Scalable Parallel Implementation of Exact Inference in Bayesian Networks

I
ICPADS
2006
12th International Conference on Parallel and Distributed Systems - Volume 1 (ICPADS'06)
Abstract - Scalable Parallel Implementation of Exact Inference in Bayesian Networks

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12th International Conference on Parallel and Distributed Systems - Volume 1 (ICPADS'06)

Minneapolis, Minnesota

July 12-July 15

ISBN: 0-7695-2612-8

	ASCII Text	x
	Vasanth Krishna Namasivayam, Viktor K. Prasanna, "Scalable Parallel Implementation of Exact Inference in Bayesian Networks," Parallel and Distributed Systems, International Conference on, vol. 1, pp. 143-150, 12th International Conference on Parallel and Distributed Systems - Volume 1 (ICPADS'06), 2006.

	BibTex	x
	@article{ 10.1109/ICPADS.2006.96, author = {Vasanth Krishna Namasivayam and Viktor K. Prasanna}, title = {Scalable Parallel Implementation of Exact Inference in Bayesian Networks}, journal ={Parallel and Distributed Systems, International Conference on}, volume = {1}, year = {2006}, issn = {1521-9097}, pages = {143-150}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICPADS.2006.96}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Parallel and Distributed Systems, International Conference on TI - Scalable Parallel Implementation of Exact Inference in Bayesian Networks SN - 1521-9097 SP143 EP150 A1 - Vasanth Krishna Namasivayam, A1 - Viktor K. Prasanna, PY - 2006 KW - Bayesian Networks KW - Junction Tree KW - Partitioning KW - Scalability KW - Pointer-Jumping KW - Loop level parallelization. VL - 1 JA - Parallel and Distributed Systems, International Conference on ER -

Vasanth Krishna Namasivayam, University of Southern California, Los Angeles, USA

Viktor K. Prasanna, University of Southern California, Los Angeles, USA

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPADS.2006.96

We present a scalable parallel implementation for exact inference in Bayesian Networks. We explore two levels of parallelization: top level parallelization which uses pointer jumping to stride across nodes; and node level parallelization which parallelizes the node.

We have implemented the algorithm using MPI and OpenMP. We consider three different types of input junction trees: linear junction trees, balanced trees and random junction trees, and obtained speedups of 203, 181 and 190 respectively over 256 processors.

Index Terms:

Bayesian Networks, Junction Tree, Partitioning, Scalability, Pointer-Jumping, Loop level parallelization.

Citation:

Vasanth Krishna Namasivayam, Viktor K. Prasanna, "Scalable Parallel Implementation of Exact Inference in Bayesian Networks," icpads, vol. 1, pp.143-150, 12th International Conference on Parallel and Distributed Systems - Volume 1 (ICPADS'06), 2006

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