GraSP: distributed streaming graph partitioning

Battaglino, Casey; Pienta, Pienta; Vuduc, Richard

doi:10.5821/hpgm15.3

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…To the best of our knowledge, GraSP [44] is the only work on online edge-cut partitioning. Its implementation follows the restreaming partitioning [36] that shows the singlepass algorithms of FENNEL [10] and WDG [35] can be repeated over the same data in the same order, yielding a convergent improvement in quality.…”

Section: E Multi-loader Online Graph Partitioningmentioning

confidence: 99%

Boosting Vertex-Cut Partitioning for Streaming Graphs

Sajjad

Payberah

Rahimian

et al. 2016

2016 IEEE International Congress on Big Data (BigData Congress)

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Abstract-While the algorithms for streaming graph partitioning are proved promising, they fall short of creating timely partitions when applied on large graphs. For example, it takes 415 seconds for a state-of-the-art partitioner to work on a social network graph with 117 millions edges. We introduce an efficient platform for boosting streaming graph partitioning algorithms. Our solution, called HoVerCut, is Horizontally and Vertically scalable. That is, it can run as a multi-threaded process on a single machine, or as a distributed partitioner across multiple machines. Our evaluations, on both real-world and synthetic graphs, show that HoVerCut speeds up the process significantly without degrading the quality of partitioning. For example, HoVerCut partitions the aforementioned social network graph with 117 millions edges in 11 seconds that is about 37 times faster.

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Section: E Multi-loader Online Graph Partitioningmentioning

confidence: 99%