Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.552686
Title: Load balancing of irregular parallel applications on heterogeneous computing environments
Author: Janjic, Vladimir
Awarding Body: University of St Andrews
Current Institution: University of St Andrews
Date of Award: 2012
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Abstract:
Large-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.
Supervisor: Hammond, Kevin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.552686  DOI: Not available
Keywords: QA76.88J2 ; Heterogeneous computing ; Parallel programming (Computer science) ; Electronic data processing--Distributed processing ; Computer algorithms
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