NDLI: Self-Adaptive Evidence Propagation on Manycore Processors

Content Provider	IEEE Xplore Digital Library
Author	Yinglong Xia Prasanna, V.K.
Copyright Year	2011
Abstract	Evidence propagation is a major step in exact inference, a key problem in exploring probabilistic graphical models. Evidence propagation is essentially a series of computations between the potential tables in cliques and separators of a given junction tree. In real applications, the size of the potential tables varies dramatically. Thus, to achieve scalability over dozens of threads remains a fundamental challenge for evidence propagation on many core processors. In this paper, we propose a self-adaptive method for evidence propagation on many core processors. Given an arbitrary junction tree, we convert evidence propagation in the junction tree into a task dependency graph. The proposed self-adaptive scheduler dynamically adjusts the number of threads for scheduling or executing tasks according to the task dependency graph. Such a self-adaptability prevents the schedulers being too idle or too busy during the scheduling process. We implemented the proposed method on the Sun UltraSPARC T2 (Niagara~2) platform that supports up to 64 hardware threads. Through a set of experiments, we show that the proposed method scales well with respect to various input junction trees and exhibits superior performance when compared with several baseline methods for evidence propagation.
Starting Page	1407
Ending Page	1416
File Size	1280787
Page Count	10
File Format	PDF
ISBN	9781612844251
ISSN	15302075
DOI	10.1109/IPDPS.2011.298
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Processor scheduling Multicore processing Bayesian methods Instruction sets Random variables Junctions
Content Type	Text
Resource Type	Article

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