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PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs
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BibTeX
@MISC{Gonzalez_powergraph:distributed,
author = {Joseph E. Gonzalez and Danny Bickson and Yucheng Low and Carlos Guestrin and Haijie Gu},
title = {PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs},
year = {}
}
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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 graphparallel 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. 1
Keyphrases
natural graph graph-parallel computation powergraph abstraction natural language processing empirical evaluation graph placement graphparallel abstraction popular graph-parallel system several graph-parallel abstraction experimental evaluation large-scale graph-structured computation power-law degree distribution magnitude gain new approach different implementation strategy large-scale real-world problem graph program power-law graph internal structure detailed analysis relative merit
