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Parallel computing in railway research

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  • Qing Wu
  • Maksym Spiryagin
  • Colin Cole
  • Tim McSweeney

Abstract

Available computing power for researchers has been increasing exponentially over the last decade. Parallel computing is possibly the best way to harness computing power provided by multiple computing units. This paper reviews parallel computing applications in railway research as well as the enabling techniques used for the purpose. Nine enabling techniques were reviewed and Message Passing Interface, Domain Decomposition and Hadoop & Apache are the top three most widely used enabling techniques. Seven major application topics were reviewed and iterative optimisations, continuous dynamics and data & signal analysis are the most widely reported applications. The reasons why these applications are suitable for parallel computing were discussed as well as the suitability of various enabling techniques for different applications. Computing time speed-ups that were reported from these applications were summarised. The challenges for applying parallel computing for railway research are discussed.

Suggested Citation

  • Qing Wu & Maksym Spiryagin & Colin Cole & Tim McSweeney, 2020. "Parallel computing in railway research," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 8(2), pages 111-134, April.
    • Handle: RePEc:taf:tjrtxx:v:8:y:2020:i:2:p:111-134
    DOI: 10.1080/23248378.2018.1553115
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    Cited by:

    1. Yujie Qi & Buddhima Indraratna & Trung Ngo & Fernanda Bessa Ferreira, 2021. "Advancements in Geo-Inclusions for Ballasted Track: Constitutive Modelling and Numerical Analysis," Sustainability, MDPI, vol. 13(16), pages 1-20, August.

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