Merge branch 'linus/variant_cutoff' into 'main'
Cutoff variant (skip far pedestrian pairs) See merge request course-asl-fastcode26/team70!19
This commit is contained in:
[Quy Anh] «Elliot» Nguyen
@@ -36,8 +36,7 @@ System Version: IdeaPad Slim 5 14AKP10
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| 0 | [Baseline](optimization_baseline.md) | 1'058'400'000.00 | 850'352'900.00 | 1.24 | |
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| 1 | [Inlining](optimization_inlining.md) | 1'058'400'000.00 | 756'088'260.00 | 1.40 | Elliot |
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| 2 | [Symmetry](optimization_symmetry.md) | | | | Smail |
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| 2 | [Symmetry](optimization_ilp.md) | | | | Smail |
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| 3 | [ILP](optimization_ilp.md) | | | | Smail |
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### ARM (Apple)
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@@ -0,0 +1,145 @@
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# Optimization: Cutoff (close-neighbors only)
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- **Author**: Linus
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- **Predecessor**: variant_baseline.cpp
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- **File(s)**: variants/variant_cutoff_callsite.cpp (main version),
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variants/variant_cutoff.cpp (first attempt, the cutoff check inside the function)
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- **Status**: probably positive (approx. 1.4-1.8 times faster on my laptop)
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## What this changes
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Skip the full force computation for pedestrian pairs whose squared distance
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exceeds `CUTOFF_SQ` (CUTOFF = 5.0 m in `headers/constants.hpp`). At that
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distance the truncated `exp(-r/sigma)` factor is `~6e-8` per pair
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Two placements of the check were tried:
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- **First attempt** (`variant_cutoff.cpp`): check at the top of
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`pedestrian_repulsion_dt`. Function call still happens for every pair, only
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the actuall calculation is skipped for far pairs.
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- **Main version** (`variant_cutoff_callsite.cpp`): check is before te
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call in `run_simulation`. Far pairs skip the function call entirely,
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and the redundant in-function check was removed.
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We picked CUTOFF=12 (not 5) so that `make verify` passes the full 4000-step
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diff. At 12 m, the truncated per-pair force is around 1e-20, below the
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double-precision rounding threshold for the accumulator, so the FP result
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stays bit-identical to baseline. Trade-off: speedup drops from ~3-4x at
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CUTOFF=5 to 1.4-1.8x at CUTOFF=12, we kept the strict version for
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correctness.
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For the CUTOFF=5:
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Default `make verify` fails because cutoff causes tiny FP drift, and once a pedestrian wraps around the corridor end one step earlier than the baseline, its position diff jumps by ~47 m and the comparison breaks. Workaround: verify against a short 200-step reference where no teleports have happened yet.
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```bash
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./main_baseline 60 200 reference/baseline_n60_i200.csv # one-time setup
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./main_cutoff_callsite 60 200 verify_cutoff_callsite_short.csv
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python3 scripts/verify.py reference/baseline_n60_i200.csv verify_cutoff_callsite_short.csv 1e-3
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```
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## Code (relevant snippet)
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In `run_simulation`, the inner pair loop:
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```cpp
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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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// Cutoff at the call site
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Vector2d r_ab_pre = p.curr_pos - pedestrians[j].curr_pos;
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if (r_ab_pre.x * r_ab_pre.x + r_ab_pre.y * r_ab_pre.y > CUTOFF_SQ)
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continue;
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f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
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}
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```
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`pedestrian_repulsion_dt` no longer has its own cutoff.
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Constants added to `headers/constants.hpp`:
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```cpp
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const double CUTOFF = 5.0;
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const double CUTOFF_SQ = CUTOFF * CUTOFF;
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```
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## Benchmark
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Conditions: min of <N> runs, N_ITER=1000, -O3 -march=native -flto.
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A preliminary run on my laptop (Intel ULTRA 9 185H @ 2.2 GHz, WSL
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Ubuntu) showed both variants 3-4x faster than the baseline across N=50 to
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N=1000, with the callsite version about 4-5% faster than the in-function
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version. The speedup was roughly constant across N.
