Unbiased Branches: An Open Problem

A. Gellert, A. Florea, M. Vintan, C. Egan, L. Vintan

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)
    82 Downloads (Pure)

    Abstract

    The 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.
    Original languageEnglish
    Pages (from-to)16-27
    JournalLecture Notes in Computer Science (LNCS)
    Volume4697
    Publication statusPublished - 2007
    Event12th Asia-Pacific Conference on Advances in Computer Systems Architecture (ACSAC '07) - Seoul, Korea, Republic of
    Duration: 1 Aug 2007 → …

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