Articles | Volume 19, issue 9
https://doi.org/10.5194/gmd-19-3783-2026
https://doi.org/10.5194/gmd-19-3783-2026
Development and technical paper
 | 
08 May 2026
Development and technical paper |  | 08 May 2026

Actionable reporting of CPU-GPU performance comparisons: insights from a CLUBB case study

Gunther Huebler, Vincent E. Larson, John M. Dennis, and Sheri A. Voelz

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Cited articles

Andersch, M., Lucas, J., Álvarez-Mesa, M. A., and Juurlink, B.: On latency in GPU throughput microarchitectures, in: 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 169–170, IEEE, https://doi.org/10.1109/ISPASS.2015.7095801, 2015. a
Bertagna, L., Deakin, M., Guba, O., Sunderland, D., Bradley, A. M., Tezaur, I. K., Taylor, M. A., and Salinger, A. G.: HOMMEXX 1.0: a performance-portable atmospheric dynamical core for the Energy Exascale Earth System Model, Geosci. Model Dev., 12, 1423–1441, https://doi.org/10.5194/gmd-12-1423-2019, 2019. a
Bogenschutz, P. A., Gettelman, A., Hannay, C., Larson, V. E., Neale, R. B., Craig, C., and Chen, C.-C.: The path to CAM6: coupled simulations with CAM5.4 and CAM5.5, Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, 2018. a
Brown, A. R., Cederwall, R. T., Chlond, A., Duynkerke, P. G., Golaz, J.-C., Khairoutdinov, M., Lewellen, D. C., Lock, A. P., MacVean, M. K., Moeng, C.-H., Neggers, R. A. J., Siebesma, A. P., and Stevens, B.: Large‐Eddy Simulation of the Diurnal Cycle of Shallow Cumulus Convection over Land, Q. J. Roy. Meteor. Soc., 128, 1075–1093, https://doi.org/10.1256/003590002320373210, 2002. a
Coleman, D. M. and Feldman, D. R.: Porting Existing Radiation Code for GPU Acceleration, IEEE J. Sel. Top. Appl., 6, 2486–2491, https://doi.org/10.1109/JSTARS.2013.2247379, 2013. a
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Short summary
Central processing units (CPUs) and graphics processing units (GPUs) are different devices that suit different kinds of work. Using a climate modeling component, we provide a clearer way to tell which device type is faster for a given task. This matters because runs usually use only one device type. Our results are actionable: they guide device choice, report performance gains fairly, highlight code areas to improve, and show how code structure and optimization can change conclusions.
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