Parallelizing Sequential Graph Computations. Fan, W., Xu, J., Wu, Y., Yu, W., Jiang, J., Zheng, Z., Zhang, B., Cao, Y., & Tian, C. Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17, 10(12):495-510, 2017.
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Paper Website abstract bibtex This paper presents GRAPE, a parallel system for graph computations. GRAPE di ers from prior systems in its abil- ity 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 al- gorithms can be \plugged into" GRAPE with minor changes, and get parallelized. As long as the sequential algorithms are correct, their GRAPE parallelization guarantees to ter- minate 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.
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abstract = {This paper presents GRAPE, a parallel system for graph computations. GRAPE di ers from prior systems in its abil- ity 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 al- gorithms can be \plugged into" GRAPE with minor changes, and get parallelized. As long as the sequential algorithms are correct, their GRAPE parallelization guarantees to ter- minate 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.},
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