›› 2015, Vol. 30 ›› Issue (1): 20-29.doi: 10.1007/s11390-015-1501-x

Special Issue: Computer Architecture and Systems; Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Computer Architecture and Systems for Big Data • Previous Articles     Next Articles

Bipartite-Oriented Distributed Graph Partitioning for Big Learning

Rong Chen(陈榕), Member, CCF, ACM, IEEE, Jia-Xin Shi(施佳鑫), Hai-Bo Chen(陈海波), Senior Member, CCF, Member, ACM, IEEE, Bin-Yu Zang(臧斌宇), Senior Member, CCF, Member, ACM, IEEE   

  1. Shanghai Key Laboratory of Scalable Computing and Systems, Institute of Parallel and Distributed Systems Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2014-07-15 Revised:2014-10-14 Online:2015-01-05 Published:2015-01-05
  • About author:Rong Chen received his B.S., M.S., and Ph.D. degrees in computer science from Fudan University, Shanghai, in 2004, 2007, and 2011, respectively. He is currently an assistant professor of the Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University, China. He is a member of CCF, ACM, and IEEE. His current research interests include, but are not limited to, distributed systems, operating systems and virtualization.
  • Supported by:

    This work was supported in part by the Doctoral Fund of Ministry of Education of China under Grant No. 20130073120040, the Program for New Century Excellent Talents in University of Ministry of Education of China, the Shanghai Science and Technology Development Funds under Grant No. 12QA1401700, a foundation for the Author of National Excellent Doctoral Dissertation of China, the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing under Grant No. 2014A05, the National Natural Science Foundation of China under Grant Nos. 61003002, 61402284, the Shanghai Science and Technology Development Fund for High-Tech Achievement Translation under Grant No. 14511100902, and the Singapore National Research Foundation under Grant No. CREATE E2S2.

Many machine learning and data mining (MLDM) problems like recommendation, topic modeling, and medical diagnosis can be modeled as computing on bipartite graphs. However, most distributed graph-parallel systems are oblivious to the unique characteristics in such graphs and existing online graph partitioning algorithms usually cause excessive replication of vertices as well as significant pressure on network communication. This article identifies the challenges and opportunities of partitioning bipartite graphs for distributed MLDM processing and proposes BiGraph, a set of bipartite-oriented graph partitioning algorithms. BiGraph leverages observations such as the skewed distribution of vertices, discriminated computation load and imbalanced data sizes between the two subsets of vertices to derive a set of optimal graph partitioning algorithms that result in minimal vertex replication and network communication. BiGraph has been implemented on PowerGraph and is shown to have a performance boost up to 17.75X (from 1.16X) for four typical MLDM algorithms, due to reducing up to 80% vertex replication, and up to 96% network traffic.

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