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Understanding the evolution and changes of global climate is of utmost importance in the 21st century. The complexity of climate simulation is reflected in the structure of codes in the field. In our first project DEEP, the Cyprus Institute (CYI) optimised an application called EMAC which consists of two coupled models. The atmospheric model represents pressures, currents, temperatures and related magnitudes of Earth's atmosphere. Coupled to this base model, a chemical simulation package analyses fine grain interactions between chemical elements.


The atmospheric model requires a significant number of transformations and data transpositions, resulting in constant global communication and lack of overall scalability. On top of that, photochemical effects caused by changes in sun light over the Earth result in a very significant load imbalance and therefore worsen an already suboptimal scalability. The processing of these local photochemical effects consumes most of the time in these simulations, due to the synchronicity of the model and its heavy computation requirements.


This was addressed by offloading individual tasks of the chemical model to the DEEP Booster dynamically, which effectively reduced the load imbalance and allowed the code to scale further than before, mainly for  two reasons: 1) The atmospheric model could be kept as small as possible to avoid excessive communication, without hindering the heavy computing parts of the code; and 2) the load imbalance, the main concern to scale the code, was effectively eliminated. These benefits gave an extra edge in scalability and performance for the EMAC community.