PyFR v2.0.3: Towards Industrial Adoption of Scale-Resolving Simulations
Freddie D. Witherden, Peter E. Vincent, Will Trojak, Yoshiaki Abe,, Amir Akbarzadeh, Semih Akkurt, Mohammad Alhawwary, Lidia Caros, Tarik Dzanic,, Giorgio Giangaspero, Arvind S. Iyer, Antony Jameson, Marius Koch, Niki Loppi,, Sambit Mishra, Rishit Modi, Gonzalo S\'aez-Mischlich
TL;DR
PyFR v2.0.3 advances a high-order CFD framework for industrial-scale simulations, with new features, community growth efforts, and performance results on large GPU clusters to facilitate industrial adoption.
Contribution
This paper introduces new capabilities in PyFR v2.0.3 aimed at enabling industrial adoption of high-accuracy scale-resolving simulations.
Findings
Successful scaling on 1024 AMD GPUs at ORNL
Performance improvements in PyFR v2.0.3
Community engagement efforts for PyFR
Abstract
PyFR is an open-source cross-platform computational fluid dynamics framework based on the high-order Flux Reconstruction approach, specifically designed for undertaking high-accuracy scale-resolving simulations in the vicinity of complex engineering geometries. Since the initial release of PyFR v0.1.0 in 2013, a range of new capabilities have been added to the framework, with a view to enabling industrial adoption of the capability. This paper provides details of those enhancements as released in PyFR v2.0.3, explains efforts to grow an engaged developer and user community, and provides latest performance and scaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at ORNL (each with two GCDs), and up to 2048 NVIDIA GH200 GPUs on Alps at CSCS.
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Taxonomy
TopicsScientific Computing and Data Management