LAUSR

In SC20, (Srivastava et al. 2020) proposed a Parallel Framework for Bayesian Learning, or ramBLe, for short, which is a highly parallel and efficient framework for learning the structure of Bayesian Networks (BNs) from samples, There was a discrepancy in Bibliography in the PDF and the source file. We have followed the source file. ?> particularly large genome-scale networks. As part of our participation in the SC21 Student Cluster Competition, our task was to verify conclusions from the original work (Srivastava et al. 2020). Here we present the outcome of our experiments, which were performed on a four-node cluster from the Oracle Cloud HPC platform. We reproduce the numerical results from (Srivastava et al. 2020), namely the algorithm's performance and scaling behavior using MPI and different Python and Boost libraries on the Oracle cloud.