research-article

A scalable distributed graph partitioner

Editors: Chen Li, Volker Markl Authors:

Margo SeltzerAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 8, Issue 12

Pages 1478 - 1489

https://doi.org/10.14778/2824032.2824046

Published: 01 August 2015 Publication History

Abstract

We present Scalable Host-tree Embeddings for Efficient Partitioning (Sheep), a distributed graph partitioning algorithm capable of handling graphs that far exceed main memory. Sheep produces high quality edge partitions an order of magnitude faster than both state of the art offline (e.g., METIS) and streaming partitioners (e.g., Fennel). Sheep's partitions are independent of the input graph distribution, which means that graph elements can be assigned to processing nodes arbitrarily without affecting the partition quality.

Sheep transforms the input graph into a strictly smaller elimination tree via a distributed map-reduce operation. By partitioning this tree, Sheep finds an upper-bounded communication volume partitioning of the original graph.

We describe the Sheep algorithm and analyze its space-time requirements, partition quality, and intuitive characteristics and limitations. We compare Sheep to contemporary partitioners and demonstrate that Sheep creates competitive partitions, scales to larger graphs, and has better runtime.

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Shi MPeng PZhou XLiu JXiao GLi K(2024)Connectivity-Oriented Property Graph Partitioning for Distributed Graph Pattern Query ProcessingProceedings of the ACM on Management of Data10.1145/36988042:6(1-26)Online publication date: 20-Dec-2024
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Zheng JLu YZheng W(2024)Distributed k-Hop Query Powered by an Asynchronous FrameworkWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0579-8_22(304-319)Online publication date: 2-Dec-2024
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A scalable distributed graph partitioner
1. Information systems
  1. Data management systems
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 8, Issue 12

Proceedings of the 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii

August 2015

728 pages

ISSN:2150-8097

Editors:
Chen Li
University of California, Irvine
,
Volker Markl
TU Berlin

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VLDB Endowment

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Published: 01 August 2015

Published in PVLDB Volume 8, Issue 12

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