面向全球数值天气预报模式的加权等积并行数据划分算法

A Weighted-Equal-Area Parallel Data Partitioning Algorithm for Global Numerical Weather Prediction Models

极区计算对全球数值预报模式设计的重要性主要体现在2个方面:模式动力框架中的极区处理和极区并行数据划分带来的并行负载不平衡问题.其中后者是全球数值预报模式大规模并行计算的性能瓶颈,对此提出一种新的基于加权等积的球面数据划分算法.该算法以球带数目和权函数为参数,将南北两极分别划分到单独的子区域,形成极点通区,使从极点到赤道方向每个纬度对应的子区域数目逐渐增多,灵活地实现球面网格的高质量划分.从理论上分析该算法的划分质量后,以基于球谐谱的浅水波模式PSTSWM为实验平台,验证了提出的划分算法具有很好的并行划分性能以及可扩展性.结合我国自主设计的GRAPES全球模式,展望了该算法的应用前景.

The computation on the polar regions plays an crucial role in the design of global numerical weather prediction（NWP） models,which details itself in the following two aspects：the particular treatment of polar regions in the model＇s dynamic framework and the load-balancing problem caused by the parallel data partitioning strategies.The latter has become the bottleneck of massive parallelization of NWP models.To address this problem,a novel spherical data partitioning algorithm based on the weighted-equal-area approach is proposed.The weight describes the computational distribution across the entire sphere.The new algorithm takes the collar amount and the weight function as its parameters and performs the spherical partitioning as follows：the north and the south polar regions are partitioned into a singular subdomain;then the remaining sphere surface is partitioned into some collars along the latitude;and finally each collar is partitioned into subdomains along the longitude.This partitioning method can result in two polar caps plus a number of collars with increasing partition counts as we approach the equator.After a theoretical analysis of the quality relevant to the partition performed by the algorithm,we take the PSTSWM,which is a spectral shallow water model based on the spherical harmonic transform technique,as our test-bed to validate our method.The preliminary results indicate that the algorithm can result in good parallel load balance and a promising prospect can be expected for its application within the global atmospheric model of GRAPES.