计算机科学 ›› 2019, Vol. 46 ›› Issue (3): 327-331.doi: 10.11896/j.issn.1002-137X.2019.03.048
梁媛,袁景凌,陈旻骋
LIANG Yuan, YUAN Jing-ling, CHEN Min-cheng
摘要: 数据中心是高性能计算机的集群中心,CPU集群运行繁忙,不规则的数据结构和算法频繁使用,使得大多数基于时空局部性的预取技术不再适用。文中引用语义局部性的概念,使用增强学习Sarsa算法来近似语义位置,预测不规则数据结构和算法未来的内存访问。由于状态空间和动态空间过大,采用Deep Q-learning方法优化状态-动作空间,将新状态与旧状态拟合,相似则采取相似的做法,从而提高泛化能力。在标准数据集SPECCPU 2006上的实验证明,所提方法的泛化能力强,能够有效提高Cache的命中率。
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