Unprecedented cloud resolution in a GPU-enabled full-physics atmospheric climate simulation on OLCF's summit supercomputer. (January 2022)

• Record Type: Journal Article
• Authors: Norman, Matthew R; Bader, David C; Eldred, Christopher; Hannah, Walter M; Hillman, Benjamin R; Jones, Christopher R; Lee, Jungmin M; Leung, LR; Lyngaas, Isaac; Pressel, Kyle G; Sreepathi, Sarat; Taylor, Mark A; Yuan, Xingqiu
• Other Names: Evans Thomas M guest-editor.
• Is Part Of: International journal of high performance computing applications. Volume 36:Number 1(2022)
• Page Start: 93
• Page End: 105
• Publication Date: 2022-01
• Subjects: climate -- clouds -- GPU -- OpenACC; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/10943420211027539
• Languages: English
• ISSNs: 1094-3420

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.