Nguyen Quy Anh
308 lines
8.8 KiB
C++
308 lines
8.8 KiB
C++
#include "board.hpp"
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#include "constants.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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#include <fstream>
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#include <iostream>
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#include <random>
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#include <tuple>
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#include <vector>
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#ifdef __x86_64__
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#include "tsc_x86.h"
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#endif
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#ifdef __aarch64__
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#include "kperf.h"
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#endif
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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 BOARD_LENGTH_MINUS_ONE BOARD_LENGTH - 1
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#define BOARD_LENGTH_MINUS_ONE_POINT_FIVE BOARD_LENGTH - 1.5
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#define WALL_EPS 1e-3
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#define BOARD_WIDTH_MINUS_WALL_EPS BOARD_WIDTH - WALL_EPS
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#define BOARD_LENGTH_HALF BOARD_LENGTH / 2
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#define BOARD_LENGTH_MINUS_TWO BOARD_LENGTH - 2
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#define BOARD_WIDTH_MINUS_ZERO_POINT_THREE BOARD_WIDTH - 0.3
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#define BOARD_LENGTH_PLUS_THIRTY BOARD_LENGTH + 30
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using namespace std;
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Pedestrian *init_pedestrians(int n_p) {
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Pedestrian *P = new Pedestrian[n_p];
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// Platform-specific non-deterministic random number generator (if available)
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// random_device rd;
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// mt19937 gen(rd()); // Use rd for true randomness
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mt19937 gen(SEED); // Use fixed seed for reproducibility
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normal_distribution<double> velocity_gen(V0_MEAN, V0_STD_DEV);
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uniform_real_distribution<double> x_gen_left(2, BOARD_LENGTH_HALF);
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uniform_real_distribution<double> x_gen_right(BOARD_LENGTH_HALF, BOARD_LENGTH_MINUS_TWO);
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uniform_real_distribution<double> y_gen(0.3, BOARD_WIDTH_MINUS_ZERO_POINT_THREE);
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const 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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Vector2d start_pos = Vector2d(x, y);
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Vector2d destination = Vector2d(BOARD_LENGTH_PLUS_THIRTY, y);
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Vector2d init_e = destination - start_pos;
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double init_e_norm = init_e.norm();
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Vector2d init_e_unit = init_e / init_e_norm;
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double desired_v0;
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// Avoid values are too small since they would make the pedestrians walk backwards
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do {
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desired_v0 = velocity_gen(gen);
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} while (desired_v0 <= 0.1);
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double v_max = 1.3 * desired_v0;
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P[i] = Pedestrian(WHITE, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination, Vector2d());
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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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Vector2d start_pos = Vector2d(x, y);
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Vector2d destination = Vector2d(-30, y);
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Vector2d init_e = destination - start_pos;
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double init_e_norm = init_e.norm();
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Vector2d init_e_unit = init_e / init_e_norm;
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double desired_v0;
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do {
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desired_v0 = velocity_gen(gen);
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} while (desired_v0 <= 0.1);
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double v_max = 1.3 * desired_v0;
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P[i] = Pedestrian(BLACK, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination, Vector2d());
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}
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return P;
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}
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int main(int argc, const char *argv[]) {
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// Ensure that arguments are passed
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if (argc < 3) {
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printf("Usage: ./sim <number_of_pedestrians> <number_of_iterations>\n");
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printf("Each iteration takes %.2f seconds in the simulation.\n", DT);
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return 1;
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}
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// Parse command-line arguments
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int n_p = atoi(argv[1]);
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int n_iter = atoi(argv[2]);
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if (n_p <= 0) {
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printf("Error: <number_of_pedestrians> must be a positive integer, got '%s' instead\n", argv[1]);
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return 1;
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}
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if (n_iter <= 0) {
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printf("Error: <number_of_iterations> must be a positive integer, got '%s' instead\n", argv[2]);
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return 1;
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}
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Obstacle obs[] = {};
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Board board = Board(BOARD_LENGTH, BOARD_WIDTH, 0, obs);
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// Logic for importing/exporting the simulation data
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const int dump_interval = 1;
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const char *dump_path = (argc >= 4) ? argv[3] : nullptr;
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vector<tuple<int, int, double, double, double, double, int>> samples; // t, id, x, y, e.x, e.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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}
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Pedestrian *pedestrians = init_pedestrians(n_p);
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#ifdef __x86_64__
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uint64_t start = start_tsc();
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#endif
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#ifdef __aarch64__
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kperf_init();
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struct performance_counters startperf, endperf;
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startperf = kperf_get_counters();
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#endif
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for (int t = 0; t < n_iter; t++) {
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// Pass 1: Conmpute all the forces given 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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p.f_i.reset();
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// # 4 ADDs, 4 MULTs
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//
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// $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
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//
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add_acceleration_term(&p);
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// # (n_p - 1) * (14 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP)
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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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add_repulsive_effects_from_pedestrians(pedestrians, &p, n_p);
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// # board.n_obstacles * (12 ADDs, 18 MULTs, 1 DIV, 2 SQRTs, 1 EXP)
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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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add_repulsive_effects_from_borders(board, &p);
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}
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// Pass 2: 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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// # 2 ADDs
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//
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// $\frac{d\vec{w_\alpha}}{dt} = \vec{F}_{\alpha}(t)$
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//
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//
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p.w = p.w + p.f_i;
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// # 1 ADD, 2 MULTs, 1 SQRT
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double w_norm = p.w.norm();
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p.curr_vel = p.w;
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// # Assumption for calculating W(n): The "if" statement always evaluates to true.
