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dc.contributor.authorMa, RLen_US
dc.contributor.authorChung, CPen_US
dc.date.accessioned2014-12-08T15:03:35Z-
dc.date.available2014-12-08T15:03:35Z-
dc.date.issued1995en_US
dc.identifier.issn0010-4620en_US
dc.identifier.urihttp://hdl.handle.net/11536/2117-
dc.identifier.urihttp://dx.doi.org/10.1093/comjnl/38.6.457en_US
dc.description.abstractBranch instructions create barriers to instruction prefetching, greatly reducing the fine-grained parallelism of programs. Branch prediction is a common method for solving this problem. We first present four lemmata in this paper describing the relationships among branch prediction hit rate and system performance, hardware efficiency, and branch prediction overhead. We then propose a branch prediction method called PAM (Periodic Adaptive Method). An abstract model and detailed implementation of PAM are described. PAM's prediction hit rate as measured by 10 Prolog benchmark programs is 97%. When implemented in a superscalar Prolog system, PAM enhances the degree of system parallelism by 68.8%. PAM can be applied to languages and applications other then the Prolog system we used in this study.en_US
dc.language.isoen_USen_US
dc.titlePeriodic adaptive branch prediction and its application in superscalar processing in Prologen_US
dc.typeArticleen_US
dc.identifier.doi10.1093/comjnl/38.6.457en_US
dc.identifier.journalCOMPUTER JOURNALen_US
dc.citation.volume38en_US
dc.citation.issue6en_US
dc.citation.spage457en_US
dc.citation.epage470en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:A1995TR92300005-
dc.citation.woscount0-
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