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Massively Parallel EEG Algorithms for Pre-exascale Architectures

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Euro-Par 2023: Parallel Processing Workshops (Euro-Par 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14352))

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Abstract

High-density EEG is a non-invasive measurement method with millisecond temporal resolution that allows us to monitor how the human brain operates under different conditions. The large amount of data combined with complex algorithms results in unmanageable execution times. Large-scale GPU parallelism provides the means to drastically reduce the execution time of EEG analysis and bring the execution of large cohort studies (over thousand subjects) within reach. This paper describes our effort to implement various EEG algorithms for multi-GPU pre-exascale supercomputers. Several challenges arise during this work, such as the high cost of data movement and synchronisation compared to computation. A performance-oriented end-to-end design approach is chosen to develop highly-scalable, GPU-only implementations of full processing pipelines and modules. Work related to the parallel design of the family of Empirical Mode Decomposition algorithms is described in detail with preliminary performance results of single-GPU implementations. The research will continue with multi-GPU algorithm design and implementation aiming to achieve scalability up to thousands of GPU cards.

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Notes

  1. 1.

    https://eurohpc-ju.europa.eu/.

  2. 2.

    LUMI: 375 petaFLOPS , Leonardo: 249 petaFLOPS, Meluxina: 10 petaFLOPS, Vega: 6.9 peta-FLOPS, Karolina: 9.13 petaFLOPS, Discoverer: 4.5 petaFLOPS and Deucalion: 7.22 petaFLOPS.

References

  1. Leiserson, C.E., et al.: There’s plenty of room at the top: what will drive computer performance after Moore’s law? Science (1979), 368 (2020)

    Google Scholar 

  2. Cardoso, J.F., Comon, P.: Independent component analysis, a survey of some algebraic methods. In: Proceedings of the IEEE International Symposium on Circuits and Systems, vol. 2, pp. 93–96 (1996)

    Google Scholar 

  3. Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. RSPSA 454, 903–998 (1998)

    Article  MathSciNet  Google Scholar 

  4. Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10, 626–634 (1999)

    Article  Google Scholar 

  5. Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159 (1995)

    Article  Google Scholar 

  6. Keith, D.B., Hoge, C.C., Frank, R.M., Malony, A.D.: Parallel ICA methods for EEG neuroimaging. In: 20th International Parallel and Distributed Processing Symposium, IPDPS 2006 (2006)

    Google Scholar 

  7. Raimondo, F., Kamienkowski, J.E., Sigman, M., Fernandez Slezak, D.: CUDAICA: GPU optimization of infomax-ICA EEG analysis. Comput. Intell. Neurosci. 2012 (2012)

    Google Scholar 

  8. Waskito, P., Miwa, S., Mitsukura, Y., Nakajo, H.: Parallelizing Hilbert-Huang transform on a GPU. In: Proceedings of the 2010 1st International Conference on Networking and Computing, ICNC 2010, pp. 184–190 (2010)

    Google Scholar 

  9. Mujahid, T., Rahman, A.U., Khan, M.M.: GPU-accelerated multivariate empirical mode decomposition for massive neural data processing. IEEE Access 5, 8691–8701 (2017)

    Article  Google Scholar 

  10. Wu, Z., Huang, N.E.: On the filtering properties of the empirical mode decomposition. Adv. Adapt. Data Anal. 2, 397–414 (2010)

    Article  MathSciNet  Google Scholar 

  11. Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Data Sci. Adapt. Anal. 1, 1–41 (2009)

    Article  Google Scholar 

  12. Colominas, M.A., Schlotthauer, G., Torres, M.E.: Improved complete ensemble EMD: a suitable tool for biomedical signal processing. Biomed. Signal Process. Control 14, 19–29 (2014)

    Article  Google Scholar 

  13. Rehman, N.U., Aftab, H.: Multivariate variational mode decomposition. IEEE Trans. Signal Process. 67, 6039–6052 (2019)

    Article  MathSciNet  Google Scholar 

  14. Zhang, Y., et al.: Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals. Biomed. Eng. 16 (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Pannonia, Veszprem, Hungary

    Zeyu Wang & Zoltan Juhasz

Authors
  1. Zeyu Wang
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  2. Zoltan Juhasz
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Corresponding author

Correspondence to Zeyu Wang .

Editor information

Editors and Affiliations

  1. University of Cyprus, Nicosia, Cyprus

    Demetris Zeinalipour

  2. University of Santiago de Compostela, Santiago de Compostela, Spain

    Dora Blanco Heras

  3. University of Cyprus, Nicosia, Cyprus

    George Pallis

  4. Cyprus University of Technology, Limassol, Cyprus

    Herodotos Herodotou

  5. University of Nicosia, Nicosia, Cyprus

    Demetris Trihinas

  6. Inria, Nantes, France

    Daniel Balouek

  7. Louisiana State University, Baton Rouge, LA, USA

    Patrick Diehl

  8. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Terry Cojean

  9. Ludwig-Maximilians-Universität, Munich, Germany

    Karl Fürlinger

  10. Roskilde University, Roskilde, Denmark

    Maja Hanne Kirkeby

  11. Bank of Italy, Rome, Italy

    Matteo Nardelli

  12. Roma Tre University, Rome, Italy

    Pierangelo Di Sanzo

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Wang, Z., Juhasz, Z. (2024). Massively Parallel EEG Algorithms for Pre-exascale Architectures. In: Zeinalipour, D., et al. Euro-Par 2023: Parallel Processing Workshops. Euro-Par 2023. Lecture Notes in Computer Science, vol 14352. Springer, Cham. https://doi.org/10.1007/978-3-031-48803-0_34

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  • DOI: https://doi.org/10.1007/978-3-031-48803-0_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48802-3

  • Online ISBN: 978-3-031-48803-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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