bmssp-rs 0.1.0

Fast single-source shortest paths for grid and pipeline networks using BMSSP algorithm

BMSSP: Fast Single-Source Shortest Paths

A Python package providing fast single-source shortest path (SSSP) computation using the BMSSP algorithm, with a high-performance Rust backend.

Status

Beta (v0.1.0) - This package is ready for use. The API is stable, but we welcome feedback and contributions.

Features

• Fast SSSP computation using BMSSP algorithm
• Optimized for large sparse graphs (hundreds of thousands to millions of edges)
• Support for dynamic weights and edge outages
• Clean Python API with NumPy integration
• Rust backend for maximum performance
• Support for both f32 and f64 precision
• Predecessor tracking for path reconstruction

Installation

Note: This package is not yet available on PyPI. For detailed installation instructions, see the Installation Guide.

Quick Installation

# Install Rust toolchain first
brew install rust  # or use rustup

# Clone repository and install
cd bmssp-py/python
maturin develop

Quick Start

import numpy as np
from bmssp import Graph, sssp

# Create a graph from edges
n = 4
edges = np.array([[0, 1], [1, 2], [0, 2], [2, 3]], dtype=np.int64)
weights = np.array([1.0, 2.0, 1.5, 1.0], dtype=np.float32)

graph, _ = Graph.from_edges(n, edges, weights=weights)

# Compute shortest paths from vertex 0
result = sssp(graph, weights, source=0)
print(result.dist)  # Distances from source to each vertex

# With path reconstruction
result = sssp(graph, weights, source=0, return_predecessors=True)
from bmssp import reconstruct_path
path = reconstruct_path(result.pred, target=3)
print(f"Path: {path}")

Performance Highlights

• Optimized for large sparse graphs: Handles hundreds of thousands to millions of edges efficiently
• Fast repeated computations: Ideal for scenario analysis with many SSSP calls
• Dynamic weight updates: Update weights without rebuilding graph topology
• Rust backend: High-performance implementation with minimal Python overhead

For detailed performance information, see the Performance Guide.

Example: Grid Network Optimization

See python/examples/grid_pipeline.py for a complete example demonstrating:

• Building grid networks
• Applying load flows and congestion models
• Handling outages
• Recomputing paths

Documentation

• Installation Guide - Detailed installation instructions
• Tutorial - Step-by-step guide with examples
• API Reference - Complete API documentation
• Performance Guide - Performance characteristics and optimization tips
• Algorithm Description - BMSSP algorithm overview

Development

For development setup, install the Rust toolchain and use maturin develop from the python/ directory.

Citation

If you use this implementation in your research, please cite the original paper:

Ran Duan, Jiayi Mao, Xiao Mao, Xinkai Shu, Longhui Yin. "Breaking the Sorting Barrier for Directed Single-Source Shortest Paths." arXiv preprint arXiv:2504.17033 (2025).
https://arxiv.org/abs/2504.17033

License

Licensed under the MIT License. See LICENSE for details.