diff --git a/docs/optimization_branchless.md b/docs/optimization_branchless.md new file mode 100644 index 0000000..2385f12 --- /dev/null +++ b/docs/optimization_branchless.md @@ -0,0 +1,129 @@ +# Optimization: Branchless w() (negative result) + +- **Author**: Linus +- **Predecessor**: variant_baseline.cpp +- **File(s)**: variants/variant_branchless.cpp, variants/variant_branchless_sqrtfree.cpp +- **Status**: probably: negative (slower) + +## What this changes + +Two attempts at making the field-of-view weight function `w()` branchless. + +**Attempt 1** (`variant_branchless.cpp`): replace the if/else in `w()` with an +arithmetic blend `c_neg_times_dt + cond * (dt_neg - c_neg_times_dt)`, where +`cond` is a 0.0/1.0 mask cast from a bool comparison. The `sqrt` inside +`f.norm()` is kept. + +**Attempt 2** (`variant_branchless_sqrtfree.cpp`): on top of attempt 1, also +eliminate the `sqrt` by squaring the comparison. Uses the fact that +`cos_phi = cos(100 deg)` is negative, so `f.norm() * cos_phi <= 0`, meaning +the condition is automatically true whenever `e.dot(f) >= 0`. For +`e.dot(f) < 0`, both sides are non-positive so we can square them safely: +`dot^2 <= f.dot(f) * cos_phi^2`. Needs the constant +`cos_phi_sq = cos_phi * cos_phi` in `headers/constants.hpp`. + + +Additionaly i split the mainloop into two loops, so we where able to remove the if-statement from the loop. Once again that didn't made a difference in the runtime analysis + +## Code (relevant snippet) + +Attempt 1 - arithmetic blend (still has sqrt): + +```cpp +double w(Vector2d e, Vector2d f) { + double dot = e.dot(f); + double f_norm = f.norm(); + double cond = (double)(dot >= f_norm * cos_phi); // 1.0 or 0.0 + return c_neg_times_dt + cond * (dt_neg - c_neg_times_dt); +} +``` + +Attempt 2 - blend + sqrt-free (squared comparison): + +```cpp +double w(Vector2d e, Vector2d f) { + double dot = e.x * f.x + e.y * f.y; + double f_dot_f = f.x * f.x + f.y * f.y; + // Bitwise | (not ||) keeps both sides evaluated -> no branch. + double cond = (dot >= 0.0) | (dot * dot <= f_dot_f * cos_phi_sq); + return c_neg_times_dt + cond * (dt_neg - c_neg_times_dt); +} +``` + +## Benchmark + +Conditions: min of runs, N_ITER=1000, -O3 -march=native -flto. + +A preliminary run on my laptop (Intel ULTRA 9 185H @ 2.2 GHz, WSL +Ubuntu) showed both attempts about 8-13% slower than the baseline across +N=50 to N=1000, with a roughly flat gap that did not scale with N. +For the official numbers run on the testmachines + +### x86 + +**Machine**: + +``` + +``` + +**Frequency**: + +Attempt 1 - branchless (arithmetic blend): + +| N | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Baseline | +| ---- | ------------ | ------------- | ------------------------- | ---------------- | +| 50 | | | | | +| 100 | | | | | +| 200 | | | | | +| 500 | | | | | +| 1000 | | | | | + +Attempt 2 - branchless + sqrt-free: + +| N | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Baseline | +| ---- | ------------ | ------------- | ------------------------- | ---------------- | +| 50 | | | | | +| 100 | | | | | +| 200 | | | | | +| 500 | | | | | +| 1000 | | | | | + +### ARM (Apple) + +**Machine**: + +``` + +``` + +**Frequency**: + +Attempt 1 - branchless (arithmetic blend): + +| N | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Baseline | +| ---- | ------------ | ------------- | ------------------------- | ---------------- | +| 50 | | | | | +| 100 | | | | | +| 200 | | | | | +| 500 | | | | | +| 1000 | | | | | + +Attempt 2 - branchless + sqrt-free: + +| N | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Baseline | +| ---- | ------------ | ------------- | ------------------------- | ---------------- | +| 50 | | | | | +| 100 | | | | | +| 200 | | | | | +| 500 | | | | | +| 1000 | | | | | + +## Analysis + + + +## Takeaway + + diff --git a/headers/constants.hpp b/headers/constants.hpp index 15841b4..a673de4 100644 --- a/headers/constants.hpp +++ b/headers/constants.hpp @@ -96,4 +96,7 @@ const double EPS = 1e-9; // Squared numerical tolerance const double EPS_SQ = EPS * EPS; + +const double cos_phi_sq = cos_phi * cos_phi; + const size_t CACHE_BLOCK_SIZE = std::hardware_destructive_interference_size; \ No newline at end of file diff --git