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×.
Highlights
The radiative process, one of the important atmospheric physics processes, is often used for calculating atmospheric radiative fluxes and heating rates [1]
The WRF long-wave rapid radiative transfer model (RRTM) code was ported to Graphics processing unit (GPU) using CUDA Fortran [28]
Using one K20 GPU in the case without I/O transfer, the G-RRTMG_LW achieved a speedup of 25.47× as compared to its counterpart running on one central processing unit (CPU) core of an Intel Xeon E5-2680 v2
Summary
The radiative process, one of the important atmospheric physics processes, is often used for calculating atmospheric radiative fluxes and heating rates [1]. The RRTM that is a validated model computing longwave and shortwave radiative fluxes and heating rates, uses the correlated-k method to provide the required accuracy and computing efficiency [4], but it still demands enormous computing resources for long-term climatic simulation To address this issue, as an accelerated version of RRTM, the rapid radiative transfer model for general circulation models (RRTMG) provides improved efficiency with minimal loss of accuracy for atmospheric general circulation models (GCMs) [5]. To further accelerate the RRTMG_LW in the CAS-ESM, a GPU-based acceleration algorithm in the g-point dimension is proposed. To further accelerate the RRTMG_LW with a massively parallel computing technology, a GPU-based accelerating algorithm in the g-point dimension is proposed. The proposed algorithm adapts well to the advances in multi-threading computing technology of GPUs and can be generalized to accelerate the RRTMG shortwave radiation scheme (RRTMG_SW). The last section concludes the paper with a summary and proposal for future work
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