Many problems in science and engineering are best simulated as a set of mutually interacting models, resulting in a coupled or multiphysics model. These models present challenges stemming from their interdisciplinary nature and from their computational and algorithmic complexities. The computational complexity of individual models, combined with the popularity of the distributed-memory parallel programming model used on commodity microprocessor-based clusters, results in a parallel coupling problem when building a coupled model. We define and elucidate this problem and how it results in a set of requirements for software capable of simplifying the construction of parallel coupled models. We describe the package, the Model Coupling Toolkit (MCT), which we have developed to meet these general requirements and the specific requirements of a parallel climate model. We present the MCT programming model with illustrative code examples. We present representative results that measure MCT's scalability, performance portability, and a proxy for coupling overhead.

The Model Coupling Toolkit (MCT) is a software library for constructing parallel coupled models from individual parallel models. MCT was created to address the challenges of creating a parallel coupler for the Community Climate System Model (CCSM). Each of the submodels that make up CCSM is a separate parallel application with its own domain decomposition, running on its own set of processors. This application contains multiple instances of the M × N problem, the problem of transferring data between two parallel programs running on disjoint sets of processors. CCSM also requires efficient data transfer to facilitate its interpolation algorithms. MCT was created as a generalized solution to handle these and other common functions in parallel coupled models. Here we describe MCT’s implementation of the data transfer infrastructure needed for a parallel coupled model. The performance of MCT scales satisfactorily as processors are added to the system. However, the types of decompositions used in the submodels can affect performance. MCT’s infrastructure provides a flexible and high-performing set of tools for enabling interoperability between parallel applications.

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.

Coupled climate models are large, multiphysics applications designed to simulate the Earth’s climate and predict the response of the climate to any changes in the forcing or boundary conditions. The Community Climate System Model (CCSM) is a widely used state-of-the-art climate model that has released several versions to the climate community over the past ten years. Like many climate models, CCSM employs a coupler, a functional unit that coordinates the exchange of data between parts of the climate system such as the atmosphere and ocean. In this paper we describe the new coupler, cpl6, contained in the latest version of CCSM, CCSM3. Cpl6 introduces distributed-memory parallelism to the coupler, a class library for important coupler functions, and a standardized interface for component models. Cpl6 is implemented entirely in Fortran90 and uses the Model Coupling Toolkit as the base for most of its classes. Cpl6 gives improved performance over previous versions and scales well on multiple platforms.

Abstract. This paper presents a review of the software currently used in climate modelling in general and in CMIP5 in particular to couple the numerical codes representing the different components of the Earth System. The coupling technologies presented show common features, such as the ability to communicate and regrid data, and also offer different functions and implementations. Design characteristics of the different approaches are discussed as well as future challenges arising from the increasing complexity of scientific problems and computing platforms.