CUDAICA: GPU optimization of infomax-ICA EEG analysis
Computational Intelligence and Neuroscience - Special issue on Advanced Computational Techniques and Tools for Neuroscience
Euro-Par'12 Proceedings of the 18th international conference on Parallel processing workshops
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Several problems in the signal processing field require generating suitable representations of data. One possible form of representation is given by independent component analysis (ICA). The computation of these representations can be quite expensive, especially if large datasizes are used. Over the last few years graphics processing units (GPUs) have emerged as inexpensive general-purpose computation accelerators. This paper presents an implementation of FastICA, an ICA algorithm, on a multicore GPU. The resulting implementation achieved an overall speedup of 55 for estimating 256 independent components, each with 1000 samples, regarding the implementation on a general purpose processor running at 2 GHz.