Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee

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 Microph...

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Bibliographic Details
Published in:arXiv.org 2024-09
Main Authors: Chayanon, Wichitrnithed, Woo-Sun-Yang, Yun, He, Richardson, Brad, Sakaguchi, Koichi, Arenaz, Manuel, Gustafson, William I, Shpund, Jacob, Ulises Costi Blanco, Alvaro Goldar Dieste
Format: Article
Language:English
Subjects:
Distributed memory
Microphysics
Thunderstorms
Weather forecasting
Workflow
Online Access:Get full text
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Summary: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.
ISSN:2331-8422