一种快速有效的L2 Cache可靠性预测方法

Fast and Efficient Prediction of L2 Cache Reliability

随着集成电路工艺的不断进步,微处理器的软错误问题日益突出.体系结构弱点因子AVF(architectural vulnerability factor)作为可靠性评估指标之一,常用于软错误的评估.AVF在程序执行过程中呈现明显的动态变化特性,使得基于AVF预测的动态容错管理技术成为当前软错误研究领域的热门课题.即根据AVF的变化来动态选择是否对微处理器部件进行容错设计,从而在满足软错误可靠性要求的前提下尽量降低容错技术的开销.因此,基于L2 Cache AVF的动态特性研究,提出使用贝叶斯累加树模型BART(Bayesian additive regression trees)对L2 Cache AVF进行准确预测,并使用块搜索(bump hunting)技术来提取由少数几个性能参数组成的、对具有高L2 Cache AVF的执行阶段进行判定的规则,从而实现了对L2 Cache AVF的快速有效预测.

With continuous technology scaling, microprocessors are becoming more and more susceptible to soft errors. Architectural vulnerability factor（AVF）, which has been introduced to quantify the vulnerability of on-chip structures to soft errors, has demonstrated to exhibit significant runtime variations. While traditional fault tolerant techniques which take no account of the dynamic characteristics of AVF provide protection throughout the entire lifetime of programs, possibly leading to the over-protection and inducing significant costs. AVF prediction based dynamic fault tolerant techniques provide error protection only at the execution points with high AVF rather than the whole execution lifetime of programs, thereby maintaining the reliability goal with minimum cost. In this paper, we aim at developing an efficient online AVF predictor which can be used in dynamic fault tolerant management schemes for L2 Cache. We firstly improve the method of Cache AVF computation and characterize the dynamic vulnerability behavior of L2 Cache. Then based on the observations of the dynamic behavior of L2 Cache AVF, we propose to employ the Bayesian additive regression trees（BART） method to accurately model the variation of L2 Cache AVF and employ bump hunting technique to extract some simple selecting rules on several key performance metrics, thus enabling a fast and efficient prediction of L2 Cache AVF.