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The practical realization of managing and executing large scale scientificcomputations efficiently and reliably is quite challenging. Scientificcomputations often involve thousands or even millions of tasks operating onlarge quantities of data, such data are often diversely structured and storedin heterogeneous physical formats, and scientists must specify and run suchcomputations over extended periods on collections of compute, storage andnetwork resources that are heterogeneous, distributed and may changeconstantly. We present the integration of several advanced systems: Swift,Karajan, and Falkon, to address the challenges in running various large scalescientific applications in Grid environments. Swift is a parallel programmingtool for rapid and reliable specification, execution, and management oflarge-scale science and engineering workflows. Swift consists of a simplescripting language called SwiftScript and a powerful runtime system that isbased on the CoG Karajan workflow engine and integrates the Falkon light-weighttask execution service that uses multi-level scheduling and a streamlineddispatcher. We showcase the scalability, performance and reliability of theintegrated system using application examples drawn from astronomy, cognitiveneuroscience and molecular dynamics, which all comprise large number offine-grained jobs. We show that Swift is able to represent dynamic workflowswhose structures can only be determined during runtime and reduce largely thecode size of various workflow representations using SwiftScript; schedule theexecution of hundreds of thousands of parallel computations via the Karajanengine; and achieve up to 90% reduction in execution time when compared totraditional batch schedulers.

Realizing Fast, Scalable and Reliable Scientific Computations in Grid Environments