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dc.contributor.authorKelefouras, Vasileios
dc.contributor.authorDjemame, K
dc.date.accessioned2022-05-24T20:25:39Z
dc.date.issued2022-05-30
dc.identifier.issn0743-7315
dc.identifier.issn1096-0848
dc.identifier.urihttp://hdl.handle.net/10026.1/19261
dc.description.abstract

Efficient application scheduling is critical for achieving high performance in heterogeneous computing systems. This problem has proved to be NP-complete even for the homogeneous case, heading research efforts in obtaining low complexity heuristics that produce good quality schedules. Such an example is HEFT, one of the most efficient list scheduling heuristics in terms of makespan and robustness. In this paper, we propose two task scheduling methods for heterogeneous computing systems that can be integrated to several task scheduling algorithms. First, a method that improves the scheduling time (the time for obtaining the output schedule) of a family of task scheduling algorithms is delivered without sacrificing the schedule length, when the computation costs of the application tasks are unknown. Second, a method that improves the scheduling length (makespan) of several task scheduling algorithms is proposed, by identifying which tasks are going to be executed as single-threaded and which as multi-threaded implementations, as well as the number of the threads used. We showcase both methods by using HEFT popular algorithm, but they can be integrated to other algorithms too, such as HCPT, HPS, PETS and CPOP. The experimental results, which consider 14580 random synthetic graphs and five real world applications, show that by enhancing HEFT algorithm with the two proposed methods, significant makespan gains and high scheduling time gains, are achieved.

dc.format.extent17-32
dc.languageen
dc.language.isoen
dc.publisherElsevier
dc.subjectTask scheduling
dc.subjectHEFT
dc.subjectHeterogeneity
dc.subjectScheduling time
dc.subjectMakespan
dc.titleWorkflow Simulation and Multi-Threading Aware Task Scheduling for Heterogeneous Computing
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000812363600002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume168
plymouth.publication-statusPublished
plymouth.journalJournal of Parallel and Distributed Computing (Elsevier)
dc.identifier.doi10.1016/j.jpdc.2022.05.011
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2022-05-24
dc.rights.embargodate2022-6-2
dc.identifier.eissn1096-0848
rioxxterms.versionofrecord10.1016/j.jpdc.2022.05.011
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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