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Merge branch 'elliot/inlining' into 'main'

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Inlining + Reorganization + Roofline

See merge request course-asl-fastcode26/team70!17
This commit is contained in:
Linus Baumberger
2026-05-19 23:17:44 +02:00
parent 43f11a661b b86c856e08
commit 1718a2122f
34 changed files with 2303 additions and 221 deletions
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Makefile
+16 -6
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@@ -47,12 +47,12 @@ CXXFLAGS := -O3 -march=native -std=c++23 -Wall -Wextra -flto
VARIANT ?= baseline
VARIANT_SRC := variant_$(VARIANT).cpp
VARIANT_SRC := ./variants/variant_$(VARIANT).cpp
SRC := main.cpp $(VARIANT_SRC)
HEADERS := board.hpp forces.hpp pedestrian.hpp vector2d.hpp constants.hpp social_force.hpp util.hpp
HEADERS := ./headers/board.hpp ./headers/pedestrian.hpp ./headers/vector2d.hpp ./headers/constants.hpp ./headers/util.hpp
TEST_SRC := test_vector2d.cpp test_board.cpp test_forces.cpp $(VARIANT_SRC)
TEST_SRC := ./tests/test_vector2d.cpp ./tests/test_board.cpp ./tests/test_forces.cpp $(VARIANT_SRC)
TEST_TARGET := run_tests
TEST_LIBS := -lgtest -lgtest_main -pthread
@@ -109,7 +109,7 @@ run: main
# make verify VARIANT=v1_cutoff # check a specific variant
verify: main
./$(TARGET) $(REFERENCE_N_P) $(REFERENCE_N_ITER) $(VERIFY_CSV)
@if python3 verify.py $(REFERENCE_CSV) $(VERIFY_CSV); then \
@if python3 ./scripts/verify.py $(REFERENCE_CSV) $(VERIFY_CSV); then \
echo "PASS: variant '$(VARIANT)' matches $(REFERENCE_CSV)"; \
rm -f $(VERIFY_CSV); \
else \
@@ -139,7 +139,7 @@ bench-suite: bench
# make video : compile the code, run the simulation, and generate the video.
video: run
python visualization.py $(CSV) $(MP4)
python ./scripts/visualization.py $(CSV) $(MP4)
# make sizes : compile the size-check helper.
sizes: clean
@@ -151,7 +151,17 @@ test: clean
./$(TEST_TARGET)
# make asm : compile and generate assembly files.
asm: clean main.s variant_$(VARIANT).s
ASM_CPP_SRCS := main.cpp $(VARIANT_SRC) sizes.cpp
ASM_HPP_SRCS := ./headers/util.hpp
ASM_SRCS := $(ASM_CPP_SRCS) $(ASM_HPP_SRCS)
ASM_TARGETS := $(patsubst %.cpp,%.s,$(patsubst %.hpp,%.s,$(ASM_SRCS)))
asm: clean
$(foreach src,$(ASM_CPP_SRCS),$(CXX) -O3 -march=native -std=c++23 -Wall -Wextra -DRANDOM_SEED=$(RANDOM_SEED) -S $(src) -o $(src:.cpp=.s); )
$(foreach src,$(ASM_HPP_SRCS),$(CXX) -O3 -march=native -std=c++23 -Wall -Wextra -DRANDOM_SEED=$(RANDOM_SEED) -x c++ -S $(src) -o $(src:.hpp=.s); )
%.s: %.cpp $(HEADERS)
$(CXX) -O3 -march=native -std=c++23 -Wall -Wextra -DRANDOM_SEED=$(RANDOM_SEED) -S $< -o $@
OPTIMIZATIONS.md
-163
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@@ -1,163 +0,0 @@
# Optimizations of the Social Force Model
## Table of Contents
1. [Performance Results](#performance-results)
2. [Baseline](#2-baseline)
3. [Optimization 1](#3-optimization-1)
4. [Optimization 2](#4-optimization-2)
5. [Optimization 3](#5-optimization-3)
6. [Optimization 4](#6-optimization-4)
7. [Optimization 5](#7-optimization-5)
8. [Optimization 6](#8-optimization-6)
9. [Optimization 7](#9-optimization-7)
10. [Optimization 8](#10-optimization-8)
11. [Optimization 9](#11-optimization-9)
---
## Performance Results
| # | Optimization | Cycle Count | Author |
|---|---|---|---|
| 0 | Baseline | - | - |
| 1 | | | |
| 2 | | | |
| 3 | | | |
| 4 | | | |
| 5 | | | |
| 6 | | | |
| 7 | | | |
| 8 | | | |
| 9 | | | |
---
## 2. Baseline
**Author**: [Name]
**Description**:
Standard implementation without optimizations.
```cpp
// Paste your code here only the relevant part
```
---
## 3. Optimization 1
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 4. Optimization 2
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 5. Optimization 3
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 6. Optimization 4
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 7. Optimization 5
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 8. Optimization 6
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 9. Optimization 7
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 10. Optimization 8
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 11. Optimization 9
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
9805244v1.pdf → docs/9805244v1.pdf
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docs/OPTIMIZATIONS.md
+196
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@@ -0,0 +1,196 @@
# Optimizations of the Social Force Model
## Table of Contents
1. [Performance Results](#performance-results)
2. [Baseline](#2-baseline)
3. [Optimization 1](#3-optimization-1)
4. [Optimization 2](#4-optimization-2)
5. [Optimization 3](#5-optimization-3)
6. [Optimization 4](#6-optimization-4)
7. [Optimization 5](#7-optimization-5)
8. [Optimization 6](#8-optimization-6)
9. [Optimization 7](#9-optimization-7)
10. [Optimization 8](#10-optimization-8)
11. [Optimization 9](#11-optimization-9)
---
## Performance Results
### x86
**Machine**:
```
Processors: 16 × AMD Ryzen AI 7 350 w/ Radeon 860M
Memory: 32 GiB of RAM (30.6 GiB usable)
Graphics Processor: AMD Radeon 860M Graphics
Manufacturer: LENOVO
Product Name: 83HX
System Version: IdeaPad Slim 5 14AKP10
```
**Frequency**: 2 GHz
| # | Optimization | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Author |
| --- | ------------ | ---------------- | -------------- | ------------------------- | ------ |
| 0 | Baseline | 1'058'400'000.00 | 850'352'900.00 | 1.24 | |
| 1 | Inlining | 1'058'400'000.00 | 756'088'260.00 | 1.40 | Elliot |
| 2 | | | | | |
| 3 | | | | | |
| 4 | | | | | |
| 5 | | | | | |
| 6 | | | | | |
| 7 | | | | | |
| 8 | | | | | |
| 9 | | | | | |
### ARM
**Machine**:
```
```
**Frequency**: ...
| # | Optimization | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Author |
| --- | ------------ | ------------ | ------------- | ------------------------- | ------ |
| 0 | Baseline | | | | |
| 1 | | | | | |
| 2 | | | | | |
| 3 | | | | | |
| 4 | | | | | |
| 5 | | | | | |
| 6 | | | | | |
| 7 | | | | | |
| 8 | | | | | |
| 9 | | | | | |
## 2. Baseline
**Author**: [Name]
**Description**:
Standard implementation without optimizations.
