DOE OSTI · 2519665
Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee
Abstract
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 routine Fast Spectral Bin Microphysics (FSBM) 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 an 2.24x overall speedup for the CONUS-12km storm test case.
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Wichitrnithed, Chayanon (Namo) [Odin Institute], Yang, Woo- Sung [Lawrence Berkeley National Laboratory], He, Yun (Helen) [Lawrence Livermore National Laboratory], Richardson, Brad [Lawrence Berkeley National Laboratory], Sakaguchi, Koichi [BATTELLE (PACIFIC NW LAB)], Arenaz, Manuel [Appentra Solutions S.L], Gustafson, William I. [BATTELLE (PACIFIC NW LAB)] (ORCID:0000000199271393), Shpund, Jacob [BATTELLE (PACIFIC NW LAB)], Costi Blanco, Ulises [Appentra Solutions], Goldar Dieste, Alvaro [Appentra Solutions]. 2024-12-30. Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee. https://doi.org/10.1109/scw63240.2024.00243
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