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A High-Performance Distributed Object-Store for Exascale Numerical Weather Prediction and Climate

Simon Smart, Tiago Quintino, Baudouin Raoult (all ECWMF)

Paper presented by Simon Smart (ECMWF) at PASC19, Zürich, Switzerland (12-14 June 2019).

Abstract: Numerical Weather Prediction (NWP) and Climate simulations sit at the intersection between classically understood High Performance Computing (HPC) and Big Data / High Performance Data Analytics (HPDA). Driven by ever more ambitious scientific goals, both the size and number of output data-elements generated as part of NWP operations have grown by several orders of magnitude, and are expected to continue growing exponentially in the future. Over the last 30 years this increase has been approximately 40% per year. To cope with this projected growth, ECMWF has been actively exploring novel hardware and software approaches to workflow and data management. ECMWF's meteorological object store acts as a hot-cache for meteorological objects within the forecast pipeline, and supports multiple backends to enable the use of different storage technologies. This paper presents extensions to this object store to allow it to operate in a distributed fashion on a wider range of hardware without assuming the presence of high-performance, parallel, globally namespaced storage systems. The improvements include a flexible, configurable front end which gives control of where data is to be stored without requiring code changes in the calling application.