Critique of “ Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team from Peking University

ABSTRACT

Ankit Srivastava et al. [1] 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 ramBLe, evaluate its strong and weak scalability of the algorithms using real COVID-19 data sets. We verify part of the conclusions in the paper and propose our explanation of the differences. IEEE