Abstract
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/cores required to execute a simulation effectively. To test the scalability, a hindcast cluster of 28 computational threads/cores (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 using the speed-up ratio, time-saving ratio and efficiency tests. Generally, a computational node configuration of six threads/cores produced the most effective computational set-up based on wave hindcasts of 1-week duration. The use of more than 20 threads/cores 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.
Highlights
The computational efficiency of metocean modelling has been the topic of ongoing deliberation for decades
They considered the physical distances between computational threads/cores and found that this parameter has a negligible effect compared to differences between OMP and Message Passing Interface (MPI), over an increasing number of threads/cores
The benchmarking of a single node (28 threads/cores) is evaluated compared with a serial computation on a single thread. Without performance metrics, they found that the wall clock times, for the iterations and not a full simulation, reached a minimum at 16 nodes (16 × 24 threads/cores) for the MPI Simulating WAves in the Nearshore (SWAN) and 64 nodes (64 × 24 threads/cores) for the hybrid SWAN
Summary
The computational efficiency of metocean (meteorology and oceanography) modelling has been the topic of ongoing deliberation for decades. Long-duration simulations are usually associated with climate-change-related research, with simulation periods of at least 30 years across multiple spatial and temporal resolutions needed to capture key oscillations (Babatunde et al, 2013). Such hindcasts are frequently used by coastal and offshore engineering consultancies for purposes such as those related to infrastructure design (Kamphuis, 2020) or environmental impact assessments (Frihy, 2001; Liu et al, 2013). The main stakeholders are usually other governmental agencies (e.g. disaster response or environmental affairs departments), commercial entities, and the public Both atmospheric and marine forecasts share similar numerical schemes that solve the governing equations and share a similar need in computational efficiency. Significant advancement in operational forecasting can be made by examining the way in which the code interfaces with the computation nodes, and how results are stored during simulation
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