改进基于历史的分支预测器性能的方法

PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005. Pub Date : 2005-10-17 DOI:10.1109/PACRIM.2005.1517240

Tieling Xie, Y. Chu, J. H. Park

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引用次数: 0

摘要

研究了基于全局历史和局部历史的分支预测器中的混叠问题，提出了两种改进全局历史分支预测器性能的方法。基于全局历史的预测器存在更严重的混叠问题，但表现出比基于局部历史的预测器更好的性能。因此，我们的方法主要侧重于缓解基于全球历史的预测器的混叠问题。使用Simplescalar模拟器和SPEC95CINT基准程序对每种方法的性能进行了评估和比较。实验结果表明，该方法优于传统的基于全局历史的分支预测器。

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Approaches to improve performance for history-based branch predictors

This paper investigates the aliasing problems in global-history-based and local-history-based branch predictors and presents two approaches to improve the performance of global-history-based branch predictors. Global-history-based predictors have more critical aliasing problems but show the better performance than local-history-based predictors. Therefore, our approaches mainly focus on alleviating the aliasing problems for global-history-based predictors. The performance of each approach is evaluated and compared by using the Simplescalar simulator with SPEC95CINT benchmark programs. Our experimental results show that the approaches outperform conventional global-history-based branch predictors.

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来源期刊

PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005.

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