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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20211207T055409Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211116T083000
DTEND;TZID=America/Chicago:20211116T170000
UID:submissions.supercomputing.org_SC21_sess241_spostg107@linklings.com
SUMMARY:Parallel Algorithms and Generalized Frameworks for Learning Large-
 Scale Bayesian Networks
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nParallel Algorithm
 s and Generalized Frameworks for Learning Large-Scale Bayesian Networks\n\
 nSrivastava\n\nBayesian networks (BNs) are an important subclass of graphi
 cal machine learning (ML) models that enable probabilistic reasoning about
  interactions between variables of interest. Their interpretability makes 
 them an ideal model for making high-stakes decisions in fields where expla
 inability is desirable. However, learning BNs with even few thousand varia
 bles using existing software libraries requires an infeasible amount of ti
 me. This has prevented BNs from becoming a viable alternative to other ML 
 models. To address this, we have developed scalable high-performance libra
 ries for learning large-scale BNs. In this poster, we present our work on 
 parallelizing a variety of popular BN learning algorithms, including a met
 hod for constructing parameter-sharing specialization of BNs – module netw
 orks. Our experiments show that the optimized open-source implementations 
 of our parallel algorithms reduce the time required for learning networks 
 with tens of thousands of variables from multiple months to a few hours by
  efficiently utilizing thousands of cores.\n\nTag: In-Person Only\n\nRegis
 tration Category: Tech Program Reg Pass, Exhibit Hall Only
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