Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2020

Abstract

Recent studies showed that single-machine graph processing systems can be as highly competitive as cluster-based approaches on large-scale problems. While several out-of-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge shards on disk. Third, we use a compressed edge cache mechanism to fully utilize the available memory of a machine to reduce the amount of disk accesses for edges. Extensive evaluations have shown that GraphMP could outperform existing single-machine out-of-core systems such as GraphChi, X-Stream and GridGraph by up to 30, and can be as highly competitive as distributed graph engines like Pregel+, PowerGraph and Chaos.

Keywords

Graph Processing, Big Data, Parallel Computing, Vertex-Centric Programming Model

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Big Data

Volume

6

Issue

4

First Page

816

Last Page

829

ISSN

2332-7790

Identifier

10.1109/TBDATA.2019.2908384

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TBDATA.2019.2908384

Download

Share

COinS