GRAPE: parallelizing sequential graph computations

Wenfei Fan, Jingbo Xu, Yinghui Wu, Wenyuan Yu, Jiaxin Jiang

Research output: Contribution to journalConference articlepeer-review

29 Citations (Scopus)

Abstract

We demonstrate GRAPE, a parallel GRAPh query Engine. GRAPE advocates a parallel model based on a simultaneous fixed point computation in terms of partial and incremental evaluation. It differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole, without the need for recasting the entire algorithms into a new model. One of its unique features is that under a monotonic condition, GRAPE parallelization guarantees to terminate with correct answers as long as the sequential algorithms "plugged in" are correct. We demonstrate its parallel computations, ease-of-use and performance compared with the start-of-the-art graph systems. We also demonstrate a use case of GRAPE in social media marketing.

Original languageEnglish
Pages (from-to)1889-1892
Number of pages4
JournalProceedings of the VLDB Endowment
Volume10
Issue number12
DOIs
Publication statusPublished - 1 Aug 2017
Event43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany
Duration: 28 Aug 20171 Sept 2017

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