The more the merrier

Author:

Then Manuel1,Kaufmann Moritz1,Chirigati Fernando2,Hoang-Vu Tuan-Anh2,Pham Kien2,Kemper Alfons1,Neumann Thomas1,Vo Huy T.2

Affiliation:

1. Technische Universität München

2. New York University

Abstract

Graph analytics on social networks, Web data, and communication networks has been widely used in a plethora of applications. Many graph analytics algorithms are based on breadth-first search (BFS) graph traversal, which is not only time-consuming for large datasets but also involves much redundant computation when executed multiple times from different start vertices. In this paper, we propose Multi-Source BFS (MS-BFS), an algorithm that is designed to run multiple concurrent BFSs over the same graph on a single CPU core while scaling up as the number of cores increases. MS-BFS leverages the properties of small-world networks , which apply to many real-world graphs, and enables efficient graph traversal that: (i) shares common computation across concurrent BFSs; (ii) greatly reduces the number of random memory accesses; and (iii) does not incur synchronization costs. We demonstrate how a real graph analytics application---all-vertices closeness centrality---can be efficiently solved with MS-BFS. Furthermore, we present an extensive experimental evaluation with both synthetic and real datasets, including Twitter and Wikipedia, showing that MS-BFS provides almost linear scalability with respect to the number of cores and excellent scalability for increasing graph sizes, outperforming state-of-the-art BFS algorithms by more than one order of magnitude when running a large number of BFSs.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

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