders(board, &p);
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}
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// Pass2: integrate the forces and update the current state
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for (int i = 0; i < n_p; i++) {
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Pedestrian &p = pedestrians[i];
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// # 4 flops = 2 ADDs, 2 MULTs
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// integrate preferred velocity: dw/dt = F (Eq. 10)
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p.w = p.w + p.f_i * dt;
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// Vector2d w_alpha = p.curr_vel + p.f_i * dt; // commented since this was using p.curr_vel instead of p.w (different from paper)
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// # 8 flops = 2 MULTs, 2 DIVs, 1 SQRT, 2 MULTs, 1 ADD
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p.curr_vel = p.w * g(p.v_max, p.w.norm());
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// # 4 flops = 2 ADDs, 2 MULTs
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//
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// $\vec{r}_i^{t+1} += \vec{r}^t_i + v^t_idt$
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//
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p.curr_pos = p.curr_pos + p.curr_vel * dt;
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// how to handle if a pedestrian reached the end of our simulation/end of the hallway
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// The option we do here is to just "teleport" the person back to the beginning
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// the good thing is we will have a constant flow of pedestrians and in the simulation we should see how lines build up
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if (p.color == WHITE && p.curr_pos.x > (BOARD_LENGTH - 1.5)) {
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// basically just keep the y position and reset the x
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p.curr_pos.x = 1.0;
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// p.destination.y = p.curr_pos.y;
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}
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if (p.color == BLACK && p.curr_pos.x < 1.5) {
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p.curr_pos.x = BOARD_LENGTH - 1.0;
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// p.destination.y = p.curr_pos.y;
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}
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// # 10 flops = 5 ADDs, 2 DIVs, 1 SQRT, 2 MULTs
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p.e = (p.destination - p.curr_pos) / (p.destination - p.curr_pos).norm();
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// if we have a dump path/file we wanna save the data for visualisation
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if (dump_path && (t % dump_interval == 0)) {
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samples.emplace_back(t, i, p.curr_pos.x, p.curr_pos.y, p.color);
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}
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}
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}
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long long W = (long long)(8 + 55 * n_p - 55 + 192 + 4 + 8 + 4 + 10) * n_p * n_iter;
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std::cout << "W(n): ";
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std::cout << W;
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std::cout << " flops\n";
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double T = stop_tsc(start);
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std::cout << "T(n): ";
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std::cout << T;
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std::cout << " cycles\n";
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double P = W / T;
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std::cout << "P(n): ";
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std::cout << P;
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std::cout << " flops/cycle\n";
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// now we wanna save the data to a csv
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if (dump_path) {
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std::ofstream out(dump_path);
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out << "time,id,x,y,color\n";
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for (auto &[t, id, x, y, c] : samples) {
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out << t << ',' << id << ',' << x << ',' << y << ',' << c << '\n';
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}
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}
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delete[] pedestrians;
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return 0;
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}
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