标签表

Sean Franey, Mikko H. Lipasti
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引用次数: 32

摘要

标签表可以为非常大的集合关联缓存存储标签-例如3D DRAM集成提供的标签-具有细粒度块大小(例如64B),开销足够低,可以在SRAM的处理器芯片上实现。这种方法不同于先前利用小块大小的建议,这些建议假设用于DRAM缓存的片上标签阵列太昂贵,因此将它们与数据一起存储在DRAM本身中。标记表可以利用应用程序的自然空间局部性,通过紧凑的“基数加偏移量”编码来跟踪缓存中数据的位置,从而避免传统标记数组的昂贵开销。此外,标记表利用转发页表结构的随需应变特性,仅为与缓存中当前存在的数据相对应的条目分配存储,这与传统标记数组带来的静态成本相反。通过高关联性,我们发现标签表提供的平均性能比先前最先进的合金缓存提高10%以上,比Loh-Hill缓存提高44%以上,由于快速的片上查找,通过一系列多线程和多编程工作负载,比无l4系统提高58%,具有高L3失分率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tag tables
Tag Tables enable storage of tags for very large set-associative caches - such as those afforded by 3D DRAM integration - with fine-grained block sizes (e.g. 64B) with low enough overhead to be feasibly implemented on the processor die in SRAM. This approach differs from previous proposals utilizing small block sizes which have assumed that on-chip tag arrays for DRAM caches are too expensive and have consequently stored them with the data in the DRAM itself. Tag Tables are able to avoid the costly overhead of traditional tag arrays by exploiting the natural spatial locality of applications to track the location of data in the cache via a compact "base-plus-offset" encoding. Further, Tag Tables leverage the on-demand nature of a forward page table structure to only allocate storage for those entries that correspond to data currently present in the cache, as opposed to the static cost imposed by a traditional tag array. Through high associativity, we show that Tag Tables provide an average performance improvement of more than 10% over the prior state-of-the-art - Alloy Cache - 44% more than the Loh-Hill Cache due to fast on-chip lookups, and 58% over a no-L4 system through a range of multithreaded and multiprogrammed workloads with high L3 miss rates.
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