2008/08/26 by Yong Zhao, Zhao, Yong, Ioan Raicu +9 · 1 citation
Computer Science · Decision Sciences · #D.1.3 #D.4.7 #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Parallel #Parallel Computing and Optimization Techniques #Programming Languages (cs.PL) #Scientific Computing and Data Management #and Cluster Computing (cs.DC) #cs.DC #cs.PL
paper · pdf · doi:10.48550/arxiv.0808.3548
Book chapter in Grid Computing Research Progress, ISBN: 978-1-60456-404-4, Nova Publisher 2008
arxiv created 2008/08/26 · openalex publication_date 2008/08/26 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The practical realization of managing and executing large scale scientific computations efficiently and reliably is quite challenging. Scientific computations often involve thousands or even millions of tasks operating on large quantities of data, such data are often diversely structured and stored in heterogeneous physical formats, and scientists must specify and run such computations over extended periods on collections of compute, storage and network resources that are heterogeneous, distributed and may change constantly. We present the integration of several advanced systems: Swift, Karajan, and Falkon, to address the challenges in running various large scale scientific applications in Grid environments. Swift is a parallel programming tool for rapid and reliable specification, execution, and management of large-scale science and engineering workflows. Swift consists of a simple scripting language called SwiftScript and a powerful runtime system that is based on the CoG Karajan workflow engine and integrates the Falkon light-weight task execution service that uses multi-level scheduling and a streamlined dispatcher. We showcase the scalability, performance and reliability of the integrated system using application examples drawn from astronomy, cognitive neuroscience and molecular dynamics, which all comprise large number of fine-grained jobs. We show that Swift is able to represent dynamic workflows whose structures can only be determined during runtime and reduce largely the code size of various workflow representations using SwiftScript; schedule the execution of hundreds of thousands of parallel computations via the Karajan engine; and achieve up to 90% reduction in execution time when compared to traditional batch schedulers.