Fast and scalable NUMA-based thread parallel breadth-first search

Abstract

The breadth-first search (BFS) is one of the most centric kernels in graph processing. Beamer's direction-optimizing BFS algorithm, which selects one of two traversal directions at each level, can reduce unnecessary edge traversals. In a previous paper, we presented an efficient BFS for a non-uniform memory access (NUMA)-based system, in which the NUMA architecture was carefully considered. In this paper, we investigate the locality of memory accesses in terms of the communication with remote memories in a BFS for a NUMA system, and describe a fast and highly scalable implementation. Our new implementation achieves performance rates of 174.704 billion edges per second for a Kronecker graph with 233 vertices and 237 edges on two racks of a SGI UV 2000 system with 1,280 threads. The implementations described in this paper achieved the fastest entries for a shared-memory system in the June 2014 and November 2014 Graph500 lists, and produced the most energy-efficient entries in the second, third, and fourth Green Graph500 lists (big data category).