Periodic adaptive branch prediction and its application in superscalar processing in prolog

Ruey Liang Ma*, Chung-Ping Chung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Branch 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.

Original languageEnglish
Pages (from-to)457-470
Number of pages14
JournalComputer Journal
Volume38
Issue number6
DOIs
StatePublished - 1 Dec 1995

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