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

doi:10.1109/CCGrid.2015.35

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

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
Pages	565-575
Number of pages	11
ISBN (Electronic)	9781479980062
DOIs	https://doi.org/10.1109/CCGrid.2015.35
State	Published - 7 Jul 2015
Event	15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015 - Shenzhen, China Duration: 4 May 2015 → 7 May 2015

Publication series

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

Conference

Conference	15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
Country/Territory	China
City	Shenzhen
Period	4/05/15 → 7/05/15

Keywords

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

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10.1109/CCGrid.2015.35

Cite this

Jin, C., Fu, Q., Wang, H., Hendrix, W., Chen, Z., Agrawal, A., Banerjee, A., & Choudhary, A. (2015). Running MAP inference on million node graphical models: A high performance computing perspective. In Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015 (pp. 565-575). Article 7152522 (Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015). https://doi.org/10.1109/CCGrid.2015.35

Jin, Chen ; Fu, Qiang ; Wang, Huahua et al. / Running MAP inference on million node graphical models : A high performance computing perspective. Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015. 2015. pp. 565-575 (Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015).

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title = "Running MAP inference on million node graphical models: A high performance computing perspective",

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.",

keywords = "Alternating direction method of multipliers, Markov random field, Maximum a posteriori inference, Message passing interface",

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

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year = "2015",

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doi = "10.1109/CCGrid.2015.35",

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series = "Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015",

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Jin, C, Fu, Q, Wang, H, Hendrix, W, Chen, Z, Agrawal, A, Banerjee, A & Choudhary, A 2015, Running MAP inference on million node graphical models: A high performance computing perspective. in Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015., 7152522, Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015, pp. 565-575, 15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015, Shenzhen, China, 4/05/15. https://doi.org/10.1109/CCGrid.2015.35

Running MAP inference on million node graphical models: A high performance computing perspective. / Jin, Chen; Fu, Qiang; Wang, Huahua et al.
Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015. 2015. p. 565-575 7152522 (Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Jin C, Fu Q, Wang H, Hendrix W, Chen Z, Agrawal A et al. Running MAP inference on million node graphical models: A high performance computing perspective. In Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015. 2015. p. 565-575. 7152522. (Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015). doi: 10.1109/CCGrid.2015.35

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

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