Adaptive Optimizations for Parallel Single-Source Shortest Paths

The single-source shortest path (SSSP) problem is essential in graph theory with applications in navigation, biology, social networks, and traffic analysis. The Δ-Stepping algorithm enhances parallelism by grouping vertices into "buckets" based on their tentative distances. However, its performance depends on Δ values and graph properties. This paper introduces an adaptive parallel Δ-Stepping implementation with three innovations: neighbor reordering, bucket fusion, and graph type-aware Δ selection. Tested on 11 diverse graphs, it achieves an average 7.1× speedup over serial Dijkstra's algorithm on a 48-threads CPU.