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### x86
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**Machine**:
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```
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<paste machine info here>
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```
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**Frequency**: <e.g., 2 GHz>
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Main version (`variant_cutoff_callsite.cpp`):
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| N | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Baseline |
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| ---- | ------------ | ------------- | ------------------------- | ---------------- |
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| 50 | | | | |
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| 100 | | | | |
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| 200 | | | | |
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| 1000 | | | | |
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First attempt (`variant_cutoff.cpp`) for comparison:
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| N | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Baseline | Diff vs Main |
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| ---- | ------------ | ------------- | ------------------------- | ---------------- | ------------ |
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| 50 | | | | | |
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| 100 | | | | | |
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| 200 | | | | | |
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| 500 | | | | | |
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| 1000 | | | | | |
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### ARM (Apple)
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**Machine**:
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```
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<paste machine info here>
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```
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**Frequency**: <e.g., 3.2 GHz>
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Main version (`variant_cutoff_callsite.cpp`):
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| N | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Baseline |
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| ---- | ------------ | ------------- | ------------------------- | ---------------- |
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| 50 | | | | |
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| 100 | | | | |
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| 200 | | | | |
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| 500 | | | | |
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| 1000 | | | | |
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First attempt (`variant_cutoff.cpp`):
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| N | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Baseline | Diff vs Main |
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| ---- | ------------ | ------------- | ------------------------- | ---------------- | ------------ |
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| 50 | | | | | |
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| 100 | | | | | |
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| 200 | | | | | |
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| 500 | | | | | |
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| 1000 | | | | | |
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## Analysis
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<Why it worked or did not. What surprised you. What bottleneck this hits
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or moves. Mention generated assembly or a profile if relevant.>
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## Takeaway
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<One short paragraph that could be lifted straight into the report.>
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@@ -96,6 +96,9 @@ const double EPS = 1e-9;
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// Squared numerical tolerance
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const double EPS_SQ = EPS * EPS;
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// Cutoff for pedestrian-pedestrian repulsion
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const double CUTOFF = 12.0;
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const double CUTOFF_SQ = CUTOFF * CUTOFF;
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const double cos_phi_sq = cos_phi * cos_phi;
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File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,406 @@
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// This file is copied from the baseline and modified
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// Our goal is to only calculate pedestrian-pedestrian forces for pedestrian which are near
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// Our tolerance is 1cm and our cutoff is a 5m radius.
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// Since we have some tolerance and we put pedestrians back at the end of the hallway once they are on one end. We only test/verify on a smaller subsample.
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// Otherwise we had huge gaps (because of those jumps back)
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#include "../headers/board.hpp"
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#include "../headers/constants.hpp"
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#include "../headers/pedestrian.hpp"
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#include "../headers/vector2d.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> // keep — forces code uses unqualified sqrt/exp
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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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// forces.cpp
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// Flop count: 2 ADDs, 5 MULTs, 1 SQRT
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double w(Vector2d e, Vector2d f) {
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if (e.dot(f) >= f.norm() * cos_phi)
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return dt_neg;
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else
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return c_neg_times_dt;
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}
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// Flop count: 2 ADDs, 4 MULTs
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Vector2d acceleration_term_dt(const Pedestrian &alpha) {
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// # 2 MULTs
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Vector2d desired_velocity = alpha.e * alpha.desired_v0;
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Vector2d actual_velocity = alpha.curr_vel;
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// # 2 ADDs, 2 MULTs
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Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv;
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return f_alpha_0_dt;
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}
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// Flop count: 12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP
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Vector2d pedestrian_repulsion_dt(const Pedestrian &alpha, const Pedestrian &beta) {
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// # 2 ADDs
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// $\vec{r}_{\alpha\beta}$
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Vector2d r_ab = alpha.curr_pos - beta.curr_pos;
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// # 1 ADD, 2 MULTs, 1 SQRT
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// $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$
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// double r_ab_norm = r_ab.norm();
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// Now we wanna check if it is a close pedestrian or wheter it is far away and can be ignored
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double r_ab_sq = r_ab.x * r_ab.x + r_ab.y * r_ab.y; // squared distance
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if (r_ab_sq > CUTOFF_SQ)
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return Vector2d(0.0, 0.0); // cheap reject
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double r_ab_norm = sqrt(r_ab_sq); // reuse r_ab_sq, no recompute
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r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS; // Avoid division by zero
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// $v_\beta \Delta t \vec{e}_\beta$
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Vector2d s_b_e = beta.s_b_e;
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// # 2 ADDs
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// $\vec{r}_{\alpha\beta} - \vec{v}_{\beta}\varDelta_{t}\vec{e}_{\beta}$
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Vector2d r_ab2 = r_ab - s_b_e;
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// # 1 ADD, 2 MULTs, 1 SQRT
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// $\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
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double r_ab2_norm = r_ab2.norm();
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r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS; // Avoid division by zero
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// # 1 ADD
