Parallel Construction of Module Networks
Event Type
Paper
Machine Learning and Artificial Intelligence
Best Reproducibility Advancement Finalist
TP
TimeTuesday, 16 November 20212pm - 2:30pm CST
Location230-231-232
DescriptionModule networks (MoNets) are a parameter-sharing specialization of Bayesian networks that are used for reasoning about multidimensional entities with concerted interactions between groups of variables. Construction of MoNets is compute-intensive, with sequential methods requiring months for learning networks with a few thousand variables. In this paper, we present the first scalable distributed-memory parallel solution for constructing MoNets by parallelizing Lemon-Tree, a widely used sequential software. We demonstrate the scalability of our parallel method on a key application of MoNets – the construction of genome-scale gene regulatory networks. Using 4096 cores, our parallel implementation constructs regulatory networks for 5,716 and 18,373 genes of two model organisms in 24 minutes and 4.2 hours, compared to an estimated 49 and 1561 days using Lemon-Tree for generating exactly the same networks, respectively. Our method is application-agnostic and broadly applicable to the learning of high-dimensional MoNets for any of its wide array of applications.
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