Couplers for linking environmental models: scoping study and potential next steps

This report scopes out what couplers there are available in the hydrology and atmospheric modelling fields. The work reported here examines both dynamic runtime and one way file based coupling. Based on a review of the peer-reviewed literature and other open sources, there are a plethora of coupling technologies and standards relating to file formats. The available approaches have been evaluated against criteria developed as part of the DREAM project. Based on these investigations, the following recommendations are made: • The most promising dynamic coupling technologies for use within BGS are OpenMI 2.0 and CSDMS (either 1.0 or 2.0) • Investigate the use of workflow engines: Trident and Pyxis, the latter as part of the TSB/AHRC project “Confluence” • There is a need to include database standards CSW and GDAL and use data formats from the climate community NetCDF and CF standards. • Development of a “standard” composition which will consist of two process models and a 3D geological model all linked to data stored in the BGS corporate database and flat file format. Web Feature Services should be included in these compositions. There is also a need to investigate other approaches in different disciplines: The Loss Modelling Framework, OASIS-LMF is the best candidate

Earth system data cubes unravel global multivariate dynamics

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Earth system data cubes unravel global multivariate dynamics

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

O2ATH: An OpenMP Offloading Toolkit for the Sunway Heterogeneous Manycore Platform

The next generation Sunway supercomputer employs the SW26010pro processor, which features a specialized on-chip heterogeneous architecture. Applications with significant hotspots can benefit from the great computation capacity improvement of Sunway many-core architectures by carefully making intensive manual many-core parallelization efforts. However, some legacy projects with large codebases, such as CESM, ROMS and WRF, contain numerous lines of code and do not have significant hotspots. The cost of manually porting such applications to the Sunway architecture is almost unaffordable. To overcome such a challenge, we have developed a toolkit named O2ATH. O2ATH forwards GNU OpenMP runtime library calls to Sunway's Athread library, which greatly simplifies the parallelization work on the Sunway architecture.O2ATH enables users to write both MPE and CPE code in a single file, and parallelization can be achieved by utilizing OpenMP directives and attributes. In practice, O2ATH has helped us to port two large projects, CESM and ROMS, to the CPEs of the next generation Sunway supercomputers via the OpenMP offload method. In the experiments, kernel speedups range from 3 to 15 times, resulting in 3 to 6 times whole application speedups.Furthermore, O2ATH requires significantly fewer code modifications compared to manually crafting CPE functions.This indicates that O2ATH can greatly enhance development efficiency when porting or optimizing large software projects on Sunway supercomputers.Comment: 15 pages, 6 figures, 5 tables

Workflow environments for advanced cyberinfrastructure platforms

Progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources that follow a cycle composed of pre-processing steps for data curation and preparation for subsequent computing steps, and later analysis and analytics steps applied to the results. However, scientific workflows are currently fragmented in multiple components, with different processes for computing and data management, and with gaps in the viewpoints of the user profiles involved. Our vision is that future workflow environments and tools for the development of scientific workflows should follow a holistic approach, where both data and computing are integrated in a single flow built on simple, high-level interfaces. The topics of research that we propose involve novel ways to express the workflows that integrate the different data and compute processes, dynamic runtimes to support the execution of the workflows in complex and heterogeneous computing infrastructures in an efficient way, both in terms of performance and energy. These infrastructures include highly distributed resources, from sensors and instruments, and devices in the edge, to High-Performance Computing and Cloud computing resources. This paper presents our vision to develop these workflow environments and also the steps we are currently following to achieve it.This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contract 2014-SGR-1051). Javier Conejero postdoctoral contract is co-financed by the Ministry of Economy and Competitiveness under Juan de la Cierva Formacion´ postdoctoral fellowship number FJCI-2015-24651. This work is supported by the H2020 mF2C project (730929) and the CLASS project (780622). The participation of Rosa M Badia in the BDEC2 meetings is supported by the EXDCI project (800957). The dislib library developments are partially funded under the project agreement between BSC and FUJITSU.Peer ReviewedPostprint (author's final draft

Evaluation and optimisation of the I/O scalability for the next generation of Earth system models: IFS CY43R3 and XIOS 2.0 integration as a case study

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.This research has been supported by Horizon 2020 (ESiWACE2 (grant no. 823988) and PRIMAVERA (grant no. 641727)).Peer ReviewedPostprint (published version