dc.creatorYero, EJH
dc.creatorHenriques, MAA
dc.date2006
dc.dateMAY
dc.date2014-11-16T18:03:35Z
dc.date2015-11-26T16:24:33Z
dc.date2014-11-16T18:03:35Z
dc.date2015-11-26T16:24:33Z
dc.date.accessioned2018-03-28T23:05:27Z
dc.date.available2018-03-28T23:05:27Z
dc.identifierPerformance Evaluation. Elsevier Science Bv, v. 63, n. 41763, n. 265, n. 277, 2006.
dc.identifier0166-5316
dc.identifierWOS:000236425200001
dc.identifier10.1016/j.peva.2005.01.008
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/56938
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/56938
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/56938
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1268448
dc.descriptionPerformance prediction for parallel applications running in heterogeneous clusters is difficult to accomplish due to the unpredictable resource contention patterns that can be found in such environments. Typically, components of a parallel application will contend for the use of resources among themselves and with entities external to the application, such as other processes running in the computers of the cluster. The performance modeling approach should be able to represent these sources of contention and to produce an estimate of the execution time, preferably in polynomial time. This paper presents a polynomial time static performance prediction approach in which the prediction takes the form of an interval of values instead of a single value. The extra information given by an interval of values represents the variability of the underlying environment more accurately, as indicated by the practical examples presented. (c) 2005 Elsevier B.V. All rights reserved.
dc.description63
dc.description41763
dc.description265
dc.description277
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationPerformance Evaluation
dc.relationPerform. Eval.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectparallel computing
dc.subjectstatic performance prediction
dc.subjectresource contention
dc.titleContention-sensitive static performance prediction for parallel distributed applications
dc.typeArtículos de revistas


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