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// $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
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double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
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// # 1 ADD, 1 MULTs, 1 SQRT
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//
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// $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
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//
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double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - beta.s_b_sq;
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two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ; // Avoid sqrt of negative and avoid division by zero
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double two_b = sqrt(two_b_sq);
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// # 5 MULTs, 1 DIV, 1 EXP
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//
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// $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
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//
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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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// # 1 MULT
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//
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// $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}$
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//
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double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
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// # 1 MULT
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//
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// $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
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//
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double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
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// # 2 ADDs, 4 MULTs
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//
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// $\vec{-f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{-V^0_{\alpha \beta}}{2\sigma}e^{-b / \sigma} 2\grad_{\vec{r}_{\alpha \beta}}b $
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//
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//
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// $= \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} -
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// v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
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//
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//
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//
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// $2\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)(\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{2b}$
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//
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//
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// $= \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
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//
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//
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Vector2d f_alpha_beta_neg = r_ab * scalar_mult_r_ab + r_ab2 * scalar_mult_r_ab2;
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// # 2 ADDs, 7 MULTs, 1 SQRT
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//
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// $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha \beta}).dt = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta})).\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta}).dt$
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//
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Vector2d f_alpha_beta_dt = f_alpha_beta_neg * w(alpha.e, f_alpha_beta_neg);
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return f_alpha_beta_dt;
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}
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// Flop count: 10 ADDs, 18 MULTs, 1 DIVs, 2 SQRTs, 1 EXP
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Vector2d obstacle_repulsion_dt(const Pedestrian &alpha, const Obstacle &B) {
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// # 7 ADDs, 5 MULTs
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// $\vec{r}_{\alpha B}$
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Vector2d r_aB = alpha.curr_pos - B.find_nearest_obstacle_point(alpha.curr_pos);
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// # 1 ADD, 2 MULTs, 1 SQRT
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// $\Arrowvert \vec{r}_{\alpha B} \Arrowvert$
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double r_aB_norm = r_aB.norm();
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r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS; // Avoid division by zero
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// # 2 MULTs, 1 DIV, 1 EXP
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//
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// $\frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R}$
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//
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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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// # 2 MULTs
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//
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// $-\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = \frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R} \frac{\vec{r}_{\alpha B}}{\Arrowvert \vec{r}_{\alpha B} \Arrowvert}$
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//
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Vector2d f_alpha_B_neg = r_aB * scalar_mult;
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// # 2 ADDs, 7 MULTs, 1 SQRT
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//
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// $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha B}).dt = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B}(\vec{r}_{\alpha B})).\vec{F}_{\alpha B}(\vec{r}_{\alpha B}).dt$
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//
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Vector2d f_alpha_B_dt = f_alpha_B_neg * w(alpha.e, f_alpha_B_neg);
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return f_alpha_B_dt;
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}
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// social_force_baseline.cpp
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Pedestrian *init_pedestrians(int n_p) {
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#if RANDOM_SEED
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random_device rd; // Platform-provided random seed source.
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mt19937 gen(rd()); // Non-reproducible initialization.
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#else
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mt19937 gen(SEED); // Reproducible initialization.
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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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Pedestrian *P = new Pedestrian[n_p];
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int n_p_half = n_p / 2;
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// Group of pedestrians walking from left to right.
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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 + 30.0, 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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|
||||
// Reject near-zero desired speeds to avoid pedestrians that barely move,
|
||||
// and reject negative speeds because they would reverse the intended
|
||||
// walking direction.
|
||||
double desired_v0;
|
||||
do {
|
||||
desired_v0 = velocity_gen(gen);
|
||||
} while (desired_v0 <= 0.1);
|
||||
|
||||
double v_max = 1.3 * desired_v0;
|
||||
|
||||
P[i] = Pedestrian(WHITE, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
|
||||
}
|
||||
|
||||
// Group of pedestrians walking from right to left.
|
||||
for (int i = n_p_half; i < n_p; i++) {
|
||||
double x = x_gen_right(gen);
|
||||
double y = y_gen(gen);
|
||||
|
||||
Vector2d start_pos = Vector2d(x, y);
|
||||
Vector2d destination = Vector2d(-30.0, y);
|
||||
|
||||
Vector2d init_e = destination - start_pos;
|
||||
double init_e_norm = init_e.norm();
|
||||
Vector2d init_e_unit = init_e / init_e_norm;
|
||||
|
||||
// Reject near-zero desired speeds to avoid pedestrians that barely move,
|
||||
// and reject negative speeds because they would reverse the intended
|
||||
// walking direction.
|
||||
double desired_v0;
|
||||
do {
|
||||
desired_v0 = velocity_gen(gen);
|
||||
} while (desired_v0 <= 0.1);
|
||||
|
||||
double v_max = 1.3 * desired_v0;
|
||||
|
||||
P[i] = Pedestrian(BLACK, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
|
||||
}
|
||||
|
||||
return P;
|
||||
}
|
||||
|
||||
Board init_board_without_obstacles() { return Board(BOARD_LENGTH, BOARD_WIDTH, 0, nullptr); }
|
||||
|
||||
int run_simulation(Pedestrian *pedestrians, Board &board, int n_p, int n_iter, const char *dump_path) {
|
||||
|
||||
#if BENCHMARK
|
||||
// Avoid compiler unused parameter warning
|
||||
(void)dump_path;
|
||||
#endif
|
||||
|
||||
#if !BENCHMARK
|
||||
const int dump_interval = 1;
|
||||
|
||||
// Each sample stores: time step, pedestrian id, x, y, e.x, e.y, color.
