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dc.contributor.advisorHammond, Kevin
dc.contributor.authorJanjic, Vladimir
dc.coverage.spatialvi, 256en_US
dc.date.accessioned2012-04-05T13:32:02Z
dc.date.available2012-04-05T13:32:02Z
dc.date.issued2012
dc.identifieruk.bl.ethos.552686
dc.identifier.urihttp://hdl.handle.net/10023/2540
dc.description.abstractLarge-scale heterogeneous distributed computing environments (such as Computational Grids and Clouds) offer the promise of access to a vast amount of computing resources at a relatively low cost. In order to ease the application development and deployment on such complex environments, high-level parallel programming languages exist that need to be supported by sophisticated runtime systems. One of the main problems that these runtime systems need to address is dynamic load balancing that ensures that no resources in the environment are underutilised or overloaded with work. This thesis deals with the problem of obtaining good speedups for irregular applications on heterogeneous distributed computing environments. It focuses on workstealing techniques that can be used for load balancing during the execution of irregular applications. It specifically addresses two problems that arise during work-stealing: where thieves should look for work during the application execution and how victims should respond to steal attempts. In particular, we describe and implement a new Feudal Stealing algorithm and also we describe and implement new granularity-driven task selection policies in the SCALES simulator, which is a work-stealing simulator developed for this thesis. In addition, we present the comprehensive evaluation of the Feudal Stealing algorithm and the granularity-driven task selection policies using the simulations of a large class of regular and irregular parallel applications on a wide range of computing environments. We show how the Feudal Stealing algorithm and the granularity-driven task selection policies bring significant improvements in speedups of irregular applications, compared to the state-of-the-art work-stealing algorithms. Furthermore, we also present the implementation of the task selection policies in the Grid-GUM runtime system [AZ06] for Glasgow Parallel Haskell (GpH) [THLPJ98], in addition to the implementation in SCALES, and we also present the evaluation of this implementation on a large set of synthetic applications.en_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.subject.lccQA76.88J2
dc.subject.lcshHeterogeneous computingen_US
dc.subject.lcshParallel programming (Computer science)en_US
dc.subject.lcshElectronic data processing--Distributed processingen_US
dc.subject.lcshComputer algorithmsen_US
dc.titleLoad balancing of irregular parallel applications on heterogeneous computing environmentsen_US
dc.typeThesisen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US


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