diff --git a/.gitignore b/.gitignore index 13913ce..da05fd2 100644 --- a/.gitignore +++ b/.gitignore @@ -7,11 +7,17 @@ *.csv *.gif *.mp4 +!reference/*.csv + +# Benchmark outputs (keep .gitkeep, ignore results) +benchmarks/* +!benchmarks/.gitkeep # Compiled binaries -main +main_* sizes # Editor files .vscode .DS_Store + diff --git a/Makefile b/Makefile index 6a7f050..652296b 100644 --- a/Makefile +++ b/Makefile @@ -45,16 +45,21 @@ endif # cross-file optimizations such as function inlining CXXFLAGS := -O3 -march=native -std=c++23 -Wall -Wextra -flto -SRC := main.cpp forces.cpp social_force_baseline.cpp +VARIANT ?= baseline + +VARIANT_SRC := variant_$(VARIANT).cpp + +SRC := main.cpp $(VARIANT_SRC) HEADERS := board.hpp forces.hpp pedestrian.hpp vector2d.hpp constants.hpp social_force.hpp -TEST_SRC := test_vector2d.cpp test_board.cpp test_forces.cpp forces.cpp +TEST_SRC := test_vector2d.cpp test_board.cpp test_forces.cpp $(VARIANT_SRC) TEST_TARGET := run_tests TEST_LIBS := -lgtest -lgtest_main -pthread -TARGET := main +TARGET := main_$(VARIANT) SIZES_TARGET := sizes + # Default simulation parameters and output files. # These can be overridden from the make command line; see examples above. N_P ?= 60 # Default number of pedestrians. @@ -70,7 +75,11 @@ RANDOM_SEED ?= 0 # Pass random-seeding mode to the C++ code. CXXFLAGS += -DRANDOM_SEED=$(RANDOM_SEED) -.PHONY: all main run bench run-bench video sizes asm test clean clean-all +# Benchmark suite parameters. +BENCH_SIZES ?= 50 100 200 500 1000 2000 5000 10000 +BENCH_ITER ?= 1000 + +.PHONY: all main run bench run-bench bench-suite video sizes asm test clean clean-all all: main @@ -91,6 +100,17 @@ bench: clean run-bench: bench $(SUDO_BENCH) ./$(TARGET) $(N_P) $(N_ITER) +# make bench-suite : compile once and run benchmarks across multiple pedestrian counts. +# Results are printed to stdout; redirect to a file to save them. +# make bench-suite > benchmarks/baseline.txt +# make bench-suite VARIANT=v1_cutoff BENCH_SIZES="100 500 1000" > benchmarks/v1_cutoff.txt +bench-suite: bench + @for n in $(BENCH_SIZES); do \ + echo "=== N_P=$$n N_ITER=$(BENCH_ITER) ==="; \ + $(SUDO_BENCH) ./$(TARGET) $$n $(BENCH_ITER); \ + echo ""; \ + done + # make video : compile the code, run the simulation, and generate the video. video: run python visualization.py $(CSV) $(MP4) @@ -105,14 +125,14 @@ test: clean ./$(TEST_TARGET) # make asm : compile and generate assembly files. -asm: clean main.s forces.s social_force_baseline.s +asm: clean main.s variant_$(VARIANT).s %.s: %.cpp $(HEADERS) $(CXX) -O3 -march=native -std=c++23 -Wall -Wextra -DRANDOM_SEED=$(RANDOM_SEED) -S $< -o $@ # make clean : delete generated files from previous builds. clean: - rm -f $(TARGET) $(SIZES_TARGET) $(TEST_TARGET) *.o *.s + rm -f main_* $(SIZES_TARGET) $(TEST_TARGET) *.o *.s # make clean-all : delete all generated files from previous builds and runs. # WARNING: this deletes all *.csv and *.mp4 files in this directory. diff --git a/README.md b/README.md index 3b35b94..7e04a2a 100644 --- a/README.md +++ b/README.md @@ -77,6 +77,31 @@ make run-bench Compiles and runs benchmark measurements. +All build commands accept a `VARIANT` parameter to select an optimization variant: + +```bash +make run-bench VARIANT=v1_cutoff N_P=100 N_ITER=10000 +``` + +The default is `VARIANT=baseline`. Each variant compiles its own binary (`main_baseline`, `main_v1_cutoff`, etc.). + +```bash +make bench-suite +``` + +Compiles once and runs benchmarks across multiple pedestrian counts (default: 50, 100, 200, 500, 1000, 2000, 5000, 10000 with 1000 iterations each). Redirect output to save results: + +```bash +make bench-suite > benchmarks/baseline.txt +make bench-suite VARIANT=v1_cutoff > benchmarks/v1_cutoff.txt +``` + +Both `BENCH_SIZES` and `BENCH_ITER` can be overridden: + +```bash +make bench-suite BENCH_SIZES="100 500 1000" BENCH_ITER=500 +``` + ```bash make test ``` @@ -121,6 +146,7 @@ N_ITER=4000 CSV=positions.csv MP4=simulation.mp4 RANDOM_SEED=0 +VARIANT=baseline ``` They can be overridden from the shell. Parameters can be provided selectively and in any order. @@ -170,7 +196,7 @@ make main This produces the executable: ```text -main +main_baseline (or main_) ``` The Makefile uses the following compiler flags: @@ -202,13 +228,16 @@ This compiles the code and runs the simulation with the default parameters. It p You can also run