1Princeton University, Cooperative Institute of Climate Science,
Princeton, NJ, USA
2Centre Européen de Recherche Avancée en
Calcul Scientifique (CERFACS), Toulouse, France
3Engility Inc., Dover, NJ, USA
4NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
5National Centre for Atmospheric Science and University of Reading, Reading, UK
6Science and Technology Facilities Council, Abingdon, UK
7Deutsches Klimarechenzentrum GmbH, Hamburg, Germany
8Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
9Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) Foundation, Lecce, Italy
10University of Salento, Lecce, Italy
11Laboratoire des Sciences du Climat et de
l'Environnement LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191
Gif-sur-Yvette CEDEX, France
12Institut Pierre-Simon Laplace, CNRS/UPMC, Paris, France
Received: 23 Jul 2016 – Discussion started: 24 Aug 2016
Abstract. A climate model represents a multitude of processes on a variety of timescales and space scales: a canonical example of multi-physics multi-scale modeling. The underlying climate system is physically characterized by sensitive dependence on initial conditions, and natural stochastic variability, so very long integrations are needed to extract signals of climate change. Algorithms generally possess weak scaling and can be I/O and/or memory-bound. Such weak-scaling, I/O, and memory-bound multi-physics codes present particular challenges to computational performance.
Revised: 26 Nov 2016 – Accepted: 29 Nov 2016 – Published: 02 Jan 2017
Traditional metrics of computational efficiency such as performance counters and scaling curves do not tell us enough about real sustained performance from climate models on different machines. They also do not provide a satisfactory basis for comparative information across models. codes present particular challenges to computational performance.
We introduce a set of metrics that can be used for the study of computational performance of climate (and Earth system) models. These measures do not require specialized software or specific hardware counters, and should be accessible to anyone. They are independent of platform and underlying parallel programming models. We show how these metrics can be used to measure actually attained performance of Earth system models on different machines, and identify the most fruitful areas of research and development for performance engineering. codes present particular challenges to computational performance.
We present results for these measures for a diverse suite of models from several modeling centers, and propose to use these measures as a basis for a CPMIP, a computational performance model intercomparison project (MIP).
Balaji, V., Maisonnave, E., Zadeh, N., Lawrence, B. N., Biercamp, J., Fladrich, U., Aloisio, G., Benson, R., Caubel, A., Durachta, J., Foujols, M.-A., Lister, G., Mocavero, S., Underwood, S., and Wright, G.: CPMIP: measurements of real computational performance of Earth system models in CMIP6, Geosci. Model Dev., 10, 19-34, doi:10.5194/gmd-10-19-2017, 2017.