|
||||
vector<tuple<int, int, double, double, double, double, int>> samples;
|
||||
|
||||
if (dump_path) {
|
||||
samples.reserve(static_cast<size_t>(n_iter / dump_interval + 1) * n_p);
|
||||
|
||||
// Store the initial state before the first simulation step.
|
||||
for (int i = 0; i < n_p; i++) {
|
||||
Pedestrian &p = pedestrians[i];
|
||||
samples.emplace_back(0, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
for (int t = 0; t < n_iter; t++) {
|
||||
// Pass 1: Compute all forces from the current state.
|
||||
for (int i = 0; i < n_p; i++) {
|
||||
Pedestrian &p = pedestrians[i];
|
||||
|
||||
// # 2 ADDs, 4 MULTs
|
||||
//
|
||||
// $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
|
||||
//
|
||||
Vector2d f_alpha_dt = acceleration_term_dt(p);
|
||||
|
||||
// # (N-1) * (12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP)
|
||||
// + (N-1) * 2 ADDs
|
||||
//
|
||||
// $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha},\vec{r}_{\alpha} - \vec{r}_{\beta})$
|
||||
//
|
||||
for (int j = 0; j < n_p; j++) {
|
||||
if (i == j)
|
||||
continue;
|
||||
|
||||
f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
|
||||
}
|
||||
|
||||
// # board.n_obstacles * (10 ADDs, 18 MULTs, 1 DIV, 2 SQRTs, 1 EXP)
|
||||
// + board.n_obstacles * 2 ADDs
|
||||
//
|
||||
// $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
|
||||
//
|
||||
for (int k = 0; k < board.n_obstacles; k++) {
|
||||
f_alpha_dt += obstacle_repulsion_dt(p, board.obstacles[k]);
|
||||
}
|
||||
|
||||
// # 2 ADDs
|
||||
//
|
||||
//
|
||||
// $\frac{d\vec{w}_\alpha}{dt} = \vec{F}_{\alpha}(t)$
|
||||
//
|
||||
p.w += f_alpha_dt;
|
||||
}
|
||||
|
||||
// Pass 2: Update pedestrian states after all forces have been computed.
|
||||
// # (7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p
|
||||
for (int i = 0; i < n_p; i++) {
|
||||
Pedestrian &p = pedestrians[i];
|
||||
|
||||
// # 1 ADD, 2 MULTs, 1 SQRT
|
||||
double w_norm = p.w.norm();
|
||||
|
||||
double w_norm2 = w_norm > EPS ? w_norm : EPS; // Avoid division by zero.
|
||||
|
||||
// # 1 DIV
|
||||
double correction_factor = std::min(1.0, p.v_max / w_norm2);
|
||||
|
||||
// # 2 MULTs
|
||||
p.curr_vel = p.w * correction_factor;
|
||||
|
||||
// # 2 ADDs, 2 MULTs
|
||||
//
|
||||
// $\vec{r}_i^{t+1} = \vec{r}_i^t + \vec{v}_i^t dt$
|
||||
//
|
||||
p.curr_pos = p.curr_pos + p.curr_vel * dt;
|
||||
|
||||
// When a pedestrian reaches the end of the corridor, teleport them back
|
||||
// to the opposite entrance while keeping the same y-coordinate. This
|
||||
// maintains a constant pedestrian flow and makes lane formation easier to
|
||||
// observe.
|
||||
if (p.color == WHITE && p.curr_pos.x > (BOARD_LENGTH - 1.5)) {
|
||||
p.curr_pos.x = 1.0;
|
||||
p.destination.y = p.curr_pos.y;
|
||||
p.w = Vector2d(p.desired_v0, 0.0);
|
||||
p.curr_vel = p.w;
|
||||
}
|
||||
if (p.color == BLACK && p.curr_pos.x < 1.5) {
|
||||
p.curr_pos.x = BOARD_LENGTH - 1.0;
|
||||
p.destination.y = p.curr_pos.y;
|
||||
p.w = Vector2d(-p.desired_v0, 0.0);
|
||||
p.curr_vel = p.w;
|
||||
}
|
||||
|
||||
// Keep pedestrians inside the board boundaries.
|
||||
p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
|
||||
|
||||
// # 2 ADDs
|
||||
Vector2d e = p.destination - p.curr_pos;
|
||||
|
||||
// # 1 ADD, 2 MULTs, 1 DIV, 1 SQRT
|
||||
double e_norm = e.norm();
|
||||
double e_norm2 = e_norm > EPS ? e_norm : EPS; // Avoid division by zero.
