The predictability of data values
The predictability of data values
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DOI：
10.1109/micro.1997.645815
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发表时间：
1997-12
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通讯作者：
Yiannakis Sazeides;James E. Smith
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文献类型：
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作者：
Yiannakis Sazeides;James E. Smith
从基础层面研究数据值的可预测性。定义了两个基本预测器模型：计算预测器对先前值执行运算以产生预测的下一个值。我们研究的示例是步幅值预测（将增量添加到先前的值）和最后值预测（对先前的值执行简单的恒等操作）。基于上下文的预测器将最近的值历史（上下文）与先前的值历史相匹配，并完全基于先前观察到的模式来预测值。为了了解价值预测的潜力，我们使用无界预测表进行模拟，这些预测表会使用正确的数据值立即更新。整数 SPEC95 基准的模拟表明数据值是高度可预测的。使用基于上下文的预测器可获得最佳性能；总体预测准确度在 56% 到 91% 之间。基于上下文的预测器的准确度通常比计算预测器（最后一个值和步长）高出约 20%。基于上下文的预测和步幅预测的比较表明，基于上下文的预测的较高准确度是由于相对较少的静态指令带来了很大的改进；这表明混合预测器的有用性。在不同的指令类型中，可预测性差异很大。一般来说，加载和移位指令更难以正确预测，而加法指令则更容易预测。
The predictability of data values is studied at a fundamental level. Two basic predictor models are defined: computational predictors perform an operation on previous values to yield predicted next values. Examples we study are stride value prediction (which adds a delta to a previous value) and last value prediction (which performs the trivial identity operation on the previous value). Context based predictors match recent value history (context) with previous value history and predict values based entirely on previously observed patterns. To understand the potential of value prediction we perform simulations with unbounded prediction tables that are immediately updated using correct data values. Simulations of integer SPEC95 benchmarks show that data values can be highly predictable. Best performance is obtained with context based predictors; overall prediction accuracies are between 56% and 91%. The context based predictor typically has an accuracy about 20% better than the computational predictors (last value and stride). Comparison of context based prediction and stride prediction shows that the higher accuracy of context based prediction is due to relatively few static instructions giving large improvements; this suggests the usefulness of hybrid predictors. Among different instruction types, predictability varies significantly. In general, load and shift instructions are more difficult to predict correctly, whereas add instructions are more predictable.