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This document is one of the deliverable reports created for the ESCAPEproject. ESCAPE stands for Energy-efficient Scalable Algorithms for WeatherPrediction at Exascale. The project develops world-class, extreme-scalecomputing capabilities for European operational numerical weather predictionand future climate models. This is done by identifying Weather & Climate dwarfswhich are key patterns in terms of computation and communication (in the spiritof the Berkeley dwarfs). These dwarfs are then optimised for different hardwarearchitectures (single and multi-node) and alternative algorithms are explored.Performance portability is addressed through the use of domain specificlanguages.  In this deliverable we focus on the RMI-EPS ensemble prediction suite. Wefirst provide a detailed report on the workflow of the suite in which 5 maincategories of jobs are defined; pre-processing, lateral boundary conditions(LBCs), data assimilation, forecast and post-processing.  Combined Energy and wall-clock time measurements of the entire RMI-EPS suitewere performed. They indicate that the wall-clock times are relatively spreadbetween the various defined job categories, with the forecast accounting forthe largest fraction at about 35%. As far as energy consumption is concerned,the forecast part dwarfs everything else and is responsible for up to 99% ofthe total energy consumption. This means that energy optimizations for theforecast part will translate almost proportionally into optimizations of thewhole suite, while the maximum theoretical speed-up due to forecastoptimizations cannot exceed a factor of about 3/2. Therefore, in terms ofenergy consumption, optimizations should first focus on the forecast part. Forwall-clock time performance gains, however, optimizations (and possiblyadditional dwarfs) can be considered for the categories outside of the forecastpart.

Report on workflow analysis for specific LAM applications