使用MALI进行高性能便携式冰盖建模
Performance portable ice-sheet modeling with MALI
摘要：
极地冰盖的高分辨率模拟在为概率海平面预测开发更准确和可靠的地球系统模型的持续努力中发挥着至关重要的作用。这些模拟通常需要来自大型超级计算集群的大量存储器和计算，以提供足够的精度和分辨率。即将上线的最新亿亿次级计算机包含一组不同的计算架构。为了避免架构特定的编程并保持跨平台的生产力，称为MALI的冰盖建模代码使用高级抽象来集成Trilinos库和Kokkos编程模型，以实现跨各种不同架构的性能可移植代码。本文通过对当前基于CPU和基于GPU的超级计算机的性能分析，分析了MALI的性能移植特性。分析强调了MALI中有限元组装和多重网格预处理的性能可移植性改进，CPU和GPU架构的加速比在1.26- 1.82倍之间，但也确定了进一步提高GPU上软件耦合和预处理性能的需求。我们还进行了弱可扩展性研究，并表明在基于GPU的机器上进行模拟时，使用GPU的速度要快1.24- 1.92倍。最好的性能是在有限元组装，实现了高达8.65倍的加速比和82.9%的弱缩放效率与GPU。我们还描述了一个自动化的性能测试框架开发的代码库使用的变化点检测方法。该框架用于在MALI内部就绩效做出可操作的决策。我们提供了几个具体的例子的情况下，该框架已确定的性能回归，改进，并在两年的发展过程中的算法差异。
英文摘要：
High resolution simulations of polar ice-sheets play a crucial role in the ongoing effort to develop more accurate and reliable Earth-system models for probabilistic sea-level projections. These simulations often require a massive amount of memory and computation from large supercomputing clusters to provide sufficient accuracy and resolution. The latest exascale machines poised to come online contain a diverse set of computing architectures. In an effort to avoid architecture specific programming and maintain productivity across platforms, the ice-sheet modeling code known as MALI uses high level abstractions to integrate Trilinos libraries and the Kokkos programming model for performance portable code across a variety of different architectures. In this paper, we analyze the performance portable features of MALI via a performance analysis on current CPU-based and GPU-based supercomputers. The analysis highlights performance portable improvements made in finite element assembly and multigrid preconditioning within MALI with speedups between 1.26-1.82x across CPU and GPU architectures but also identifies the need to further improve performance in software coupling and preconditioning on GPUs. We also perform a weak scalability study and show that simulations on GPU-based machines perform 1.24-1.92x faster when utilizing the GPUs. The best performance is found in finite element assembly which achieved a speedup of up to 8.65x and a weak scaling efficiency of 82.9% with GPUs. We additionally describe an automated performance testing framework developed for this code base using a changepoint detection method. The framework is used to make actionable decisions about performance within MALI. We provide several concrete examples of scenarios in which the framework has identified performance regressions, improvements, and algorithm differences over the course of two years of development.
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。
