SCHEDULE: NOV 15-20, 2015

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Full Correlation Matrix Analysis of fMRI Data on Intel Xeon Phi Coprocessors

SESSION: Applications: Folding, Imaging, and Proteins

EVENT TYPE: Papers

EVENT TAG(S): Applications

TIME: 3:30PM - 4:00PM

SESSION CHAIR(S): Evelyn Duesterwald

AUTHOR(S):Yida Wang, Michael J. Anderson, Jonathan D. Cohen, Alexander Heinecke, Kai Li, Nadathur Satish, Narayanan Sundaram, Nicholas B. Turk-Browne, Theodore L. Willke

ROOM:18AB

ABSTRACT:

Full correlation matrix analysis (FCMA) is an unbiased approach for exhaustively studying interactions among brain regions in functional magnetic resonance imaging (fMRI) data from human participants. In order to answer neuroscientific questions efficiently, we are developing a closed-loop analysis system with FCMA on a cluster of nodes with Intel Xeon Phi coprocessors. We have proposed several ideas which involve data driven algorithmic modification to improve the performance on the coprocessor. Our experiments with real datasets show that the optimized single-node code runs 5x-16x faster than the baseline implementation using the well-known Intel MKL and LibSVM libraries, and that the cluster implementation achieves near linear speedup on 5760 cores.

Chair/Author Details:

Evelyn Duesterwald (Chair) - IBM Corporation|

Yida Wang - Princeton University

Michael J. Anderson - Intel Corporation

Jonathan D. Cohen - Princeton University

Alexander Heinecke - Intel Corporation

Kai Li - Princeton University

Nadathur Satish - Intel Corporation

Narayanan Sundaram - Intel Corporation

Nicholas B. Turk-Browne - Princeton University

Theodore L. Willke - Intel Corporation

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