The description, composition, and execution of even logically simple scientific workflows are often complicated by the need to deal with "messy" issues like heterogeneous storage formats and ad-hoc file system structures. We show how these difficulties can be overcome via a typed, compositional workflow notation within which issues of physical representation are cleanly separated from logical typing, and by the implementation of this notation within the context of a powerful runtime system that supports distributed execution. The resulting notation and system are capable both of expressing complex workflows in a simple, compact form, and of enacting those workflows in distributed environments. We apply our technique to cognitive neuroscience workflows that analyze functional MRI image data, and demonstrate significant reductions in code size relative to other approaches.

SUMMARYScientific applications like neuroscience data analysis are usually compute and data-intensive. With the use of the additional capacity offered by distributed resources and suitable middlewares, we can achieve much shorter execution time, distribute compute and storage load, and add greater flexibility to the execution of these scientific applications than we could ever achieve in a single compute resource. In this paper, we present the processing of image registration (IR) for functional magnetic resonance imaging studies on Global Grids. We characterize the application, list its requirements and then transform it to a workflow. We use Gridbus Broker and Gridbus Workflow Engine technologies for executing the neuroscience application on the Grid. We developed a complete web-based portal integrating GUI-based workflow editor, execution management, monitoring and visualization of tasks and resources. We describe each component of the system in detail. We then execute the application on Grid'5000 platform and present extensive performance results. We show that the IR application can have (1) significantly improved makespan, (2) distribution of compute and storage load among resources used, and (3) flexibility when executing multiple times on Grid resources.

SUMMARYThe GriPhyN virtual data system provides a suite of components and services for data-intensive sciences that enables scientists to systematically and efficiently describe, discover, and share large-scale data and computational resources. We describe the design and implementation of such middleware services in terms of a virtual data system interface called Chiron, and present virtual data integration examples from the QuarkNet education project and from functional-MRI-based neuroscience research. The Chiron interface also serves as an online 'educator' for virtual data applications.

We describe the services, architecture and application of the GriPhyN Virtual Data System, a suite of components and services that allow users to describe virtual data products in declarative terms, discover definitions and assemble workflows based on those definitions, and execute the resulting workflows on Grid resources. We show how these middleware-level services have been applied by specific communities to manage scientific data and workflows. In particular, we highlight and introduce Chiron, a portal facility that enables the interactive use of the virtual data system. Chiron has been used within the QuarkNet education project and as an online "educator" for virtual data applications. We also present applications from functional MRI-based neuroscience research.