PAGCM: A scalable parallel spectral-based atmospheric general circulation model

The Atmospheric General Circulation Model (AGCM) as one of the most important components of Climate System Model (CSM), has been proved to be an effective way for weather forecasting and climate prediction. Although lots of efforts have been conducted to improve the computing efficiency of AGCMs, such as exploit parallel algorithms, migrating codes, and even redesigning systems to adapt to the emerging computer architectures, it is not enough to match the real requirement, due to the limited scalability of the parallel algorithms themselves. Therefore, we design and implement a scalable parallel spectral-based atmospheric circulation mode called PAGCM in this paper. Specifically, we first analyze the data dependencies of the dimensions in different spaces according to the calculation characteristics of spectral models, and based on which we propose a two-dimensional decomposition algorithm in PAGCM to effectively increase the involving cores for the parallel computing, and thus reduce the overall computing time. Furthermore, to adapt to the novel data decomposition in each computing stage of dynamic framework, we propose three-dimensional data transposition algorithms and data collection algorithms correspondingly, by considering of load balancing and communication optimization. Extensive experiments are conducted on Tianhe-2 to validate the effectiveness and scalability of our proposals.