Constructing Bisimulation Summaries on a Multi-Core Graph Processing Framework
Proceedings of the GRADES'15 on - GRADES'15
Bisimulation summaries of graph data have multiple applications, including facilitating graph exploration and enabling query optimization techniques, but efficient, scalable, summary construction is challenging. The literature describes parallel construction algorithms using message-passing, and these have been recently adapted to MapReduce environments. The fixpoint nature of bisimulation is well suited to iterative graph processing, but the existing MapReduce solutions do not drastically
... ot drastically decrease per-iteration times as the computation progresses. In this paper, we focus on leveraging parallel multi-core graph frameworks with the goal of constructing summaries in roughly the same amount of time that it takes to input the data into the framework (for a range of real world data graphs) and output the summary. To achieve our goal we introduce a singleton optimization that significantly reduces per-iteration times after only a few iterations. We present experimental results validating that our scalable GraphChi implementation achieves our goal with bisimulation summaries of million to billion edge graphs. General Terms Bisimulation, graph summaries, scalable, parallel Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.