Journal Article
The International Journal of High Performance Computing Applications, vol. 36, iss. 1, pp. 93-105, 2021
Authors
Matthew R Norman, David A Bader, Christopher Eldred, Walter M Hannah, Benjamin R Hillman, Christopher R Jones, Jungmin M Lee, LR Leung, Isaac Lyngaas, Kyle G Pressel, Sarat Sreepathi, Mark A Taylor, Xingqiu Yuan
Abstract
Clouds represent a key uncertainty in future climate projection. While explicit cloud resolution remains beyond our computational grasp for global climate, we can incorporate important cloud effects through a computational middle ground called the Multi-scale Modeling Framework (MMF), also known as Super Parameterization. This algorithmic approach embeds high-resolution Cloud Resolving Models (CRMs) to represent moist convective processes within each grid column in a Global Climate Model (GCM). The MMF code requires no parallel data transfers and provides a self-contained target for acceleration. This study investigates the performance of the Energy Exascale Earth System Model-MMF (E3SM-MMF) code on the OLCF Summit supercomputer at an unprecedented scale of simulation. Hundreds of kernels in the roughly 10K lines of code in the E3SM-MMF CRM were ported to GPUs with OpenACC directives. A high-resolution benchmark using 4600 nodes on Summit demonstrates the computational capability of the GPU-enabled E3SM-MMF code in a full physics climate simulation.
