Preprints
https://doi.org/10.5194/gmd-2021-387
https://doi.org/10.5194/gmd-2021-387

Submitted as: development and technical paper 10 Dec 2021

Submitted as: development and technical paper | 10 Dec 2021

Review status: this preprint is currently under review for the journal GMD.

University of Warsaw Lagrangian Cloud Model (UWLCM) 2.0: Adaptation of a mixed Eulerian-Lagrangian numerical model for heterogeneous computing clusters

Piotr Dziekan and Piotr Zmijewski Piotr Dziekan and Piotr Zmijewski
  • Institute of Geophysics, Faculty of Physics, University of Warsaw, Poland

Abstract. A numerical cloud model with Lagrangian particles coupled to an Eulerian flow is adapted for distributed memory systems. Eulerian and Lagrangian calculations can be done in parallell on CPUs and GPUs, respectively. Scaling efficiency and the amount of parallelization of CPU and GPU calculations both exceed 50 % for up to 40 nodes. A sophisticated Lagrangian microphysics model slows down simulation by only 50 % compared to a simplistic bulk microphysics model, thanks to the use of GPUs. Overhead of communications between cluster nodes is mostly related to the pressure solver. Presented method of adaptation for computing clusters can be used in any numerical model with Lagrangian particles coupled to an Eulerian fluid flow.

Piotr Dziekan and Piotr Zmijewski

Status: open (until 04 Feb 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-387', Anonymous Referee #1, 11 Jan 2022 reply

Piotr Dziekan and Piotr Zmijewski

Piotr Dziekan and Piotr Zmijewski

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Short summary
Detailed computer simulations of clouds are important for understanding Earth's atmosphere and climate. The paper describes how UWLCM model has been adapted to work on supercomputers. A distinctive feature of UWLCM is that air flow is calculated by processors at the same time as cloud droplets are modeled by graphics cards. Thanks to that, simulations in which droplets are modeled with high fidelity do not take much more time than simulations with simplistic representation of droplets.