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 Sáez-Mischlich, Jin Seok Park, Brian C. Vermeire, Lai Wang
{"title":"PyFR v2.0.3: Towards Industrial Adoption of Scale-Resolving Simulations","authors":"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 Sáez-Mischlich, Jin Seok Park, Brian C. Vermeire, Lai Wang","doi":"arxiv-2408.16509","DOIUrl":null,"url":null,"abstract":"PyFR is an open-source cross-platform computational fluid dynamics framework\nbased on the high-order Flux Reconstruction approach, specifically designed for\nundertaking high-accuracy scale-resolving simulations in the vicinity of\ncomplex engineering geometries. Since the initial release of PyFR v0.1.0 in\n2013, a range of new capabilities have been added to the framework, with a view\nto enabling industrial adoption of the capability. This paper provides details\nof those enhancements as released in PyFR v2.0.3, explains efforts to grow an\nengaged developer and user community, and provides latest performance and\nscaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at\nORNL (each with two GCDs), and up to 2048 NVIDIA GH200 GPUs on Alps at CSCS.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Computational Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.16509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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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