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2DCC: Cache Compression in Two Dimensions

机译:2DCC:二维的缓存压缩

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摘要

The importance of caches for performance, and their high silicon area cost, have motivated hardware solutions that transparently compress the cached data to increase effective capacity without sacrificing silicon area. To this end, prior work has taken one of two approaches: either (a) deduplicating identical cache blocks across the cache to take advantage of inter-block redundancy or (b) compressing common patterns within each cache block to take advantage of intra-block redundancy.(p)(/p)In this paper, we demonstrate that leveraging only one of these redundancy types leads to a significant loss in compression opportunities for several applications: some workloads exhibit either inter-block or intra-block redundancy, while others exhibit both. We propose 2DCC (Two Dimensional Cache Compression), a simple technique that takes advantage of both types of redundancy. Across the SPEC and Parsec benchmark suites, 2DCC results in a 2.12× compression factor (geomean) compared to 1.44–1.49× for best prior techniques on an iso-silicon basis. For the cache-sensitive subset of these benchmarks run in isolation, 2DCC also achieves a 11.7% speedup (geomean).
机译:高速缓存对于性能的重要性及其较高的硅面积成本,推动了硬件解决方案的发展,这些解决方案可以透明地压缩高速缓存的数据,以在不牺牲硅面积的情况下增加有效容量。为此,现有工作采用了以下两种方法之一:(a)在整个缓存中对相同的缓存块进行重复数据删除以利用块间冗余;或(b)压缩每个缓存块内的通用模式以利用块内(p)(/ p)在本文中,我们证明了仅利用其中一种冗余类型会导致几种应用程序的压缩机会大量损失:某些工作负载呈现块间或块内冗余,而其他工作负载则呈现块间或块内冗余同时展示。我们提出2DCC(二维高速缓存压缩),这是一种利用两种冗余类型的简单技术。在SPEC和Parsec基准套件中,2DCC的压缩系数(几何数)为2.12倍,而基于等硅技术的最佳现有技术则为1.44-1.49倍。由于这些基准测试中对缓存敏感的子集是独立运行的,因此2DCC还可实现11.7%的加速(geomean)。

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