Running MAP inference on million node graphical models: A high performance computing perspective

Chen Jin, Qiang Fu, Huahua Wang, William Hendrix, Zhengzhang Chen, Ankit Agrawal, Arindam Banerjee, Alok Choudhary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

An important problem in discrete graphical models is the maximum a posterior (MAP) inference problem. Recent research has been focusing on the development of parallel MAP inference algorithm, which scales to graphical models of millions of nodes. In this paper, we introduce a parallel implementation of the recently proposed Bethe-ADMM algorithm using Message Passing Interface (MPI), which allows us to fully utilize the computing power provided by the modern supercomputers with thousands of cores. Experimental results demonstrate that for a broad class of problems, our parallel implementation of Bethe-ADMM scales almost linearly even with thousands of cores.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages565-575
Number of pages11
ISBN (Electronic)9781479980062
DOIs
StatePublished - Jul 7 2015
Event15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015 - Shenzhen, China
Duration: May 4 2015May 7 2015

Publication series

NameProceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015

Other

Other15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
CountryChina
CityShenzhen
Period5/4/155/7/15

Keywords

  • Alternating direction method of multipliers
  • Markov random field
  • Maximum a posteriori inference
  • Message passing interface

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Networks and Communications
  • Software

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