|
||||
double e_norm_inv = 1.0 / e_norm2;
|
||||
|
||||
// # 2 MULTs
|
||||
p.e = e * e_norm_inv;
|
||||
|
||||
// # 1 ADD, 2 MULTs
|
||||
double velocity_norm_sq = p.curr_vel.dot(p.curr_vel);
|
||||
|
||||
// # 1 SQRT
|
||||
double velocity_norm = std::sqrt(velocity_norm_sq);
|
||||
|
||||
// # 1 MULT
|
||||
p.s_b_sq = velocity_norm_sq * delta_t_sq;
|
||||
|
||||
// # 3 MULTs
|
||||
p.s_b_e = p.e * (velocity_norm * delta_t);
|
||||
|
||||
#if !BENCHMARK
|
||||
// Store the current pedestrian state for visualization.
|
||||
if (dump_path && (t % dump_interval == 0)) {
|
||||
samples.emplace_back(t + 1, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
#if !BENCHMARK
|
||||
// Export recorded simulation states to CSV.
|
||||
if (dump_path) {
|
||||
ofstream out(dump_path);
|
||||
out << "time,id,x,y,p.e.x,p.e.y,color\n";
|
||||
|
||||
for (auto &[t, id, x, y, e_x, e_y, c] : samples) {
|
||||
out << t << ',' << id << ',' << x << ',' << y << ',' << e_x << ',' << e_y << ',' << c << '\n';
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -0,0 +1,412 @@
|
||||
// This file is copied from the baseline and modified
|
||||
// Our goal is to only calculate pedestrian-pedestrian forces for pedestrian which are near
|
||||
// Our tolerance is 1cm and our cutoff is a 5m radius.
|
||||
// Since we have some tolerance and we put pedestrians back at the end of the hallway once they are on one end. We only test/verify on a smaller subsample.
|
||||
// Otherwise we had huge gaps (because of those jumps back)
|
||||
// In this version we do the cutoff before we call the pedestrian forces function
|
||||
|
||||
#include "../headers/board.hpp"
|
||||
#include "../headers/constants.hpp"
|
||||
#include "../headers/pedestrian.hpp"
|
||||
#include "../headers/vector2d.hpp"
|
||||
|
||||
#include <algorithm>
|
||||
#include <cmath>
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <math.h> // keep — forces code uses unqualified sqrt/exp
|
||||
#include <random>
|
||||
#include <tuple>
|
||||
#include <vector>
|
||||
|
||||
#define SEED 42
|
||||
#define BOARD_WIDTH 10.0
|
||||
#define BOARD_LENGTH 50.0
|
||||
#define WALL_EPS 1e-3
|
||||
#define BOARD_WIDTH_MINUS_WALL_EPS 9.999
|
||||
|
||||
#ifndef RANDOM_SEED
|
||||
#define RANDOM_SEED 0
|
||||
#endif
|
||||
|
||||
#ifndef BENCHMARK
|
||||
#define BENCHMARK 0
|
||||
#endif
|
||||
|
||||
using namespace std;
|
||||
|
||||
// forces.cpp
|
||||
|
||||
// Flop count: 2 ADDs, 5 MULTs, 1 SQRT
|
||||
double w(Vector2d e, Vector2d f) {
|
||||
if (e.dot(f) >= f.norm() * cos_phi)
|
||||
return dt_neg;
|
||||
else
|
||||
return c_neg_times_dt;
|
||||
}
|
||||
|
||||
// Flop count: 2 ADDs, 4 MULTs
|
||||
Vector2d acceleration_term_dt(const Pedestrian &alpha) {
|
||||
|
||||
// # 2 MULTs
|
||||
Vector2d desired_velocity = alpha.e * alpha.desired_v0;
|
||||
|
||||
Vector2d actual_velocity = alpha.curr_vel;
|
||||
|
||||
// # 2 ADDs, 2 MULTs
|
||||
Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv;
|
||||
|
||||
return f_alpha_0_dt;
|
||||
}
|
||||
|
||||
// Flop count: 12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP
|
||||
Vector2d pedestrian_repulsion_dt(const Pedestrian &alpha, const Pedestrian &beta) {
|
||||
|
||||
// # 2 ADDs
|
||||
// $\vec{r}_{\alpha\beta}$
|
||||
Vector2d r_ab = alpha.curr_pos - beta.curr_pos;
|
||||
|
||||
// # 1 ADD, 2 MULTs, 1 SQRT
|
||||
// $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$
|
||||
double r_ab_norm = r_ab.norm();
|
||||
// Now we wanna check if it is a close pedestrian or wheter it is far away and can be ignored
|
||||
// double r_ab_sq = r_ab.x * r_ab.x + r_ab.y * r_ab.y; // squared distance
|
||||
// if (r_ab_sq > CUTOFF_SQ)
|
||||
// return Vector2d(0.0, 0.0); // cheap reject
|
||||
// double r_ab_norm = sqrt(r_ab_sq); // reuse r_ab_sq, no recompute
|
||||
|
||||
r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS; // Avoid division by zero
|
||||
|
||||
// $v_\beta \Delta t \vec{e}_\beta$
|
||||
Vector2d s_b_e = beta.s_b_e;
|
||||
|
||||
// # 2 ADDs
|
||||
// $\vec{r}_{\alpha\beta} - \vec{v}_{\beta}\varDelta_{t}\vec{e}_{\beta}$
|
||||
Vector2d r_ab2 = r_ab - s_b_e;
