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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.16509 |
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| _version_ | 1866916374517907456 |
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| author | Witherden, Freddie D. Vincent, Peter E. Trojak, Will Abe, Yoshiaki Akbarzadeh, Amir Akkurt, Semih Alhawwary, Mohammad Caros, Lidia Dzanic, Tarik Giangaspero, Giorgio Iyer, Arvind S. Jameson, Antony Koch, Marius Loppi, Niki Mishra, Sambit Modi, Rishit Sáez-Mischlich, Gonzalo Park, Jin Seok Vermeire, Brian C. Wang, Lai |
| author_facet | Witherden, Freddie D. Vincent, Peter E. Trojak, Will Abe, Yoshiaki Akbarzadeh, Amir Akkurt, Semih Alhawwary, Mohammad Caros, Lidia Dzanic, Tarik Giangaspero, Giorgio Iyer, Arvind S. Jameson, Antony Koch, Marius Loppi, Niki Mishra, Sambit Modi, Rishit Sáez-Mischlich, Gonzalo Park, Jin Seok Vermeire, Brian C. Wang, Lai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16509 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PyFR v2.0.3: Towards Industrial Adoption of Scale-Resolving Simulations Witherden, Freddie D. Vincent, Peter E. Trojak, Will Abe, Yoshiaki Akbarzadeh, Amir Akkurt, Semih Alhawwary, Mohammad Caros, Lidia Dzanic, Tarik Giangaspero, Giorgio Iyer, Arvind S. Jameson, Antony Koch, Marius Loppi, Niki Mishra, Sambit Modi, Rishit Sáez-Mischlich, Gonzalo Park, Jin Seok Vermeire, Brian C. Wang, Lai Computational Physics 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. |
| title | PyFR v2.0.3: Towards Industrial Adoption of Scale-Resolving Simulations |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2408.16509 |