高效混合预测策略的设计

Design of efficient hybrid predicting strategies

现有的分支预测模型无法完全准确预测处理器中各种指令的行为,导致处理效率受限。为此提出了两种混合预测解决方案,旨在结合多种分支预测模型,以提高预测的准确性和处理器的执行效率。将TAGE(tagged geometric history length)分支预测模型与BATAGE(Bayesian tagged geometric history length)分支预测模型的预测结果转交Hybrid模型。在预测阶段中,Hybrid模型会根据TAGE和BATAGE的历史表现去选择表现最佳分支预测模型的预测结果。而在更新阶段中,Hybrid模型会根据设计的混合预测策略对需要更新条目的饱和计数器进行更新。在CBP(championship branch prediction)软件仿真平台提供的440个测试程序上进行实验,实验结果表明:与多种最新主流分支预测模型相比,两种混合预测解决方案的预测错误率均低于它们。该研究为预测所有指令模式行为问题提供了有效解决方案。在实际CPU的分支指令预测,该研究提供了一些实用价值。

The existing branch prediction models can t predict the behaviors of various instructions in the processor accurately,which leads to the limitation of processing efficiency.Therefore,this paper proposed two hybrid prediction solutions,which aimed to combine multiple branch prediction models to improve prediction accuracy and processor execution efficiency.This paper transferred the prediction results of TAGE branch prediction model and BATAGE branch prediction model to the Hybrid model.In the prediction phase,the Hybrid model selected the prediction results of the best-performing branch prediction model based on the historical performance of TAGE and BATAGE.In the update phase,the Hybrid model updated the saturation counter of the entries that needed to be updated according to the designed hybrid prediction strategy.Experiments on 440 test programs provided by the CBP software simulation platform show that the prediction error rates of both hybrid prediction solutions are lower than those of many of the latest mainstream branch prediction models.This study provides an effective solution to the problem of predicting all command mode behaviors.In the branch instruction prediction of real CPU,this research provides some practical value.