A Heuristic Algorithm for Shortest Path Search

2025-06-24

AI Summary

To address challenges in parallel single-source shortest paths (SSSP) algorithms—including excessive redundant computation, high synchronization overhead, and load imbalance—this paper proposes a work-efficient parallel search framework. Our method introduces three key innovations: (1) an asynchronous search mechanism supporting concurrent expansion of paths of multiple lengths; (2) a dynamic step-size heuristic that adaptively adjusts the expansion stride to minimize synchronization frequency; and (3) a traversal optimization strategy employing edge-level load redistribution to mitigate workload imbalance. Evaluated on 73 real-world and synthetic graphs, our approach achieves 2.5×–5.83× speedup over five state-of-the-art implementations on the GAP benchmark—excluding Road-class sparse graphs—while significantly improving throughput and scalability.

Technology Category

• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Social Networks and Social Media: Social mining and social search on the Web

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.

Problem

• Develops parallel algorithm for Single-Source Shortest Path problem
• Proposes dynamic-stepping heuristic to reduce path extensions
• Introduces traversal-optimization heuristic to balance workload

Innovation

• Parallel path extension with negligible relaxations
• Dynamic-stepping heuristic reduces synchronizations
• Traversal-optimization heuristic balances workload