An analysis of correlation and predictability: what makes two-level branch predictors work
An analysis of correlation and predictability: what makes two-level branch predictors work
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DOI：
10.1109/isca.1998.694762
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发表时间：
1998-04
期刊：
影响因子：
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通讯作者：
M. Evers;Sanjay J. Patel;R. Chappell;Y. Patt
中科院分区：
文献类型：
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作者：
M. Evers;Sanjay J. Patel;R. Chappell;Y. Patt
由分支预测错误引起的流水线刷新是深度流水线超标量处理器设计者面临的最严重问题之一。已经提出了许多分支预测器来帮助缓解这个问题，包括两级自适应分支预测器和混合分支预测器。大量的研究已经表明，在给定的一组基准测试中，哪些预测器和配置最能预测分支。一些研究还调查了可能对这些预测器的性能有害的影响，例如模式历史表干扰。然而，很少有研究已经做了哪些特性的分支行为，使预测器表现良好。在本文中，我们调查和量化的原因，为什么分行是可预测的。我们发现，这种可预测性是不捕获的两级自适应分支预测。对分支的可预测性的理解可能会导致最终产生更好或更简单的预测器的见解。我们还调查和量化的分支在每个基准的功能是可预测的使用本文中描述的每一种方法。
Pipeline flushes due to branch mispredictions is one of the most serious problems facing the designer of a deeply pipelined, superscalar processor. Many branch predictors have been proposed to help alleviate this problem, including two-level adaptive branch predictors and hybrid branch predictors. Numerous studies have shown which predictors and configurations best predict the branches in a given set of benchmarks. Some studies have also investigated effects, such as pattern history table interference, that can be detrimental to the performance of these predictors. However, little research has been done on which characteristics of branch behavior make predictors perform well. In this paper we investigate and quantify reasons why branches are predictable. We show that some of this predictability is not captured by the two-level adaptive branch predictors. An understanding of the predictability of branches may lead to insights ultimately resulting in better or less complex predictors. We also investigate and quantify what function of the branches in each benchmark is predictable using each of the methods described in this paper.