Projects per year
Abstract / Description of output
This paper presents GRAPE, a parallel system for graph computations. GRAPE differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole. Underlying GRAPE are a simple programming model and a principled approach, based on partial evaluation and incremental computation. We show that sequential graph algorithms can be “plugged into” GRAPE with minor changes, and get parallelized. As long as the sequential algorithms are correct, their GRAPE parallelization guarantees to terminate with correct answers under a monotonic condition. 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.
|Title of host publication
|Proceedings of the 43rd International Conference on Very Large Data Bases
|Very Large Data Base Endowment Inc.
|Number of pages
|Published - 31 Aug 2017
|43rd International Conference on Very Large Data Bases - Technical University of Munich, Munich, Germany
Duration: 28 Aug 2017 → 1 Sept 2017
|Proceedings of the VLDB Endowment
|43rd International Conference on Very Large Data Bases
|28/08/17 → 1/09/17