A Heuristic Algorithm for Shortest Path Search

TL;DR

This paper presents a novel parallel heuristic algorithm for the Single-Source Shortest Path problem that significantly improves speedup over existing methods by reducing workload and synchronization overhead.

Contribution

It introduces a new shortest path search method with heuristics for path reduction and load balancing, advancing practical parallel SSSP algorithms.

Findings

Achieves 2.5x to 5.83x speedup on benchmark graphs.

Effectively reduces workload and synchronization overhead.

Outperforms five state-of-the-art SSSP implementations.

Abstract

The Single-Source Shortest Path (SSSP) problem is well-known for the challenges in developing fast, practical, and work-efficient parallel algorithms. This work introduces a novel shortest path search method. It allows paths with different lengths to be extended in parallel at the cost of almost negligible repeated relaxations. A dynamic-stepping heuristic is proposed for the method to efficiently reduce the extended paths and the synchronizations. A traversal-optimization heuristic is proposed to improve the method by efficiently reducing the created paths and alleviating the load imbalance. Based on the method, the two heuristics are used to develop a practical SSSP algorithm, which tactfully reduces workload and overhead. The heuristics and the algorithm were evaluated on 73 real-world and synthetic graphs. The algorithm was also compared with five state-of-the-art SSSP implementations. On each GAP benchmark suite graph except Road, its speedup to the best achieved by these five implementations is 2.5x to 5.83x.