Name
Abstract | ||
|---|---|---|
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 SPEC~CPU~2017 suite and is significantly higher than prior proposals scaled to an iso-silicon budget.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3373376.3378518 | ASPLOS '20: Architectural Support for Programming Languages and Operating Systems
Lausanne
Switzerland
March, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7102-5 | 1 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amin Ghasemazar | 1 | 3 | 1.04 |
| Prashant J. Nair | 2 | 346 | 15.74 |
| Mieszko Lis | 3 | 3 | 2.41 |