王玉柱：GPUs-RRTMG_LW: high-efficient and scalable computing for a longwave radiative transfer model on multiple GPUs

2021-03-04     发布：[bat365正版唯一官网]    点击：123次

Atmospheric radiation physical process plays an important role in climate simulations. As a radiative transfer scheme, the rapid radiative transfer model for general circulation models (RRTMG) is widely used in weather forecasting and climate simulation systems. However, its expensive computational overhead poses a severe challenge to system performance. Therefore, improving the radiative transfer model's computational performance has significant scientific research and practical value. Numerous radiative transfer models have benefited from a widely used and powerful GPU. Nevertheless, few of them have exploited CPU/GPU cluster resources within heterogeneous high-performance computing platforms. In this paper, we endeavor to demonstrate an approach that runs a large-scale, computationally intensive, longwave radiative transfer model on a GPU cluster. First, a CUDA-based acceleration algorithm of the RRTMG longwave radiation scheme (RRTMG_LW) on multiple GPUs is proposed. Then, a heterogeneous, hybrid programming paradigm (MPI+CUDA) is presented and utilized with the RRTMG_LW on a GPU cluster. After implementing the algorithm in CUDA Fortran, a multi-GPU version of the RRTMG_LW, namely GPUs-RRTMG_LW, was developed. The experimental results demonstrate that the multi-GPU acceleration algorithm is valid, scalable, and highly efficient when compared to a single GPU or CPU. Running the GPUs-RRTMG_LW on a K20 cluster achieved a 77.78x speedup when compared to a single Intel Xeon E5-2680 CPU core.

上述成果发表在期刊《JOURNAL OF SUPERCOMPUTING》上：Wang,YZ(Wang,Yuzhu)[1];Guo,MX(Guo,Mingxin)[1];Zhao,Y(Zhao,Yuan)[1];Jiang,JR(Jiang,Jinrong)[2]. GPUs‑RRTMG_LW: high‑efficient and scalable computing for a longwave radiative transfer model on multiple GPUs. JOURNAL OF SUPERCOMPUTING.

全文链接：https://link.springer.com/article/10.1007%2Fs11227-020-03451-3