Optimizing Full Correlation Matrix Analysis of fMRI Data on Intel R Xeon PhiTM Coprocessors
Report ID: TR-983-15Author: Wang, Yida / Anderson, Michael / Cohen, Jonathan / Heinecke, Alexander / Li, Kai / Satish, Nadathur / Sundaram, Narayanan / Turk-Browne, Nicholas / Willke, Ted
Date: 2015-05-13
Pages: 12
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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 to modify the algorithm 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.