research-article

PG2S+: Stack Distance Construction Using Popularity, Gap and Machine Learning

Authors:

Jiangwei Zhang,

Y.C. TayAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 973 - 983

https://doi.org/10.1145/3366423.3380176

Published: 20 April 2020 Publication History

Abstract

Stack distance characterizes temporal locality of workloads and plays a vital role in cache analysis since the 1970s. However, exact stack distance calculation is too costly, and impractical for online use. Hence, much work was done to optimize the exact computation, or approximate it through sampling or modeling.

This paper introduces a new approximation technique PG2S that is based on reference popularity and gap distance. This approximation is exact under the Independent Reference Model (IRM). The technique is further extended, using machine learning, to PG2S+ for non-IRM reference patterns. Extensive experiments show that PG2S+ is much more accurate and robust than other state-of-the-art algorithms for determining stack distance.

PG2S+ is the first technique to exploit the strong correlation among reference popularity, gap distance and stack distance.

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Cited By

Sultan SShakiba KLee AChen PStumm M(2024)TTLs Matter: Efficient Cache Sizing with TTL-Aware Miss Ratio Curves and Working Set SizesProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650066(387-404)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1145/3627703.3650066
Bender MDeLayo DKuszmaul BKuszmaul WWest EAgrawal KShun J(2023)Increment - and - Freeze: Every Cache, Everywhere, All of the TimeProceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3558481.3591085(129-139)Online publication date: 17-Jun-2023
https://dl.acm.org/doi/10.1145/3558481.3591085
Lim SPark D(2022)Efficient Stack Distance Approximation Based on Workload CharacteristicsIEEE Access10.1109/ACCESS.2022.318032710(59792-59805)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3180327

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PG2S+: Stack Distance Construction Using Popularity, Gap and Machine Learning

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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Published: 20 April 2020

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Cited By

Sultan SShakiba KLee AChen PStumm M(2024)TTLs Matter: Efficient Cache Sizing with TTL-Aware Miss Ratio Curves and Working Set SizesProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650066(387-404)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1145/3627703.3650066
Bender MDeLayo DKuszmaul BKuszmaul WWest EAgrawal KShun J(2023)Increment - and - Freeze: Every Cache, Everywhere, All of the TimeProceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3558481.3591085(129-139)Online publication date: 17-Jun-2023
https://dl.acm.org/doi/10.1145/3558481.3591085
Lim SPark D(2022)Efficient Stack Distance Approximation Based on Workload CharacteristicsIEEE Access10.1109/ACCESS.2022.318032710(59792-59805)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3180327

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