Abstract

We introduce ClimateMachine, a new open-source atmosphere modeling framework using the Julia language to be performance portable on central processing units (CPUs) and graphics processing units (GPUs). ClimateMachine uses a common framework both for coarser-resolution global simulations and for high-resolution, limited-area large-eddy simulations (LES). Here, we demonstrate the LES configuration of the atmosphere model in canonical benchmark cases and atmospheric flows, using an energy-conserving nodal discontinuous-Galerkin (DG) discretization of the governing equations. Resolution dependence, conservation characteristics and scaling metrics are examined in comparison with existing LES codes. They demonstrate the utility of ClimateMachine as a modelling tool for limited-area LES flow configurations.

Highlights

  • Hybrid computer architectures and the need to exploit the power of graphics processing units (GPUs) are increasingly driving 10 developments in atmosphere and climate modeling (e.g., Schalkwijk et al, 2012; Palmer, 2014; Schalkwijk et al, 2015; Marras et al, 2015; Abdi et al, 2017b, a; Fuhrer et al, 2018; Schär et al, 2020)

  • We introduce ClimateMachine, a new open-source atmosphere modeling framework using the Julia language to be performance portable on central processing units (CPUs) and graphics processing units (GPUs)

  • 15 In this paper, we introduce ClimateMachine, a new open-source atmosphere model written in the Julia programming language (Bezanson et al, 2017) to provide a computational framework that is portable across CPU and GPU architectures

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Summary

Introduction

Hybrid computer architectures and the need to exploit the power of graphics processing units (GPUs) are increasingly driving 10 developments in atmosphere and climate modeling (e.g., Schalkwijk et al, 2012; Palmer, 2014; Schalkwijk et al, 2015; Marras et al, 2015; Abdi et al, 2017b, a; Fuhrer et al, 2018; Schär et al, 2020). The sheer computing power available on modern hardware architectures presents opportunities to accelerate atmosphere and climate modeling. Exploiting this computing power requires re-coding atmosphere and climate models to an extent not seen in decades, and portable performance and scaling across different platforms remain difficult to achieve (Fuhrer et al, 2014; Balaji, 2021). It lends itself well to modern high-performance computing architectures because its communication overhead is low, enabling scaling on manycore processors including GPUs (Abdi et al, 2017b). Another im portant consideration within ClimateMachine is the use of total energy of moist air as a prognostic variable, ensuring energetic consistency of the simulations. Additional details about the model, boundary 45 conditions, statistical definitions, and computer hardware are summarized in the appendices

Working fluid
Mass balance
Total water balance
Momentum balance
Energy balance
Saturation adjustment
Space discretization
Time discretization
Smagorinsky-Lilly model
Vreman eddy viscosity model
Numerical stability
Numerical experiments and discussions
Passive transport over warped grids
Mountain-triggered gravity waves
Linear non-hydrostatic
Mass and energy conservation
CPU strong-scaling
Conclusions
Solid walls and wall fluxes
Non-reflecting top boundary
Numerical implementation
705 References
Findings
Methods
Full Text
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