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

Chayanon; Wichitrnithed; Woo-Sun-Yang; Yun; He; Brad Richardson; Koichi Sakaguchi; Manuel Arenaz; William I. Gustafson; Jacob Shpund; Ulises Costi Blanco; Alvaro Goldar Dieste

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

Distributed, Parallel, and Cluster Computing 2024-09-12 v1 Computational Engineering, Finance, and Science

Authors: Chayanon , Wichitrnithed , Woo-Sun-Yang , Yun , He , Brad Richardson , Koichi Sakaguchi , Manuel Arenaz , William I. Gustafson , Jacob Shpund , Ulises Costi Blanco , Alvaro Goldar Dieste

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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.

Keywords

time series forecasting fpga accelerator physics simulation

Cite

@article{arxiv.2409.07232,
  title  = {Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee},
  author = {Chayanon and Wichitrnithed and Woo-Sun-Yang and Yun and He and Brad Richardson and Koichi Sakaguchi and Manuel Arenaz and William I. Gustafson and Jacob Shpund and Ulises Costi Blanco and Alvaro Goldar Dieste},
  journal= {arXiv preprint arXiv:2409.07232},
  year   = {2024}
}

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

Abstract

Keywords

Cite

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