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Improving branch prediction and predicated execution in out-of-order processors

机译:改进无序处理器中的分支预测和预测执行

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摘要

If-conversion is a compiler technique that reduces the misprediction penalties caused by hard-to-predict branches, transforming control dependencies into data dependencies. Although it is globally beneficial, it has a negative side-effect because the removal of branches eliminates useful correlation information necessary for conventional branch predictors. The remaining branches may become harder to predict. However, in predicated ISAs with a compare-branch model, the correlation information not only resides in branches, but also in compare instructions that compute their guarding predicates. When a branch is removed, its correlation information is still available in its compare instruction. We propose a branch prediction scheme based on predicate prediction. It has three advantages: First, since the prediction is not done on a branch basis but on a predicate define basis, branch removal after if-conversion does not lose any correlation information, so accuracy is not degraded. Second, the mechanism we propose permits using the computed value of the branch predicate when available, instead of the predicted value, thus effectively achieving 100% accuracy on such early-resolved branches. Third, as shown in previous work, the selective predicate prediction is a very effective technique to implement if-conversion on out-of-order processors, since it avoids the problem of multiple register definitions and reduces the unnecessary resource consumption of nullified instructions. Hence, our approach enables a very efficient implementation of if-conversion for an out-of-order processor, with almost no additional hardware cost, because the same hardware is used to predict the predicates of if-converted code and to predict branches without accuracy degradation.
机译:If-conversion是一种编译器技术,可减少由难以预测的分支引起的错误预测损失,从而将控制依赖项转换为数据依赖项。尽管它在全球范围内都是有益的,但它具有负面影响,因为删除分支会消除常规分支预测器所必需的有用的相关信息。其余分支可能变得更难以预测。但是,在具有比较分支模型的谓词ISA中,相关信息不仅驻留在分支中,而且还驻留在计算其保护谓词的比较指令中。删除分支后,其相关信息仍可在其比较指令中使用。我们提出了一种基于谓词预测的分支预测方案。它具有三个优点:首先,由于预测不是基于分支而是基于谓词定义,因此if转换后的分支删除不会丢失任何相关信息,因此不会降低准确性。其次,我们提出的机制允许使用分支谓词的计算值(如果有),而不是预测值,从而有效地在此类早期解析的分支上实现100%的准确性。第三,如先前的工作所示,选择性谓词预测是一种在无序处理器上执行if转换的非常有效的技术,因为它避免了多个寄存器定义的问题并减少了无效指令的不必要的资源消耗。因此,我们的方法可以为乱序处理器实现if转换的非常有效的实现,而几乎没有额外的硬件成本,因为相同的硬件用于预测if转换后的代码的谓词和预测精度不高的分支降解。

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