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

Yida Wang, Michael Anderson, Jonathan D. Cohen, Alexander Heinecke, Kai Li, Nadathur Satish, Narayanan Sundaram, Nicholas B. Turk-Browne, Ted Willke
Department of Computer Science, Princeton University
Princeton University Technical Report TR-983-15, 2015

@article{wang2015optimizing,

   title={Optimizing Full Correlation Matrix Analysis of fMRI Data on IntelR Xeon PhiTM Coprocessors},

   author={Wang, Yida and Anderson, Michael and Cohen, Jonathan D and Heinecke, Alexander and Li, Kai and Satish, Nadathur and Sundaram, Narayanan and Turk-Browne, Nicholas B and Willke, Ted},

   year={2015}

}

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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 neuro-scientific questions efficiently, we are developing a closedloop 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.
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