```cpp
// Paste your code here only the relevant part
```
---
## 3. Optimization 1
**Author**: [Quy Anh] «Elliot» Nguyen
**Description**:
Manually inlined functions, namely `w`, `acceleration_term_dt`, `pedestrian_repulsion_dt`, `obstacle_repulsion_dt`, `norm`, `dot`, and `find_nearest_obstacle_point`, all into `run_simulation`. Got rid of small number checks after verifying they do not affect results. Removed all references to `Vector2d` and rewrote `Pedestrian` into a struct of doubles. Reduced `sqrt()` calls using mathematical properties.
**Relevant Files**: `variant_inlining_1.cpp`, `variant_inlining_2.cpp`, `variant_inlining_3.cpp`, `variant_inlining_4.cpp`, `variant_inlining_5.cpp`, `variant_inlining_6.cpp`, `variant_inlining_7.cpp`, `pedestrian_reduced.hpp`
## 4. Optimization 2
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 5. Optimization 3
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 6. Optimization 4
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 7. Optimization 5
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 8. Optimization 6
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 9. Optimization 7
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 10. Optimization 8
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
## 11. Optimization 9
**Author**: [Name]
**Description**:
[Briefly describe the optimization technique]
```cpp
// Paste your code here only the relevant part
```
---
Overview_Formulas_Variables.md → docs/Overview_Formulas_Variables.md
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board.hpp → headers/board.hpp
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constants.hpp → headers/constants.hpp
+5
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@@ -47,6 +47,11 @@ const double phi = 100.0 / 180.0 * M_PI;
// $cos(\varphi)$
const double cos_phi = cos(phi);
//
// $\frac{1}{cos(\varphi)}$
//
const double cos_phi_inv = 1 / cos(phi);
// $<v^0>$
const double v0_mean = 1.34;
forces.hpp → headers/forces.hpp
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kperf.h → headers/kperf.h
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pedestrian.hpp → headers/pedestrian.hpp
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headers/pedestrian_reduced.hpp
+35
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@@ -0,0 +1,35 @@
#pragma once
#include "constants.hpp"
struct Pedestrian {
int color;
double curr_vel_x;
double curr_vel_y;
double w_x;
double w_y;
double curr_pos_x;
double curr_pos_y;
double e_x;
double e_y;
double v_max;
double desired_v0;
double destination_x;
double destination_y;
double s_b_sq;
double s_b_e_x;
double s_b_e_y;
Pedestrian() : color(0), curr_vel_x(0.0), curr_vel_y(0), w_x(0.0), w_y(0.0), curr_pos_x(0.0), curr_pos_y(0.0), e_x(0.0), e_y(0.0), v_max(0.0), desired_v0(0.0), destination_x(0.0), destination_y(0.0), s_b_sq(0.0), s_b_e_x(0.0), s_b_e_y(0.0) {}
Pedestrian(int clr, double c_v_x, double c_v_y, double c_p_x, double c_p_y, double e_x, double e_y, double v_max, double desired_v0, double d_x, double d_y)
: color(clr), curr_vel_x(c_v_x), curr_vel_y(c_v_y), w_x(c_v_x), w_y(c_v_y), curr_pos_x(c_p_x), curr_pos_y(c_p_y), e_x(e_x), e_y(e_y), v_max(v_max), desired_v0(desired_v0), destination_x(d_x), destination_y(d_y), s_b_sq((c_v_x * c_v_x + c_v_y * c_v_y) * delta_t_sq), s_b_e_x(e_x * (sqrt(c_v_x * c_v_x + c_v_y * c_v_y)) * delta_t), s_b_e_y(e_y * (sqrt(c_v_x * c_v_x + c_v_y * c_v_y)) * delta_t) {}
};
social_force.hpp → headers/social_force.hpp
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test_helpers.hpp → headers/test_helpers.hpp
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tsc_x86.h → headers/tsc_x86.h
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util.hpp → headers/util.hpp
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vector2d.hpp → headers/vector2d.hpp
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main.cpp
+6 -6
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@@ -1,7 +1,7 @@
#include "board.hpp"
#include "constants.hpp"
#include "pedestrian.hpp"
#include "social_force.hpp"
#include "./headers/board.hpp"
#include "./headers/constants.hpp"
#include "./headers/pedestrian.hpp"
#include "./headers/social_force.hpp"
#include <algorithm>
#include <cstdio>
@@ -14,11 +14,11 @@
#endif
#ifdef __x86_64__
#include "tsc_x86.h"
#include "./headers/tsc_x86.h"
#endif
#ifdef __aarch64__
#include "kperf.h"
#include "./headers/kperf.h"
#endif
#define N_OBSTACLES 2
pseudocode.md
-26
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@@ -1,26 +0,0 @@
```c++
for each time_step t:
for each pedestrian p:
reset_social_force(p)
// (1)
compute_acceleration_term(p)
// (2)
compute_repulsive_effects_from_pedestrians(p, pedestrians)
// (3)
compute_repulsive_effects_from_borders(p, board)
for each pedestrian p:
// next pos, desired dir, desired v, curr v
update_state(p)
```
(1) $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) \vec{e}_\alpha(t) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
(2) $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta})$
(3) $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
disable-frequency-scaling.sh → scripts/disable-frequency-scaling.sh
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scripts/roofline.py
+159
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@@ -0,0 +1,159 @@
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import numpy as np
ram_beta = 1.91
l1d_l2_beta = 64
l3_beta = 32
pi_scalar = 6.0
pi_vector = 48.0
I_vals = np.logspace(-50, 8, 500, base=2)
plt.figure(figsize=(10, 7))
plt.gca().set_facecolor('lightgrey')
plt.plot(I_vals, ram_beta * I_vals, 'k-', label='_nolegend_')
plt.plot(I_vals, l1d_l2_beta * I_vals, 'k-', label='_nolegend_')
plt.plot(I_vals, l3_beta * I_vals, 'k-', label='_nolegend_')
ram_bound_x_pos = 0.25
ram_bound_y_pos = ram_beta * ram_bound_x_pos + 0.175
plt.text(
ram_bound_x_pos,
ram_bound_y_pos,
f'Read β mem. ({ram_beta} bytes/cycle)',
color='black',
va='center',
ha='center',
fontsize=8,
rotation=20.5,
)
l1d_l2_bound_x_pos = 0.25
l1d_l2_bound_y_pos = l1d_l2_beta * l1d_l2_bound_x_pos + 3.5
plt.text(
l1d_l2_bound_x_pos,
l1d_l2_bound_y_pos,
f'Read β L1D/L2 ({l1d_l2_beta} bytes/cycle)',
color='black',
va='center',
ha='center',
fontsize=8,
rotation=20.5,
)
l3_bound_x_pos = 0.375
l3_bound_y_pos = l3_beta * l3_bound_x_pos + 3.5
plt.text(
l3_bound_x_pos,
l3_bound_y_pos,
f'Read β L3 ({l3_beta} bytes/cycle)',
color='black',
va='center',
ha='center',
fontsize=8,
rotation=20.5,
)
plt.plot(I_vals, [pi_scalar] * len(I_vals), 'k-', label='_nolegend_')
plt.text(
85,
pi_scalar + 1.5,
f'Peak π seq. ({pi_scalar} flops/cycle)',
color='black',
va='center',
ha='center',
fontsize=8,
)
plt.plot(I_vals, [pi_vector] * len(I_vals), 'k-', label='_nolegend_')
plt.text(
85,
pi_vector + 12.5,
f'Peak π par. ({pi_vector} flops/cycle)',
color='black',
va='center',
ha='center',
fontsize=8,
)
def fraction_formatter(x, _pos):
if x == 0:
return '0'
log2_x = np.log2(x)
if abs(log2_x - round(log2_x)) < 1e-6:
exponent = int(round(log2_x))
if exponent < 0:
denominator = 2**(-exponent)
return f'{1/denominator:.2f}'
elif exponent == 0:
return '1'
else:
return str(int(x))
return f'{x:.2f}'
formatter = FuncFormatter(fraction_formatter)
plt.xscale('log', base=2)
plt.yscale('log', base=2)
plt.gca().xaxis.set_major_formatter(formatter)
plt.gca().yaxis.set_major_formatter(formatter)
plt.xlabel('Operational Intensity [flops/byte]', fontsize=10)
plt.title("""Roofline Plot for the Social Force Model
CPU: AMD Ryzen AI 7 350 w/ Radeon 860M @ 2 GHz
Compiler: g++ (Debian 15.2.0-16) 15.2.0
Command: ./main 100 10000
Performance [flops/cycle]""",
loc='left',
size=10)
plt.scatter([53], [0.0936126],
c='black',
marker='o',
edgecolors='black',
label='baseline (-O0)')
plt.text(
53,
0.0936126 - 0.03,
f'0.09 flops/cycle',
color='black',
va='center',
ha='center',
fontsize=8,
)
plt.scatter([53], [0.627801],
c='black',
marker='s',
edgecolors='black',
label='baseline (-O3 -march=native)')
plt.text(
53,
0.627801 - 0.2,
f'0.63 flops/cycle',
color='black',
va='center',
ha='center',
fontsize=8,
)
plt.legend(loc='lower right',
fontsize=11,
frameon=True,
facecolor='white',
edgecolor='black')
plt.grid(True, which="both", ls="-", color='white', alpha=0.8, linewidth=1)
plt.xlim(0.05, 200)
plt.ylim(0.0005, 100)
plt.tight_layout()
plt.show()
verify.py → scripts/verify.py
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visualization.py → scripts/visualization.py
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sizes.cpp
+2 -2
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@@ -1,5 +1,5 @@
#include "pedestrian.hpp"
#include "board.hpp"
#include "./headers/pedestrian.hpp"
#include "./headers/board.hpp"
#include <iostream>
#include <math.h>
test_board.cpp → tests/test_board.cpp
+2 -2
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@@ -1,5 +1,5 @@
#include "board.hpp"
#include "test_helpers.hpp"
#include "../headers/board.hpp"
#include "../headers/test_helpers.hpp"
#include <cmath>
test_forces.cpp → tests/test_forces.cpp
+6 -6
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@@ -2,12 +2,12 @@
// Tests for forces.hpp
// =============================================================================
#include "board.hpp"
#include "constants.hpp"
#include "forces.hpp"
#include "pedestrian.hpp"
#include "test_helpers.hpp"
#include "vector2d.hpp"
#include "../headers/board.hpp"
#include "../headers/constants.hpp"
#include "../headers/forces.hpp"
#include "../headers/pedestrian.hpp"
#include "../headers/vector2d.hpp"
#include "../headers/test_helpers.hpp"
#include <algorithm>
#include <cmath>
test_vector2d.cpp → tests/test_vector2d.cpp
+2 -2
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@@ -1,7 +1,7 @@
#include "test_helpers.hpp"
#include "vector2d.hpp"
#include "../headers/test_helpers.hpp"
#include "../headers/vector2d.hpp"
#include <cmath>
#include <limits>
variant_baseline.cpp → variants/variant_baseline.cpp
+4 -6
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@@ -3,12 +3,10 @@
// This should make it more easy in the future to implement optimizations
// Our naming convention from now on is: variant_<variant_name>.cpp (Please make sure to keep that consistent, otherwise our Makefile wont work anymore)
#include "board.hpp"
#include "constants.hpp"
#include "forces.hpp"
#include "pedestrian.hpp"
#include "social_force.hpp"
#include "vector2d.hpp"
#include "../headers/board.hpp"
#include "../headers/constants.hpp"
#include "../headers/pedestrian.hpp"
#include "../headers/vector2d.hpp"
#include <algorithm>
#include <cmath>
variants/variant_inlining_1.cpp
+244
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@@ -0,0 +1,244 @@
// # This file performs inlining for run_simulation.
// # Changes: Inlined w, acceleration_term_dt, pedestrian_repulsion_dt, and obstacle_repulsion_dt
// # Predecessor: variant_baseline.cpp
#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>
#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;
Pedestrian *init_pedestrians(int n_p) {
#if RANDOM_SEED
random_device rd;
mt19937 gen(rd());
#else
mt19937 gen(SEED);
#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;
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;
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);
}
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;
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
(void)dump_path;
#endif
#if !BENCHMARK
const int dump_interval = 1;
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);
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++) {
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
Vector2d desired_velocity = p.e * p.desired_v0;
Vector2d actual_velocity = p.curr_vel;
Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv;
Vector2d f_alpha_dt = f_alpha_0_dt;
for (int j = 0; j < n_p; j++) {
if (i == j)
continue;
Vector2d r_ab = p.curr_pos - pedestrians[j].curr_pos;
double r_ab_norm = r_ab.norm();
r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS;
Vector2d s_b_e = pedestrians[j].s_b_e;
Vector2d r_ab2 = r_ab - s_b_e;
double r_ab2_norm = r_ab2.norm();
r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS;
double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - pedestrians[j].s_b_sq;
two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ;
double two_b = sqrt(two_b_sq);
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));
double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
Vector2d f_alpha_beta_neg = r_ab * scalar_mult_r_ab + r_ab2 * scalar_mult_r_ab2;
double w_val;
if (p.e.dot(f_alpha_beta_neg) >= f_alpha_beta_neg.norm() * cos_phi)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
Vector2d f_alpha_beta_dt = f_alpha_beta_neg * w_val;
f_alpha_dt += f_alpha_beta_dt;
}
for (int k = 0; k < board.n_obstacles; k++) {
Vector2d r_aB = p.curr_pos - board.obstacles[k].find_nearest_obstacle_point(p.curr_pos);
double r_aB_norm = r_aB.norm();
r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS;
double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
Vector2d f_alpha_B_neg = r_aB * scalar_mult;
double w_val;
if (p.e.dot(f_alpha_B_neg) >= f_alpha_B_neg.norm() * cos_phi)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
Vector2d f_alpha_B_dt = f_alpha_B_neg * w_val;
f_alpha_dt += f_alpha_B_dt;
}
p.w += f_alpha_dt;
}
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double w_norm = p.w.norm();
double w_norm2 = w_norm > EPS ? w_norm : EPS;
double correction_factor = std::min(1.0, p.v_max / w_norm2);
p.curr_vel = p.w * correction_factor;
p.curr_pos = p.curr_pos + p.curr_vel * dt;
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;
}
p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
Vector2d e = p.destination - p.curr_pos;
double e_norm = e.norm();
double e_norm2 = e_norm > EPS ? e_norm : EPS;
double e_norm_inv = 1.0 / e_norm2;
p.e = e * e_norm_inv;
double velocity_norm_sq = p.curr_vel.dot(p.curr_vel);
double velocity_norm = std::sqrt(velocity_norm_sq);
p.s_b_sq = velocity_norm_sq * delta_t_sq;
p.s_b_e = p.e * (velocity_norm * delta_t);
#if !BENCHMARK
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
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;
}
variants/variant_inlining_2.cpp
+243
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// # This file performs inlining for run_simulation.