a/variants/variant_branchless.cpp b/variants/variant_branchless.cpp new file mode 100644 index 0000000..2d3b736 --- /dev/null +++ b/variants/variant_branchless.cpp @@ -0,0 +1,407 @@ +// This file is merged from the forces.cpp and social_forces_baseline.cpp +// The goal is that we from now on only have one file as our baseline +// This should make it more easy in the future to implement optimizations +// Our naming convention from now on is: variant_.cpp (Please make sure to keep that consistent, otherwise our Makefile wont work anymore) + +#include "../headers/board.hpp" +#include "../headers/constants.hpp" +#include "../headers/forces.hpp" +#include "../headers/pedestrian.hpp" +#include "../headers/social_force.hpp" +#include "../headers/vector2d.hpp" + +#include +#include +#include +#include +#include // keep — forces code uses unqualified sqrt/exp +#include +#include +#include + +#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) { + + double dot = e.dot(f); + double f_norm = f.norm(); + double cond = (double)(dot >= f_norm * cos_phi); // Should be 1 if in the field of view, else 0 + return c_neg_times_dt + cond * (dt_neg - 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(); + 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 velocity_gen(v0_mean, v0_std_dev); + uniform_real_distribution y_gen(0.3, BOARD_WIDTH - 0.3); + uniform_real_distribution x_gen_left(2.0, BOARD_LENGTH / 2.0); + uniform_real_distribution 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> samples; + + if (dump_path) { + samples.reserve(static_cast(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]); + //} + for (int j = 0; j < i; j++) { + f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]); + } + for (int j = i + 1; j < n_p; j++) { + 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 = std::fmax(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; +} \ No newline at end of file diff --git a/variants/variant_branchless_sqrtfree.cpp b/variants/variant_branchless_sqrtfree.cpp new file mode 100644 index 0000000..d8f4097 --- /dev/null +++ b/variants/variant_branchless_sqrtfree.cpp @@ -0,0 +1,412 @@ +// This file is merged from the forces.cpp and social_forces_baseline.cpp +// The goal is that we from now on only have one file as our baseline +// This should make it more easy in the future to implement optimizations +// Our naming convention from now on is: variant_.cpp (Please make sure to keep that consistent, otherwise our Makefile wont work anymore) + +#include "../headers/board.hpp" +#include "../headers/constants.hpp" +#include "../headers/forces.hpp" +#include "../headers/pedestrian.hpp" +#include "../headers/social_force.hpp" +#include "../headers/vector2d.hpp" + +#include +#include +#include +#include +#include // keep — forces code uses unqualified sqrt/exp +#include +#include +#include + +#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: 3 ADDs, 7 MULTs (no SQRT, no branch) +// Branchless + sqrt-free: cos_phi < 0 so f.norm()*cos_phi <= 0. +// - If dot >= 0: condition is trivially true. +// - If dot < 0: both sides non-positive; square them safely: +// dot^2 <= f.dot(f) * cos_phi^2. +// Bitwise | (not ||) keeps both sides evaluated and avoids a branch. +double w(Vector2d e, Vector2d f) { + + double dot = e.x * f.x + e.y * f.y; + double f_dot_f = f.x * f.x + f.y * f.y; + double cond = (dot >= 0.0) | (dot * dot <= f_dot_f * cos_phi_sq); + return c_neg_times_dt + cond * (dt_neg - 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(); + 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 velocity_gen(v0_mean, v0_std_dev); + uniform_real_distribution y_gen(0.3, BOARD_WIDTH - 0.3); + uniform_real_distribution x_gen_left(2.0, BOARD_LENGTH / 2.0); + uniform_real_distribution 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> samples; + + if (dump_path) { + samples.reserve(static_cast(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]); + //} + for (int j = 0; j < i; j++) { + f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]); + } + for (int j = i + 1; j < n_p; j++) { + 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 = std::fmax(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; +} \ No newline at end of file