From Think Parallel to Think Sequential – SIGMOD Record

Type: Research Highlight

Title: From Think Parallel to Think Sequential

Wenfei Fan, Yang Cao, Jingbo Xu, Wenyuan Yu, Yinghui Wu, Chao Tian, Jiaxin Jiang, Bohan Zhang

Available in: PDF

This paper presents GRAPE, a parallel GRAPh Engine for graph computations. GRAPE differs from previous graph 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. Underlying GRAPE are a simple programming model, and a principled approach based on fixpoint computation with partial evaluation and incremental computation. Under a monotonic condition, GRAPE guarantees to converge at correct answers as long as the sequential algorithms are correct. We show how our familiar sequential graph algorithms can be parallelized by GRAPE. In addition to the ease of programming, we experimentally verify that GRAPE achieves comparable performance to the state-of-theart graph systems, using real-life and synthetic graphs.

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