Value Speculation through Equality Prediction
Value Speculation through Equality Prediction
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DOI：
10.1109/iccd46524.2019.00101
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Kleovoulos Kalaitzidis;André Seznec
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文献类型：
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作者：
Kleovoulos Kalaitzidis;André Seznec
现代基于上下文的值预测器通过在指令和上下文上建立信任，将重复出现的值与指令和上下文紧密关联。然而，当执行单调性以间隔的形式存在时，潜在的预测覆盖率是有限的，因为预测置信度在每个新的间隔开始时被重置。在本文中，我们通过引入相等预测（EP）的概念来解决这一挑战，它代表了值预测的二进制方面。遵循双重决策方案（类似于分支预测），EP利用控制流历史来确定在获取时间读取的最后提交结果与所获取的发生的结果之间的相等性。当以高置信度预测相等时，使用读取值。我们的实验表明，这种技术获得了相同的性能水平，以前提出的最先进的基于上下文的预测。然而，凭借更好地利用区间相等的模式，我们的设计补充了执行值预测的既定方式，并且当与当代预测模型相结合时，平均将交付的加速提高了19%。
Modern context-based value predictors tightly associate recurring values with instructions and contexts by building confidence upon them. However, when execution monotony exists in the form of intervals, the potential prediction coverage is limited, since prediction confidence is reset at the beginning of each new interval. In this paper, we address this challenge by introducing the notion of Equality Prediction (EP), which represents the binary facet of value prediction. Following a twofold decision scheme (similar to branch prediction), EP makes use of control-flow history to determine equality between the last committed result read at fetch time, and the result of the fetched occurrence. When equality is predicted with high confidence, the read value is used. Our experiments show that this technique obtains the same level of performance as previously proposed state-of-the-art context-based predictors. However, by virtue of better exploiting patterns of interval equality, our design complements the established way that value prediction is performed, and when combined with contemporary prediction models, improves the delivered speedup by 19% on average.