Value Prediction (VP) has recently been gaining interest in the research community, since prior work has established practical solutions for its implementation that provide meaningful performance gains. A constant challenge of contemporary context-based value predictors is to sufficiently capture value redundancy and exploit the predictable execution paths. To do so, modern context-based VP techniques tightly associate recurring values with instructions and contexts by building confidence upon them after a plethora of repetitions. 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 study, we address this challenge by introducing the notion of Equality Prediction (EP), which represents the binary facet of VP. Following a twofold decision scheme (similar to branch prediction), at fetch time, EP makes use of control-flow history to predict equality between the last committed result for this instruction and the result of the currently fetched occurrence. When equality is predicted with high confidence, the last committed value is used. Our simulation results show that this technique obtains the same level of performance as previously proposed state-of-the-art context-based value predictors. However, by virtue of exploiting equality patterns that are not captured by previous VP schemes, our design can improve the speedup of standard VP by 19% on average, when combined with contemporary prediction models.

中文翻译：

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利用价值平等预测进行价值投机

价值预测 (VP) 最近引起了研究界的兴趣，因为之前的工作已经为其实施建立了实用的解决方案，从而提供了有意义的性能提升。当代基于上下文的价值预测器的一个持续挑战是充分捕获价值冗余并利用可预测的执行路径。为此，现代基于上下文的 VP 技术通过在大量重复后建立对它们的信心，将重复值与指令和上下文紧密联系起来。然而，当执行单调以区间的形式存在时，潜在的预测覆盖范围是有限的，因为预测置信度在每个新区间开始时被重置。在本研究中，我们通过引入平等预测(EP)，表示 VP 的二进制方面。遵循双重决策方案（类似于分支预测），在获取时，EP 利用控制流历史来预测该指令的最后提交结果与当前获取的结果之间的相等性。当以高置信度预测相等时，使用最后提交的值。我们的模拟结果表明，该技术获得了与先前提出的最先进的基于上下文的值预测器相同的性能水平。然而，由于利用了以前的 VP 方案未捕获的相等模式，我们的设计与当代预测模型相结合时，可以将标准 VP 的平均加速提高 19%。