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Parallel Implementation of a Sensitivity Operator-Based Source Identification Algorithm for Distributed Memory Computers

Author

Listed:
  • Alexey Penenko

    (Institute of Computational Mathematics and Mathematical Geophysics SB RAS, pr. Akademika Lavrentjeva 6, 630090 Novosibirsk, Russia)

  • Evgeny Rusin

    (Institute of Computational Mathematics and Mathematical Geophysics SB RAS, pr. Akademika Lavrentjeva 6, 630090 Novosibirsk, Russia)

Abstract

Large-scale inverse problems that require high-performance computing arise in various fields, including regional air quality studies. The paper focuses on parallel solutions of an emission source identification problem for a 2D advection–diffusion–reaction model where the sources are identified by heterogeneous measurement data. In the inverse modeling approach we use, a source identification problem is transformed to a quasi-linear operator equation with a sensitivity operator, which allows working in a unified way with heterogeneous measurement data and provides natural parallelization of numeric algorithms by concurrent calculation of the rows of a sensitivity operator matrix. The parallel version of the algorithm implemented with a message passing interface (MPI) has shown a 40× speedup on four Intel Xeon Gold 6248R nodes in an inverse modeling scenario for the Lake Baikal region.

Suggested Citation

  • Alexey Penenko & Evgeny Rusin, 2022. "Parallel Implementation of a Sensitivity Operator-Based Source Identification Algorithm for Distributed Memory Computers," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4522-:d:988934
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    References listed on IDEAS

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    1. Alberto Carrassi & Marc Bocquet & Laurent Bertino & Geir Evensen, 2018. "Data assimilation in the geosciences: An overview of methods, issues, and perspectives," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 9(5), September.
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