论文标题
动态合并点预测
Dynamic Merge Point Prediction
论文作者
论文摘要
尽管进行了数十年的研究，但有条件的分支错误预测仍然为性能带来一个重大问题。此外，对无限规模预测因子的限制研究表明，许多其余的分支是无法通过当前策略来预测的。我们的工作着重于在无法预测分支机构的情况下减轻绩效损失。本文提出了一个动态合并点预测器，该预测器使用在分支的错误路径上获取的指令来动态检测合并点。我们的预测因子以95％的精度定位合并点，即使面对无法预测的方向的分支。此外，我们引入了一种新颖的信心成本系统，该系统确定了昂贵的难以预测的分支机构。我们的完整系统用正确的合并点预测取代了所有分支错误预测的58％，实际上将MPKI降低了43％。该结果证明了动态合并点预测的潜力，可以显着提高性能。
Despite decades of research, conditional branch mispredictions still pose a significant problem for performance. Moreover, limit studies on infinite size predictors show that many of the remaining branches are impossible to predict by current strategies. Our work focuses on mitigating performance loss in the face of impossible to predict branches. This paper presents a dynamic merge point predictor, which uses instructions fetched on the wrong path of the branch to dynamically detect the merge point. Our predictor locates the merge point with an accuracy of 95%, even when faced with branches whose direction is impossible to predict. Furthermore, we introduce a novel confidence-cost system, which identifies costly hard-to-predict branches. Our complete system replaces 58% of all branch mispredictions with a correct merge point prediction, effectively reducing MPKI by 43%. This result demonstrates the potential for dynamic merge point prediction to significantly improve performance.
