Articles | Volume 16, issue 9
https://doi.org/10.5194/gmd-16-2719-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2719-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| Highlight paper
| 
17 May 2023
Development and technical paper | Highlight paper |
| 17 May 2023
| 17 May 2023
Pace v0.2: a Python-based performance-portable atmospheric model
Johann Dahm
Climate Modeling, Allen Institute for Artificial Intelligence, Seattle, WA, USA
Eddie Davis
Climate Modeling, Allen Institute for Artificial Intelligence, Seattle, WA, USA
Florian Deconinck
Climate Modeling, Allen Institute for Artificial Intelligence, Seattle, WA, USA
Oliver Elbert
Climate Modeling, Allen Institute for Artificial Intelligence, Seattle, WA, USA
Weather and Climate Dynamics Division, Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA
Rhea George
Climate Modeling, Allen Institute for Artificial Intelligence, Seattle, WA, USA
Jeremy McGibbon
Climate Modeling, Allen Institute for Artificial Intelligence, Seattle, WA, USA
Tobias Wicky
Climate Modeling, Allen Institute for Artificial Intelligence, Seattle, WA, USA
Elynn Wu
Climate Modeling, Allen Institute for Artificial Intelligence, Seattle, WA, USA
Christopher Kung
Global Modeling and Assimilation Office, Goddard Space Flight Center, NASA, Greenbelt, MD, USA
Tal Ben-Nun
Department of Computer Science, ETH Zurich, Zurich, Switzerland
Lucas Harris
Weather and Climate Dynamics Division, Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA
Linus Groner
Swiss National Supercomputing Centre (CSCS), ETH Zurich, Lugano, Switzerland
Oliver Fuhrer
CORRESPONDING AUTHOR
Climate Modeling, Allen Institute for Artificial Intelligence, Seattle, WA, USA
Numerical Prediction, Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
Model code and software
ai2cm/pace: v0.2.0 Rhea George, Elynn Wu, Jeremy McGibbon, Johann Dahm, Eddie Davis, Tobias Wicky, Florian Deconinck, Christopher Kung, Oliver Fuhrer, Oliver Elbert, Ajda Savarin, Noah D. Brenowitz, Mark Cheeseman, Brian Henn, Spencer Clark, and Yannick Niedermayr https://doi.org/10.5281/zenodo.7464843
ai2cm/gt4py: v0.1.0 GMD release Johann Dahm, Linus Groner, Enrique G. Paredes, Felix Thaler, Hannes Vogt, Eddie Davis, Rico Haeuselmann, Till Ehrengruber, Stefano Ubbiali, Tobias Wicky, Florian Deconinck, Tal Ben-Nun, and Rhea George https://doi.org/10.5281/zenodo.7080260
Executive editor
Achieving both performance and portability in a whole dynamical core implemented in a high-productivity language such as Python is an eye-opening result which rebuts some widely held assumptions in the geoscientific modelling community. This is a paper which everyone who writes geoscientific models should read.
Achieving both performance and portability in a whole dynamical core implemented in a...
Short summary
It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
It is hard for scientists to write code which is efficient on different kinds of supercomputers....
