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. Effective and accurate ocean and coastal wave predictionsare necessary for engineering, safety and recreational purposes. Refiningpredictive capabilities is increasingly critical to reduce the uncertaintiesfaced with a changing global wave climatology. Simulating WAves in theNearshore (SWAN) is a widely used spectral wave modelling tool employed bycoastal engineers and scientists, including for operational wave forecastingpurposes. Fore- and hindcasts can span hours to decades, and a detailedunderstanding of the computational efficiencies is required to designoptimized operational protocols and hindcast scenarios. To date, thereexists limited knowledge on the relationship between the size of a SWANcomputational domain and the optimal amount of parallel computationalthreads/cores required to execute a simulation effectively. To test thescalability, a hindcast cluster of 28 computational threads/cores (1 node)was used to determine the computation efficiencies of a SWAN modelconfiguration for southern Africa. The model extent and resolution emulatethe current operational wave forecasting configuration developed by theSouth African Weather Service (SAWS). We implemented and compared bothOpenMP and the Message Passing Interface (MPI) distributing memoryarchitectures. Three sequential simulations (corresponding to typical gridcell numbers) were compared to various permutations of parallel computationsusing the speed-up ratio, time-saving ratio and efficiency tests. Generally,a computational node configuration of six threads/cores produced the mosteffective 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-upratio for the smallest computation domain, owing to the increased sub-domaincommunication times for limited domain sizes.

Parallel computing efficiency of SWAN 40.91