Performance control of scientific coupled models in Grid environments

• Published in: Concurrency and computation Vol. 17; no. 2-4; pp. 259 - 295
• Main Authors: Armstrong, Christopher, Ford, Rupert W., Gurd, John R., Luján, Mikel, Mayes, Kenneth R., Riley, Graham D.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01-02-2005
• Subjects: component-oriented programming; coupled models; distributed computing; Grid computing; performance control
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.926

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 PERCO. Copyright © 2005 John Wiley & Sons, Ltd.