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...
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| Veröffentlicht in: | IEEE transactions on parallel and distributed systems 2023-06, Vol.34 (6), p.1720-1722 |
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| container_end_page | 1722 |
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| container_issue | 6 |
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| container_title | IEEE transactions on parallel and distributed systems |
| container_volume | 34 |
| creator | Si, Jiaqi Guo, Junyi Hao, Zhewen He, Wenyang Li, Ruihan Pan, Yueyang Fu, Zhenxin Fan, Chun |
| description | 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. |
| doi_str_mv | 10.1109/TPDS.2022.3206099 |
| format | Article |
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(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. 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| subjects | Bioinformatics Computational modeling Computer science Genomics Gold Probabilistic logic Reproducible computation Silicon Student Cluster Competition |
| title | Critique of "A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery" by SCC Team From Peking University |
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