Parallel Bayesian Network Structure Learning for Genome-Scale Gene Networks

Learning Bayesian networks is NP-hard. Even with recent progress in heuristic and parallel algorithms, modeling capabilities still fall short of the scale of the problems encountered. In this paper, we present a massively parallel method for Bayesian network structure learning, and demonstrate its capability by constructing genome-scale gene networks of the model plant Arabidopsis thaliana from over 168.5 million gene expression values. We report strong scaling efficiency of 75% and demonstrate scaling to 1.57 million cores of the Tianhe-2 supercomputer. Our results constitute three and five orders of magnitude increase over previously published results in the scale of data analyzed and computations performed, respectively. We achieve this through algorithmic innovations, using efficient techniques to distribute work across all compute nodes, all available processors and coprocessors on each node, all available threads on each processor and coprocessor, and vectorization techniques to maximize single thread performance.