Title :
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
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;
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
DOI :
10.1109/IPDPSW.2012.257
