Coupling technologies for Earth System Modelling

Valcke, Sophie; Balaji, V.; Craig, Anthony P; DeLuca, Cecelia; Dunlap, Rocky; Ford, Rupert; Jacob, Robert; Larson, Jay; O'Kuinghttons, R.; Riley, Graham; Vertenstein, Mariana

doi:10.5194/gmd-5-1589-2012

Cited by 66 publications

(56 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It links component models together to construct a coupled model, and controls the integration of the whole coupled model (Valcke et al, 2012). A number of couplers are now available, e.g.…”

Section: Introductionmentioning

confidence: 99%

A new adaptive data transfer library for model coupling

et al. 2016

View full text Add to dashboard Cite

Abstract. Data transfer means transferring data fields from a sender to a receiver. It is a fundamental and frequently used operation of a coupler. Most versions of state-of-the-art couplers currently use an implementation based on the pointto-point (P2P) communication of the message passing interface (MPI) (referred to as "P2P implementation" hereafter). In this paper, we reveal the drawbacks of the P2P implementation when the parallel decompositions of the sender and the receiver are different, including low communication bandwidth due to small message size, variable and high number of MPI messages, as well as network contention. To overcome these drawbacks, we propose a butterfly implementation for data transfer. Although the butterfly implementation outperforms the P2P implementation in many cases, it degrades the performance when the sender and the receiver have similar parallel decompositions or when the number of processes used for running models is small. To ensure data transfer with optimal performance, we design and implement an adaptive data transfer library that combines the advantages of both butterfly implementation and P2P implementation.

show abstract

“…It links component models together to construct a coupled model, and controls the integration of the whole coupled model (Valcke et al, 2012). A number of couplers are now available, e.g.…”

Section: Introductionmentioning

confidence: 99%

A new adaptive data transfer library for model coupling

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The growing complexity of the coupling task means that couplers require more expertise to develop, and that labs can benefit by comparing their approaches, sharing lessons learnt, and re-using coupling code (Valcke et al, 2012). At the same time, there has been a move towards general reusable subcomponents (e.g., both atmosphere and ocean models using the same numerical solver), compared to earlier model generations, where the code for each component was developed entirely separately.…”

Section: Introductionmentioning

confidence: 99%

The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations

Alexander

Easterbrook

2015

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. We analyze the source code of eight coupled climate models, selected from those that participated in the CMIP5 (Taylor et al., 2012) or EMICAR5 Zickfeld et al., 2013) intercomparison projects. For each model, we sort the preprocessed code into components and subcomponents based on dependency structure. We then create software architecture diagrams that show the relative sizes of these components/subcomponents and the flow of data between them. The diagrams also illustrate several major classes of climate model design; the distribution of complexity between components, which depends on historical development paths as well as the conscious goals of each institution; and the sharing of components between different modeling groups. These diagrams offer insights into the similarities and differences in structure between climate models, and have the potential to be useful tools for communication between scientists, scientific institutions, and the public.

show abstract

“…The challenges and intricacies of coupling technologies of earth system models were reviewed by Valcke et al (2012), who focused on the central features of different established systems consisting of data transfers, re-gridding, time step management, and parallel efficiency. Prominent examples of coupled modeling systems are the Community Climate System Model, CCSM (Gent, 2006), and the Earth System Modeling Framework, ESMF (Hill et al, 2006), which have also been shown to scale to processor numbers on the order of 10 4 .…”

Section: Introductionmentioning

confidence: 99%

Implementation and scaling of the fully coupled Terrestrial Systems Modeling Platform (TerrSysMP v1.0) in a massively parallel supercomputing environment – a case study on JUQUEEN (IBM Blue Gene/Q)

Gasper¹,

Goergen

Shrestha

et al. 2014

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. Continental-scale hyper-resolution simulations constitute a grand challenge in characterizing nonlinear feedbacks of states and fluxes of the coupled water, energy, and biogeochemical cycles of terrestrial systems. Tackling this challenge requires advanced coupling and supercomputing technologies for earth system models that are discussed in this study, utilizing the example of the implementation of the newly developed Terrestrial Systems Modeling Platform (TerrSysMP v1.0) on JUQUEEN (IBM Blue Gene/Q) of the Jülich Supercomputing Centre, Germany. The applied coupling strategies rely on the Multiple Program Multiple Data (MPMD) paradigm using the OASIS suite of external couplers, and require memory and load balancing considerations in the exchange of the coupling fields between different component models and the allocation of computational resources, respectively. Using the advanced profiling and tracing tool Scalasca to determine an optimum load balancing leads to a 19 % speedup. In massively parallel supercomputer environments, the coupler OASIS-MCT is recommended, which resolves memory limitations that may be significant in case of very large computational domains and exchange fields as they occur in these specific test cases and in many applications in terrestrial research. However, model I/O and initialization in the petascale range still require major attention, as they constitute true big data challenges in light of future exascale computing resources. Based on a factor-two speedup due to compiler optimizations, a refactored coupling interface using OASIS-MCT and an optimum load balancing, the problem size in a weak scaling study can be increased by a factor of 64 from 512 to 32 768 processes while maintaining parallel efficiencies above 80 % for the component models.

show abstract

Coupling technologies for Earth System Modelling

Cited by 66 publications

References 23 publications

A new adaptive data transfer library for model coupling

A new adaptive data transfer library for model coupling

The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations

Implementation and scaling of the fully coupled Terrestrial Systems Modeling Platform (TerrSysMP v1.0) in a massively parallel supercomputing environment – a case study on JUQUEEN (IBM Blue Gene/Q)

Contact Info

Product

Resources

About