sponsored byIEEEACMThe International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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Fast Iterative Graph Computation: A Path Centric Approach

SESSION: Big Data Analysis


TIME: 10:30AM - 11:00AM

SESSION CHAIR: Kelly Gaither

AUTHOR(S):Pingpeng Yuan, Wenya Zhang, Changfeng Xie, Ling Liu, Hai Jin, Kisung Lee



Large scale graph processing represents an interesting challenge due to the lack of locality. This paper presents PathGraph for improving iterative graph computation on graphs with billions of edges. Our system design has three unique features: First, we model a large graph using a collection of tree-based partitions and use an path-centric computation rather than vertex-centric or edge-centric computation. Our parallel computation model significantly improves the memory and disk locality for performing iterative computation algorithms. Second, we design a compact storage that further maximize sequential access and minimize random access on storage media. Third, we implement the path-centric computation model by using a scatter/gather programming model, which parallels the iterative computation at partition tree level and performs sequential updates for vertices in each partition tree. The experimental results show that the path-centric approach outperforms vertex-centric and edge-centric systems on a number of graph algorithms for both in-memory and out-of-core graphs.

Chair/Author Details:

Kelly Gaither (Chair) - Texas Advanced Computing Center

Pingpeng Yuan - Huazhong University of Science & Technology

Wenya Zhang - Huazhong University of Science & Technology

Changfeng Xie - Huazhong University of Science & Technology

Ling Liu - Georgia Institute of Technology

Hai Jin - Huazhong University of Science & Technology

Kisung Lee - Georgia Institute of Technology

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society