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Highly Effective GPU Realization of Discrete Wavelet Transform for Big-Data Problems

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12742))

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Abstract

Discrete wavelet transform (DWT) is widely used in the tasks of signal processing, analysis and recognition. Moreover it’s practical applications are not limited to the case of one-dimensional signals but also apply to images and multidimensional data. From the moment of introduction of the dedicated libraries that enable to use graphics processing units (GPUs) for mass-parallel general purpose calculations the development of effective GPU based implementations of one-dimensional DWT is an important field of scientific research. It is also important because with use of one-dimensional procedure we can calculate DWT in multidimensional case if only the transform’s separability is assumed. In this paper the authors propose a novel approach to calculation of one-dimensional DWT based on lattice structure which takes advantage of shared memory and registers in order to implement necessary inter-thread communication. The experimental analysis reveals high time-effectiveness of the proposed approach which can be even 5 times higher for Maxwell architecture, and up to 2 times for Turing family GPU cards, than the one characteristic for the convolution based approach in computational tasks that can be classified as big-data problems.

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Notes

  1. 1.

    We measured times of kernel launch and calculations with NVIDA’s \(\texttt {nvprof}\) profiler. During experiments we used Intel i7-9700, 12 MB cache, 32 GB RAM, Windows 10 platform, and CUDA 10, C++ implementations.

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Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, Ul. Wolczanska 215, 90-924, Lodz, Poland

    Dariusz Puchala & Kamil Stokfiszewski

Authors
  1. Dariusz Puchala
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  2. Kamil Stokfiszewski
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Corresponding author

Correspondence to Dariusz Puchala .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Puchala, D., Stokfiszewski, K. (2021). Highly Effective GPU Realization of Discrete Wavelet Transform for Big-Data Problems. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_19

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  • DOI: https://doi.org/10.1007/978-3-030-77961-0_19

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  • Print ISBN: 978-3-030-77960-3

  • Online ISBN: 978-3-030-77961-0

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