An unstructured CFD mini-application for the performance prediction of a production CFD code
An unstructured CFD mini-application for the performance prediction of a production CFD code
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用于预测生产 CFD 代码性能的非结构化 CFD 迷你应用程序
DOI：
10.1002/cpe.5443
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Owenson A
中科院分区：
文献类型：
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作者：
Owenson A
维持大型科学规范的性能是一项艰巨的任务。为了帮助完成这一任务，已经开发了许多迷你应用程序，这些应用程序比大规模生产代码更易于分析，同时保留了它们的性能特征。这些“迷你应用程序”还可以更快地进行硬件评估，对于敏感的商用代码，可以在访问批准流程之外评估代码和系统更改。在本文中，我们开发了MG‐CFD，这是一个代表几何多重网格的小型应用程序，非结构化计算流体动力学（CFD）代码，旨在表现出类似的性能特征，而无需共享商业敏感代码。我们详细介绍了我们的经验，开发此应用程序使用现有研究中详细的指导方针，并进一步促进这些。我们的应用程序是根据HYDRONIC的无粘通量程序进行验证的，HYDRONIC是由罗尔斯罗伊斯公司开发的一种用于涡轮设计的CFD代码。本文（1）记录了MG‐CFD的发展，（2）介绍了一个相关的性能模型，它可以评估新的HPC架构上的HYPERT的性能，（3）证明了使用MG‐CFD和性能模型来预测HYPERT的性能是可能的，对于强缩放研究，平均误差为9.2%。
Maintaining the performance of large scientific codes is a difficult task. To aid in this task, a number of mini‐applications have been developed that are more tractable to analyze than large‐scale production codes while retaining the performance characteristics of them. These “mini‐apps” also enable faster hardware evaluation and, for sensitive commercial codes, allow evaluation of code and system changes outside of access approval processes. In this paper, we develop MG‐CFD, a mini‐application that represents a geometric multigrid, unstructured computational fluid dynamics (CFD) code, designed to exhibit similar performance characteristics without sharing commercially sensitive code. We detail our experiences of developing this application using guidelines detailed in existing research and contributing further to these. Our application is validated against the inviscid flux routine of HYDRA, a CFD code developed by Rolls‐Royce plc for turbomachinery design. This paper (1) documents the development of MG‐CFD, (2) introduces an associated performance model with which it is possible to assess the performance of HYDRA on new HPC architectures, and (3) demonstrates that it is possible to use MG‐CFD and the performance models to predict the performance of HYDRA with a mean error of 9.2% for strong‐scaling studies.