Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee
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arXiv
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| Format: | Preprint |
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2024
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| author | Chayanon Wichitrnithed Woo-Sun-Yang Yun He Richardson, Brad Sakaguchi, Koichi Arenaz, Manuel Gustafson Jr., William I. Shpund, Jacob Blanco, Ulises Costi Dieste, Alvaro Goldar |
| author_facet | Chayanon Wichitrnithed Woo-Sun-Yang Yun He Richardson, Brad Sakaguchi, Koichi Arenaz, Manuel Gustafson Jr., William I. Shpund, Jacob Blanco, Ulises Costi Dieste, Alvaro Goldar |
| contents | Currently, the Weather Research and Forecasting model (WRF) utilizes shared memory (OpenMP) and distributed memory (MPI) parallelisms. To take advantage of GPU resources on the Perlmutter supercomputer at NERSC, we port parts of the computationally expensive routines of the Fast Spectral Bin Microphysics (FSBM) microphysical scheme to NVIDIA GPUs using OpenMP device offloading directives. To facilitate this process, we explore a workflow for optimization which uses both runtime profilers and a static code inspection tool Codee to refactor the subroutine. We observe a 2.08x overall speedup for the CONUS-12km thunderstorm test case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07232 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee Chayanon Wichitrnithed Woo-Sun-Yang Yun He Richardson, Brad Sakaguchi, Koichi Arenaz, Manuel Gustafson Jr., William I. Shpund, Jacob Blanco, Ulises Costi Dieste, Alvaro Goldar Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science Currently, the Weather Research and Forecasting model (WRF) utilizes shared memory (OpenMP) and distributed memory (MPI) parallelisms. To take advantage of GPU resources on the Perlmutter supercomputer at NERSC, we port parts of the computationally expensive routines of the Fast Spectral Bin Microphysics (FSBM) microphysical scheme to NVIDIA GPUs using OpenMP device offloading directives. To facilitate this process, we explore a workflow for optimization which uses both runtime profilers and a static code inspection tool Codee to refactor the subroutine. We observe a 2.08x overall speedup for the CONUS-12km thunderstorm test case. |
| title | Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee |
| topic | Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2409.07232 |