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Productivity, Performance, and Portability for Computational Fluid Dynamics Applications
Computers & Fluids ( IF 2.8 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.compfluid.2020.104425
István Z. Reguly , Gihan R. Mudalige

Abstract Hardware trends over the last decade show increasing complexity and heterogeneity in high performance computing architectures, which presents developers of CFD applications with three key challenges; the need for achieving good performance, being able to utilise current and future hardware by being portable, and doing so in a productive manner. These three appear to contradict each other when using traditional programming approaches, but in recent years, several strategies such as template libraries and Domain Specific Languages have emerged as a potential solution; by giving up generality and focusing on a narrower domain of problems, all three can be achieved. This paper gives an overview of the state-of-the-art for delivering performance, portability, and productivity to CFD applications, ranging from high-level libraries that allow the symbolic description of PDEs to low-level techniques that target individual algorithmic patterns. We discuss advantages and challenges in using each approach, and review the performance benchmarking literature that compares implementations for hardware architectures and their programming methods, giving an overview of key applications and their comparative performance.

中文翻译:

计算流体动力学应用的生产力、性能和便携性

摘要 过去十年的硬件趋势表明,高性能计算架构的复杂性和异构性不断增加,这给 CFD 应用程序的开发人员带来了三个关键挑战:需要获得良好的性能,能够通过便携性利用当前和未来的硬件,并以高效的方式这样做。这三者在使用传统编程方法时似乎相互矛盾,但近年来,模板库和领域特定语言等几种策略已经成为潜在的解决方案;通过放弃一般性并专注于更狭窄的问题领域,这三者都可以实现。本文概述了为 CFD 应用程序提供性能、便携性和生产力的最新技术,从允许对偏微分方程进行符号描述的高级库到针对单个算法模式的低级技术。我们讨论了使用每种方法的优势和挑战,并回顾了比较硬件架构实现及其编程方法的性能基准测试文献,概述了关键应用程序及其比较性能。
更新日期:2020-03-01
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