现代存储层次结构的实用数据压缩
Practical Data Compression for Modern Memory Hierarchies
摘要：
在这篇论文中，我们描述了一种新的，实用的方法来集成基于硬件的数据压缩的存储层次结构，包括片上缓存，主存储器，片上和片外互连。这种新方法快速，简单，有效地节省了存储空间。我们的方法中的一个关键见解是，访问时间（包括解压缩延迟）是现代存储层次结构的关键。通过将廉价的硬件支持与适度的操作系统支持相结合，我们的整体压缩方法在整个存储层次结构中实现了性能和能源效率的大幅提高。利用这种新的方法，我们在这篇论文中做出了几个主要贡献。首先，我们提出了一种新的压缩算法，Base-Delta-Immediate Compression（BDI），它可以以很低的压缩/解压缩延迟实现高压缩比。BDI利用许多缓存行中存在的值的现有低动态范围，使用Base+Delta编码将它们压缩到更小的大小。其次，我们观察到缓存块的压缩大小可以指示其重用。我们使用这个观察开发一个新的缓存插入策略的压缩缓存，基于大小的插入策略（SIP），它使用的压缩块的大小作为度量之一，以预测其潜在的未来重用。第三，我们提出了一个新的主存压缩框架，线性压缩页面（LCP），显着降低了支持主存压缩的复杂性和功耗成本。我们证明，任何压缩算法都可以适应LCP的要求，LCP可以有效地与现有的高速缓存压缩设计，避免额外的压缩/解压缩。
英文摘要：
In this thesis, we describe a new, practical approach to integrating hardware-based data compression within the memory hierarchy, including on-chip caches, main memory, and both on-chip and off-chip interconnects. This new approach is fast, simple, and effective in saving storage space. A key insight in our approach is that access time (including decompression latency) is critical in modern memory hierarchies. By combining inexpensive hardware support with modest OS support, our holistic approach to compression achieves substantial improvements in performance and energy efficiency across the memory hierarchy. Using this new approach, we make several major contributions in this thesis. First, we propose a new compression algorithm, Base-Delta-Immediate Compression (BDI), that achieves high compression ratio with very low compression/decompression latency. BDI exploits the existing low dynamic range of values present in many cache lines to compress them to smaller sizes using Base+Delta encoding. Second, we observe that the compressed size of a cache block can be indicative of its reuse. We use this observation to develop a new cache insertion policy for compressed caches, the Size-based Insertion Policy (SIP), which uses the size of a compressed block as one of the metrics to predict its potential future reuse. Third, we propose a new main memory compression framework, Linearly Compressed Pages (LCP), that significantly reduces the complexity and power cost of supporting main memory compression. We demonstrate that any compression algorithm can be adapted to fit the requirements of LCP, and that LCP can be efficiently integrated with the existing cache compression designs, avoiding extra compression/decompression.
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