High-throughput Bayesian network learning using heterogeneous multicore computers

Michael D. Linderman, Robert Bruggner, Vivek Athalye, Teresa H. Meng, Narges B. Asadi, Garry P. Nolan
Computer Systems Laboratory, Stanford University
In Proceedings of the 24th ACM International Conference on Supercomputing (2010), pp. 95-104


   title={High-throughput Bayesian network learning using heterogeneous multicore computers},

   author={Linderman, M.D. and Bruggner, R. and Athalye, V. and Meng, T.H. and Bani Asadi, N. and Nolan, G.P.},

   booktitle={Proceedings of the 24th ACM International Conference on Supercomputing},





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Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20–50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations.
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