Analysis of high performance conjugate heat transfer with the OpenPALM coupler

By F. Duchaine, S. Jaure, Damien Poitou, Eric Quémerais, Gabriel Staffelbach, Thierry Morel, Laurent Y. M. Gicquel. 2015

377 citations recorded by the source corpus.

In many communities such as climate science or industrial design, to solve complex coupled problems with high fidelity external coupling of legacy solvers puts a lot of pressure on the tool used for the coupling. The precision of such predictions not only largely depends on simulation resolutions and the use of huge meshes but also on high performance computing to reduce restitution times. In this context, the current work aims at studying the scalability of code coupling on high performance computing architectures for a conjugate heat transfer problem. The flow solver is a Large Eddy Simulation code that has been already ported on massively parallel architectures. The conduction solver is based on the same data structure and thus shares the flow solver scalability properties. Accurately coupling solvers on massively parallel architectures while maintaining their scalability is challenging. It requires exchanging and treating information based on two different computational grids that are partitioned differently on a different number of cores. Such transfers have to be thought to maintain code scalabilities while maintaining numerical accuracy. This raises communication and high performance computing issues: transferring data from a distributed interface to another distributed interface in a parallel way and on a very large number of processors is not straightforward and solutions are not clear. Performance tests have been carried out up to 12 288 cores on the CURIE supercomputer (TGCC/CEA). Results show a good behavior of the coupled model when increasing the number of cores thanks to the fully distributed exchange process implemented in the coupler. Advanced analyses are carried out to draw new paths for future developments for coupled simulations: i.e. optimization of the data transfer protocols through asynchronous communications or coupling-aware preprocessing of the coupled models (mesh partitioning phase).

Subfields: Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/0041de8815a44fe61f94a728d8274165ac7ea892

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In Computational Mechanics

Citations in the corpus

The Athena++ Adaptive Mesh Refinement Framework: Design and Magnetohydrodynamic Solvers (2020, methodology): “While this speedup from mesh refinement is slightly lower than that reported for the PLUTO code (Mignone et al. 2012, table 3), this is in part because the run time on a uniform mesh is significantly lower using Athena++. 4.3.2.”
Gamma-ray binaries and related systems (2013, methodology): “– Bosch-Ramon et al. (2012) presented 2D (cylindrical) simulations including orbital motion using the relativistic hydro version of PLUTO, a grid-based code (Mignone et al. 2012).”