SCHEDULE: NOV 12-18, 2011

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A Scalable Eigensolver for Large Scale-Free Graphs Using 2D Partitioning

SESSION: Applications

EVENT TYPE: Paper

TIME: 1:30PM - 2:00PM

AUTHOR(S):Andy Yoo, Allison Baker, Roger Pearce, Van Henson

ROOM:TCC 305

ABSTRACT:
Eigensolvers are important tools for analyzing and mining useful information from scale-free graphs. Such graphs are used in many applications and can be extremely large. Unfortunately, existing parallel eigensolvers do not scale well for these graphs due to the high communication overhead in the parallel matrix-vector multiplication (MatVec). We develop a MatVec algorithm based on 2D edge partitioning that significantly reduces the communication costs and embed it into a popular eigensolver library. We demonstrate that the enhanced eigensolver can attain two orders of magnitude performance improvement compared to the original on a state-of-art massively parallel machine. We illustrate the performance of the embedded MatVec by computing eigenvalues of a scale-free graph with 300 million vertices and 5 billion edges, the largest scale-free graph analyzed by any in-memory parallel eigensolver, to the best of our knowledge.

Chair/Author Details:

Andy Yoo - Lawrence Livermore National Laboratory

Allison Baker - Lawrence Livermore National Laboratory

Roger Pearce - Texas A&M University

Van Henson - Lawrence Livermore National Laboratory

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The full paper can be found in the ACM Digital Library