A scalable parallel algorithm for atmospheric general circulation models on a multi-core cluster

Abstract

High-performance computing of atmospheric general circulation models (AGCMs) has been receiving increasing attention in earth science research. However, when scaling to large-scale multi-core computing, the parallelization of an AGCM which demands fast parallel computing for long-time integration or climate simulation becomes extremely challenging due to its inner complex numerical calculation. The previous Institute of Atmospheric Physics of the Chinese Academy of Sciences Atmospheric General Circulation Model version 4.0 (IAP AGCM4.0) with one-dimensional domain decomposition can only run on dozens of CPU cores, so the paper proposes a two-dimensional domain decomposition parallel algorithm for it. In the parallel implementation of the IAP AGCM4.0, its dynamical core utilizes a hybrid form of latitude/longitude decomposition and vertical direction/longitude circle direction decomposition. Through experiments on a multi-core cluster, we confirmed that our algorithm is efficient and scalable. The parallel efficiency of the IAP AGCM4.0 can reach up to 50.88% on 512 CPU cores, and the IAP AGCM4.0 can be run long-term simulations for climate change research.