The Library
Predictive analysis of large-scale coupled CFD simulations with the CPX mini-app
Tools
Powell, Archie, Choudry, Kabir, Prabhakar, A., Reguly, I. Z., Amirante, D., Jarvis, S. A. and Mudalige, Gihan R. (2021) Predictive analysis of large-scale coupled CFD simulations with the CPX mini-app. In: IEEE International Conference on High Performance Computing, Data and Analytics (HiPC 2021), Bangalore, India, 17-20 Dec 2021. Published in: 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC) ISBN 9781665410168. doi:10.1109/HiPC53243.2021.00028 ISSN 2640-0316.
|
PDF
WRAP-Predictive-analysis-large-scale-coupled-CFD-2021.pdf - Accepted Version - Requires a PDF viewer. Download (1358Kb) | Preview |
Official URL: https://doi.org/10.1109/HiPC53243.2021.00028
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.
Item Type: | Conference Item (Paper) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery |
||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
Library of Congress Subject Headings (LCSH): | Computational fluid dynamics , Turbomachines -- Performance | ||||||||||||
Journal or Publication Title: | 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC) | ||||||||||||
Publisher: | IEEE | ||||||||||||
ISBN: | 9781665410168 | ||||||||||||
ISSN: | 2640-0316 | ||||||||||||
Official Date: | December 2021 | ||||||||||||
Dates: |
|
||||||||||||
DOI: | 10.1109/HiPC53243.2021.00028 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Re-use Statement: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
Date of first compliant deposit: | 11 October 2021 | ||||||||||||
Date of first compliant Open Access: | 11 October 2021 | ||||||||||||
RIOXX Funder/Project Grant: |
|
||||||||||||
Conference Paper Type: | Paper | ||||||||||||
Title of Event: | IEEE International Conference on High Performance Computing, Data and Analytics (HiPC 2021) | ||||||||||||
Type of Event: | Conference | ||||||||||||
Location of Event: | Bangalore, India | ||||||||||||
Date(s) of Event: | 17-20 Dec 2021 | ||||||||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year