论文标题
Interplay：一个智能模型，用于预测由于多缓存方式降解性能降解的智能模型
INTERPLAY: An Intelligent Model for Predicting Performance Degradation due to Multi-cache Way-disabling
论文作者
论文摘要
现代和未来的处理器需要在存在永久性故障的情况下保持功能正确，以维持扩展福利并限制现场回报。本文介绍了一个称为Interplay的基于相互作用的基于仿真的结合分析和微体系模拟的框架，该框架可以在设计时迅速预测，从而在现场时使用的方法可以通过使用方法来处理固定断层的处理器预期的性能下降。提出的模型可以预测程序的性能，对于具有两级高速缓存层次结构的处理器的准确性高达98.40％，当时多个caches遭受了故障的损失，并且需要禁用一种或多种方式。相互作用比详尽的仿真方法快9.2倍，因为它仅需要用于单速度驱动方式的配置的训练模拟来预测任何多缓存方式删除配置的性能。
Modern and future processors need to remain functionally correct in the presence of permanent faults to sustain scaling benefits and limit field returns. This paper presents a combined analytical and microarchitectural simulation-based framework called INTERPLAY, that can rapidly predict, at design-time, the performance degradation expected from a processor employing way-disabling to handle permanent faults in caches while in-the-field. The proposed model can predict a program's performance with an accuracy of up to 98.40% for a processor with a two-level cache hierarchy, when multiple caches suffer from faults and need to disable one or more of their ways. INTERPLAY is 9.2x faster than an exhaustive simulation approach since it only needs the training simulation runs for the single-cache way-disabling configurations to predict the performance for any multi-cache way-disabling configuration.
