optiVLSI

optiVLSI is a Python package that implements a collection of classic graph and circuit algorithms, including:

• Bellman‑Ford
• Dijkstra
• Prim
• Kruskal
• Lee (maze solver)
• BDD (binary decision diagrams)
• Simulation engines (compiled‑code and event‑driven)

The project has been refactored into a proper Python package with a modern pyproject.toml, type hints, comprehensive tests, documentation, and CI/CD pipelines.

Quick Start

# Install the package
pip install optivlsi

# Run a quick demo
python -m optivlsi.lee.algorithms.lee_algorithm

Documentation

• API Reference
• Algorithms Overview
• Benchmarks

Contributing

See the CONTRIBUTING.md file for guidelines.

Implemented Algorithms

Graph Algorithms

Routing

Digital Circuit Simulation

Binary Decision Diagrams

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=optivlsi --cov-report=term

# Run benchmarks
pytest tests/test_benchmarks.py --benchmark-only

Documentation

Full documentation is available in the docs/ directory:

• API Reference
• Algorithm Details

Detailed research paper: OptiVLSI.pdf

Benchmarking

Each algorithm module includes an automate.py file for automan-based benchmarking across various problem sizes. The package also provides pytest-benchmark integration for performance regression detection.

A live benchmark comparison report is automatically generated on every push:
👉 View Benchmark Report

Optimization Tools Used

• Numba: All algorithms have Numba-accelerated variants with JIT compilation
• Automan: Automated simulation and benchmarking infrastructure
• NetworkX: Reference implementations using standard graph library

Project Structure

optivlsi/                   # Main package
├── bellman_ford/           # Bellman-Ford algorithm
├── dijkstra/               # Dijkstra's algorithm
├── kruskal/                # Kruskal's MST
├── prim/                   # Prim's MST
├── lee/                    # Lee maze routing
├── simulation/
│   ├── compiled_code/      # Compiled-code simulator
│   ├── compiled_code_numba/# Numba-accelerated variant
│   └── event_driven/       # Event-driven simulator
├── bdd/                    # ROBDD
└── utils/                  # Shared utilities
bellman-ford/               # Original standalone modules
dijkstra/                   # (preserved for reproducibility)
kruskal/                    #
prim/                       #
lee-algorithm/              #
compiled-code-simulator/    #
event-driven-sim/           #
ROBDD/                      #

Original Research

This open-source codebase started as a course project for AE6102 - Parallel Scientific Computing and Visualization at IIT Bombay. The original standalone research modules are preserved in their respective directories.

Collaborators

• Rohan Rajesh Kalbag
• Neeraj Prabhu

License

MIT