Accelerating Extreme-Scale Numerical Weather Prediction View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Willem Deconinck , Mats Hamrud , Christian Kühnlein , George Mozdzynski , Piotr K. Smolarkiewicz , Joanna Szmelter , Nils P. Wedi

ABSTRACT

Numerical Weather Prediction (NWP) and climate simulations have been intimately connected with progress in supercomputing since the first numerical forecast was made about 65 years ago. The biggest challenge to state-of-the-art computational NWP arises today from its own software productivity shortfall. The application software at the heart of most NWP services is ill-equipped to efficiently adapt to the rapidly evolving heterogeneous hardware provided by the supercomputing industry. If this challenge is not addressed it will have dramatic negative consequences for weather and climate prediction and associated services. This article introduces Atlas, a flexible data structure framework developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) to facilitate a variety of numerical discretisation schemes on heterogeneous architectures, as a necessary step towards affordable exascale high-performance simulations of weather and climate. A newly developed hybrid MPI-OpenMP finite volume module built upon Atlas serves as a first demonstration of the parallel performance that can be achieved using Atlas’ initial capabilities. More... »

PAGES

583-593

Book

TITLE

Parallel Processing and Applied Mathematics

ISBN

978-3-319-32151-6
978-3-319-32152-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-32152-3_54

DOI

http://dx.doi.org/10.1007/978-3-319-32152-3_54

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007451700


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