NDLI: Data Parallel Implementation of Belief Propagation in Factor Graphs on Multi-core Platforms

Content Provider	Springer Nature Link
Author	Ma, Nam Xia, Yinglong Prasanna, Viktor K.
Copyright Year	2013
Abstract	We investigate data parallel techniques for belief propagation in acyclic factor graphs on multi-core systems. Belief propagation is a key inference algorithm in factor graph, a probabilistic graphical model that has found applications in many domains. In this paper, we explore data parallelism for basic operations over the potential tables in belief propagation. Data parallel techniques for these table operations are developed for shared memory platforms. We then propose a complete belief propagation algorithm using these table operations to perform exact inference in factor graphs. The proposed algorithms are implemented on state-of-the-art multi-socket multi-core systems with additional NUMA-aware optimizations. Our proposed algorithms exhibit good scalability using a representative set of factor graphs. On a four-socket Intel Westmere-EX system with 40 cores, we achieve 39.5 $$\times $$ speedup for the table operations and 39 $$\times $$ speedup for the complete algorithm using factor graphs with large potential tables.
Starting Page	219
Ending Page	237
Page Count	19
File Format	PDF
ISSN	08857458
Journal	International Journal of Parallel Programming
Volume Number	42
Issue Number	1
e-ISSN	15737640
Language	English
Publisher	Springer US
Publisher Date	2013-04-24
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data parallelism Belief propagation Factor graph Multi-core NUMA Theory of Computation Processor Architectures Software Engineering/Programming and Operating Systems
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Information Systems Software

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