基于GPGPU的多变量信号非线性相互依赖分析的大规模并行方法

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum Pub Date : 2012-05-21 DOI:10.1109/IPDPSW.2012.257

Dan Chen, Lizhe Wang, D. Cui, Dongchuan Lu, Xiaoli Li, S. Khan, J. Kolodziej

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引用次数: 2

摘要

非线性相互依赖(NLI)分析是测量脑区域间同步性的有效方法，是脑功能正常与异常的重要特征。但是，NLI算法的超高复杂性长期以来在很大程度上阻碍了它在实践中的应用。我们开发了一种大规模并行方法来解决这个问题。这种方法极大地提高了运行时性能。它还使NLI能够对多元信号进行分析，这在以前是不可能的。

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

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.

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2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum

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