// # Changes: Inlined norm() and dot()
// # Predecessor: variant_inlining_1.cpp
#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>
#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;
Pedestrian *init_pedestrians(int n_p) {
#if RANDOM_SEED
random_device rd;
mt19937 gen(rd());
#else
mt19937 gen(SEED);
#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;
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 = sqrt(init_e.x * init_e.x + init_e.y * init_e.y);
Vector2d init_e_unit = init_e / init_e_norm;
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);
}
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 = sqrt(init_e.x * init_e.x + init_e.y * init_e.y);
Vector2d init_e_unit = init_e / init_e_norm;
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
(void)dump_path;
#endif
#if !BENCHMARK
const int dump_interval = 1;
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);
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++) {
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
Vector2d desired_velocity = p.e * p.desired_v0;
Vector2d actual_velocity = p.curr_vel;
Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv;
Vector2d f_alpha_dt = f_alpha_0_dt;
for (int j = 0; j < n_p; j++) {
if (i == j)
continue;
Vector2d r_ab = p.curr_pos - pedestrians[j].curr_pos;
double r_ab_norm = sqrt(r_ab.x * r_ab.x + r_ab.y * r_ab.y);
r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS;
Vector2d s_b_e = pedestrians[j].s_b_e;
Vector2d r_ab2 = r_ab - s_b_e;
double r_ab2_norm = sqrt(r_ab2.x * r_ab2.x + r_ab2.y * r_ab2.y);
r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS;
double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - pedestrians[j].s_b_sq;
two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ;
double two_b = sqrt(two_b_sq);
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));
double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
Vector2d f_alpha_beta_neg = r_ab * scalar_mult_r_ab + r_ab2 * scalar_mult_r_ab2;
double w_val;
if ((p.e.x * f_alpha_beta_neg.x + p.e.y * f_alpha_beta_neg.y) >= sqrt(f_alpha_beta_neg.x * f_alpha_beta_neg.x + f_alpha_beta_neg.y * f_alpha_beta_neg.y) * cos_phi)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
Vector2d f_alpha_beta_dt = f_alpha_beta_neg * w_val;
f_alpha_dt += f_alpha_beta_dt;
}
for (int k = 0; k < board.n_obstacles; k++) {
Vector2d r_aB = p.curr_pos - board.obstacles[k].find_nearest_obstacle_point(p.curr_pos);
double r_aB_norm = sqrt(r_aB.x * r_aB.x + r_aB.y * r_aB.y);
r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS;
double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
Vector2d f_alpha_B_neg = r_aB * scalar_mult;
double w_val;
if ((p.e.x * f_alpha_B_neg.x + p.e.y * f_alpha_B_neg.y) >= sqrt(f_alpha_B_neg.x * f_alpha_B_neg.x + f_alpha_B_neg.y * f_alpha_B_neg.y) * cos_phi)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
Vector2d f_alpha_B_dt = f_alpha_B_neg * w_val;
f_alpha_dt += f_alpha_B_dt;
}
p.w += f_alpha_dt;
}
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double w_norm = sqrt(p.w.x * p.w.x + p.w.y * p.w.y);
double w_norm2 = w_norm > EPS ? w_norm : EPS;
double correction_factor = min(1.0, p.v_max / w_norm2);
p.curr_vel = p.w * correction_factor;
p.curr_pos = p.curr_pos + p.curr_vel * dt;
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;
}
p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
Vector2d e = p.destination - p.curr_pos;
double e_norm = sqrt(e.x * e.x + e.y * e.y);
double e_norm2 = e_norm > EPS ? e_norm : EPS;
double e_norm_inv = 1.0 / e_norm2;
p.e = e * e_norm_inv;
double velocity_norm_sq = p.curr_vel.x * p.curr_vel.x + p.curr_vel.y * p.curr_vel.y;
double velocity_norm = sqrt(velocity_norm_sq);
p.s_b_sq = velocity_norm_sq * delta_t_sq;
p.s_b_e = p.e * (velocity_norm * delta_t);
#if !BENCHMARK
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
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;
}
variants/variant_inlining_3.cpp
+255
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// # This file performs in-lining for run_simulation.
// # Changes: Inlined find_nearest_obstacle_point()
// # Predecessor: variant_inlining_2.cpp
#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>
#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;
Pedestrian *init_pedestrians(int n_p) {
#if RANDOM_SEED
random_device rd;
mt19937 gen(rd());
#else
mt19937 gen(SEED);
#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;
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 = sqrt(init_e.x * init_e.x + init_e.y * init_e.y);
Vector2d init_e_unit = init_e / init_e_norm;
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);
}
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 = sqrt(init_e.x * init_e.x + init_e.y * init_e.y);
Vector2d init_e_unit = init_e / init_e_norm;
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
(void)dump_path;
#endif
#if !BENCHMARK
const int dump_interval = 1;
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);
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++) {
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
Vector2d desired_velocity = p.e * p.desired_v0;
Vector2d actual_velocity = p.curr_vel;
Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv;
Vector2d f_alpha_dt = f_alpha_0_dt;
for (int j = 0; j < n_p; j++) {
if (i == j)
continue;
Vector2d r_ab = p.curr_pos - pedestrians[j].curr_pos;
double r_ab_norm = sqrt(r_ab.x * r_ab.x + r_ab.y * r_ab.y);
r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS;
Vector2d s_b_e = pedestrians[j].s_b_e;
Vector2d r_ab2 = r_ab - s_b_e;
double r_ab2_norm = sqrt(r_ab2.x * r_ab2.x + r_ab2.y * r_ab2.y);
r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS;
double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - pedestrians[j].s_b_sq;
two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ;
double two_b = sqrt(two_b_sq);
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));
double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
Vector2d f_alpha_beta_neg = r_ab * scalar_mult_r_ab + r_ab2 * scalar_mult_r_ab2;
double w_val;
if ((p.e.x * f_alpha_beta_neg.x + p.e.y * f_alpha_beta_neg.y) >= sqrt(f_alpha_beta_neg.x * f_alpha_beta_neg.x + f_alpha_beta_neg.y * f_alpha_beta_neg.y) * cos_phi)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
Vector2d f_alpha_beta_dt = f_alpha_beta_neg * w_val;
f_alpha_dt += f_alpha_beta_dt;
}
for (int k = 0; k < board.n_obstacles; k++) {
Vector2d v1 = p.curr_pos - board.obstacles[k].start;
Vector2d v2 = board.obstacles[k].obstacle_v;
double projection = (v1.x * v2.x + v1.y * v2.y) * board.obstacles[k].length_sq_inv;
if (projection < 0.0)
projection = 0.0;
else if (projection > 1.0)
projection = 1.0;
Vector2d r_aB = p.curr_pos - (board.obstacles[k].start + board.obstacles[k].obstacle_v * projection);
double r_aB_norm = sqrt(r_aB.x * r_aB.x + r_aB.y * r_aB.y);
r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS;
double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
Vector2d f_alpha_B_neg = r_aB * scalar_mult;
double w_val;
if ((p.e.x * f_alpha_B_neg.x + p.e.y * f_alpha_B_neg.y) >= sqrt(f_alpha_B_neg.x * f_alpha_B_neg.x + f_alpha_B_neg.y * f_alpha_B_neg.y) * cos_phi)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
Vector2d f_alpha_B_dt = f_alpha_B_neg * w_val;
f_alpha_dt += f_alpha_B_dt;
}
p.w += f_alpha_dt;
}
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double w_norm = sqrt(p.w.x * p.w.x + p.w.y * p.w.y);
double w_norm2 = w_norm > EPS ? w_norm : EPS;
double correction_factor = min(1.0, p.v_max / w_norm2);
p.curr_vel = p.w * correction_factor;
p.curr_pos = p.curr_pos + p.curr_vel * dt;
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;
}
p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
Vector2d e = p.destination - p.curr_pos;
double e_norm = sqrt(e.x * e.x + e.y * e.y);
double e_norm2 = e_norm > EPS ? e_norm : EPS;
double e_norm_inv = 1.0 / e_norm2;
p.e = e * e_norm_inv;
double velocity_norm_sq = p.curr_vel.x * p.curr_vel.x + p.curr_vel.y * p.curr_vel.y;
double velocity_norm = sqrt(velocity_norm_sq);
p.s_b_sq = velocity_norm_sq * delta_t_sq;
p.s_b_e = p.e * (velocity_norm * delta_t);
#if !BENCHMARK
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
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;
}
variants/variant_inlining_4.cpp
+282
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// # This file performs in-lining for run_simulation.
