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DTSTART:19700308T020000
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LOCATION:Track 2
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UID:submissions.supercomputing.org_SC20_sess179_pap296@linklings.com
SUMMARY:A Parallel Framework for Constraint-Based Bayesian Network Learnin
 g via Markov Blanket Discovery
DESCRIPTION:Paper\n\nA Parallel Framework for Constraint-Based Bayesian Ne
 twork Learning via Markov Blanket Discovery\n\nSrivastava, Chockalingam, A
 luru\n\nBayesian networks (BNs) are a widely used graphical model in machi
 ne learning. As learning the structure of BNs is NP-hard, high-performance
  computing methods are necessary for constructing large-scale networks. In
  this paper, we present a parallel framework to scale BN structure learnin
 g algorithms to tens of thousands of variables. Our framework is applicabl
 e to learning algorithms that rely on the discovery of Markov blankets (MB
 s) as an intermediate step. We demonstrate the applicability of our framew
 ork by parallelizing three different algorithms: Grow-Shrink (GS), Increme
 ntal Association MB (IAMB), and Interleaved IAMB (Inter-IAMB). Our impleme
 ntations are able to construct BNs from real data sets with tens of thousa
 nds of variables and thousands of observations in less than a minute on 10
 24 cores, with a speedup of up to 845X and 82.5% efficiency. Furthermore, 
 we demonstrate using simulated data sets that our proposed parallel framew
 ork can scale to BNs of even higher dimensionality.\n\nTag: Applications, 
 Machine Learning, Deep Learning and Artificial Intelligence, Parallel Prog
 ramming Languages, Libraries, and Models\n\nRegistration Category: Tech Pr
 ogram Reg Pass
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