Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2017

Abstract

It is common for real-world applications to analyze big graphs using distributed graph processing systems. Popular in-memory systems require an enormous amount of resources to handle big graphs. While several out-of-core approaches have been proposed for processing big graphs on disk, the high disk I/O overhead could significantly reduce performance. In this paper, we propose GraphH to enable highperformance big graph analytics in small clusters. Specifically, we design a two-stage graph partition scheme to evenly divide the input graph into partitions, and propose a GAB (GatherApply-Broadcast) computation model to make each worker process a partition in memory at a time. We use an edge cache mechanism to reduce the disk I/O overhead, and design a hybrid strategy to improve the communication performance. GraphH can efficiently process big graphs in small clusters or even a single commodity server. Extensive evaluations have shown that GraphH could be up to 7.8x faster compared to popular in-memory systems, such as Pregel+ and PowerGraph when processing generic graphs, and more than 100x faster than recently proposed out-of-core systems, such as GraphD and Chaos when processing big graphs.

Keywords

Graph Processing, Distributed Computing System, Network

Discipline

Numerical Analysis and Scientific Computing | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 2017 IEEE International Conference on Cluster Computing (CLUSTER), Honolulu, USA, September 5-8

First Page

1

Last Page

11

ISBN

2168-9253

Identifier

10.1109/CLUSTER.2017.51

Publisher

IEEE

City or Country

Honolulu, USA

Additional URL

https://doi.org/10.1109/CLUSTER.2017.51

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