使用决策树分析的插入策略选择

S. Khan, Daniel A. Jiménez
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引用次数: 21

摘要

最后一级缓存(LLC)减轻了当前微体系结构中长内存访问延迟的影响。LLC中的插入策略对缓存效率有很大的影响。固定的插入策略可能允许无用的块在缓存中保留的时间超过必要的时间,从而导致效率低下。引入了基于决策树分析(DTA)的插入策略选择方法。与最近最少使用(least-recently used, LRU)替换策略相比,该技术只需要最小的硬件修改。此策略使用LLC过滤时间局部性的事实。许多被带到缓存中的行永远不会再被访问。即使它们被重新访问,它们也不会经历突发,而是当它们接近LRU堆栈中的LRU位置时被重用。我们使用多集决斗的决策树分析来选择LRU堆栈中的最优插入位置。插入到这个位置,零重用行最小化了它们的死区时间,而非零重用行在缓存中保留足够长的时间以被重用并避免丢失。对于单核处理器中的1MB 16路集合关联的最后一级缓存,我们的策略在LRU替换策略上只使用2,069个额外的位。与LRU相比,平均减少了5.16%的失误率,实现了7.19%的IPC改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Insertion policy selection using Decision Tree Analysis
The last-level cache (LLC) mitigates the impact of long memory access latencies in today's microarchitectures. The insertion policy in the LLC has a significant impact on cache efficiency. A fixed insertion policy can allow useless blocks to remain in the cache longer than necessary, resulting in inefficiency. We introduce insertion policy selection using Decision Tree Analysis (DTA). The technique requires minimal hardware modification over the least-recently-used (LRU) replacement policy. This policy uses the fact that the LLC filters temporal locality. Many of the lines brought to the cache are never accessed again. Even if they are reaccessed they do not experience bursts, but rather they are reused when they are near to the LRU position in the LRU stack. We use decision tree analysis of multi-set-dueling to choose the optimal insertion position in the LRU stack. Inserting in this position, zero reuse lines minimize their dead time while the non-zero reuse lines remain in the cache long enough to be reused and avoid a miss. For a 1MB 16 way set-associative last level cache in a single core processor, our policy uses only 2,069 additional bits over the LRU replacement policy. On average it reduces misses by 5.16% and achieves 7.19% IPC improvement over LRU.
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