Predictive Analysis of Code Optimisations on Large-Scale Coupled CFD-Combustion Simulations using the CPX Mini-App

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 a gas turbine aero engine, where instances of rotor/stator CFD and combustion simulations are coupled. Allocating resources correctly and efficiently during production simulations is a significant challenge due to the large HPC resources required and the varying scalability of specific components, a result of differences between solver physics. In this research, we develop a coupled mini-app simulation and an accompanying performance model to help support this process. We integrate an existing Particle-In-Cell mini-app, SIMPIC, as a ‘performance proxy’ for production combustion codes in industry, into a coupled mini-app CFD simulation using the CPX mini-coupler. The bottlenecks of the workload are examined, and the performance behavior are replicated using the mini-app. A selection of optimizations are examined, allowing us to estimate the workload’s theoretical performance. The coupling of mini-apps is supported by an empirical performance model which is then used to load balance and predict the speedup of a full-scale compressor-combustor-turbine simulation of 1.2Bn cells, a production representative problem size. The model is validated on 40K-cores of an HPE-Cray EX system, predicting the runtime of the mini-app work-flow with over 75% accuracy. The developed coupled mini-apps and empirical model combination demonstrates how rapid design space and run-time setup exploration studies can be carried out to obtain the best performance from full-scale Combustion-CFD coupled simulations.