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Parallel Efficiency of Time-Integration Strategies for the Next Generation Global Weather Prediction Model

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Supercomputing (RuSCDays 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1331))

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Abstract

Next generation global numerical weather prediction models will have horizontal resolution of 3–5 km that lead to the problem of about \(10^{10}\) degrees of freedom. To meet operational requirements for medium-range weather forecast, \(O(10^4)\)-\(O(10^5)\) processor cores have to be used efficiently. The non-hydrostatic equation set will be used so models have to treat efficiently a number of fast-propagating wave families. Therefore, time-integration scheme is crucial for the scalability and computational efficiency.

The next generation global atmospheric model is currently under development at INM RAS and Hydrometcentre of Russia. To choose the time integration scheme for the new model, we evaluate scalability and efficiency of several options (including exponential propagation integrator) using linearized equation set. We also present a parallel framework developed for the solution of atmospheric dynamic equations on the spherical cube grid.

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Authors and Affiliations

  1. G.I. Marchuk Institute of Numerical Mathematics, RAS, 8, Gubkina st., Moscow, Russia

    Vladimir Shashkin & Gordey Goyman

  2. Hydrometeorological center of Russia, 11-13, Bol.Predtechenskiy per., Moscow, Russia

    Vladimir Shashkin & Gordey Goyman

  3. Moscow Institute of Physics and Technology, 9, Institutskiy per., Dolgoprudny, Moscow Region, Russia

    Vladimir Shashkin & Gordey Goyman

Authors
  1. Vladimir Shashkin
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  2. Gordey Goyman
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Correspondence to Vladimir Shashkin .

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Editors and Affiliations

  1. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Vladimir Voevodin

  2. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Sergey Sobolev

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Shashkin, V., Goyman, G. (2020). Parallel Efficiency of Time-Integration Strategies for the Next Generation Global Weather Prediction Model. In: Voevodin, V., Sobolev, S. (eds) Supercomputing. RuSCDays 2020. Communications in Computer and Information Science, vol 1331. Springer, Cham. https://doi.org/10.1007/978-3-030-64616-5_25

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  • DOI: https://doi.org/10.1007/978-3-030-64616-5_25

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-64616-5

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