4.1 Article

From Think Parallel to Think Sequential

期刊

SIGMOD RECORD
卷 47, 期 1, 页码 15-22

出版社

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3277006.3277011

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资金

  1. 973 Program [2014CB340302]
  2. ERC [652976]
  3. EPSRC [EP/M025268/1]
  4. NSFC [61421003, 61602023]
  5. Beijing Advanced Innovation Center for Big Data and Brain Computing
  6. NSF [IIS-1633629]
  7. EPSRC [EP/M025268/1] Funding Source: UKRI

向作者/读者索取更多资源

This paper presents G RAPE, 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 G RAPE are a simple programming model, and a principled approach based on fixpoint computation with partial evaluation and incremental computation. Under a monotonic condition, G RAPE 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 G RAPE. 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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