Abstract

Abstract. Earth system models have considerably increased their spatial resolution to solve more complex problems and achieve more realistic solutions. However, this generates an enormous amount of model data which requires proper management. Some Earth system models use inefficient sequential input/output (I/O) schemes that do not scale well when many parallel resources are used. In order to address this issue, the most commonly adopted approach is to use scalable parallel I/O solutions that offer both computational performance and efficiency. In this paper we analyse the I/O process of the European Centre for Medium-Range Weather Forecasts (ECMWF) operational Integrated Forecasting System (IFS) CY43R3. IFS can use two different output schemes: a parallel I/O server developed by Météo-France used operationally and an obsolete sequential I/O scheme. The latter is the only scheme that is being exposed by the OpenIFS variant of IFS. “Downstream” Earth system models that have adopted older versions of an IFS derivative as a component – such as the EC-Earth 3 climate model – also face a bottleneck due to the limited I/O capabilities and performance of the sequential output scheme. Moreover, it is often desirable to produce grid-point-space Network Common Data Format (NetCDF) files instead of the IFS native spectral and grid-point output fields in General Regularly-distributed Information in Binary form (GRIB), which requires the development of model-specific post-processing tools. We present the integration of the XML Input/Output Server (XIOS) 2.0 into IFS CY43R3. XIOS is an asynchronous Message Passing Interface (MPI) I/O server that offers features especially targeted at climate models: NetCDF output files, inline diagnostics, regridding, and, when properly configured, the capability to produce CMOR-compliant data. We therefore expect our work to reduce the computational cost of data-intensive (high-resolution) climate runs, thereby shortening the critical path of EC-Earth 4 experiments. The performance evaluation suggests that the use of XIOS 2.0 in IFS CY43R3 to output data achieves an adequate performance as well, outperforming the sequential I/O scheme. Furthermore, when we also take into account the post-processing task, which is needed to convert GRIB files to NetCDF files and also transform IFS spectral output fields to grid-point space, our integration not only surpasses the sequential output scheme but also the operational IFS I/O server.

Highlights

  • Over the years, the computing power of high-performance computing (HPC) has grown exponentially (Poyraz et al, 2014)

  • We present the integration of the XML Input/Output Server (XIOS) 2.0 into Integrated Forecasting System (IFS) CY43R3

  • The work we present in this paper aims to shorten the critical path for Earth system models that incorporate IFS or OpenIFS by optimising the I/O scheme and providing the foundations to absorb the post-processing into the model execution

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Summary

Introduction

The computing power of high-performance computing (HPC) has grown exponentially (Poyraz et al, 2014). One Earth system model that comes to mind is EC-Earth 3 (Hazeleger et al, 2010), a global circulation model (GCM) that couples IFS (based on the ECMWF IFS CY36R4 operational code) to the Nucleus for European Modelling of the Ocean (NEMO) 3.6 and other Earth system components using the Ocean Atmosphere Sea Ice Soil 3 Model Coupling Toolkit (OASIS3-MCT) Climate models such as ECEarth are a good example to demonstrate why an efficient I/O scheme for the atmospheric sub-model is needed. For a typical ECEarth simulation requiring over 2 million core hours, this represents a significant waste of processor time caused by the serial I/O blocking the parallel model execution This time does not include the post-processing stage, which adds a significant additional computation time; EC-Earth atmospheric output needs to be converted from General Regularly-distributed Information in Binary form (GRIB) to Network Common Data Format (NetCDF) files.

Motivation and related work
Framework design
Implemented optimisations
Model setup
Output scheme comparison
Comparison including post-processing
Data validation
Summary and conclusions
Findings
IFS and OpenIFS
Full Text
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