Article

PowerGraph: distributed graph-parallel computation on natural graphs

Authors:

Joseph E. Gonzalez,

Carlos GuestrinAuthors Info & Claims

OSDI'12: Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation

Pages 17 - 30

Published: 08 October 2012 Publication History

Abstract

Large-scale graph-structured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graph-parallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the real-world have highly skewed power-law degree distributions, which challenge the assumptions made by these abstractions, limiting performance and scalability.

In this paper, we characterize the challenges of computation on natural graphs in the context of existing graph-parallel abstractions. We then introduce the PowerGraph abstraction which exploits the internal structure of graph programs to address these challenges. Leveraging the PowerGraph abstraction we introduce a new approach to distributed graph placement and representation that exploits the structure of power-law graphs. We provide a detailed analysis and experimental evaluation comparing PowerGraph to two popular graph-parallel systems. Finally, we describe three different implementation strategies for PowerGraph and discuss their relative merits with empirical evaluations on large-scale real-world problems demonstrating order of magnitude gains.

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cover image ACM Other conferences

OSDI'12: Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation

October 2012

362 pages

ISBN:9781931971966

Program Chairs:
Amin Vahdat
Google and University of California, San Diego
,
Chandu Thekkath
Microsoft Research Silicon Valley

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Infosys
EMC2: EMC2
Microsoft Reasearch: Microsoft Reasearch
ORACLE: ORACLE
USENIX Assoc: USENIX Assoc

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SIGOPS: ACM Special Interest Group on Operating Systems

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Published: 08 October 2012

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Cited By

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https://dl.acm.org/doi/10.1177/10943420231214532
Zhao XChen SKang Y(2024)Load Balanced PIM-Based Graph ProcessingACM Transactions on Design Automation of Electronic Systems10.1145/365995129:4(1-22)Online publication date: 21-Jun-2024
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