Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 9
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.
https://doi.org/10.5194/gmd-16-2719-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 9
Development and technical paper
 | Highlight paper
 | 
17 May 2023
Development and technical paper | Highlight paper |  | 17 May 2023

Pace v0.2: a Python-based performance-portable atmospheric model

Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer

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Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, Zenodo [software], https://doi.org/10.5281/zenodo.4724125, 2015. a
Adams, S., Ford, R., Hambley, M., Hobson, J., Kavčič, I., Maynard, C., Melvin, T., Müller, E., Mullerworth, S., Porter, A., Rezny, M., Shipway, B., and Wong, R.: LFRic: Meeting the challenges of scalability and performance portability in Weather and Climate models, J. Parallel Distr. Com., 132, 383–396, https://doi.org/10.1016/j.jpdc.2019.02.007, 2019. a
Alnaes, M. S., Blechta, J., Hake, J., Johansson, A., Kehlet, B., Logg, A., Richardson, C., Ring, J., and Rognes, M. E., and Wells, G. N.: The FEniCS Project Version 1.5, Archive of Numerical Software [software], https://doi.org/10.11588/ans.2015.100.20553, 2015. a
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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.
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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.
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