MPAS中浅水方程的局部时间推进
Local time stepping for the shallow water equations in MPAS
摘要：
我们评估了一组本地时间步长（LTS）计划的浅水方程中实现跨尺度预测模型（MPAS）的性能。LTS的目标是通过在计算网格的不同区域上允许不同的时间步长来加速模拟。这里考虑的LTS方案最初由Hoang等人（J. Comput.物理、第382卷，第152 -176页，2019年），他奠定了方法的数学基础。在这里，作者承担的任务是在高性能计算机器上提出这些LTS方法的快速，高效和可扩展的并行实现，目的是为其他可能有兴趣在代码中采用LTS算法的气候建模组提供一个配方。事实上，即使MPAS是我们选择的框架，我们的方法也足够通用，可能会引起MPAS社区以外的其他团体的兴趣。由于其性质，LTS方法具有固有的负载不平衡，需要仔细解决，以获得有效的可扩展性。更重要的是，在时间步进过程中，仅在特定LTS区域上计算右侧项的任务远非微不足道。对该任务的低效处理导致CPU时间性能的急剧衰减，使得LTS算法实际上没有用处。因此，本工作的重点是LTS方法的计算和并行方面，其适当的处理是至关重要的，使方法运行速度更快，对现有的战略，例如高阶显式全球时间步进计划。这实际上是使用LTS程序的最终目标，也是我们所有优化工作的目标。
英文摘要：
We assess the performance of a set of local time-stepping (LTS) schemes for the shallow water equations implemented in the Model for Prediction Across Scales (MPAS). The goal of LTS is to speed up the simulation by allowing different time-steps on different regions of the computational grid. The LTS schemes considered here were originally introduced by Hoang et al. (J. Comput. Phys., Vol. 382, p.152-176, 2019), who laid out the mathematical foundation of the methods. Here, the authors take on the task of presenting a fast, efficient and scalable parallel implementation of these LTS methods on high performance computing machines, with the aim to provide a recipe for other climate modeling groups that may be interested in employing LTS algorithms in their codes. As a matter of fact, even if MPAS is our framework of choice, our approach is general enough and could be of interest to other groups beyond the MPAS community. Due to their nature, LTS methods possess an inherent load imbalance that needs to be carefully addressed in order to obtain efficient scalability. Even more important is the far from trivial task of computing the right-hand side terms only on specific LTS regions during the time-stepping procedure. An inefficient handling of this task causes a drastic decay of the CPU time performance, making the LTS algorithms practically of no use. The emphasis of the present work is therefore on the computational and parallel aspects of the LTS methods, whose proper handling is crucial to make the methods run faster against existing strategies, such as for instance high-order explicit global time-stepping schemes. This is in fact the ultimate goal of using an LTS procedure and it is the one to which we direct all our optimization efforts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
