Mini-Combust - an Open-Source Unstructured FGM Combustion Mini-app for Co-Designing Aero-Engines at Extreme Scale

Samuel Curtis, Harry M Waugh, Tom Deakin, Gihan Mudalige

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

We present Mini-Combust, a representative mini-application to explore algorithms of interest for simulating combustion in aero turbine engines. Mini-Combust uses an asynchronous coupled Lagrangian-Eulerian (particle and FVM flow field solver) approach and incorporates basic models for heat transfer, turbulence, boundary conditions, convection schemes, fuel, spray atomisation and emissions. The mini-app is developed in a modular and scalable setup with highly optimized code-paths for executing the solvers on multi-core CPU systems and on CPU/GPU heterogeneous systems. We investigate its performance and scalability on two high-performance computing systems, exploring key performance profiles on up to 32k CPU cores and 128 GPUs, solving a test case representative of a bluff-body swirl burner from industry. Results demonstrate the highly memory-bound nature of the key kernels of the solvers. We see a speedup of 8.8× on an H100 node compared to a power-equivalent number of CPU nodes when offloading the most time-consuming component, the Eulerian solver, to GPUs, executing the asynchronous solvers in a hybrid CPU-GPU manner on an NVIDIA H100 GPU node. Additionally, we see how the communication overhead between the two solvers becomes more pronounced at increasing scale. Mini-Combust is available as open-source software.
Original languageEnglish
Title of host publication2024 IEEE 31st International Conference on High Performance Computing, Data, and Analytics (HiPC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages199-209
Number of pages11
ISBN (Electronic)9798331509095
ISBN (Print)9798331509101
DOIs
Publication statusPublished - 20 Feb 2025
Event31st IEEE International Conference on High Performance Computing, Data, and Analytics - Bengaluru, India
Duration: 18 Dec 202421 Dec 2024
https://www.hipc.org/

Publication series

NameIEEE International Conference on High Performance Computing, Data, and Analytics (HiPC)
PublisherIEEE
ISSN (Print)1094-7256
ISSN (Electronic)2640-0316

Conference

Conference31st IEEE International Conference on High Performance Computing, Data, and Analytics
Country/TerritoryIndia
CityBengaluru
Period18/12/2421/12/24
Internet address

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

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