Articles | Volume 9, issue 9
https://doi.org/10.5194/gmd-9-3483-2016
https://doi.org/10.5194/gmd-9-3483-2016
Development and technical paper
 | 
29 Sep 2016
Development and technical paper |  | 29 Sep 2016

Earth system modelling on system-level heterogeneous architectures: EMAC (version 2.42) on the Dynamical Exascale Entry Platform (DEEP)

Michalis Christou, Theodoros Christoudias, Julián Morillo, Damian Alvarez, and Hendrik Merx

Abstract. We examine an alternative approach to heterogeneous cluster-computing in the many-core era for Earth system models, using the European Centre for Medium-Range Weather Forecasts Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model as a pilot application on the Dynamical Exascale Entry Platform (DEEP). A set of autonomous coprocessors interconnected together, called Booster, complements a conventional HPC Cluster and increases its computing performance, offering extra flexibility to expose multiple levels of parallelism and achieve better scalability. The EMAC model atmospheric chemistry code (Module Efficiently Calculating the Chemistry of the Atmosphere (MECCA)) was taskified with an offload mechanism implemented using OmpSs directives. The model was ported to the MareNostrum 3 supercomputer to allow testing with Intel Xeon Phi accelerators on a production-size machine. The changes proposed in this paper are expected to contribute to the eventual adoption of Cluster–Booster division and Many Integrated Core (MIC) accelerated architectures in presently available implementations of Earth system models, towards exploiting the potential of a fully Exascale-capable platform.

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Short summary
We examine an alternative approach to heterogeneous cluster-computing for Earth system models, using the EMAC model as a pilot application on the Dynamical Exascale Entry Platform (DEEP). A set of autonomous interconnected coprocessors complements a conventional HPC cluster to increase computing performance and offer extra flexibility to expose multiple levels of parallelism and achieve better scalability, towards exploiting the potential of a fully Exascale-capable platform.