GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications
<p>Global modeling of atmospheric chemistry is a grand computational challenge because of the need to simulate large coupled systems of ∼ 100–1000 chemical species interacting with transport on all scales. Offline chemical transport models (CTMs), where the chemical continuity equations a...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-07-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/11/2941/2018/gmd-11-2941-2018.pdf |
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Article |
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DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
S. D. Eastham S. D. Eastham M. S. Long C. A. Keller C. A. Keller E. Lundgren R. M. Yantosca J. Zhuang C. Li C. J. Lee M. Yannetti B. M. Auer B. M. Auer T. L. Clune J. Kouatchou J. Kouatchou W. M. Putman M. A. Thompson M. A. Thompson A. L. Trayanov A. L. Trayanov A. M. Molod R. V. Martin R. V. Martin D. J. Jacob |
| spellingShingle |
S. D. Eastham S. D. Eastham M. S. Long C. A. Keller C. A. Keller E. Lundgren R. M. Yantosca J. Zhuang C. Li C. J. Lee M. Yannetti B. M. Auer B. M. Auer T. L. Clune J. Kouatchou J. Kouatchou W. M. Putman M. A. Thompson M. A. Thompson A. L. Trayanov A. L. Trayanov A. M. Molod R. V. Martin R. V. Martin D. J. Jacob GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications Geoscientific Model Development |
| author_facet |
S. D. Eastham S. D. Eastham M. S. Long C. A. Keller C. A. Keller E. Lundgren R. M. Yantosca J. Zhuang C. Li C. J. Lee M. Yannetti B. M. Auer B. M. Auer T. L. Clune J. Kouatchou J. Kouatchou W. M. Putman M. A. Thompson M. A. Thompson A. L. Trayanov A. L. Trayanov A. M. Molod R. V. Martin R. V. Martin D. J. Jacob |
| author_sort |
S. D. Eastham |
| title |
GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications |
| title_short |
GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications |
| title_full |
GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications |
| title_fullStr |
GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications |
| title_full_unstemmed |
GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications |
| title_sort |
geos-chem high performance (gchp v11-02c): a next-generation implementation of the geos-chem chemical transport model for massively parallel applications |
| publisher |
Copernicus Publications |
| series |
Geoscientific Model Development |
| issn |
1991-959X 1991-9603 |
| publishDate |
2018-07-01 |
| description |
<p>Global modeling of atmospheric chemistry is a grand
computational challenge because of the need to simulate large coupled systems
of ∼ 100–1000 chemical species interacting with transport on all scales.
Offline chemical transport models (CTMs), where the chemical continuity
equations are solved using meteorological data as input, have usability
advantages and are important vehicles for developing atmospheric chemistry
knowledge that can then be transferred to Earth system models. However, they
have generally not been designed to take advantage of massively parallel
computing architectures. Here, we develop such a high-performance capability
for GEOS-Chem (GCHP), a CTM driven by meteorological data from the NASA
Goddard Earth Observation System (GEOS) and used by hundreds of research
groups worldwide. GCHP is a grid-independent implementation of GEOS-Chem
using the Earth System Modeling Framework (ESMF) that permits the same
standard model to operate in a distributed-memory framework for massive
parallelization. GCHP also allows GEOS-Chem to take advantage of the native
GEOS cubed-sphere grid for greater accuracy and computational efficiency in
simulating transport. GCHP enables GEOS-Chem simulations to be conducted with
high computational scalability up to at least 500 cores, so that global
simulations of stratosphere–troposphere oxidant–aerosol chemistry at C180
spatial resolution ( ∼ 0.5° × 0.625°) or finer
become routinely feasible.</p> |
| url |
https://www.geosci-model-dev.net/11/2941/2018/gmd-11-2941-2018.pdf |
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doaj-71ba9db8726f454695bb1807b76fc70d2020-11-25T02:46:32ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032018-07-01112941295310.5194/gmd-11-2941-2018GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applicationsS. D. Eastham0S. D. Eastham1M. S. Long2C. A. Keller3C. A. Keller4E. Lundgren5R. M. Yantosca6J. Zhuang7C. Li8C. J. Lee9M. Yannetti10B. M. Auer11B. M. Auer12T. L. Clune13J. Kouatchou14J. Kouatchou15W. M. Putman16M. A. Thompson17M. A. Thompson18A. L. Trayanov19A. L. Trayanov20A. M. Molod21R. V. Martin22R. V. Martin23D. J. Jacob24Laboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, Massachusetts, USAJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USAJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USANASA Global Modeling and Assimilation Office, Greenbelt, Maryland, USAUniversities Space Research Association, Columbia, Maryland, USAJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USAJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USAJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USADepartment of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, CanadaDepartment of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, CanadaJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USANASA Global Modeling and Assimilation Office, Greenbelt, Maryland, USAScience Systems and Applications, Inc., Lanham, Maryland, USANASA Global Modeling and Assimilation Office, Greenbelt, Maryland, USANASA Global Modeling and Assimilation Office, Greenbelt, Maryland, USAScience Systems and Applications, Inc., Lanham, Maryland, USANASA Global Modeling and Assimilation Office, Greenbelt, Maryland, USANASA Global Modeling and Assimilation Office, Greenbelt, Maryland, USAScience Systems and Applications, Inc., Lanham, Maryland, USANASA Global Modeling and Assimilation Office, Greenbelt, Maryland, USAScience Systems and Applications, Inc., Lanham, Maryland, USANASA Global Modeling and Assimilation Office, Greenbelt, Maryland, USADepartment of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, CanadaSmithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USAJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA<p>Global modeling of atmospheric chemistry is a grand computational challenge because of the need to simulate large coupled systems of ∼ 100–1000 chemical species interacting with transport on all scales. Offline chemical transport models (CTMs), where the chemical continuity equations are solved using meteorological data as input, have usability advantages and are important vehicles for developing atmospheric chemistry knowledge that can then be transferred to Earth system models. However, they have generally not been designed to take advantage of massively parallel computing architectures. Here, we develop such a high-performance capability for GEOS-Chem (GCHP), a CTM driven by meteorological data from the NASA Goddard Earth Observation System (GEOS) and used by hundreds of research groups worldwide. GCHP is a grid-independent implementation of GEOS-Chem using the Earth System Modeling Framework (ESMF) that permits the same standard model to operate in a distributed-memory framework for massive parallelization. GCHP also allows GEOS-Chem to take advantage of the native GEOS cubed-sphere grid for greater accuracy and computational efficiency in simulating transport. GCHP enables GEOS-Chem simulations to be conducted with high computational scalability up to at least 500 cores, so that global simulations of stratosphere–troposphere oxidant–aerosol chemistry at C180 spatial resolution ( ∼ 0.5° × 0.625°) or finer become routinely feasible.</p>https://www.geosci-model-dev.net/11/2941/2018/gmd-11-2941-2018.pdf |