The partial correlation coefficient with information theory PCIT method is an important technique for detecting interactions between networks. The PCIT algorithm has been used in the biological context to infer complex regulatory mechanisms and interactions in genetic networks, in genome wide association studies, and in other similar problems. In this work, the PCIT algorithm is re-implemented with exemplary parallel, vector, input/output I/O, memory, and instruction optimizations for today's multi-core and many-core architectures. The evolution and performance of the new code targets the processor architectures of the Stampede supercomputer but will also benefit other architectures. The Stampede system consists of an Intel Xeon E5 processor base system with an innovative component consist of Intel Xeon Phi Coprocessors. Optimized results and an analysis are presented for both the Xeon and the Xeon Phi. Copyright © 2014 John Wiley & Sons, Ltd.
['Lars Koesterke', 'James E. Koltes', 'Nathan T. Weeks', 'Kent Milfeld', 'Matthew W. Vaughn', 'James M. Reecy', 'Dan Stanzione']
Discovery of biological networks using an optimized partial correlation coefficient with information theory algorithm on Stampede's Xeon and Xeon Phi processors