Preprints
https://doi.org/10.5194/gmd-2020-314
https://doi.org/10.5194/gmd-2020-314

Submitted as: development and technical paper 02 Dec 2020

Submitted as: development and technical paper | 02 Dec 2020

Review status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Parallel computing efficiency of SWAN

Christo Rautenbach1,2,3,4, Julia C. Mullarney4, and Karin R. Bryan4 Christo Rautenbach et al.
  • 1Institute for Coastal and Marine Research, Nelson Mandela University, South Africa
  • 2Department of Oceanography and Marine Research Institute, University of Cape Town, South Africa
  • 3Research and development, MetOcean (a division of the Metrological Service), Raglan, New Zealand
  • 4Environmental Research Institute, University of Waikato, Hamilton, New Zealand

Abstract. Effective and accurate ocean and coastal wave predictions are necessary for engineering, safety and recreational purposes. Refining predictive capabilities is increasingly critical to reduce the uncertainties faced with a changing global wave climatology. Simulating WAves in the Nearshore (SWAN) is a widely used spectral wave modelling tool employed by coastal engineers and scientists, including for operational wave forecasting purposes. Fore- and hindcasts can span hours to decades and a detailed understanding of the computational efficiencies is required to design optimized operational protocols and hindcast scenarios. To date, there exists limited knowledge on the relationship between the size of a SWAN computational domain and the optimal amount of parallel computational threads required to execute a simulation effectively. To test this, a hindcast cluster of 28 computational threads (1 node) was used to determine the computation efficiencies of a SWAN model configuration for southern Africa. The model extent and resolution emulate the current operational wave forecasting configuration developed by the South African Weather Service (SAWS). We implemented and compared both OpenMP and the Message Passing Interface (MPI) distributing memory architectures. Three sequential simulations (corresponding to typical grid cell numbers) were compared to various permutations of parallel computations via the speed-up ratio, time saving ratio and efficiency tests. Generally, a computational node configuration of 6 threads produced the most effective computational set-up based on wave hindcasts of one-week duration. The use of more than 20 threads resulted in a decrease in speed-up ratio for the smallest computation domain, owing to the increased sub-domain communication times for limited domain sizes.

Christo Rautenbach et al.

 
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Status: closed
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Christo Rautenbach et al.

Christo Rautenbach et al.

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Short summary
The simulation of ocean waves is important for various reasons, e.g. ship route safety and coastal vulnerability assessments. SWAN is a popular tool with which ocean waves may be predicted. Simulations using this tool can be computationally expensive. The present study thus aimed at understanding which parallel computing, typical SWAN model setup, will be most effective. There thus do exist configurations where these simulations are most time-saving and effective.