Optimizing Full Correlation Matrix Analysis of fMRI Data on Intel R Xeon PhiTM Coprocessors

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.