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Enabling Energy-Efficient Analysis of Massive Neural Signals Using GPGPU

Dan Chen, Lizhe Wang, Shuaiting Wang, Muzhou Xiong, Gregor von Laszewski, Xiaoli Li
School of Computer Science, China University of Geosciences, Wuhan, Hubei, China, 430074
IEEE/ACM International Conference on Cyber, Physical and Social Computing (CPSCom), Green Computing and Communications (GreenCom), 2010

@article{chen2010enabling,

   title={Enabling Energy-efficient Analysis of Massive Neural Signals using GPGPU},

   author={Chen, D. and Wang, L. and Wang, S. and Xiong, M. and von Laszewski, G. and Li, X.},

   year={2010}

}

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Analysis of neural signals (such as EEG) has long been a hot topic in neuroscience community due to neural signals’ nonlinear and non-stationary features. Recent advances of experimental methods and neuroscience research have made neural signals constantly massive and analysis of these signals highly compute-intensive. Analysis of neural signals has been routinely performed upon CPU-based computer clusters with rapidly increasing scale and CPU clock speed. This inevitably incurs additional problems of excessive energy consumption, greenhouse emission, and extra cost of heat dissemination. This study proposes parallelized neural signal analysis approach based on a many-cores high performance computing technique, i.e., General-purpose computing on the graphics processing unit (GPGPU). Experimental results indicate that the GPGPU-aided approach achieved a dramatic speed-up with energy consumption minimized in contrast to using a CPU-based high-end computer cluster.
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