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Window-based streaming graph partitioning algorithm

Patwary, MAK ORCID: 0000-0003-0760-3835, Garg, SK ORCID: 0000-0003-3510-2464 and Kang, BH ORCID: 0000-0003-3476-8838 2019 , 'Window-based streaming graph partitioning algorithm', in Proceedings of the 2019 Australasian Computer Science Week Multiconference (ACSW 2019) , Association for Computing Machinery, United States, pp. 1-10 , doi: 10.1145/3290688.3290711.

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Abstract

In the recent years, the scale of graph datasets has increased to such a degree that a single machine is not capable of efficiently processing large graphs. Thereby, efficient graph partitioning is necessary for those large graph applications. Traditional graph partitioning generally loads the whole graph data into the memory before performing partitioning; this is not only a time consuming task but it also creates memory bottlenecks. These issues of memory limitation and enormous time complexity can be resolved using stream-based graph partitioning. A streaming graph partitioning algorithm reads vertices once and assigns that vertex to a partition accordingly. This is also called an one-pass algorithm. This paper proposes an efficient window-based streaming graph partitioning algorithm called WStream. The WStream algorithm is an edge-cut partitioning algorithm, which distributes a vertex among the partitions. Our results suggest that the WStream algorithm is able to partition large graph data efficiently while keeping the load balanced across different partitions, and communication to a minimum. Evaluation results with real workloads also prove the effectiveness of our proposed algorithm, and it achieves a significant reduction in load imbalance and edge-cut with different ranges of dataset.

Item Type: Conference Publication
Authors/Creators:Patwary, MAK and Garg, SK and Kang, BH
Keywords: streaming, graph partitioning
Journal or Publication Title: Proceedings of the 2019 Australasian Computer Science Week Multiconference (ACSW 2019)
Publisher: Association for Computing Machinery
DOI / ID Number: 10.1145/3290688.3290711
Copyright Information:

Copyright 2019 Association for Computing Machinery

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