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Developing parallel graph algorithms with correctness guarantees is nontrivial even for experienced programmers. Is it possible to parallelize existing sequential graph algorithms, without recasting the algorithms into a parallel model? Better yet, can the parallelization guarantee to converge at correct answers as long as the sequential algorithms provided are correct? GRAPE tackles these questions, to make parallel graph computations accessible to a large group of users. This paper presents (a) the parallel model of GRAPE, based on partial evaluation and incremental computation, and (b) a performance study, showing that GRAPE achieves performance comparable to the state-of-the-art systems.
|Number of pages||12|
|Journal||IEEE Data Engineering Bulletin|
|Publication status||Published - 30 Sep 2017|