Distributed Single-Source Shortest Path with Only Local Relaxation

Abstract

Finding the shortest path from a source vertex to any other vertices on a graph (single-source shortest path, SSSP) is used in a wide range of applications. With the rapid expansion of graph data volume, graphs are too large to be stored and processed in a standalone machine. Therefore, performing SSSP distributively in the computer clusters becomes an inevitable way. We found that the performance of existing distributed SSSP algorithms is limited by the communication cost between workers, which is caused by global relaxation. To eliminate the expensive communication cost, we propose an efficient distributed SSSP algorithm LR-SSSP that replaces global relaxation with local relaxation. Furthermore, we propose two optimizations, i.e., lazy synchronization and forward relaxation, to reduce invalid synchronization and communication. Our results show that LR-SSSP can achieve up to 6–20× speedup over the state-of-the-art Δ-stepping++ algorithm.