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Performance-effective and low-complexity task scheduling for heterogeneous computing

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dc.contributor.authorTOPCUOĞLU, HALUK RAHMİ
dc.contributor.authorsTopcuoglu, H; Hariri, S; Wu, MY
dc.date.accessioned2022-03-14T10:13:05Z
dc.date.available2022-03-14T10:13:05Z
dc.date.issued2002-03
dc.description.abstractEfficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results, and average scheduling time metrics.
dc.identifier.doi10.1109/71.993206
dc.identifier.eissn1558-2183
dc.identifier.issn1045-9219
dc.identifier.urihttps://hdl.handle.net/11424/244213
dc.identifier.wosWOS:000174338100005
dc.language.isoeng
dc.publisherIEEE COMPUTER SOC
dc.relation.ispartofIEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDAG scheduling
dc.subjecttask graphs
dc.subjectheterogeneous systems
dc.subjectlist scheduling
dc.subjectmapping
dc.subjectSYSTEMS
dc.subjectGRAPHS
dc.titlePerformance-effective and low-complexity task scheduling for heterogeneous computing
dc.typearticle
dspace.entity.typePublication
local.avesis.ida103f6ad-c082-4c45-8e51-db93bbf91e34
local.import.packageSS16
local.indexed.atWOS
local.indexed.atSCOPUS
local.journal.numberofpages15
oaire.citation.endPage274
oaire.citation.issue3
oaire.citation.startPage260
oaire.citation.titleIEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
oaire.citation.volume13
relation.isAuthorOfPublication54c6a927-2146-44b3-90ee-33dac6503317
relation.isAuthorOfPublication.latestForDiscovery54c6a927-2146-44b3-90ee-33dac6503317

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