Using dataflow based context for accurate value prediction
Using dataflow based context for accurate value prediction
复制标题
使用基于数据流的上下文进行准确的价值预测
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
M. Franklin
中科院分区：
文献类型：
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作者：
Renju Thomas;M. Franklin
我们探讨了现有数据值预测因子的预测准确性相当低的原因。我们的研究表明，仅根据静态指令的最后几个实例的结果形成的上下文并不总是封装正确预测所需的所有信息。数据流与控制流之间的复杂相互作用以导致大量动态指令的可预测性损失的方式改变上下文。为了提高预测准确性，我们提出了使用从数据流图的可预测部分得出的上下文的概念。也就是说，可以通过利用在数据流图中易于预测的指令的可预测性来提高难以预测的指令的可预测性。我们提出并研究了一种运行时方案，用于从先前指令的预测值中产生这种改进的上下文。我们还提出了一个新的预测指标，称为动态数据流媒体上的投机上下文（DDISC）预测指标，以专门预测难以预测的指令。仿真结果验证了基于数据流的上下文的使用可在预测准确性方面显着改善，范围从35％到99％。这意味着总体预测准确度为68％至99.9％。
We explore the reasons behind the rather low prediction accuracy of existing data value predictors. Our studies show that contexts formed only from the outcomes of the last several instances of a static instruction do not always encapsulate all of the information required for correct prediction. Complex interactions between data flow and control flow change the context in ways that result in predictability loss for a significant number of dynamic instructions. For improving the prediction accuracy, we propose the concept of using contexts derived from the predictable portions of the data flow graph. That is, the predictability of hard-to-predict instructions can be improved by taking advantage of the predictability of the easy-to-predict instructions that precede it in the data flow graph. We propose and investigate a run-time scheme for producing such an improved context from the predicted values of previous instructions. We also propose a novel predictor called dynamic dataflow-inherited speculative context (DDISC) based predictor for specifically predicting hard-to-predict instructions. Simulation results verify that the use of dataflow-based contexts yields significant improvements in prediction accuracies, ranging from, 35% to 99%. This translates to an overall prediction accuracy of 68% to 99.9%.