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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-304
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-304
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 21 Jan 2019

Development and technical paper | 21 Jan 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Andreas Müller1, Willem Deconinck1, Christian Kühnlein1, Gianmarco Mengaldo1, Michael Lange1, Nils Wedi1, Peter Bauer1, Piotr K. Smolarkiewicz1, Michail Diamantakis1, Sarah-Jane Lock1, Mats Hamrud1, Sami Saarinen1, George Mozdzynski1, Daniel Thiemert1, Michael Glinton2, Pierre Bénard2, Fabrice Voitus2, Charles Colavolpe2, Philippe Marguinaud2, Yongjun Zheng2, Joris Van Bever3, Daan Degrauwe3, Geert Smet3, Piet Termonia3,4, Kristian P. Nielsen5, Bent H. Sass5, Jacob W. Poulsen5, Per Berg5, Carlos Osuna6, Oliver Fuhrer6, Valentin Clement7, Michael Baldauf8, Mike Gillard9, Joanna Szmelter9, Enda O'Brien10, Alastair McKinstry10, Oisín Robinson10, Parijat Shukla10, Michael Lysaght10, Michał Kulczewski11, Milosz Ciznicki11, Wojciech Pia̧tek11, Sebastian Ciesielski11, Marek Błażewicz11, Krzysztof Kurowski11, Marcin Procyk11, Pawel Spychala11, Bartosz Bosak11, Zbigniew Piotrowski12, Andrzej Wyszogrodzki12, Erwan Raffin13, Cyril Mazauric13, David Guibert13, Louis Douriez13, Xavier Vigouroux13, Alan Gray14, Peter Messmer14, Alexander J. Macfaden15, and Nick New15 Andreas Müller et al.
  • 1European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield Park, Reading RG2 9AX, UK
  • 2Centre National de Recherches Météorologiques, Météo-France, Toulouse, France
  • 3Royal Meteorological Institute (RMI), Ringlaan 3, Brussels, Belgium
  • 4Department of Physics and Astronomy, Ghent University, Ghent, Belgium
  • 5The Danish Meteorological Institute (DMI), Copenhagen, Denmark
  • 6Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
  • 7Center for Climate System Modeling, Zurich, Switzerland
  • 8Deutscher Wetterdienst (DWD), Offenbach, Germany
  • 9Loughborough University, Leicestershire LE11 3TU, UK
  • 10Irish Centre for High-End Computing (ICHEC), National University of Ireland, Galway, Ireland
  • 11Poznan Supercomputing and Networking Center (PSNC), Jana Pawła II 10 Street, 61-139, Poznań, Poland
  • 12Institute of Meteorology and Water Management – National Research institute (IMGW-PIB), Podleśna 61, Warsaw, Poland
  • 13Bull, an ATOS company, Bezons, France
  • 14NVIDIA Switzerland, Technoparkstr. 1, 8005 Zurich, Switzerland
  • 15Optalysys Ltd., Flemming Court, Whistler Drive, Glasshoughton, WF10 5HW, UK

Abstract. In the simulation of complex multi-scale flow problems, such as those arising in weather and climate modelling, one of the biggest challenges is to satisfy operational requirements in terms of time-to-solution and energy-to-solution yet without compromising the accuracy and stability of the calculation. These competing factors require the development of state-of-the-art algorithms that can optimally exploit the targeted underlying hardware and efficiently deliver the extreme computational capabilities typically required in operational forecast production. These algorithms should (i) minimise the energy footprint along with the time required to produce a solution, (ii) maintain a satisfying level of accuracy, (iii) be numerically stable and resilient, in case of hardware or software failure.

The European Centre for Medium Range Weather Forecasts (ECMWF) is leading a project called ESCAPE (Energy-efficient SCalable Algorithms for weather Prediction on Exascale supercomputers) which is funded by Horizon 2020 (H2020) under initiative Future and Emerging Technologies in High Performance Computing (FET-HPC). The goal of the ESCAPE project is to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres and hardware vendors.

This paper presents an overview of results obtained in the ESCAPE project in which weather prediction have been broken down into smaller building blocks called dwarfs. The participating weather prediction models are: IFS (Integrated Forecasting System), ALARO – a combination of AROME (Application de la Recherche à l'Opérationnel a Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International) and COSMO-EULAG – a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian/semi-Lagrangian fluid solver). The dwarfs are analysed and optimised in terms of computing performance for different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi). The ESCAPE project includes the development of new algorithms that are specifically designed for better energy efficiency and improved portability through domain specific languages. In addition, the modularity of the algorithmic framework, naturally allows testing different existing numerical approaches, and their interplay with the emerging heterogeneous hardware landscape. Throughout the paper, we will compare different numerical techniques to solve the main building blocks that constitute weather models, in terms of energy efficiency and performance, on a variety of computing technologies.

Andreas Müller et al.
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Short summary
This paper presents an overview of the ESCAPE project in which weather prediction models are broken down into smaller building blocks called dwarfs. These are optimised for different hardware architectures. New algorithms are developed that are specifically designed for better energy efficiency and improved portability through domain specific languages. Different numerical techniques are compared in terms of energy efficiency and performance on a variety of computing technologies.
This paper presents an overview of the ESCAPE project in which weather prediction models are...
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