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Implementing Independent Component Analysis in General-Purpose GPU Architectures

  • Conference paper
Digital Information Processing and Communications (ICDIPC 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 189))

Abstract

New computational architectures, such as multi-core processors and graphics processing units (GPUs), pose challenges to application developers. Although in the case of general-purpose GPU programming, environments and toolkits such as CUDA and OpenCL have simplified application development, different ways of thinking about memory access, storage, and program execution are required. This paper presents a strategy for implementing a specific signal processing technique for blind-source separation: infomax independent component analysis (ICA). Common linear algebra operations are mapped to a low cost programmable graphics card using the OpenCL programming toolkit. Because many components of ICA are inherently parallel, ICA computations can be accelerated by low cost parallel hardware. Experimental results on simulated and speech signals indicate that efficiency gains and scalability are achievable through general-purpose GPU implementation, and suggest that important applications in telecommunications, speech processing, and biomedical signal analysis can benefit from these new architectures. The utilization of low cost GPUs for programming may potentially facilitate real-time applications of previously offline algorithms.

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Author information

Authors and Affiliations

  1. University of Western Ontario, London, ON, Canada

    Jacquelyne Forgette

  2. Nipissing University, North Bay, ON, Canada

    Renata Wachowiak-Smolíková & Mark Wachowiak

Authors
  1. Jacquelyne Forgette
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  2. Renata Wachowiak-Smolíková
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  3. Mark Wachowiak
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Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, VĹ B-TUO, 17. listopadu 15, 708 33, Ostrava, Poruba, Czech Republic

    Vaclav Snasel  & Jan Platos  & 

  2. Information Systems Department, King Saud University, 11543, Riyadh, Saudi Arabia

    Eyas El-Qawasmeh

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© 2011 Springer-Verlag Berlin Heidelberg

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Forgette, J., Wachowiak-Smolíková, R., Wachowiak, M. (2011). Implementing Independent Component Analysis in General-Purpose GPU Architectures. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22410-2_20

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  • DOI: https://doi.org/10.1007/978-3-642-22410-2_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22409-6

  • Online ISBN: 978-3-642-22410-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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