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// # This is not true in practice, but we assume the worst-case scenario for the flop count calculation.
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if (w_norm > p.v_max) {
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// # 2 MULTs, 1 DIV
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p.curr_vel = p.curr_vel * (p.v_max / w_norm);
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}
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// # 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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// When a pedestrian reaches the end of the hallway we simply "teleport" the person back to the beginning.
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// This way, we have a constant flow of pedestrians, and it is easier to see lanes forming.
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if (p.color == WHITE && p.curr_pos.x > BOARD_LENGTH_MINUS_ONE_POINT_FIVE) {
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// keep y, reset 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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p.w = Vector2d(p.desired_v0, 0);
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p.curr_vel = p.w;
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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_MINUS_ONE;
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p.destination.y = p.curr_pos.y;
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p.w = Vector2d(-p.desired_v0, 0);
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p.curr_vel = p.w;
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}
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if (p.curr_pos.y <= 0.0) {
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p.curr_pos.y = WALL_EPS;
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if (p.curr_vel.y < 0)
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p.curr_vel.y = 0;
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if (p.w.y < 0)
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p.w.y = 0;
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}
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if (p.curr_pos.y >= BOARD_WIDTH) {
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p.curr_pos.y = BOARD_WIDTH_MINUS_WALL_EPS;
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if (p.curr_vel.y > 0)
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p.curr_vel.y = 0;
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if (p.w.y > 0)
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p.w.y = 0;
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}
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// # 2 ADDs
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Vector2d e = p.destination - p.curr_pos;
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// # 1 ADD, 2 MULTs, 1 DIV, 1 SQRT
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double e_norm_inv = 1.0 / e.norm();
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// # 2 MULTs
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p.e = e * e_norm_inv;
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// # 1 ADD, 3 MULTs, 1 SQRT
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double s_b = p.curr_vel.norm() * DELTA_T;
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// # 2 MULTs
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p.s_b_e_b = p.e * s_b;
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// # 1 MULT
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p.s_b_sq = s_b * s_b;
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// Save the data for visualization
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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.e.x, p.e.y, p.color);
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}
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}
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}
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#ifdef __x86_64__
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long double T = stop_tsc(start);
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#endif
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#ifdef __aarch64__
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endperf = kperf_get_counters();
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long double T = endperf.cycles - startperf.cycles;
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#endif
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long long add_count_per_iter = n_p * (4 + 14 * (n_p - 1) + 12 * board.n_obstacles) + n_p * 9;
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long long mult_count_per_iter = n_p * (4 + 23 * (n_p - 1) + 18 * board.n_obstacles) + n_p * 16;
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long long div_count_per_iter = n_p * (n_p - 1 + board.n_obstacles) + n_p * 2;
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long long sqrt_count_per_iter = n_p * (4 * (n_p - 1) + 2 * board.n_obstacles) + n_p * 3;
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long long exp_count_per_iter = n_p * (n_p - 1 + board.n_obstacles);
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long double W_per_iter = add_count_per_iter * COST_ADD + mult_count_per_iter * COST_MULT + div_count_per_iter * COST_DIV + sqrt_count_per_iter * COST_SQRT + exp_count_per_iter * COST_EXP;
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long long Q_per_iter = sizeof(Pedestrian) * n_p + sizeof(Obstacle) * board.n_obstacles; // TODO: Refine this
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long double I = W_per_iter / Q_per_iter;
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long double W = W_per_iter * n_iter;
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long double P = W / T;
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printf("W(n) per iteration: %.2Lf flops\n", W_per_iter);
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printf("Q(n) per iteration: %lld bytes\n", Q_per_iter);
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printf("I(n): %.2Lf flops/byte\n", I);
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printf("W(n): %.2Lf flops\n", W);
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printf("T(n): %.2Lf cycles\n", T);
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printf("P(n): %.2Lf flops/cycle\n", P);
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// Export the data to a .csv file
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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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delete[] pedestrians;
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return 0;
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}
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