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Concurrency and Computation Practice and Experience
Article . 2019 . Peer-reviewed
License: CC BY
Data sources: Crossref
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DBLP
Article . 2020
Data sources: DBLP
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An unstructured CFD mini‐application for the performance prediction of a production CFD code

Authors: Andrew Owenson; Steven A. Wright 0001; Richard A. Bunt; Yoon Ho; Matthew J. Street; Stephen A. Jarvis;

An unstructured CFD mini‐application for the performance prediction of a production CFD code

Abstract

SummaryMaintaining 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.

Country
United Kingdom
Keywords

computational fluid dynamics, performance modeling, QA76, high performance computing, scientific computing, 1712, 2614, mini-application, performance analysis, 1703, 1706, 1705

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Top 10%
Top 10%
Green
hybrid