We are excited to announce that the SC21 Reproducibility Challenge Committee has selected the SC20 paper “A parallel framework for constraint-based Bayesian network learning via Markov blanket discovery” by Ankit Srivastava, Sriram P Chockalingam, and Srinivas Aluru to serve as the Student Cluster Competition (SCC) benchmark for this year’s Reproducibility Challenge. A team of reviewers selected the paper from 96 accepted SC20 papers, based on the paper’s Artifact Descriptor (AD) and its suitability to the SCC. The authors and the Reproducibility Committee have been working to create a reproducible benchmark that builds on the paper’s results. At SC21, the SCC teams will be asked to run the benchmark, replicating the findings from the original paper under different settings and with different datasets.
What makes the work of the student teams particularly relevant is the replication of the paper’s work across the different clusters that will be fielded by the teams. In the era of heterogeneous computing, porting applications from one platform to another is not a simple task. The work of the student teams at SC21 is a fantastic way to dive into reproducibility challenges across various platforms and emerge with shareable, robust insights. It is the ensemble of each team’s implementation and execution of the challenge on sixteen different platforms that will earn this paper ACM’s “Results Replicated” badge in the ACM Digital Library. Sharing is at the core of the Reproducibility Challenge – so, the work of the SCC teams will be collected and published. We have already published three special issues in Parallel Computing from previous years.