Predictive analysis of large-scale coupled CFD simulations with the CPX mini-app

Stephen Jarvis, Archie Powell, Karim Choudry, Arun Prabhakar, Istvan Z. Reguly, Dario Amirante, Gihan R. Mudalige

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the complexity of multi-physics simulations increases, there is a need for efficient flow of information between components. Discrete ‘coupler’ codes can abstract away this process, improving solver interoperability. One such multi-physics problem is modelling the high pressure compressor of turbofan engines, where instances of rotor/stator CFD simulations are coupled. Configuring couplers and allocating resources correctly can be challenging for such problems due to the sliding interfaces between codes. In this research, we present CPX, a mini-coupler designed to model the performance behaviour of a production coupler framework at Rolls-Royce plc., used for coupling rotor/stator simulations. CPX, the first mini-coupler framework of its kind, is combined with a CFD mini-app to predict the run-time and scaling behaviour of large scale coupled CFD simulations. We demonstrate high qualitative and quantitative predictive accuracy with a less than 17 % mean error. A performance model is developed to predict the ‘optimum’ configuration of resources, and is tested to show the high accuracy of these predictions. The model is also used to project the ‘optimum’ configuration for a 6 Billion cell test case, a problem size representative of current leading-edge production workloads, on a 100,000 core cluster and a 400 GPU cluster. Further testing reveals that the ‘optimum’ configuration is unstable if not set up correctly, and therefore a trade-off needs to be made with a marginally less-than-optimal setup to ensure stability. The work illustrates the significant utility of CPX to carry out such rapid design space and run-time setup exploration studies to obtain the best performance from production CFD coupled simulations.
Original languageEnglish
Title of host publication2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC)
PublisherIEEE
Pages141-151
Number of pages11
ISBN (Electronic)9781665410168
ISBN (Print)9781665410175
DOIs
Publication statusPublished - 24 Jan 2022

Publication series

NameInternational Conference on High Performance Computing
ISSN (Print)1094-7256
ISSN (Electronic)2640-0316

Keywords

  • Coupling
  • Mini-App
  • Performance model
  • CFD

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