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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

Authors :
C. I. Efstathiou
E. Adams
C. J. Coats
R. Zelt
M. Reed
J. McGee
K. M. Foley
F. I. Sidi
D. C. Wong
S. Fine
S. Arunachalam
Source :
Geoscientific Model Development, Vol 17, Pp 7001-7027 (2024)
Publication Year :
2024
Publisher :
Copernicus Publications, 2024.

Abstract

The Community Multiscale Air Quality Model (CMAQ) is a local- to hemispheric-scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Community Modeling and Analysis System (CMAS) center. 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 version 5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of a 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 to identify specific areas of potential optimization with respect to storage and compute architectures.

Subjects

Subjects :
Geology
QE1-996.5

Details

Language :
English
ISSN :
70012024, 1991959X, and 19919603
Volume :
17
Database :
Directory of Open Access Journals
Journal :
Geoscientific Model Development
Publication Type :
Academic Journal
Accession number :
edsdoj.617edb9ca85f4cb08ee18414f83d7700
Document Type :
article
Full Text :
https://doi.org/10.5194/gmd-17-7001-2024