Critique of "A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery" by SCC Team From Peking University

Ankit Srivastava et al. (Srivastava et al. 2020) proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely, GS, IAMB and Inter-IAMB. As part of the Stude...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems 2023-06, Vol.34 (6), p.1720-1722
Main Authors: Si, Jiaqi, Guo, Junyi, Hao, Zhewen, He, Wenyang, Li, Ruihan, Pan, Yueyang, Fu, Zhenxin, Fan, Chun
Format: Article
Language:English
Subjects:
Bioinformatics
Computational modeling
Computer science
Genomics
Gold
Probabilistic logic
Reproducible computation
Silicon
Student Cluster Competition
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Ankit Srivastava et al. (Srivastava et al. 2020) proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely, GS, IAMB and Inter-IAMB. As part of the Student Cluster Competition at SC21, we reproduce the computational efficiency of ramBLe on our assigned Oracle cluster. The cluster has 4x36 cores in total with 100 Gbps RoCE v2 support and is equipped with CentOS-compatible Oracle Linux. Our experiments, covering the same three algorithms of the original ramBLe article (Srivastava et al. 2020), evaluate the strong and weak scalability of the algorithms using real COVID-19 data sets. We verify part of the conclusions from the original article and propose our explanation of the differences obtained in our results.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2022.3206099