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.

IF 4 3区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Geoscientific Model Development Pub Date : 2024-09-19 DOI:10.5194/gmd-17-7001-2024
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, Saravanan Arunachalam
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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.

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为社区多尺度空气质量模型(CMAQ)5.3.3 版启用高性能云计算:性能评估及对用户社区的益处。
社区多尺度空气质量模型(CMAQ)是由美国环境保护局(USEPA)开发的地方到半球尺度的空气质量数值模型系统,由社区建模与分析系统(CMAS)中心提供支持。CMAQ 被美国环保署项目办公室、州和地方空气机构用于监管目的,同时也被更广泛的全球研究界用于模拟和了解复杂的空气质量过程,以及计算环境归宿和迁移、气候和健康影响研究。利用用于高性能计算(HPC)应用的科学云计算资源,CMAQ现已作为经过全面测试的公开可用技术堆栈(HPC集群和软件堆栈)提供给两大云服务提供商(CSP)。具体来说,CMAQ配置和辅助材料已开发完成,可在其高性能计算集群上使用,包括大量在线文档、教程和指南,以便使用其服务扩展和优化空气质量模拟。通过这些资源,建模人员可以将 CMAQ、云托管数据集以及可视化和评估工具快速整合到可在全球范围内快速可靠部署的短暂集群上。本文介绍了 CMAQ 5.3.3 版云使用中的注意事项以及每个 CSP 的支持资源,并通过一个基准应用套件进行了介绍,该套件是作为测试和验证建模系统组件的典型模拟示例而开发的。这项工作的成果是提供使用流行供应商提供的资源在云上执行 CMAQ 仿真的结果,使用户社区能够根据自身需求进行调整,并确定在存储和计算架构方面可能进行优化的具体领域。
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来源期刊
Geoscientific Model Development
Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
8.60
自引率
9.80%
发文量
352
审稿时长
6-12 weeks
期刊介绍: Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.
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