Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors
- Resource Type
- Conference
- Authors
- Wang, Yida; Anderson, Michael J.; Cohen, Jonathan D.; Heinecke, Alexander; Li, Kai; Satish, Nadathur; Sundaram, Narayanan; Turk-Browne, Nicholas B.; Willke, Theodore L.
- Source
- SC15: International Conference for High Performance Computing, Networking, Storage and Analysis High Performance Computing, Networking, Storage and Analysis, 2015 SC - International Conference for. :1-12 Nov, 2015
- Subject
- Computing and Processing
Correlation
Coprocessors
Support vector machines
Real-time systems
Neuroscience
Hardware
Computer architecture
- Language
- ISSN
- 2167-4337
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.