Abstract. In the simulation of complex multi-scale flow problems, such as those arising in weather and climate modelling, one of the biggest challenges is to satisfy operational requirements in terms of time-to-solution and energy-to-solution yet without compromising the accuracy and stability of the calculation. These competing factors require the development of state-of-the-art algorithms that can optimally exploit the targeted underlying hardware and efficiently deliver the extreme computational capabilities typically required in operational forecast production. These algorithms should (i) minimise the energy footprint along with the time required to produce a solution, (ii) maintain a satisfying level of accuracy, (iii) be numerically stable and resilient, in case of hardware or software failure. The European Centre for Medium Range Weather Forecasts (ECMWF) is leading a project called ESCAPE (Energy-efficient SCalable Algorithms for weather Prediction on Exascale supercomputers) which is funded by Horizon 2020 (H2020) under initiative Future and Emerging Technologies in High Performance Computing (FET-HPC). The goal of the ESCAPE project is to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres and hardware vendors. This paper presents an overview of results obtained in the ESCAPE project in which weather prediction have been broken down into smaller building blocks called dwarfs. The participating weather prediction models are: IFS (Integrated Forecasting System), ALARO – a combination of AROME (Application de la Recherche à l'Opérationnel a Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International) and COSMO-EULAG – a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian/semi-Lagrangian fluid solver). The dwarfs are analysed and optimised in terms of computing performance for different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi). The ESCAPE project includes the development of new algorithms that are specifically designed for better energy efficiency and improved portability through domain specific languages. In addition, the modularity of the algorithmic framework, naturally allows testing different existing numerical approaches, and their interplay with the emerging heterogeneous hardware landscape. Throughout the paper, we will compare different numerical techniques to solve the main building blocks that constitute weather models, in terms of energy efficiency and performance, on a variety of computing technologies.
Measuring and tuning energy efficiency on large scale high performance computing platforms.
