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

2024/09/11 by Chayanon, Wichitrnithed, Woo-Sun-Yang +17
Computer Science · #Advanced Computational Techniques and Applications #Computational Engineering #Distributed #FOS: Computer and information sciences #Finance #Parallel #and Cluster Computing (cs.DC) #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2409.07232

openalex publication_date 2024/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

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 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.

Related

• Performance report and optimized implementation of Weather & Climate Dwarfs on GPU, MIC and Optalysys Optical Processor
• Performance report and optimized implementations of Weather & Climate dwarfs on multi-node systems
• New High Performance GPGPU Code Transformation Framework Applied to Large Production Weather Prediction Code
• Model Coupling between the Weather Research and Forecasting Model and the DPRI Large Eddy Simulator for Urban Flows on GPU-accelerated Multicore Systems
• Accelerating the Dutch Atmospheric Large-Eddy Simulation (DALES) model with OpenACC