Exploiting compressed block size as an indicator of future reuse
Exploiting compressed block size as an indicator of future reuse
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利用压缩块大小作为未来重用的指标
DOI：
10.1109/hpca.2015.7056021
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
T. Mowry
中科院分区：
文献类型：
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作者：
Gennady Pekhimenko;Tyler Huberty;Rui Cai;O. Mutlu;Phillip B. Gibbons;M. Kozuch;T. Mowry
我们引入了一套新的压缩感知管理策略（CAMP），以用于采用数据压缩的片上缓存。我们的管理政策基于两个关键思想。首先，我们表明，如果直接用于计算缓存块的值（重要性），则可以为压缩块大小构建更有效的管理策略。这导致了最小值驱逐（MVE），该政策根据规模和预期的未来再利用，驱逐高速公路的价值最低。其次，我们表明，在某些情况下，压缩块大小可以用作有效指标，证明了缓存块的未来重复使用。我们使用此想法来构建一种称为基于大小的插入策略（SIP）的新插入策略，该策略将其压缩大小作为指示器将高速块块动态优先级。我们将CAMP（及其全球变体G-aMP）与先前的芯片高速缓存管理政策（合并尺寸和尺寸了解）进行比较，发现我们的机制在使用压缩块大小作为缓存管理中的额外维度方面更有效决定。我们的结果表明，拟议的管理策略（i）减少了片宽带宽消耗（单核中的8.7％），（ii）与记忆子系统的能源消耗降低（单核中为7.2％），用于记忆密集型工作负载。最佳先验机制，（iii）提高性能（在单/两/四核工作负载评估中平均提高4.9％/9.0％/10.2％ 20.1％）CAMP对各种压缩算法和具有本地和全球替代策略的不同缓存设计有效。
We introduce a set of new Compression-Aware Management Policies (CAMP) for on-chip caches that employ data compression. Our management policies are based on two key ideas. First, we show that it is possible to build a more efficient management policy for compressed caches if the compressed block size is directly used in calculating the value (importance) of a block to the cache. This leads to Minimal-Value Eviction (MVE), a policy that evicts the cache blocks with the least value, based on both the size and the expected future reuse. Second, we show that, in some cases, compressed block size can be used as an efficient indicator of the future reuse of a cache block. We use this idea to build a new insertion policy called Size-based Insertion Policy (SIP) that dynamically prioritizes cache blocks using their compressed size as an indicator. We compare CAMP (and its global variant G-CAMP) to prior on-chip cache management policies (both size-oblivious and size-aware) and find that our mechanisms are more effective in using compressed block size as an extra dimension in cache management decisions. Our results show that the proposed management policies (i) decrease off-chip bandwidth consumption (by 8.7% in single-core), (ii) decrease memory subsystem energy consumption (by 7.2% in single-core) for memory intensive workloads compared to the best prior mechanism, and (iii) improve performance (by 4.9%/9.0%/10.2% on average in single-/two-/four-core workload evaluations and up to 20.1%) CAMP is effective for a variety of compression algorithms and different cache designs with local and global replacement strategies.