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Optimizing the Management of Reference Prediction Table for Prefetching and Prepromotion

机译:优化参考预测表的管理,以进行预取和预花束

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摘要

—Prefetching and prepromotion are two important techniques for hiding the memory access latency. Reference prediction tables (RPT) plays a significant role in the process of prefetching or prepromoting data with linear memory access patterns. The traditional RPT management, LRU replacement algorithm, can not manage RPT efficiently. This leads to that large RPT has to be used for the considerable performance. The cost brought from the large capacity limits the usage of RPT in real processors. This paper uses bimodal insert policy (BIP) and proposed scalar filter policy (SFP) in the RPT management. Owing to matching the using characteristics of RPT, BIP can reduce the RPT thrashing and SFP can filter the useless scalar instructions in it. After testing 8 NPB benchmarks on a fullsystem simulator, we find that our approaches improve the RPT hit rate by 53.81% averagely, and increases prefetching and prepromotion operations by 18.85% and 53.55% averagely over the traditional LRU management.
机译:- 用于隐藏存储器访问等待时间的两个重要技术。参考预测表(RPT)在具有线性存储器访问模式的预取或预付费数据的过程中起重要作用。传统的RPT管理,LRU替换算法,无法有效地管理RPT。这导致大型RPT必须用于相当大的性能。从大容量带来的成本限制了RPT在实际处理器中的使用。本文在RPT管理中使用双峰插入策略(BIP)和提出的标量过滤器策略(SFP)。由于匹配使用RPT的特性,BIP可以减少RPT THRASHING,SFP可以过滤IT中的无用标量指令。在全系统模拟器上测试8个NPB基准后,我们​​发现我们的方法平均将RPT命中率提高了53.81%,并在传统的LRU管理方面将预取和预先染色行动增加18.85%和53.55%。

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