Preprints
https://doi.org/10.5194/egusphere-2023-3045
https://doi.org/10.5194/egusphere-2023-3045
26 Mar 2024
 | 26 Mar 2024

Enabling High Performance Cloud Computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: Performance Evaluation and Benefits for the User Community

Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam

Abstract. The Community Multiscale Air Quality (CMAQ) Model is a local-to-hemispheric scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Center for Community Modeling and Analysis System (CMAS). CMAQ is used for regulatory purposes by the USEPA program offices and state and local air agencies, and is also widely used by the broader global research community to simulate and understand complex air quality processes and for computational environmental fate and transport, and climate and health impact studies. Leveraging state-of-the-science cloud computing resources for high performance computing (HPC) applications, CMAQ is now available as a fully tested, publicly available technology stack (HPC cluster and software stack) for two major cloud service providers (CSPs). Specifically, CMAQ configurations and supporting materials have been developed for use on their HPC clusters, including extensive online documentation, tutorials, and guidelines to scale and optimize air quality simulations using their services. These resources allow modelers to rapidly bring together CMAQ, cloud-hosted datasets, and visualization and evaluation tools on ephemeral clusters that can be deployed quickly and reliably worldwide. Described here are considerations in CMAQ v5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of typical simulation for testing and verifying components of the modeling system. The outcomes of this effort are to provide findings from performing CMAQ simulations on the cloud using popular vendor provided resources, to enable the user community to adapt this for their own needs and identify specific areas of potential optimization with respect to storage and compute architectures.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-3045', Anonymous Referee #1, 21 Apr 2024
  • RC2: 'Comment on egusphere-2023-3045', Anonymous Referee #2, 23 Apr 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-3045', Anonymous Referee #1, 21 Apr 2024
  • RC2: 'Comment on egusphere-2023-3045', Anonymous Referee #2, 23 Apr 2024
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam

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Short summary
We present a summary of enabling high performance computing of CMAQ – a state-of-the-science regional-scale air quality model – on two popular cloud computing platforms, through documenting the technologies, model performance, scaling and relative merits. We anticipate that this may be a new paradigm for computationally intense future model applications in space and time. We initiated this work due to a growing need to leverage cloud computing advances and to ease learning curve for new users.