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dc.contributor.authorGellert, A.
dc.contributor.authorFlorea, A.
dc.contributor.authorVintan, M.
dc.contributor.authorEgan, C.
dc.contributor.authorVintan, L.
dc.date.accessioned2007-10-29T12:07:10Z
dc.date.available2007-10-29T12:07:10Z
dc.date.issued2007
dc.identifier.citationGellert , A , Florea , A , Vintan , M , Egan , C & Vintan , L 2007 , ' Unbiased Branches: An Open Problem ' , Lecture Notes in Computer Science , vol. 4697 , pp. 16-27 .
dc.identifier.issn0302-9743
dc.identifier.otherPURE: 90207
dc.identifier.otherPURE UUID: 97ca051f-9122-436e-9863-de93a847d146
dc.identifier.otherdspace: 2299/1006
dc.identifier.otherScopus: 38049030792
dc.identifier.urihttp://hdl.handle.net/2299/1006
dc.description.abstractThe majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches on prediction accuracy. In this paper we evaluate the impact of unbiased branches on a range of branch difference predictors using prediction by partial matching, multiple Markov prediction and neural-based prediction. Our simulation results, with the SPEC2000 integer benchmark suite, are interesting even though they show that unbiased branches still restrict the ceiling of branch prediction and therefore accurately predicting unbiased branches remains an open problem.en
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science
dc.rights/dk/atira/pure/core/openaccesspermission/open
dc.titleUnbiased Branches: An Open Problemen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstyperestrictedAccess


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