@inproceedings{73b1d72788834315bc022c46f43ada90,
title = "Full correlation matrix analysis of fMRI data on Intel Xeon Phi 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. 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.",
keywords = "Intel{\textregistered} Xeon Phi{\texttrademark} coprocessor, fMRI data",
author = "Yida Wang and Anderson, {Michael J.} and Cohen, {Jonathan D.} and Alexander Heinecke and Kai Li and Nadathur Satish and Narayanan Sundaram and Nicholas Turk-Browne and Willke, {Theodore L.}",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2015 ; Conference date: 15-11-2015 Through 20-11-2015",
year = "2015",
month = nov,
day = "15",
doi = "10.1145/2807591.2807631",
language = "English (US)",
series = "International Conference for High Performance Computing, Networking, Storage and Analysis, SC",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of SC 2015",
address = "United States",
}