Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors
Yida Wang, Michael J. Anderson, Jonathan D. Cohen, Alexander Heinecke, Kai Li, Satish Nadathur, Narayanan Sundaram, Nicholas B. Turk‐Browne, Theodore L. Willke · 2015
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. Here we propose several ideas for 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.