A Probabilistic Monte Carlo Framework for Branch Prediction
A Probabilistic Monte Carlo Framework for Branch Prediction
复制标题
用于分支预测的概率蒙特卡罗框架
DOI：
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发表时间：
2017
期刊：
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通讯作者：
S. Eidenbenz
中科院分区：
文献类型：
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作者：
Bhargava Kalla;N. Santhi;Abdel;Gopinath Chennupati;S. Eidenbenz
分支预测是提高微处理器吞吐量的关键。它减少了流水线中的分支停顿，这有助于维护指令执行流。在这些指令中，条件分支在确定微处理器性能和吞吐量方面是重要的。现代微处理器使用高级分支预测技术来准确地预测分支。适当地估计分支错误预测有利于通过有效地节省CPU周期来提高应用程序的整体性能。通常，使用最先进的模拟器收集分支预测统计是耗时的并且不可扩展。我们提出了一种新的蒙特卡罗模拟框架，预测分支误预测率。我们的框架产生的结果表明，在三个科学应用的误预测率是相似的（平均差异为0.3%）的马尔可夫模型的2位饱和分支预测。
Branch prediction is crucial in improving the throughput of microprocessors. It reduces branching stalls in the pipeline, which helps to maintain the instruction execution flow. Of these instructions, conditional branches are non-trivial in determining the microprocessor performance and throughput. Modern microprocessors accurately predict the branches using advanced branch prediction techniques. Appropriately estimating the branch mis-predictions benefits to improve the overall performance of an application through effectively saving the CPU cycles. In general, collecting branch prediction statistics using state-of-the-art simulators is time consuming and not scalable. We present a novel Monte Carlo simulation framework that predicts branch mis-prediction rate. Our framework produces results that suggest that the mis-prediction rates on three scientific applications are similar (with an average difference of 0.3%) to that of a Markov model of a 2-bit saturating branch predictor.