|
||||
|
||||
// # 1 ADD, 2 MULTs, 1 SQRT
|
||||
// $\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
|
||||
double r_ab2_norm = r_ab2.norm();
|
||||
r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS; // Avoid division by zero
|
||||
|
||||
// # 1 ADD
|
||||
// $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
|
||||
double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
|
||||
|
||||
// # 1 ADD, 1 MULTs, 1 SQRT
|
||||
//
|
||||
// $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
|
||||
//
|
||||
double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - beta.s_b_sq;
|
||||
two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ; // Avoid sqrt of negative and avoid division by zero
|
||||
double two_b = sqrt(two_b_sq);
|
||||
|
||||
// # 5 MULTs, 1 DIV, 1 EXP
|
||||
//
|
||||
// $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
|
||||
//
|
||||
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));
|
||||
|
||||
// # 1 MULT
|
||||
//
|
||||
// $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}$
|
||||
//
|
||||
double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
|
||||
|
||||
// # 1 MULT
|
||||
//
|
||||
// $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
|
||||
//
|
||||
double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
|
||||
|
||||
// # 2 ADDs, 4 MULTs
|
||||
//
|
||||
// $\vec{-f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{-V^0_{\alpha \beta}}{2\sigma}e^{-b / \sigma} 2\grad_{\vec{r}_{\alpha \beta}}b $
|
||||
//
|
||||
//
|
||||
// $= \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} -
|
||||
// v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
|
||||
//
|
||||
//
|
||||
//
|
||||
// $2\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)(\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{2b}$
|
||||
//
|
||||
//
|
||||
// $= \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
|
||||
//
|
||||
//
|
||||
Vector2d f_alpha_beta_neg = r_ab * scalar_mult_r_ab + r_ab2 * scalar_mult_r_ab2;
|
||||
|
||||
// # 2 ADDs, 7 MULTs, 1 SQRT
|
||||
//
|
||||
// $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha \beta}).dt = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta})).\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta}).dt$
|
||||
//
|
||||
Vector2d f_alpha_beta_dt = f_alpha_beta_neg * w(alpha.e, f_alpha_beta_neg);
|
||||
|
||||
return f_alpha_beta_dt;
|
||||
}
|
||||
|
||||
// Flop count: 10 ADDs, 18 MULTs, 1 DIVs, 2 SQRTs, 1 EXP
|
||||
Vector2d obstacle_repulsion_dt(const Pedestrian &alpha, const Obstacle &B) {
|
||||
|
||||
// # 7 ADDs, 5 MULTs
|
||||
// $\vec{r}_{\alpha B}$
|
||||
Vector2d r_aB = alpha.curr_pos - B.find_nearest_obstacle_point(alpha.curr_pos);
|
||||
|
||||
// # 1 ADD, 2 MULTs, 1 SQRT
|
||||
// $\Arrowvert \vec{r}_{\alpha B} \Arrowvert$
|
||||
double r_aB_norm = r_aB.norm();
|
||||
r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS; // Avoid division by zero
|
||||
|
||||
// # 2 MULTs, 1 DIV, 1 EXP
|
||||
//
|
||||
// $\frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R}$
|
||||
//
|
||||
double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
|
||||
|
||||
// # 2 MULTs
|
||||
//
|
||||
// $-\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = \frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R} \frac{\vec{r}_{\alpha B}}{\Arrowvert \vec{r}_{\alpha B} \Arrowvert}$
|
||||
//
|
||||
Vector2d f_alpha_B_neg = r_aB * scalar_mult;
|
||||
|
||||
// # 2 ADDs, 7 MULTs, 1 SQRT
|
||||
//
|
||||
// $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha B}).dt = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B}(\vec{r}_{\alpha B})).\vec{F}_{\alpha B}(\vec{r}_{\alpha B}).dt$
|
||||
//
|
||||
Vector2d f_alpha_B_dt = f_alpha_B_neg * w(alpha.e, f_alpha_B_neg);
|
||||
|
||||
return f_alpha_B_dt;
|
||||
}
|
||||
|
||||
// social_force_baseline.cpp
|
||||
|
||||
Pedestrian *init_pedestrians(int n_p) {
|
||||
#if RANDOM_SEED
|
||||
random_device rd; // Platform-provided random seed source.
|
||||
mt19937 gen(rd()); // Non-reproducible initialization.
|
||||
#else
|
||||
mt19937 gen(SEED); // Reproducible initialization.
