Accelerating the Dutch Atmospheric Large-Eddy Simulation (DALES) model with OpenACC
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913711066710016 |
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| author | Esclapez, Lucas Soucasse, Laurent Jungbacker, Caspar Jansson, Fredrik de Roode, Stephan R. Costa, Pedro Oord, Gijs van den Sclocco, Alessio |
| author_facet | Esclapez, Lucas Soucasse, Laurent Jungbacker, Caspar Jansson, Fredrik de Roode, Stephan R. Costa, Pedro Oord, Gijs van den Sclocco, Alessio |
| contents | This paper presents the GPU porting through OpenACC directives of the Dutch Atmospheric Large-Eddy Simulation (DALES) application, a high-resolution atmospheric model. The code is written in Fortran~90 and features parallel (distributed) execution through spatial domain decomposition. We assess the performance of the GPU offloading, comparing the time-to-solution on regular and accelerated HPC nodes. %comparing the computational time between distributed and accelerated nodes. A weak scaling analysis is conducted and portability across NVIDIA A100 and H100 hardware %and AMD hardware is discussed. Finally, we show how targeted kernels can benefit from further optimization with Kernel Tuner, a GPU kernels auto-tuning package. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20412 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating the Dutch Atmospheric Large-Eddy Simulation (DALES) model with OpenACC Esclapez, Lucas Soucasse, Laurent Jungbacker, Caspar Jansson, Fredrik de Roode, Stephan R. Costa, Pedro Oord, Gijs van den Sclocco, Alessio Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing Atmospheric and Oceanic Physics This paper presents the GPU porting through OpenACC directives of the Dutch Atmospheric Large-Eddy Simulation (DALES) application, a high-resolution atmospheric model. The code is written in Fortran~90 and features parallel (distributed) execution through spatial domain decomposition. We assess the performance of the GPU offloading, comparing the time-to-solution on regular and accelerated HPC nodes. %comparing the computational time between distributed and accelerated nodes. A weak scaling analysis is conducted and portability across NVIDIA A100 and H100 hardware %and AMD hardware is discussed. Finally, we show how targeted kernels can benefit from further optimization with Kernel Tuner, a GPU kernels auto-tuning package. |
| title | Accelerating the Dutch Atmospheric Large-Eddy Simulation (DALES) model with OpenACC |
| topic | Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2502.20412 |