the executable manually after compiling: ```bash -./main [csv_output_path] +./main_baseline [csv_output_path] +./main_ [csv_output_path] ``` Example: ```bash -./main 60 4000 positions.csv +./main_baseline 60 4000 positions.csv +./main_ 60 4000 positions.csv + ``` The third argument is optional. If it is provided, simulation states are written to the given CSV file. @@ -292,9 +321,50 @@ make run-bench N_P=100 N_ITER=5000 ``` The benchmark output includes estimated work, operational intensity, measured cycles, and estimated performance in flops per cycle. - --- +## Optimization Variants + +The project supports multiple optimization variants through the `VARIANT` Makefile parameter. Each variant provides its own implementation of the force computations and simulation loop while sharing the same interface, benchmark harness, and test suite. + +The default variant is `baseline`: + +```bash +make run-bench # uses baseline +make run-bench VARIANT=baseline # equivalent +make run-bench VARIANT=v1_cutoff # uses a different variant + +``` +### File naming convention +Each variant consists of a single file in the project root: + +```bash +variant_.cpp # force computations and simulation loop +``` +The file must implement the interfaces defined in `forces.hpp` and `social_force.hpp`. The baseline implementation is `variant_baseline.cpp`. + +### Creating a new variant +1. Copy the baseline file: + ```text +cp variant_baseline.cpp variant_v1_cutoff.cpp +``` +2. Edit the new file with your optimization. + +3. Build and test: +```bash +make test VARIANT=v1_cutoff +make run-bench VARIANT=v1_cutoff N_P=100 N_ITER=10000 +``` +### Verifying correctness +A reference CSV is stored in reference/baseline_n60_i4000.csv. To verify that a variant produces the same simulation output: +```bash +make run VARIANT=v1_cutoff N_P=60 N_ITER=4000 CSV=test_output.csv +diff reference/baseline_n60_i4000.csv test_output.csv +``` +If the variant changes numerical results (e.g., distance cutoff skipping negligible forces), use a tolerance-based comparison instead of exact diff and justify the deviation. + + +--- ## Tests Run all unit tests with: @@ -311,7 +381,7 @@ The test target compiles and runs the unit tests for the simulation components. ## Assembly generation -To generate assembly files: +To generate assembly files for the currently selected VARIANT: ```bash make asm @@ -362,7 +432,7 @@ make clean This deletes files such as: ```text -main +main_* sizes run_tests *.o @@ -378,7 +448,7 @@ make clean-all This deletes: ```text -main +main_* sizes run_tests *.o diff --git a/benchmarks/.gitkeep b/benchmarks/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/forces.cpp b/forces.cpp deleted file mode 100644 index 5cd2aaa..0000000 --- a/forces.cpp +++ /dev/null @@ -1,140 +0,0 @@ -#include "forces.hpp" -#include "constants.hpp" -#include - -// Flop count: 2 ADDs, 5 MULTs, 1 SQRT -double w(Vector2d e, Vector2d f) { - if (e.dot(f) >= f.norm() * cos_phi) - return dt_neg; - else - return c_neg_times_dt; -} - -// Flop count: 2 ADDs, 4 MULTs -Vector2d acceleration_term_dt(const Pedestrian &alpha) { - - // # 2 MULTs - Vector2d desired_velocity = alpha.e * alpha.desired_v0; - - Vector2d actual_velocity = alpha.curr_vel; - - // # 2 ADDs, 2 MULTs - Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv; - - return f_alpha_0_dt; -} - - -// Flop count: 12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP -Vector2d pedestrian_repulsion_dt(const Pedestrian &alpha, const Pedestrian &beta) { - - // # 2 ADDs - // $\vec{r}_{\alpha\beta}$ - Vector2d r_ab = alpha.curr_pos - beta.curr_pos; - - // # 1 ADD, 2 MULTs, 1 SQRT - // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$ - double r_ab_norm = r_ab.norm(); - 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; - } diff --git a/forces.hpp b/forces.hpp index 6521b62..fe2453a 100644 --- a/forces.hpp +++ b/forces.hpp @@ -70,7 +70,7 @@ Vector2d acceleration_term_dt(const Pedestrian &alpha); // - $\vec{r}_{\alpha \beta} = \vec{r}_\alpha - \vec{r}_\beta$ is the vector from beta's position to alpha's position, // - $\vec{e}_\alpha$ is the desired direction of pedestrian alpha, // -// - $\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta}) = -\nabla_{\vec{r}_{\alpha\beta}} V_{\alpha\beta}\left[b(\vec{r}_{\alpha\beta})\right]$ is the repulsive effect from beta on alpha (The expanded formula details are