Performance evaluation of ROMS v3.6 on a commercial cloud system

Jung, Kwangwoog; Cho, Yang‐Ki; Tak, Yong-Jin

doi:10.5194/gmd-2017-270

“…The NAPCS is intended to be used in Canada, Mexico, and the United States (INEGI, n.d. [13]), though only the Canadian and US have published national implementations at this time. There are some variations between the level of detail set out in the Canadian and US implementations, with the former setting out detailed definitions and additional breakdowns (Statistics Canada, 2018 [14]), in comparison to the latter, which sets out only product category titles with no definitions (US Census Bureau, n.d. [15]).…”

Section: North American Product Classification System (Napcs)mentioning

confidence: 99%

Measuring cloud services use by businesses

Ker¹

2021

OECD Digital Economy Papers

0

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“…Other tasks, such as deploying an auto-scaling-enabled job scheduler and configuring a shared file system, involve more complicated steps. This bottom-up deployment can be done by manually clicking on the AWS web console (Appendix A of Jung et al, 2017) or be partly automated by custom scripts (Chapter 9 of Yelick et al, 2011;Chang et al, 2018) based on the AWS Command Line Interface (AWSCLI).…”

Section: Appendix a Approaches To Deploy Hpc Cluster Infrastructure mentioning

confidence: 99%

Enabling high-performance cloud computing for Earth science modeling on over a thousand cores: application to the GEOS-Chem atmospheric chemistry model

Zhuang¹,

Jacob²,

Lin³

et al. 2020

Preprint

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Cloud computing platforms can facilitate the use of Earth science models by providing immediate access to fully configured software, massive computing power, and large input datasets. However, slow inter-node communication performance has previously discouraged the use of cloud platforms for massively parallel simulations. Here we show that recent advances in the network performance on the Amazon Web Services (AWS) cloud enable efficient model simulations with over a thousand cores. The choices of Message Passing Interface (MPI) library configuration and inter-node communication protocol are critical to this success. Application to the GEOS-Chem model of atmospheric chemistry at global 50 km horizontal resolution shows efficient scaling up to at least 1152 cores, with performance and cost comparable to the NASA Pleiades supercomputing cluster.

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Enabling High‐Performance Cloud Computing for Earth Science Modeling on Over a Thousand Cores: Application to the GEOS‐Chem Atmospheric Chemistry Model

Zhuang

¹

,

Jacob

²

,

Lin

³

et al. 2020

J Adv Model Earth Syst

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Cloud computing platforms can facilitate the use of Earth science models by providing immediate access to fully configured software, massive computing power, and large input data sets. However, slow internode communication performance has previously discouraged the use of cloud platforms for massively parallel simulations. Here we show that recent advances in the network performance on the Amazon Web Services cloud enable efficient model simulations with over a thousand cores. The choices of Message Passing Interface library configuration and internode communication protocol are critical to this success. Application to the Goddard Earth Observing System (GEOS)-Chem global 3-D chemical transport model at 50-km horizontal resolution shows efficient scaling up to at least 1,152 cores, with performance and cost comparable to the National Aeronautics and Space Administration Pleiades supercomputing cluster.Plain Language Summary Earth science model simulations are computationally expensive, typically requiring the use of high-end supercomputing clusters that are managed by universities or national laboratories. Commercial cloud computing offers an alternative. However, past work found that cloud computing platforms were not efficient for large-scale simulations on over 100 CPU cores, because the network communication performance on the cloud was slow compared to local clusters. Here we show that recent advances in the cloud network performance enable efficient model simulations with over a thousand cores, and cloud platforms can now serve as a viable alternative to local clusters for simulations at large scale. Computing on the cloud has extensive advantages, such as providing immediate access to fully configured model code and large data sets for any users, allowing full reproducibility of model simulation results, offering quick access to novel hardware that might not be available on local clusters, and being able to scale to virtually unlimited amounts of compute and storage resources. Those benefits will help advance Earth science modeling research.

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Performance evaluation of ROMS v3.6 on a commercial cloud system

Cited by 5 publications

References 10 publications

Measuring cloud services use by businesses

Measuring cloud services use by businesses

Enabling high-performance cloud computing for Earth science modeling on over a thousand cores: application to the GEOS-Chem atmospheric chemistry model

Enabling High‐Performance Cloud Computing for Earth Science Modeling on Over a Thousand Cores: Application to the GEOS‐Chem Atmospheric Chemistry Model

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