// # Changes: Replace as many Vector2d declarations with doubles as possible
// # Predecessor: variant_inlining_3.cpp
#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>
#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;
Pedestrian *init_pedestrians(int n_p) {
#if RANDOM_SEED
random_device rd;
mt19937 gen(rd());
#else
mt19937 gen(SEED);
#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;
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 = sqrt(init_e.x * init_e.x + init_e.y * init_e.y);
Vector2d init_e_unit = init_e / init_e_norm;
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);
}
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 = sqrt(init_e.x * init_e.x + init_e.y * init_e.y);
Vector2d init_e_unit = init_e / init_e_norm;
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
(void)dump_path;
#endif
#if !BENCHMARK
const int dump_interval = 1;
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);
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++) {
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double desired_velocity_x = p.e.x * p.desired_v0;
double desired_velocity_y = p.e.y * p.desired_v0;
double f_alpha_dt_x = (desired_velocity_x - p.curr_vel.x) * dt_times_tau_alpha_inv;
double f_alpha_dt_y = (desired_velocity_y - p.curr_vel.y) * dt_times_tau_alpha_inv;
for (int j = 0; j < n_p; j++) {
if (i == j)
continue;
double r_ab_x = p.curr_pos.x - pedestrians[j].curr_pos.x;
double r_ab_y = p.curr_pos.y - pedestrians[j].curr_pos.y;
double r_ab_norm = sqrt(r_ab_x * r_ab_x + r_ab_y * r_ab_y);
r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS;
double r_ab2_x = r_ab_x - pedestrians[j].s_b_e.x;
double r_ab2_y = r_ab_y - pedestrians[j].s_b_e.y;
double r_ab2_norm = sqrt(r_ab2_x * r_ab2_x + r_ab2_y * r_ab2_y);
r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS;
double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - pedestrians[j].s_b_sq;
two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ;
double two_b = sqrt(two_b_sq);
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));
double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
double f_alpha_beta_neg_x = r_ab_x * scalar_mult_r_ab + r_ab2_x * scalar_mult_r_ab2;
double f_alpha_beta_neg_y = r_ab_y * scalar_mult_r_ab + r_ab2_y * scalar_mult_r_ab2;
// TODO: Get rid of sqrt
double w_val;
double a = p.e.x * f_alpha_beta_neg_x + p.e.y * f_alpha_beta_neg_y;
double b = sqrt(f_alpha_beta_neg_x * f_alpha_beta_neg_x + f_alpha_beta_neg_y * f_alpha_beta_neg_y) * cos_phi;
// TODO: Get rid of branching without impacting performance
if (a >= b)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
f_alpha_dt_x = f_alpha_dt_x + f_alpha_beta_neg_x * w_val;
f_alpha_dt_y = f_alpha_dt_y + f_alpha_beta_neg_y * w_val;
}
for (int k = 0; k < board.n_obstacles; k++) {
double v1_x = p.curr_pos.x - board.obstacles[k].start.x;
double v1_y = p.curr_pos.y - board.obstacles[k].start.y;
double projection = (v1_x * board.obstacles[k].obstacle_v.x + v1_y * board.obstacles[k].obstacle_v.y) * board.obstacles[k].length_sq_inv;
// TODO: Get rid of branching without impacting performance
if (projection < 0.0)
projection = 0.0;
else if (projection > 1.0)
projection = 1.0;
double r_aB_x = p.curr_pos.x - (board.obstacles[k].start.x + board.obstacles[k].obstacle_v.x * projection);
double r_aB_y = p.curr_pos.y - (board.obstacles[k].start.y + board.obstacles[k].obstacle_v.y * projection);
double r_aB_norm = sqrt(r_aB_x * r_aB_x + r_aB_y * r_aB_y);
r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS;
double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
double f_alpha_B_neg_x = r_aB_x * scalar_mult;
double f_alpha_B_neg_y = r_aB_y * scalar_mult;
// TODO: Get rid of sqrt
// TODO: Get rid of branching without impacting performance
double w_val;
if ((p.e.x * f_alpha_B_neg_x + p.e.y * f_alpha_B_neg_y) >= sqrt(f_alpha_B_neg_x * f_alpha_B_neg_x + f_alpha_B_neg_y * f_alpha_B_neg_y) * cos_phi)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
double f_alpha_B_dt_x = f_alpha_B_neg_x * w_val;
double f_alpha_B_dt_y = f_alpha_B_neg_y * w_val;
f_alpha_dt_x += f_alpha_B_dt_x;
f_alpha_dt_y += f_alpha_B_dt_y;
}
p.w.x += f_alpha_dt_x;
p.w.y += f_alpha_dt_y;
}
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double w_norm = sqrt(p.w.x * p.w.x + p.w.y * p.w.y);
double w_norm2 = w_norm > EPS ? w_norm : EPS;
double correction_factor = min(1.0, p.v_max / w_norm2);
p.curr_vel = p.w * correction_factor;
p.curr_pos = p.curr_pos + p.curr_vel * dt;
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.x = p.desired_v0;
p.w.y = 0.0;
p.curr_vel.x = p.w.x;
p.curr_vel.y = p.w.y;
}
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.x = -p.desired_v0;
p.w.y = 0.0;
p.curr_vel.x = p.w.x;
p.curr_vel.y = p.w.y;
}
p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
double e_x = p.destination.x - p.curr_pos.x;
double e_y = p.destination.y - p.curr_pos.y;
double e_norm = sqrt(e_x * e_x + e_y * e_y);
double e_norm2 = e_norm > EPS ? e_norm : EPS;
double e_norm_inv = 1.0 / e_norm2;
p.e.x = e_x * e_norm_inv;
p.e.y = e_y * e_norm_inv;
double velocity_norm_sq = p.curr_vel.x * p.curr_vel.x + p.curr_vel.y * p.curr_vel.y;
double velocity_norm = sqrt(velocity_norm_sq);
p.s_b_sq = velocity_norm_sq * delta_t_sq;
p.s_b_e = p.e * (velocity_norm * delta_t);
#if !BENCHMARK
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
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;
}
variants/variant_inlining_5.cpp
+275
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// # This file performs in-lining for run_simulation.
