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A Novel GPU-Based Acceleration Algorithm for a Longwave Radiative Transfer Model

Yuzhu Wang,Yuan Zhao,Jinrong Jiang,He Zhang

Published 2020 in Applied Sciences

ABSTRACT

Graphics processing unit (GPU)-based computing for climate system models is a longstanding research area of interest. The rapid radiative transfer model for general circulation models (RRTMG), a popular atmospheric radiative transfer model, can calculate atmospheric radiative fluxes and heating rates. However, the RRTMG has a high calculation time, so it is urgent to study its GPU-based efficient acceleration algorithm to enable large-scale and long-term climatic simulations. To improve the calculative efficiency of radiation transfer, this paper proposes a GPU-based acceleration algorithm for the RRTMG longwave radiation scheme (RRTMG_LW). The algorithm concept is accelerating the RRTMG_LW in the g- p o i n t dimension. After implementing the algorithm in CUDA Fortran, the G-RRTMG_LW was developed. The experimental results indicated that the algorithm was effective. In the case without I/O transfer, the G-RRTMG_LW on one K40 GPU obtained a speedup of 30.98× over the baseline performance on one single Intel Xeon E5-2680 CPU core. When compared to its counterpart running on 10 CPU cores of an Intel Xeon E5-2680 v2, the G-RRTMG_LW on one K20 GPU in the case without I/O transfer achieved a speedup of 2.35×.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Applied Sciences

  • Publication date

    2020-01-16

  • Fields of study

    Physics, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/app10020649
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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