A Massively Parallel Approach for Nonlinear Interdependency Analysis of Multivariate Signals with GPGPU

Author

Dan Chen ; Lizhe Wang ; Dong Cui ; Dongchuan Lu ; Xiaoli Li ; Khan, Samee U. ; Kolodziej, Joanna

Author_Institution

Sch. of Comput. Sci., China Univ. of Geosci., Wuhan, China

fYear

2012

fDate

21-25 May 2012

Firstpage

1971

Lastpage

1978

Abstract

Nonlinear interdependency (NLI) analysis is an effective method for measurement of synchronization among brain regions, which is an important feature of normal and abnormal brain functions. But its application in practice has long been largely hampered by the ultra-high complexity of the NLI algorithms. We developed a massively parallel approach to address this problem. The approach has dramatically improved the runtime performance. It also enabled NLI analysis on multivariate signals which was previously impossible.

Keywords

computational complexity; electroencephalography; graphics processing units; medical signal processing; neurophysiology; parallel processing; performance evaluation; synchronisation; EEG; GPGPU; NLI algorithm complexity; NLI analysis; abnormal brain functions; brain regions; massively parallel computing; multivariate signals; nonlinear interdependency analysis; normal brain functions; runtime performance improvement; synchronization measurement; Delay; Electroencephalography; Graphics processing unit; Instruction sets; Parallel processing; Synchronization; Vectors; EEG; GPGPU; massively parallel computing; nonlinear interdependency; synchronization;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International

Conference_Location

Shanghai

Print_ISBN

978-1-4673-0974-5

Type

conf

DOI

10.1109/IPDPSW.2012.257

Filename

6270404