|
||||
#endif
|
||||
|
||||
normal_distribution<double> velocity_gen(v0_mean, v0_std_dev);
|
||||
uniform_real_distribution<double> y_gen(0.3, BOARD_WIDTH - 0.3);
|
||||
uniform_real_distribution<double> x_gen_left(2.0, BOARD_LENGTH / 2.0);
|
||||
uniform_real_distribution<double> x_gen_right(BOARD_LENGTH / 2.0, BOARD_LENGTH - 2.0);
|
||||
|
||||
Pedestrian *P = new Pedestrian[n_p];
|
||||
|
||||
int n_p_half = n_p / 2;
|
||||
|
||||
// Group of pedestrians walking from left to right.
|
||||
for (int i = 0; i < n_p_half; i++) {
|
||||
double x = x_gen_left(gen);
|
||||
double y = y_gen(gen);
|
||||
|
||||
Vector2d start_pos = Vector2d(x, y);
|
||||
Vector2d destination = Vector2d(BOARD_LENGTH + 30.0, y);
|
||||
|
||||
Vector2d init_e = destination - start_pos;
|
||||
double init_e_norm = init_e.norm();
|
||||
Vector2d init_e_unit = init_e / init_e_norm;
|
||||
|
||||
// Reject near-zero desired speeds to avoid pedestrians that barely move,
|
||||
// and reject negative speeds because they would reverse the intended
|
||||
// walking direction.
|
||||
double desired_v0;
|
||||
do {
|
||||
desired_v0 = velocity_gen(gen);
|
||||
} while (desired_v0 <= 0.1);
|
||||
|
||||
double v_max = 1.3 * desired_v0;
|
||||
|
||||
P[i] = Pedestrian(WHITE, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
|
||||
}
|
||||
|
||||
// Group of pedestrians walking from right to left.
|
||||
for (int i = n_p_half; i < n_p; i++) {
|
||||
double x = x_gen_right(gen);
|
||||
double y = y_gen(gen);
|
||||
|
||||
Vector2d start_pos = Vector2d(x, y);
|
||||
Vector2d destination = Vector2d(-30.0, y);
|
||||
|
||||
Vector2d init_e = destination - start_pos;
|
||||
double init_e_norm = init_e.norm();
|
||||
Vector2d init_e_unit = init_e / init_e_norm;
|
||||
|
||||
// Reject near-zero desired speeds to avoid pedestrians that barely move,
|
||||
// and reject negative speeds because they would reverse the intended
|
||||
// walking direction.
|
||||
double desired_v0;
|
||||
do {
|
||||
desired_v0 = velocity_gen(gen);
|
||||
} while (desired_v0 <= 0.1);
|
||||
|
||||
double v_max = 1.3 * desired_v0;
|
||||
|
||||
P[i] = Pedestrian(BLACK, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
|
||||
}
|
||||
|
||||
return P;
|
||||
}
|
||||
|
||||
Board init_board_without_obstacles() { return Board(BOARD_LENGTH, BOARD_WIDTH, 0, nullptr); }
|
||||
|
||||
int run_simulation(Pedestrian *pedestrians, Board &board, int n_p, int n_iter, const char *dump_path) {
|
||||
|
||||
#if BENCHMARK
|
||||
// Avoid compiler unused parameter warning
|
||||
(void)dump_path;
|
||||
#endif
|
||||
|
||||
#if !BENCHMARK
|
||||
const int dump_interval = 1;
|
||||
|
||||
// Each sample stores: time step, pedestrian id, x, y, e.x, e.y, color.
|
||||
vector<tuple<int, int, double, double, double, double, int>> samples;
|
||||
|
||||
if (dump_path) {
|
||||
samples.reserve(static_cast<size_t>(n_iter / dump_interval + 1) * n_p);
|
||||
|
||||
// Store the initial state before the first simulation step.
|
||||
for (int i = 0; i < n_p; i++) {
|
||||
Pedestrian &p = pedestrians[i];
|
||||
samples.emplace_back(0, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
for (int t = 0; t < n_iter; t++) {
|
||||
// Pass 1: Compute all forces from the current state.
