二维缓存压缩

Amin Ghasemazar, M. Ewais, Prashant J. Nair, Mieszko Lis
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引用次数: 3

摘要

缓存对性能的重要性以及它们的高硅面积成本促使硬件解决方案透明地压缩缓存数据,以在不牺牲硅面积的情况下增加有效容量。为此,之前的工作采取了两种方法中的一种:(a)在缓存中重复删除相同的缓存块以利用块间冗余或(b)压缩每个缓存块内的公共模式以利用块内冗余。(p)(/p)在本文中,我们证明仅利用这些冗余类型中的一种会导致几个应用程序压缩机会的重大损失:一些工作负载表现出块间冗余或块内冗余,而其他工作负载则两者都表现出来。我们提出2DCC(二维缓存压缩),这是一种利用两种冗余的简单技术。在SPEC和Parsec基准测试套件中,2DCC的压缩系数(几何系数)为2.12倍,而在等硅基础上,最佳的先前技术的压缩系数为1.44 - 1.49倍。对于隔离运行的这些基准测试的缓存敏感子集,2DCC还实现了11.7%的加速(几何)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
2DCC: Cache Compression in Two Dimensions
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).
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