In recent years, there has been increasing interest in the development of computer simulations of complex biological systems, and of multi-physics and multi-scale physical phenomena. Applications have been developed t...
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In recent years, there has been increasing interest in the development of computer simulations of complex biological systems, and of multi-physics and multi-scale physical phenomena. Applications have been developed that involve the coupling together of separate executable models of individual systems, where these models may have been developed in isolation. A lightweight yet general solution is required to problems of linking coupled models, and of handling the incompatibilities between interacting models that arise from their diverse origins and natures. Many such models require high-performance computers to provide acceptable execution times, and there is increasing interest in utilizing Grid technologies. However, Grid applications need the ability to cope with heterogeneous and dynamically changing execution environments, particularly where run-time changes can affect application performance. A general coupling framework (GCF) is described that allows the construction of flexible coupled models. This approach results in a component-based implementation of a coupled model application. A semi-formal presentation of GCF is given. components under GCF are separately deployable and coupled by simple data flows, making them appropriate structures for dynamic execution platforms such as the Grid. The design and initial implementation of a performance control system (PERCo) is reviewed. PERCo acts by redeploying components, and is thus appropriate for controlling GCF coupled model applications. Redeployment decisions in PERCo require performance prediction capabilities. A proof-of-concept performance prediction algorithm is presented, based on the descriptions of GCF and PFRCo. Copyright (C) 2005 John Wiley Sons, Ltd.
We present the programming language KERIS, an extension of Java with explicit support for software evolution. KERIS introduces extensible modules as the basic building blocks for software. Modules are composed hierarc...
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We present the programming language KERIS, an extension of Java with explicit support for software evolution. KERIS introduces extensible modules as the basic building blocks for software. Modules are composed hierarchically, explicitly revealing the architecture of systems. A distinct feature of the module design is that modules do not get linked manually. Instead, the wiring of modules gets inferred. The module assembly and refinement mechanism of KERIS is not restricted to the unanticipated extensibility of atomic modules. It also allows extensions of already linked systems by replacing selected submodules with compatible versions without needing to re-link the full system. Extensibility is type-safe and noninvasive, i.e., the extension of a module preserves the original version and does not require access to source code. Copyright (c) 2005 John Wiley & Sons, Ltd.
We present the programming language KERIS, an extension of Java with explicit support for software evolution. KERIS introduces extensible modules as the basic building blocks for software. Modules are composed hierarc...
详细信息
We present the programming language KERIS, an extension of Java with explicit support for software evolution. KERIS introduces extensible modules as the basic building blocks for software. Modules are composed hierarchically, explicitly revealing the architecture of systems. A distinct feature of the module design is that modules do not get linked manually. Instead, the wiring of modules gets inferred. The module assembly and refinement mechanism of KERIS is not restricted to the unanticipated extensibility of atomic modules. It also allows extensions of already linked systems by replacing selected submodules with compatible versions without needing to re-link the full system. Extensibility is type-safe and noninvasive, i.e., the extension of a module preserves the original version and does not require access to source code. Copyright (c) 2005 John Wiley & Sons, Ltd.
Investigating the potential impact of climate on agro-ecosystems using simulation models is underpinned by the availability of climate data at the appropriate temporal scale (daily or higher resolution). The productio...
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ISBN:
(纸本)9780975840023
Investigating the potential impact of climate on agro-ecosystems using simulation models is underpinned by the availability of climate data at the appropriate temporal scale (daily or higher resolution). The production of artificial series of weather data has traditionally adopted a variety of alternative methods. These range from empirical functions where simple relationships between weather variables are used to estimate missing data from available data, to sophisticated approaches where physically-based models are used. All such approaches illustrate from different perspectives that there is actually a wealth of well developed solutions to the basic problem of estimating or generating weather data, coded in a variety of ways. The weather generation and estimation problem and the frequent need to evaluate alternative approaches in a comparative fashion has suggested that these methods be made available as one, comprehensive set of linkable components which also allow for extension by third parties of models that have already been implemented. This research describes the development of a software component called CLIMA, which provides a structured repository of methods for estimation of weather variables (synthetic production of weather variables from other weather variables) and generation (synthetic production of weather data from site-specific statistical properties). CLIMA is a synthetic weather estimator/generator used to characterize study areas and provide daily or sub-daily weather inputs (air temperature, evapotranspiration, precipitation, solar radiation, wind speed) to agro-ecological models. The model component enables users to interrogate the weather database of study areas, to estimate parameters, to produce synthetic weather data and to distribute them for more advanced analysis. There are five main subcomponents in CLIMA, including estimation/generation functions for each relevant weather variable. These basic model components are: AirT: air tempera
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