基于访问模式的最后一级缓存重引用间隔表

B. Yu, Yifan Hu, Jianliang Ma, Tianzhou Chen
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引用次数: 1

摘要

到最后一级缓存(LLC)的内存访问流是来自上层缓存的过滤版本。LLC中存在大量重引用间隔较长的缓存块,且LLC中存在大量零重用块,长期污染LLC。基于访问模式的重引用间隔表(RRIT)是一种有效的LLC管理技术,它引入了一个表来跟踪连续块的重引用间隔。然后预测新传入块的重新引用间隔。当发生缓存替换问题时,RRIT总是选择具有最大重引用间隔的块作为受害块。在此基础上,提出了一种基于RRIT的死块滤波(rit - filter)机制。rit - filter绕过了预测的死块,提高了LLC效率。我们在单核2MB LLC上使用SPEC2006工作负载进行的评估表明,在吞吐量指标上,RRIT和rit - filter比LRU替代的性能平均分别高出6.6%和8.6%。我们在一个拥有8MB共享LLC的4核CMP上对10个多编程工作负载进行了评估,结果表明,我们的策略在吞吐量指标上平均分别比LRU替代高出23%和39%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Access Pattern Based Re-reference Interval Table for Last Level Cache
The memory access stream to the last level cache (LLC) is a filtered version from the upper level caches. There are a large number of cache blocks with long re-reference interval in the LLC. What's worse, zero reuse blocks are in abundance in the LLC and pollute the LLC for a long time. Access pattern based re-reference interval table (RRIT), here we propose, is an efficient management technology for the LLC. RRIT introduces a table to track consecutive blocks' re-reference interval. And then predicts the re-reference interval for a new incoming block. RRIT always selects the block with the max re-reference interval as the victim block when a cache replacement issue occurs. Furthermore, we propose a dead block filtering (RRIT-Filter) mechanism based on RRIT. RRIT-Filter bypasses the predicted dead block and improves LLC efficiency. Our evaluations using the SPEC2006 workloads on a single-core with a 2MB LLC show that RRIT and RRIT-Filter outperform LRU replacement on the throughput metric by an average of 6.6% and 8.6% respectively. Our evaluations with 10 multi-programmed workloads on a 4-core CMP with an 8MB shared LLC show that our strategy outperforms LRU replacement on the throughput metric by an average of 23% and 39% respectively.
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