The Model Coupling Toolkit

Larson, Jay; Jacob, Robert; Foster, Ian; Guo, Jing

doi:10.1007/3-540-45545-0_27

Cited by 29 publications

(14 citation statements)

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“…Collaboration between the CCSM and the CCA is focused on three areas, prototyping use of CCA at (1) the system integration level, (2) the model subcomponent level, and (3) the algorithmic level. We are also exploring the use of CCA to package portions of the Model Coupling Toolkit (the foundation code on which CCSM's flux coupler is built; see (Larson et al 2001;Ong, Larson, and Jacob 2002;Larson, Jacob, and Ong 2004)) as CCA components. Since CCSM plans to adopt the standard interfaces being developed by the ESMF project (see Section 13.2.2), this work is also being performed in collaboration with the ESMF group.…”

Section: The Community Climate System Modelmentioning

confidence: 99%

“…The current CCSM flux coupler was implemented using the Model Coupling Toolkit (MCT) (Larson et al 2001;Ong, Larson, and Jacob 2002;Larson, Jacob, and Ong 2004). MCT is a software package for constructing parallel couplings between MPIbased distributed-memory parallel applications which supports both sequential and concurrent couplings.…”

Section: Model Coupling Toolkitmentioning

confidence: 99%

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A Component Architecture for High-Performance Scientific Computing

Allan

Armstrong²,

Bernholdt

et al. 2006

The International Journal of High Performance Computing Applica

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177

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The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry.

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Section: The Community Climate System Modelmentioning

confidence: 99%

Section: Model Coupling Toolkitmentioning

confidence: 99%

A Component Architecture for High-Performance Scientific Computing

Allan

Armstrong²,

Bernholdt

et al. 2006

The International Journal of High Performance Computing Applica

Self Cite

177

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“…However, sometimes they consist of multiple SPMD programs, partitioned on the same or different parallel machines, which are coupled with a complex data transfer (e.g. climate applications [12,13]), or a degenerate case where multiple processes are in communication with a single process (e.g. for visualization or data logging [14]).…”

Section: Scmd Design Pattern For Spmd Computingmentioning

confidence: 99%

The CCA component model for high‐performance scientific computing

Armstrong

Kumfert

McInnes

et al. 2005

Concurrency and Computation

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SUMMARYThe Common Component Architecture (CCA) is a component model for high-performance computing, developed by a grass-roots effort of computational scientists. Although the CCA is usable with CORBA-like distributed-object components, its main purpose is to set forth a component model for high-performance, parallel computing. Traditional component models are not well suited for performance and massive parallelism. We outline the design pattern for the CCA component model, discuss our strategy for language interoperability, describe the development tools we provide, and walk through an illustrative example using these tools. Performance and scalability, which are distinguishing features of CCA components, affect choices throughout design and implementation.

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“…We are developing all three cases using a model of concurrent processes directly coupled (using MPI); we are considering the use of a more flexible coupling framework [8] for the future. Several computational challenges involved in the implementation of adaptivity for coupled physical-biological forecasts remain to be researched.…”

Section: Fig 2 Approaches For Adaptive Coupled Physical-biogeochemimentioning

confidence: 99%

Adaptive Coupled Physical and Biogeochemical Ocean Predictions: A Conceptual Basis

Lermusiaux

Evangelinos

Tian

et al. 2004

Computational Science - ICCS 2004

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Abstract. Physical and biogeochemical ocean dynamics can be intermittent and highly variable, and involve interactions on multiple scales. In general, the oceanic fields, processes and interactions that matter thus vary in time and space. For efficient forecasting, the structures and parameters of models must evolve and respond dynamically to new data injected into the executing prediction system. The conceptual basis of this adaptive modeling and corresponding computational scheme is the subject of this presentation. Specifically, we discuss the process of adaptive modeling for coupled physical and biogeochemical ocean models. The adaptivity is introduced within an interdisciplinary prediction system. Model-data misfits and data assimilation schemes are used to provide feedback from measurements to applications and modify the runtime behavior of the prediction system. Illustrative examples in Massachusetts Bay and Monterey Bay are presented to highlight ongoing progress.

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The Model Coupling Toolkit

Cited by 29 publications

References 1 publication

A Component Architecture for High-Performance Scientific Computing

A Component Architecture for High-Performance Scientific Computing

The CCA component model for high‐performance scientific computing

Adaptive Coupled Physical and Biogeochemical Ocean Predictions: A Conceptual Basis

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