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Improving branch prediction accuracy by reducing pattern history table interference

机译:通过减少模式历史表干扰来提高分支预测精度

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Today's deeply pipelined, superscalar processors rely on accurate branch prediction in order to approach their performance potential. Branch mispredictions result in a flushing of the speculative information in the pipeline, thus limiting the amount of useful work that can be done. The 2-level branch predictors have been shown to achieve high prediction accuracy. However, it has also been shown that there is a high degree of pattern history table interference in 2-level branch predictors and that the interference generally has a negative effect on the prediction accuracy. This paper introduces a method for reducing the pattern history table interference by dynamically identifying some easily predictable branches and inhibiting the pattern history table update for these branches. We show how this technique reduces pattern history table interference for two versions of the 2-level branch predictor and that this significantly improves branch prediction accuracy for the SPEC 95 benchmarks. In particular, we eliminate up to 30% of the mispredictions for the gcc benchmark.
机译:如今,深度流水线化的超标量处理器依靠准确的分支预测来发挥其性能潜力。分支预测错误会导致流水线中的推测性信息泛滥,从而限制了可以完成的有用工作量。已经显示了2级分支预测器可以实现较高的预测精度。但是,还显示出在2级分支预测变量中存在高度的模式历史表干扰,并且该干扰通常对预测精度有负面影响。本文介绍了一种通过动态识别一些易于预测的分支并禁止这些分支的模式历史表更新来减少模式历史表干扰的方法。我们将展示该技术如何减少两个级别的2级分支预测器的模式历史表干扰,并显着提高SPEC 95基准的分支预测精度。特别是,对于gcc基准,我们最多可以消除30%的错误预测。

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