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configs
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cpl-opt-partitions
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ed-opt-partitions
ed-opt-partitions
 
 
inputs
inputs
 
 
makespan-opt-partitions
makespan-opt-partitions
 
 
meta_heuristic
meta_heuristic
 
 
mip-opt-partitions-json
mip-opt-partitions-json
 
 
mip-opt-partitions
mip-opt-partitions
 
 
outputs
outputs
 
 
spt-opt-partitions
spt-opt-partitions
 
 
utils
utils
 
 
.gitignore
.gitignore
 
 
README.md
README.md
 
 
all-config-all-method-results-uncertainty.csv
all-config-all-method-results-uncertainty.csv
 
 
all-config-all-seed-all-method-results.csv
all-config-all-seed-all-method-results.csv
 
 
evaluate_partition.py
evaluate_partition.py
 
 
ilp_scheduler_test.ipynb
ilp_scheduler_test.ipynb
 
 
main.py
main.py
 
 
main_ils.py
main_ils.py
 
 
meta_heuristic_main.py
meta_heuristic_main.py
 
 
meta_heuristic_playground.ipynb
meta_heuristic_playground.ipynb
 
 
milp_eval.py
milp_eval.py
 
 
parse_eval_slurm_log.py
parse_eval_slurm_log.py
 
 
parse_slurm.py
parse_slurm.py
 
 
plot_comparison.py
plot_comparison.py
 
 
requirements-pso.txt
requirements-pso.txt
 
 
requirements.txt
requirements.txt
 
 
run_mip.sh
run_mip.sh
 
 
save_results.py
save_results.py
 
 

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Task Graph Partitioning Evaluation

Evaluation of task graph partitioning

Mixed Integer Programming (MIP)

WIP

Vanilla Particle Swarm Optimization (PSO) algorithm

Description

This pipeline solves task graph hw/sw partitioning while respecting area constraints through various metaheuristics

Usage

python meta_heuristic_main.py --config <CONFIG_FILE>

Currently Implemented MetaHeuristics

  • Particle Swarm Optimization (PSO) with Sigmoid Activation
  • Discrete Binary Particle Swarm Optimization (DBPSO)
  • Comprehensive learning PSO (CLPSO)
  • Cooperative Coevolving PSO (CCPSO)
  • Global and Local genetic algorithm (GL25)
  • Enhanced Simulated Annealing (ESA)
  • Success-History based Adaptive DE (SHADE)
  • Adaptive DE (JADE)

Examples

Basic Usage

python pso_eval.py --config configs/config_default.yaml

Requirements

  • Python 3.x
  • Required dependencies (see requirements-pso.txt)

Acknowledgments

This README was generated with assistance from Claude AI.

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