[A-AS02] Weather, Climate, and Environmental Science Studies using High-Performance Computing

[AAS02-11] Efforts toward optimization of global non-hydrostatic atmospheric model on GPU supercomputer

*Hisashi Yashiro¹, Kazuya Yamazaki², Takashi Arakawa³, Shuhei Matsugishi², INTYISYAR Intyisyar¹, Kengo Nakajima² (1.National Institute for Environmental Studies, 2.The University of Tokyo, 3.ClimTech Inc.)

Keywords:GPU, model simulation, High Performance Computing, climate prediction, Machine Learning, Python

To secure more computational resources for future weather/climate simulations, it is necessary to utilize supercomputers equipped with many energy-efficient accelerators. We present the results of several strategies to port the non-hydrostatic icosahedral atmospheric model (NICAM, Satoh et al., 2014) to GPU supercomputers. 1) We conducted GPU porting of the full application using OpenACC directives and evaluated computational performance. We identified and avoided several patterns that led to deteriorated computational speed throughout the optimization process. 2) Several kernels extracted from the dycore were rewritten from Fortran to Python. Leveraging the JAX library, we conducted offloading the computations to GPU. 3) Algorithm transformation: We created data-driven model components capable of utilizing dense matrix operations by learning the simulation results based on the physics-based process model.