// # Changes: Got rid of EPS checks
// # Predecessor: variant_inlining_4.cpp
#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>
#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;
Pedestrian *init_pedestrians(int n_p) {
#if RANDOM_SEED
random_device rd;
mt19937 gen(rd());
#else
mt19937 gen(SEED);
#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;
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 = sqrt(init_e.x * init_e.x + init_e.y * init_e.y);
Vector2d init_e_unit = init_e / init_e_norm;
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);
}
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 = sqrt(init_e.x * init_e.x + init_e.y * init_e.y);
Vector2d init_e_unit = init_e / init_e_norm;
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
(void)dump_path;
#endif
#if !BENCHMARK
const int dump_interval = 1;
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);
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++) {
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double desired_velocity_x = p.e.x * p.desired_v0;
double desired_velocity_y = p.e.y * p.desired_v0;
double f_alpha_dt_x = (desired_velocity_x - p.curr_vel.x) * dt_times_tau_alpha_inv;
double f_alpha_dt_y = (desired_velocity_y - p.curr_vel.y) * dt_times_tau_alpha_inv;
for (int j = 0; j < n_p; j++) {
if (i == j)
continue;
double r_ab_x = p.curr_pos.x - pedestrians[j].curr_pos.x;
double r_ab_y = p.curr_pos.y - pedestrians[j].curr_pos.y;
double r_ab_norm = sqrt(r_ab_x * r_ab_x + r_ab_y * r_ab_y);
double r_ab2_x = r_ab_x - pedestrians[j].s_b_e.x;
double r_ab2_y = r_ab_y - pedestrians[j].s_b_e.y;
double r_ab2_norm = sqrt(r_ab2_x * r_ab2_x + r_ab2_y * r_ab2_y);
double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - pedestrians[j].s_b_sq;
double two_b = sqrt(two_b_sq);
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));
double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
double f_alpha_beta_neg_x = r_ab_x * scalar_mult_r_ab + r_ab2_x * scalar_mult_r_ab2;
double f_alpha_beta_neg_y = r_ab_y * scalar_mult_r_ab + r_ab2_y * scalar_mult_r_ab2;
// TODO: Get rid of sqrt
double w_val;
double a = p.e.x * f_alpha_beta_neg_x + p.e.y * f_alpha_beta_neg_y;
double b = sqrt(f_alpha_beta_neg_x * f_alpha_beta_neg_x + f_alpha_beta_neg_y * f_alpha_beta_neg_y) * cos_phi;
// TODO: Get rid of branching without impacting performance
if (a >= b)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
f_alpha_dt_x = f_alpha_dt_x + f_alpha_beta_neg_x * w_val;
f_alpha_dt_y = f_alpha_dt_y + f_alpha_beta_neg_y * w_val;
}
for (int k = 0; k < board.n_obstacles; k++) {
double v1_x = p.curr_pos.x - board.obstacles[k].start.x;
double v1_y = p.curr_pos.y - board.obstacles[k].start.y;
double projection = (v1_x * board.obstacles[k].obstacle_v.x + v1_y * board.obstacles[k].obstacle_v.y) * board.obstacles[k].length_sq_inv;
// TODO: Get rid of branching without impacting performance
if (projection < 0.0)
projection = 0.0;
else if (projection > 1.0)
projection = 1.0;
double r_aB_x = p.curr_pos.x - (board.obstacles[k].start.x + board.obstacles[k].obstacle_v.x * projection);
double r_aB_y = p.curr_pos.y - (board.obstacles[k].start.y + board.obstacles[k].obstacle_v.y * projection);
double r_aB_norm = sqrt(r_aB_x * r_aB_x + r_aB_y * r_aB_y);
double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
double f_alpha_B_neg_x = r_aB_x * scalar_mult;
double f_alpha_B_neg_y = r_aB_y * scalar_mult;
// TODO: Get rid of sqrt
// TODO: Get rid of branching without impacting performance
double w_val;
if ((p.e.x * f_alpha_B_neg_x + p.e.y * f_alpha_B_neg_y) >= sqrt(f_alpha_B_neg_x * f_alpha_B_neg_x + f_alpha_B_neg_y * f_alpha_B_neg_y) * cos_phi)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
double f_alpha_B_dt_x = f_alpha_B_neg_x * w_val;
double f_alpha_B_dt_y = f_alpha_B_neg_y * w_val;
f_alpha_dt_x += f_alpha_B_dt_x;
f_alpha_dt_y += f_alpha_B_dt_y;
}
p.w.x += f_alpha_dt_x;
p.w.y += f_alpha_dt_y;
}
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double w_norm = sqrt(p.w.x * p.w.x + p.w.y * p.w.y);
double correction_factor = min(1.0, p.v_max / w_norm);
p.curr_vel = p.w * correction_factor;
p.curr_pos = p.curr_pos + p.curr_vel * dt;
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.x = p.desired_v0;
p.w.y = 0.0;
p.curr_vel.x = p.w.x;
p.curr_vel.y = p.w.y;
}
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.x = -p.desired_v0;
p.w.y = 0.0;
p.curr_vel.x = p.w.x;
p.curr_vel.y = p.w.y;
}
p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
double e_x = p.destination.x - p.curr_pos.x;
double e_y = p.destination.y - p.curr_pos.y;
double e_norm = sqrt(e_x * e_x + e_y * e_y);
double e_norm_inv = 1.0 / e_norm;
p.e.x = e_x * e_norm_inv;
p.e.y = e_y * e_norm_inv;
double velocity_norm_sq = p.curr_vel.x * p.curr_vel.x + p.curr_vel.y * p.curr_vel.y;
double velocity_norm = sqrt(velocity_norm_sq);
p.s_b_sq = velocity_norm_sq * delta_t_sq;
p.s_b_e = p.e * (velocity_norm * delta_t);
#if !BENCHMARK
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
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;
}
variants/variant_inlining_6.cpp
+286
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@@ -0,0 +1,286 @@
// # This file performs in-lining for run_simulation.
