sponsored byIEEEACMThe International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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Parallel Bayesian Network Structure Learning for Genome-Scale Gene Networks

SESSION: High Performance Genomics

EVENT TYPE: Papers, Best Paper Finalists

TIME: 11:30AM - 12:00PM


AUTHOR(S):Sanchit Misra, Vasimuddin Md, Kiran Pamnany, Sriram P. Chockalingam, Yong Dong, Min Xie, Maneesha R. Aluru, Srinivas Aluru



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 Intel Xeon processors and Intel Xeon Phi coprocessors on each node, all available threads on each processor and coprocessor, and vectorization techniques to maximize single thread performance.

Chair/Author Details:

Zhong Jin (Chair) - Chinese Academy of Sciences

Sanchit Misra - Intel Corporation

Vasimuddin Md - Indian Institute of Technology Bombay

Kiran Pamnany - Intel Corporation

Sriram P. Chockalingam - Indian Institute of Technology Bombay

Yong Dong - National University of Defense Technology, China

Min Xie - National University of Defense Technology, China

Maneesha R. Aluru - Georgia Institute of Technology

Srinivas Aluru - Georgia Institute of Technology

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society