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 (US) |
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Title of host publication | Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 565-575 |
Number of pages | 11 |
ISBN (Electronic) | 9781479980062 |
DOIs | |
State | Published - Jul 7 2015 |
Event | 15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015 - Shenzhen, China Duration: May 4 2015 → May 7 2015 |
Publication series
Name | Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015 |
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Other
Other | 15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015 |
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Country/Territory | China |
City | Shenzhen |
Period | 5/4/15 → 5/7/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Alternating direction method of multipliers
- Markov random field
- Maximum a posteriori inference
- Message passing interface