|
||||
for (int i = 0; i < n_p; i++) {
|
||||
Pedestrian &p = pedestrians[i];
|
||||
|
||||
// # 2 ADDs, 4 MULTs
|
||||
//
|
||||
// $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
|
||||
//
|
||||
Vector2d f_alpha_dt = acceleration_term_dt(p);
|
||||
|
||||
// # (N-1) * (12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP)
|
||||
// + (N-1) * 2 ADDs
|
||||
//
|
||||
// $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha},\vec{r}_{\alpha} - \vec{r}_{\beta})$
|
||||
//
|
||||
for (int j = 0; j < n_p; j++) {
|
||||
if (i == j)
|
||||
continue;
|
||||
|
||||
// Cutoff check at the call site (so we dont have an additional call)
|
||||
Vector2d r_ab_pre = p.curr_pos - pedestrians[j].curr_pos;
|
||||
if (r_ab_pre.x * r_ab_pre.x + r_ab_pre.y * r_ab_pre.y > CUTOFF_SQ)
|
||||
continue;
|
||||
|
||||
f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
|
||||
}
|
||||
|
||||
// # board.n_obstacles * (10 ADDs, 18 MULTs, 1 DIV, 2 SQRTs, 1 EXP)
|
||||
// + board.n_obstacles * 2 ADDs
|
||||
//
|
||||
// $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
|
||||
//
|
||||
for (int k = 0; k < board.n_obstacles; k++) {
|
||||
f_alpha_dt += obstacle_repulsion_dt(p, board.obstacles[k]);
|
||||
}
|
||||
|
||||
// # 2 ADDs
|
||||
//
|
||||
//
|
||||
// $\frac{d\vec{w}_\alpha}{dt} = \vec{F}_{\alpha}(t)$
|
||||
//
|
||||
p.w += f_alpha_dt;
|
||||
}
|
||||
|
||||
// Pass 2: Update pedestrian states after all forces have been computed.
|
||||
// # (7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p
|
||||
for (int i = 0; i < n_p; i++) {
|
||||
Pedestrian &p = pedestrians[i];
|
||||
|
||||
// # 1 ADD, 2 MULTs, 1 SQRT
|
||||
double w_norm = p.w.norm();
|
||||
|
||||
double w_norm2 = w_norm > EPS ? w_norm : EPS; // Avoid division by zero.
|
||||
|
||||
// # 1 DIV
|
||||
double correction_factor = std::min(1.0, p.v_max / w_norm2);
|
||||
|
||||
// # 2 MULTs
|
||||
p.curr_vel = p.w * correction_factor;
|
||||
|
||||
// # 2 ADDs, 2 MULTs
|
||||
//
|
||||
// $\vec{r}_i^{t+1} = \vec{r}_i^t + \vec{v}_i^t dt$
|
||||
//
|
||||
p.curr_pos = p.curr_pos + p.curr_vel * dt;
|
||||
|
||||
// When a pedestrian reaches the end of the corridor, teleport them back
|
||||
// to the opposite entrance while keeping the same y-coordinate. This
|
||||
// maintains a constant pedestrian flow and makes lane formation easier to
|
||||
// observe.
|
||||
if (p.color == WHITE && p.curr_pos.x > (BOARD_LENGTH - 1.5)) {
|
||||
p.curr_pos.x = 1.0;
|
||||
p.destination.y = p.curr_pos.y;
|
||||
p.w = Vector2d(p.desired_v0, 0.0);
|
||||
p.curr_vel = p.w;
|
||||
}
|
||||
if (p.color == BLACK && p.curr_pos.x < 1.5) {
|
||||
p.curr_pos.x = BOARD_LENGTH - 1.0;
|
||||
p.destination.y = p.curr_pos.y;
|
||||
p.w = Vector2d(-p.desired_v0, 0.0);
|
||||
p.curr_vel = p.w;
|
||||
}
|
||||
|
||||
// Keep pedestrians inside the board boundaries.
|
||||
p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
|
||||
|
||||
// # 2 ADDs
|
||||
Vector2d e = p.destination - p.curr_pos;
|
||||
|
||||
// # 1 ADD, 2 MULTs, 1 DIV, 1 SQRT
|
||||
double e_norm = e.norm();
|
||||
double e_norm2 = e_norm > EPS ? e_norm : EPS; // Avoid division by zero.
|
||||
double e_norm_inv = 1.0 / e_norm2;
|
||||
|
||||
// # 2 MULTs
|
||||
p.e = e * e_norm_inv;
|
||||
|
||||
// # 1 ADD, 2 MULTs
|
||||
double velocity_norm_sq = p.curr_vel.dot(p.curr_vel);
|
||||
|
||||
// # 1 SQRT
|
||||
double velocity_norm = std::sqrt(velocity_norm_sq);
|
||||
|
||||
// # 1 MULT
|
||||
p.s_b_sq = velocity_norm_sq * delta_t_sq;
|
||||
|
||||
// # 3 MULTs
|
||||
p.s_b_e = p.e * (velocity_norm * delta_t);
|
||||
|
||||
#if !BENCHMARK
|
||||
// Store the current pedestrian state for visualization.
|
||||
if (dump_path && (t % dump_interval == 0)) {
|
||||
samples.emplace_back(t + 1, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
#if !BENCHMARK
|
||||
// Export recorded simulation states to CSV.
|
||||
if (dump_path) {
|
||||
ofstream out(dump_path);
|
||||
out << "time,id,x,y,p.e.x,p.e.y,color\n";
|
||||
|
||||
for (auto &[t, id, x, y, e_x, e_y, c] : samples) {
|
||||
out << t << ',' << id << ',' << x << ',' << y << ',' << e_x << ',' << e_y << ',' << c << '\n';
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
return 0;
|
||||
}
|
||||
Reference in New Issue
Block a user