From Scientific Software Libraries to Problem-Solving Environments
IEEE Computational Science & Engineering
SciNapse: A Problem-Solving Environment for Partial Differential Equations
IEEE Computational Science & Engineering
An Integrated Problem Solving Environment: The SCIRun Computational Steering System
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 7 - Volume 7
Proceedings of the 28th international conference on Software engineering
An Open Domain-Extensible Environment for Simulation-Based Scientific Investigation (ODESSI)
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Software Challenges for Extreme Scale Computing: Going From Petascale to Exascale Systems
International Journal of High Performance Computing Applications
Annual Review of Information Science and Technology
A survey of scientific software development
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
IEEE Software
Globus toolkit version 4: software for service-oriented systems
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
Velo: A Knowledge-Management Framework for Modeling and Simulation
Computing in Science and Engineering
HUBzero: A Platform for Dissemination and Collaboration in Computational Science and Engineering
Computing in Science and Engineering
Development of a Mesh Generation Code with a Graphical Front-End: A Case Study
Journal of Organizational and End User Computing
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Modern scientific software is daunting in its diversity and complexity. From massively parallel simulations running on the world's largest supercomputers, to visualizations and user support environments that manage ever growing complex data collections, the challenges for software engineers are plentiful. While high performance simulators are necessarily specialized codes to maximize performance on specific supercomputer architectures, we argue the vast majority of supporting infrastructure, data management and analysis tools can leverage commodity open source and component-based technologies. This approach can significantly drive down the effort and costs of building complex, collaborative scientific user environments, as well as increase their reliability and extensibility. In this paper we describe our experiences in creating an initial user environment for scientists involved in modeling the detailed effects of climate change on the environment of selected geographical regions. Our approach composes the user environment using the Velo scientific knowledge management platform and the MeDICi Integration Framework for scientific workflows. These established platforms leverage component-based technologies and extend commodity open source platforms with abstractions and capabilities that make them amenable for broad use in science. Using this approach we were able to deliver an operational user environment capable of running thousands of simulations in a 7 month period, and achieve significant software reuse.