Comparison of Edge Partitioners for Graph Processing

Abstract : Deploying graph on a cluster requires its partitioning into a number of subgraphs, and assigning them to different machines. Two partitioning approaches have been proposed: vertex partitioning and edge partitioning. In the edge partitioning approach edges are allocated to partitions. Recent studies show that, for power-law graphs, edge partitioning is more effective than vertex partitioning. In this paper we provide an overview of existing edge partitioning algorithms. However, based only on published work, we cannot draw a clear conclusion about the relative performances of these partitioners. For this reason, we compare all the edge partition-ers currently available for GraphX. Our preliminary results suggest that Hybrid-Cut partitioner provides the best performance.
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The 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Dec 2016, Las Vegas, United States
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Contributeur : Hlib Mykhailenko <>
Soumis le : mercredi 23 novembre 2016 - 10:58:07
Dernière modification le : jeudi 11 janvier 2018 - 16:57:54
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Hlib Mykhailenko, Fabrice Huet, Giovanni Neglia. Comparison of Edge Partitioners for Graph Processing. The 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Dec 2016, Las Vegas, United States. 〈hal-01401338〉

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