Articles | Volume 16, issue 9
https://doi.org/10.5194/gmd-16-2719-2023
https://doi.org/10.5194/gmd-16-2719-2023
Development and technical paper
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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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Cited articles

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
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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.
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.