Crossing the chasm: how to develop weather and climate models for next generation computers?Lawrence, B. N. ORCID: https://orcid.org/0000-0001-9262-7860, Rezny, M., Budich, R., Bauer, P., Behrens, J., Carter, M., Deconinck, W., Ford, R., Maynard, C., Mullerworth, S., Osuna, C., Porter, A., Serradell, K., Valcke, S., Wedi, N. and Wilson, S. (2018) Crossing the chasm: how to develop weather and climate models for next generation computers? Geoscientific Model Development, 11 (5). pp. 1799-1821. ISSN 1991-9603
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.5194/gmd-11-1799-2018 Abstract/SummaryWeather and climate models are complex pieces of software which include many individual components, each of which is evolving under pressure to exploit advances in com- puting to enhance some combination of a range of possible improvements (higher spatio-temporal resolution, increased fidelity in terms of resolved processes, more quantification of uncertainty, etc.). However, after many years of a relatively stable computing environment with little choice in process- ing architecture or programming paradigm (basically X86 processors using MPI for parallelism), the existing menu of processor choices includes significant diversity, and more is on the horizon. This computational diversity, coupled with ever increasing software complexity, leads to the very real possibility that weather and climate modelling will arrive at a chasm which will separate scientific aspiration from our ability to develop and/or rapidly adapt codes to the available hardware. In this paper we review the hardware and software trends which are leading us towards this chasm, before describing current progress in addressing some of the tools which we may be able to use to bridge the chasm. This brief intro- duction to current tools and plans is followed by a discus- sion outlining the scientific requirements for quality model codes which have satisfactory performance and portability, while simultaneously supporting productive scientific evolu- tion. We assert that the existing method of incremental model improvements employing small steps which adjust to the changing hardware environment is likely to be inadequate for crossing the chasm between aspiration and hardware at a sat- isfactory pace, in part because institutions cannot have all the relevant expertise in house. Instead, we outline a method- ology based on large community efforts in engineering and standardisation, which will depend on identifying a taxon- omy of key activities – perhaps based on existing efforts to develop domain-specific languages, identify common pat- terns in weather and climate codes, and develop community approaches to commonly needed tools and libraries – and then collaboratively building up those key components. Such a collaborative approach will depend on institutions, projects, and individuals adopting new interdependencies and ways of working.
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