// # Changes: Update Vector2d references to double references
// # Predecessor: variant_inlining_5.cpp
#include "../headers/board.hpp"
#include "../headers/constants.hpp"
#include "../headers/pedestrian_reduced.hpp"
#include <algorithm>
#include <cmath>
#include <fstream>
#include <iostream>
#include <math.h>
#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;
Pedestrian *init_pedestrians(int n_p) {
#if RANDOM_SEED
random_device rd;
mt19937 gen(rd());
#else
mt19937 gen(SEED);
#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;
for (int i = 0; i < n_p_half; i++) {
double x = x_gen_left(gen);
double y = y_gen(gen);
double start_pos_x = x;
double start_pos_y = y;
double destination_x = BOARD_LENGTH + 30.0;
double destination_y = y;
double init_e_x = destination_x - start_pos_x;
double init_e_y = destination_y - start_pos_y;
double init_e_norm = sqrt(init_e_x * init_e_x + init_e_y * init_e_y);
double init_e_unit_x = init_e_x / init_e_norm;
double init_e_unit_y = init_e_y / init_e_norm;
double desired_v0;
do {
desired_v0 = velocity_gen(gen);
} while (desired_v0 <= 0.1);
double v_max = 1.3 * desired_v0;
P[i] = Pedestrian(0, 0, 0, start_pos_x, start_pos_y, init_e_unit_x, init_e_unit_y, v_max, desired_v0, destination_x, destination_y);
}
for (int i = n_p_half; i < n_p; i++) {
double x = x_gen_right(gen);
double y = y_gen(gen);
double start_pos_x = x;
double start_pos_y = y;
double destination_x = -30;
double destination_y = y;
double init_e_x = destination_x - start_pos_x;
double init_e_y = destination_y - start_pos_y;
double init_e_norm = sqrt(init_e_x * init_e_x + init_e_y * init_e_y);
double init_e_unit_x = init_e_x / init_e_norm;
double init_e_unit_y = init_e_y / init_e_norm;
double desired_v0;
do {
desired_v0 = velocity_gen(gen);
} while (desired_v0 <= 0.1);
double v_max = 1.3 * desired_v0;
P[i] = Pedestrian(1, 0, 0, start_pos_x, start_pos_y, init_e_unit_x, init_e_unit_y, v_max, desired_v0, destination_x, destination_y);
}
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
(void)dump_path;
#endif
#if !BENCHMARK
const int dump_interval = 1;
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);
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++) {
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double desired_velocity_x = p.e_x * p.desired_v0;
double desired_velocity_y = p.e_y * p.desired_v0;
double f_alpha_dt_x = (desired_velocity_x - p.curr_vel_x) * dt_times_tau_alpha_inv;
double f_alpha_dt_y = (desired_velocity_y - p.curr_vel_y) * dt_times_tau_alpha_inv;
for (int j = 0; j < n_p; j++) {
if (i == j)
continue;
double r_ab_x = p.curr_pos_x - pedestrians[j].curr_pos_x;
double r_ab_y = p.curr_pos_y - pedestrians[j].curr_pos_y;
double r_ab_norm = sqrt(r_ab_x * r_ab_x + r_ab_y * r_ab_y);
double r_ab2_x = r_ab_x - pedestrians[j].s_b_e_x;
double r_ab2_y = r_ab_y - pedestrians[j].s_b_e_y;
double r_ab2_norm = sqrt(r_ab2_x * r_ab2_x + r_ab2_y * r_ab2_y);
double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - pedestrians[j].s_b_sq;
double two_b = sqrt(two_b_sq);
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));
double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
double f_alpha_beta_neg_x = r_ab_x * scalar_mult_r_ab + r_ab2_x * scalar_mult_r_ab2;
double f_alpha_beta_neg_y = r_ab_y * scalar_mult_r_ab + r_ab2_y * scalar_mult_r_ab2;
// TODO: Get rid of sqrt
double w_val;
double a = p.e_x * f_alpha_beta_neg_x + p.e_y * f_alpha_beta_neg_y;
double b = sqrt(f_alpha_beta_neg_x * f_alpha_beta_neg_x + f_alpha_beta_neg_y * f_alpha_beta_neg_y) * cos_phi;
// TODO: Get rid of branching without impacting performance
if (a >= b)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
f_alpha_dt_x = f_alpha_dt_x + f_alpha_beta_neg_x * w_val;
f_alpha_dt_y = f_alpha_dt_y + f_alpha_beta_neg_y * w_val;
}
for (int k = 0; k < board.n_obstacles; k++) {
double v1_x = p.curr_pos_x - board.obstacles[k].start.x;
double v1_y = p.curr_pos_y - board.obstacles[k].start.y;
double projection = (v1_x * board.obstacles[k].obstacle_v.x + v1_y * board.obstacles[k].obstacle_v.y) * board.obstacles[k].length_sq_inv;
// TODO: Get rid of branching without impacting performance
if (projection < 0.0)
projection = 0.0;
else if (projection > 1.0)
projection = 1.0;
double r_aB_x = p.curr_pos_x - (board.obstacles[k].start.x + board.obstacles[k].obstacle_v.x * projection);
double r_aB_y = p.curr_pos_y - (board.obstacles[k].start.y + board.obstacles[k].obstacle_v.y * projection);
double r_aB_norm = sqrt(r_aB_x * r_aB_x + r_aB_y * r_aB_y);
double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
double f_alpha_B_neg_x = r_aB_x * scalar_mult;
double f_alpha_B_neg_y = r_aB_y * scalar_mult;
// TODO: Get rid of sqrt
// TODO: Get rid of branching without impacting performance
double w_val;
if ((p.e_x * f_alpha_B_neg_x + p.e_y * f_alpha_B_neg_y) >= sqrt(f_alpha_B_neg_x * f_alpha_B_neg_x + f_alpha_B_neg_y * f_alpha_B_neg_y) * cos_phi)
w_val = dt_neg;
else
w_val = c_neg_times_dt;
double f_alpha_B_dt_x = f_alpha_B_neg_x * w_val;
double f_alpha_B_dt_y = f_alpha_B_neg_y * w_val;
f_alpha_dt_x += f_alpha_B_dt_x;
f_alpha_dt_y += f_alpha_B_dt_y;
}
p.w_x += f_alpha_dt_x;
p.w_y += f_alpha_dt_y;
}
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double w_norm = sqrt(p.w_x * p.w_x + p.w_y * p.w_y);
double correction_factor = min(1.0, p.v_max / w_norm);
p.curr_vel_x = p.w_x * correction_factor;
p.curr_vel_y = p.w_y * correction_factor;
p.curr_pos_x = p.curr_pos_x + p.curr_vel_x * dt;
p.curr_pos_y = p.curr_pos_y + p.curr_vel_y * dt;
if (p.color == 0 && p.curr_pos_x > (BOARD_LENGTH - 1.5)) {
p.curr_pos_x = 1.0;
p.destination_y = p.curr_pos_y;
p.w_x = p.desired_v0;
p.w_y = 0.0;
p.curr_vel_x = p.w_x;
p.curr_vel_y = p.w_y;
}
if (p.color == 1 && p.curr_pos_x < 1.5) {
p.curr_pos_x = BOARD_LENGTH - 1.0;
p.destination_y = p.curr_pos_y;
p.w_x = -p.desired_v0;
p.w_y = 0.0;
p.curr_vel_x = p.w_x;
p.curr_vel_y = p.w_y;
}
p.curr_pos_y = clamp(p.curr_pos_y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
double e_x = p.destination_x - p.curr_pos_x;
double e_y = p.destination_y - p.curr_pos_y;
double e_norm = sqrt(e_x * e_x + e_y * e_y);
double e_norm_inv = 1.0 / e_norm;
p.e_x = e_x * e_norm_inv;
p.e_y = e_y * e_norm_inv;
double velocity_norm_sq = p.curr_vel_x * p.curr_vel_x + p.curr_vel_y * p.curr_vel_y;
double velocity_norm = sqrt(velocity_norm_sq);
p.s_b_sq = velocity_norm_sq * delta_t_sq;
p.s_b_e_x = p.e_x * (velocity_norm * delta_t);
p.s_b_e_y = p.e_y * (velocity_norm * delta_t);
#if !BENCHMARK
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
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;
}
variants/variant_inlining_7.cpp
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// # This file performs in-lining for run_simulation.
