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Thesaurus: Efficient Cache Compression via Dynamic Clustering -- ASPLOS 2020

Home Page:https://prashantnair.bitbucket.io/assets/pdf/ghasemazar2020thesaurus.pdf

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Thesaurus

In this paper, we identify a previously untapped source of compressibility in cache working sets: clusters of cachelines that are similar, but not identical, to one another. To compress the cache, we can then store the "clusteroid" of each cluster together with the (much smaller) "diffs" needed to reconstruct the rest of the cluster. To exploit this opportunity, we propose a hardware-level on-line cacheline clustering mechanism based on locality-sensitive hashing. Our method dynamically forms clusters as they appear in the data access stream and retires them as they disappear from the cache. Our evaluations show that we achieve 2.25× compression on average (and up to 9.9×) on SPECCPU 2017 suite and is significantly higher than prior proposals scaled to an iso-silicon budget.

Main classes: please refer to approximatededupbdi_cache.h and approximatededupbdi_cache.cpp

Cache Compression zsim

This is a variation of the original zsim simulator that supports compressed caches. For more details please see here.

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Thesaurus: Efficient Cache Compression via Dynamic Clustering -- ASPLOS 2020

https://prashantnair.bitbucket.io/assets/pdf/ghasemazar2020thesaurus.pdf

License:GNU General Public License v2.0


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