given in the code comments given inside the function's implementation in forces.cpp), +// - $\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta}) = -\nabla_{\vec{r}_{\alpha\beta}} V_{\alpha\beta}\left[b(\vec{r}_{\alpha\beta})\right]$ is the repulsive effect from beta on alpha (The expanded formula details are given in the code comments given inside the function's implementation in variants_baseline.cpp), // // - $w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta}))$ the direction-dependent perception weighting function, for which the formula is detailed in the w() function above. // @@ -94,7 +94,7 @@ Vector2d pedestrian_repulsion_dt(const Pedestrian &alpha, const Pedestrian &beta // - $\vec{r}_{\alpha B} = \vec{r}_\alpha - \vec{r}^{\,\alpha}_B$ is the vector from the closest point on obstacle B (i.e., $\vec{r}^{\,\alpha}_B$) to pedestrian alpha's position, // - $\vec{e}_\alpha$ is the desired direction of pedestrian alpha, // -// - $\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = -\nabla_{\vec{r}_{\alpha B}} U_{\alpha B}(||\vec{r}_{\alpha B}||)$ is the repulsive effect from obstacle B on alpha (The expanded formula details are given in the code comments given inside the function's implementation in forces.cpp), +// - $\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = -\nabla_{\vec{r}_{\alpha B}} U_{\alpha B}(||\vec{r}_{\alpha B}||)$ is the repulsive effect from obstacle B on alpha (The expanded formula details are given in the code comments given inside the function's implementation in variant_baseline.cpp), // // - $w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B}(\vec{r}_{\alpha B}))$ the direction-dependent perception weighting function, for which the formula is detailed in the w() function above. // diff --git a/reference/baseline_n60_i4000.csv b/reference/baseline_n60_i4000.csv new file mode 100644 index 0000000..6d611b7 --- /dev/null +++ b/reference/baseline_n60_i4000.csv @@ -0,0 +1,240061 @@ +time,id,x,y,p.e.x,p.e.y,color +0,0,0,7.96484,1,0,0 +0,1,0,5.96831,1,0,0 +0,2,0,4.45844,1,0,0 +0,3,0,3.33742,1,0,0 +0,4,0,1.42938,1,0,0 +0,5,0,7.21954,1,0,0 +0,6,0,9.38465,1,0,0 +0,7,0,0.071649,1,0,0 +0,8,0,0.231578,1,0,0 +0,9,0,0.467563,1,0,0 +0,10,0,9.73661,1,0,0 +0,11,0,6.18362,1,0,0 +0,12,0,3.82485,1,0,0 +0,13,0,8.59868,1,0,0 +0,14,0,6.80271,1,0,0 +0,15,0,9.42113,1,0,0 +0,16,0,5.63276,1,0,0 +0,17,0,2.30948,1,0,0 +0,18,0,2.41077,1,0,0 +0,19,0,8.33128,1,0,0 +0,20,0,1.7343,1,0,0 +0,21,0,7.5531,1,0,0 +0,22,0,4.25171,1,0,0 +0,23,0,0.31407,1,0,0 +0,24,0,8.42216,1,0,0 +0,25,0,9.26574,1,0,0 +0,26,0,7.27227,1,0,0 +0,27,0,5.2083,1,0,0 +0,28,0,9.6108,1,0,0 +0,29,0,5.39684,1,0,0 +0,30,50,5.86734,-1,0,1 +0,31,50,2.76044,-1,0,1 +0,32,50,2.96314,-1,0,1 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"board.hpp" diff --git a/social_force_baseline.cpp b/variant_baseline.cpp similarity index 50% rename from social_force_baseline.cpp rename to variant_baseline.cpp index d8fe86d..38997ac 100644 --- a/social_force_baseline.cpp +++ b/variant_baseline.cpp @@ -1,3 +1,8 @@ +// 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 "board.hpp" #include "constants.hpp" #include "forces.hpp" @@ -9,29 +14,167 @@ #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 -// Fixed seeding is used by default to make simulations reproducible. #define RANDOM_SEED 0 #endif #ifndef BENCHMARK -// Benchmarking is disabled by default. #define BENCHMARK 0 #endif using namespace std; +// forces.cpp + +// Flop count: 2 ADDs, 5 MULTs, 1 SQRT +double w(Vector2d e, Vector2d f) { + if (e.dot(f) >= f.norm() * cos_phi) + return dt_neg; + else + return c_neg_times_dt; +} + +// Flop count: 2 ADDs, 4 MULTs +Vector2d acceleration_term_dt(const Pedestrian &alpha) { + + // # 2 MULTs + Vector2d desired_velocity = alpha.e * alpha.desired_v0; + + Vector2d actual_velocity = alpha.curr_vel; + + // # 2 ADDs, 2 MULTs + Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv; + + return f_alpha_0_dt; +} + +// Flop count: 12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP +Vector2d pedestrian_repulsion_dt(const Pedestrian &alpha, const Pedestrian &beta) { + + // # 2 ADDs + // $\vec{r}_{\alpha\beta}$ + Vector2d r_ab = alpha.curr_pos - beta.curr_pos; + + // # 1 ADD, 2 MULTs, 1 SQRT + // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$ + double r_ab_norm = r_ab.norm(); + 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.