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[JOB] Postdoc at CEA Cadarache πŸ‘©β€πŸ’»πŸ§‘β€πŸ’»- Autotuning for ultra-high performance computing with partitioned coupling

Contact

FAUCHER Vincent CEA DES/DTN/DIR (See pdf)

Background

Taking into account multiple and coupled physics is at the heart of many application needs in fields as varied as, but not limited to, aeronautics, defense and biology. This is also strong area of expertise for CEA’s Energy Division, with multiple domains including fluid-structure interaction, neutronics coupled with thermal-hydraulics a/o thermal-mechanics or severe accident modeling. The emergence of exascale architectures opens the way to promising new levels of high-fidelity simulations, but is also significantly increasing the complexity of many software applications in terms of total or partial rewriting. It therefore specifically encourages coupling to limit development work. The idea is to search for each physics of interest in a necessarily reduced number of highly optimized software components, rather than making specific, possibly redundant developments in standalone applications.
Once the coupled multiphysics problem has been written with the expected levels of accurracy and stability, the proposed work concentrates on the resolution algorithms to enable the coupling between applications asssumed to be themselves exascale-compatible, to be solved efficiently at exascale. It is also worth noting that, in general, the couplings under consideration can present a high level of complexity, involving numerous physics with different level of feedback between them and various communications from border exchanges to overlaping domains. The current post-doctoral internship to be carried out in the framework of the ExaMA collaborative project, is in particular dedicated to the identification and dynamic tuning of the relevant numerical parameters arising from the coupling algorithms and impacting the computational efficiency of the global simulation. Considered problems are in the general case time-evolving problems, with a significant number of time iterations allowing using the first iterations to gather data and conduct the tuning.