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.
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Acknowledgments
This work was supported in part by funding received from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2012/ERC Grant agreement no. 320375), and in part by the CRESTA project that has received funding from the European Community’s Seventh Framework Programme (ICT-2011.9.13) under Grant Agreement no. 287703.
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Deconinck, W. et al. (2016). Accelerating Extreme-Scale Numerical Weather Prediction. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds) Parallel Processing and Applied Mathematics. Lecture Notes in Computer Science(), vol 9574. Springer, Cham. https://doi.org/10.1007/978-3-319-32152-3_54
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DOI: https://doi.org/10.1007/978-3-319-32152-3_54
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