// # Changes: Removed 2 sqrt() calls from the loop bodies
// # Predecessor: variant_inlining_6.cpp
#include "../headers/board.hpp"
#include "../headers/constants.hpp"
#include "../headers/pedestrian_reduced.hpp"
#include <algorithm>
#include <cmath>
#include <fstream>
#include <iostream>
#include <math.h>
#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;
Pedestrian *init_pedestrians(int n_p) {
#if RANDOM_SEED
random_device rd;
mt19937 gen(rd());
#else
mt19937 gen(SEED);
#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;
for (int i = 0; i < n_p_half; i++) {
double x = x_gen_left(gen);
double y = y_gen(gen);
double start_pos_x = x;
double start_pos_y = y;
double destination_x = BOARD_LENGTH + 30.0;
double destination_y = y;
double init_e_x = destination_x - start_pos_x;
double init_e_y = destination_y - start_pos_y;
double init_e_norm = sqrt(init_e_x * init_e_x + init_e_y * init_e_y);
double init_e_unit_x = init_e_x / init_e_norm;
double init_e_unit_y = init_e_y / init_e_norm;
double desired_v0;
do {
desired_v0 = velocity_gen(gen);
} while (desired_v0 <= 0.1);
double v_max = 1.3 * desired_v0;
P[i] = Pedestrian(0, 0, 0, start_pos_x, start_pos_y, init_e_unit_x, init_e_unit_y, v_max, desired_v0, destination_x, destination_y);
}
for (int i = n_p_half; i < n_p; i++) {
double x = x_gen_right(gen);
double y = y_gen(gen);
double start_pos_x = x;
double start_pos_y = y;
double destination_x = -30;
double destination_y = y;
double init_e_x = destination_x - start_pos_x;
double init_e_y = destination_y - start_pos_y;
double init_e_norm = sqrt(init_e_x * init_e_x + init_e_y * init_e_y);
double init_e_unit_x = init_e_x / init_e_norm;
double init_e_unit_y = init_e_y / init_e_norm;
double desired_v0;
do {
desired_v0 = velocity_gen(gen);
} while (desired_v0 <= 0.1);
double v_max = 1.3 * desired_v0;
P[i] = Pedestrian(1, 0, 0, start_pos_x, start_pos_y, init_e_unit_x, init_e_unit_y, v_max, desired_v0, destination_x, destination_y);
}
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
(void)dump_path;
#endif
#if !BENCHMARK
const int dump_interval = 1;
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);
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++) {
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double desired_velocity_x = p.e_x * p.desired_v0;
double desired_velocity_y = p.e_y * p.desired_v0;
double f_alpha_dt_x = (desired_velocity_x - p.curr_vel_x) * dt_times_tau_alpha_inv;
double f_alpha_dt_y = (desired_velocity_y - p.curr_vel_y) * dt_times_tau_alpha_inv;
for (int j = 0; j < n_p; j++) {
if (i == j)
continue;
double r_ab_x = p.curr_pos_x - pedestrians[j].curr_pos_x;
double r_ab_y = p.curr_pos_y - pedestrians[j].curr_pos_y;
double r_ab_norm = sqrt(r_ab_x * r_ab_x + r_ab_y * r_ab_y);
double r_ab2_x = r_ab_x - pedestrians[j].s_b_e_x;
double r_ab2_y = r_ab_y - pedestrians[j].s_b_e_y;
double r_ab2_norm = sqrt(r_ab2_x * r_ab2_x + r_ab2_y * r_ab2_y);
double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - pedestrians[j].s_b_sq;
double two_b = sqrt(two_b_sq);
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));
double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
double f_alpha_beta_neg_x = r_ab_x * scalar_mult_r_ab + r_ab2_x * scalar_mult_r_ab2;
double f_alpha_beta_neg_y = r_ab_y * scalar_mult_r_ab + r_ab2_y * scalar_mult_r_ab2;
double w_val;
double a = (p.e_x * f_alpha_beta_neg_x + p.e_y * f_alpha_beta_neg_y) * cos_phi_inv;
double b_sq = f_alpha_beta_neg_x * f_alpha_beta_neg_x + f_alpha_beta_neg_y * f_alpha_beta_neg_y;
w_val = dt_neg;
if (a >= 0 && (a * a > b_sq)) {
w_val = c_neg_times_dt;
}
f_alpha_dt_x = f_alpha_dt_x + f_alpha_beta_neg_x * w_val;
f_alpha_dt_y = f_alpha_dt_y + f_alpha_beta_neg_y * w_val;
}
for (int k = 0; k < board.n_obstacles; k++) {
double v1_x = p.curr_pos_x - board.obstacles[k].start.x;
double v1_y = p.curr_pos_y - board.obstacles[k].start.y;
double projection = (v1_x * board.obstacles[k].obstacle_v.x + v1_y * board.obstacles[k].obstacle_v.y) * board.obstacles[k].length_sq_inv;
if (projection < 0.0)
projection = 0.0;
else if (projection > 1.0)
projection = 1.0;
double r_aB_x = p.curr_pos_x - (board.obstacles[k].start.x + board.obstacles[k].obstacle_v.x * projection);
double r_aB_y = p.curr_pos_y - (board.obstacles[k].start.y + board.obstacles[k].obstacle_v.y * projection);
double r_aB_norm = sqrt(r_aB_x * r_aB_x + r_aB_y * r_aB_y);
double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
double f_alpha_B_neg_x = r_aB_x * scalar_mult;
double f_alpha_B_neg_y = r_aB_y * scalar_mult;
double w_val;
double a = (p.e_x * f_alpha_B_neg_x + p.e_y * f_alpha_B_neg_y) * cos_phi_inv;
double b_sq = f_alpha_B_neg_x * f_alpha_B_neg_x + f_alpha_B_neg_y * f_alpha_B_neg_y;
w_val = dt_neg;
if (a >= 0 && (a * a > b_sq)) {
w_val = c_neg_times_dt;
}
double f_alpha_B_dt_x = f_alpha_B_neg_x * w_val;
double f_alpha_B_dt_y = f_alpha_B_neg_y * w_val;
f_alpha_dt_x += f_alpha_B_dt_x;
f_alpha_dt_y += f_alpha_B_dt_y;
}
p.w_x += f_alpha_dt_x;
p.w_y += f_alpha_dt_y;
}
for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i];
double w_norm = sqrt(p.w_x * p.w_x + p.w_y * p.w_y);
double correction_factor = min(1.0, p.v_max / w_norm);
p.curr_vel_x = p.w_x * correction_factor;
p.curr_vel_y = p.w_y * correction_factor;
p.curr_pos_x = p.curr_pos_x + p.curr_vel_x * dt;
p.curr_pos_y = p.curr_pos_y + p.curr_vel_y * dt;
if (p.color == 0 && p.curr_pos_x > (BOARD_LENGTH - 1.5)) {
p.curr_pos_x = 1.0;
p.destination_y = p.curr_pos_y;
p.w_x = p.desired_v0;
p.w_y = 0.0;
p.curr_vel_x = p.w_x;
p.curr_vel_y = p.w_y;
}
if (p.color == 1 && p.curr_pos_x < 1.5) {
p.curr_pos_x = BOARD_LENGTH - 1.0;
p.destination_y = p.curr_pos_y;
p.w_x = -p.desired_v0;
p.w_y = 0.0;
p.curr_vel_x = p.w_x;
p.curr_vel_y = p.w_y;
}
p.curr_pos_y = clamp(p.curr_pos_y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
double e_x = p.destination_x - p.curr_pos_x;
double e_y = p.destination_y - p.curr_pos_y;
double e_norm_inv = 1.0 / sqrt(e_x * e_x + e_y * e_y);
p.e_x = e_x * e_norm_inv;
p.e_y = e_y * e_norm_inv;
double velocity_norm_sq = p.curr_vel_x * p.curr_vel_x + p.curr_vel_y * p.curr_vel_y;
double velocity_norm = sqrt(velocity_norm_sq);
p.s_b_sq = velocity_norm_sq * delta_t_sq;
p.s_b_e_x = p.e_x * (velocity_norm * delta_t);
p.s_b_e_y = p.e_y * (velocity_norm * delta_t);
#if !BENCHMARK
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
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;
}