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

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1029/2020MS002064. This is version 3 of this Preprint.

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Jiawei Zhuang, Daniel J. Jacob, Haipeng Lin, Elizabeth Lundgren, Robert Yantosca, Judit Flo Gaya, Melissa P. Sulprizio, Sebastian David Eastham


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.




Computer Sciences, Earth Sciences, Physical Sciences and Mathematics


Cloud computing, Earth science modeling, High-performance computing


Published: 2020-02-20 12:25

Last Updated: 2020-02-21 22:18

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GNU Lesser General Public License (LGPL) 2.1