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Parallelizing Sequential Graph Computations

CategoryPublications 193

Authors: Wenfei Fan, Jingbo Xu , Wenyuan Yu , Jingren Zhou, Xiaojian Luo, Ping Lu, Qiang Yin, Yang Cao, and Ruiqi Xu

Published in: ACM Transactions on Database Systems (TODS 2018)

Date of Publication: Dec 16, 2018

This article presents GRAPE, a parallel graph engine for graph computations. GRAPE differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole, without the need for recasting the entire algorithm into a new model. Underlying GRAPE are a simple programming model and a principled approach based on fixpoint computation that starts with partial evaluation and uses an incremental function as the intermediate consequence operator. We show that users can devise existing sequential graph algorithms with minor additions, and GRAPE parallelizes the computation. Under a monotonic condition, the GRAPE parallelization guarantees to converge at correct answers as long as the sequential algorithms are correct. Moreover, we show that algorithms in MapReduce, BSP, and PRAM can be optimally simulated on GRAPE. In addition to the ease of programming, we experimentally verify that GRAPE achieves comparable performance to the state-of-the-art graph systems using real-life and synthetic graphs.

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Keywords2018Distributed graph systemsParallel computation modelsTODSWenfei Fan Next: