Scalable single-source shortest path algorithms on distributed memory systems

Abstract

Single-source shortest path (SSSP) is a well-known graph computation that has been studied for more than half a century. It is one of the most common graph analytical analysis in many research areas such as networks, communication, transportation, electronics and so on. In this paper, we propose scalable SSSP algorithms for distributed memory systems. Our algorithms are based on a Δ -stepping algorithm with the use of a two dimensional (2D) graph layout as an underlying graph data structure to reduce communication overhead and improve load balancing. The detailed evaluation of the algorithms on various large-scale real-world graphs is also included. Our experiments show that the algorithm with the 2D graph layout delivers up to three times the performance (in TEPS), and uses only one-fifth of the communication time of the algorithm with a one dimensional layout.