通过调整大小和重新映射来提高缓存效率

Subramanian Ramaswamy, S. Yalamanchili
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引用次数: 18

摘要

在本文中,我们提出了动态缩小或增大缓存的技术,同时关闭缓存集/行以产生高效的缓存。与以前的方法不同,调整大小伴随着将内存重新映射到调整大小的缓存中,从而避免错过关闭的集/行。本文首先对能源效率低下的原因进行了分析,揭示了一个提高效率的简单模型。基于该模型,我们提出了“折叠”的概念——将映射到不相交的缓存资源的存储区域组合在一起以共享缓存集,从而产生新的放置函数。折叠可以关闭缓存集,但代价是可能增加冲突丢失。有效的折叠启发式可以在可接受的执行时间增加的代价下大幅提高能源效率。我们的目标是12缓存,因为它的大小和能耗更大。我们的技术将缓存能源效率提高了20%,并将EDP(能量延迟产品)降低了45%,IPC退化低于4%。结果还指出了通过协作编译器交互进一步提高缓存效率的机会。
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Improving cache efficiency via resizing + remapping
In this paper we propose techniques to dynamically downsize or upsize a cache accompanied by cache set/line shutdown to produce efficient caches. Unlike previous approaches, resizing is accompanied by a non-uniform remapping of memory into the resized cache, thus avoiding misses to sets/lines that are shut off. The paper first provides an analysis into the causes of energy inefficiencies revealing a simple model for improving efficiency. Based on this model we propose the concept of "folding" - memory regions mapping to disjoint cache resources are combined to share cache sets producing a new placement function. Folding enables powering down cache sets at the expense of possibly increasing conflict misses. Effective folding heuristics can substantially increase energy efficiency at the expense of acceptable increase in execution time. We target the 12 cache because of its larger size and greater energy consumption. Our techniques increase cache energy efficiency by 20%, and reduce the EDP (energy delay product) by up to 45% with an IPC degradation of less than 4%. The results also indicate opportunity for improving cache efficiencies further via cooperative compiler interactions.
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