research-article

Analytical cache models with applications to cache partitioning

Authors:

Srinivas Devadas,

Larry RudolphAuthors Info & Claims

ACM International Conference on Supercomputing 25th Anniversary Volume

Pages 323 - 334

https://doi.org/10.1145/2591635.2667181

Published: 17 June 2001 Publication History

Abstract

An accurate, tractable, analytic cache model for time-shared systems is presented, which estimates the overall cache miss-rate of a multiprocessing system with any cache size and time quanta. The input to the model consists of the isolated miss-rate curves for each process, the time quanta for each of the executing processes, and the total cache size. The output is the overall miss-rate. Trace-driven simulations demonstrate that the estimated miss-rate is very accurate. Since the model provides a fast and accurate way to estimate the effect of context switching, it is useful for both understanding the effect of context switching on caches and optimizing cache performance for time-shared systems. A cache partitioning mechanism is also presented and is shown to improve the cache miss-rate up to 25% over the normal LRU replacement policy.

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Cited By

Zhang JLiu WZhang LTian J(2024)Enhanced In-Network Caching for Deep Learning in Edge NetworksElectronics10.3390/electronics1323463213:23(4632)Online publication date: 24-Nov-2024
https://doi.org/10.3390/electronics13234632
Xiao JXiang YWang XLuo YPimentel AWang ZGallivan KNikolopoulos DBeivide RGallopoulos E(2023)FLORIA: A Fast and Featherlight Approach for Predicting Cache PerformanceProceedings of the 37th International Conference on Supercomputing10.1145/3577193.3593740(25-36)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1145/3577193.3593740
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Analytical cache models with applications to cache partitioning

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Published In

cover image ACM Conferences

ACM International Conference on Supercomputing 25th Anniversary Volume

June 2014

94 pages

ISBN:9781450328401

DOI:10.1145/2591635

Editor:
Utpal Banerjee

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 17 June 2001

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Cited By

Zhang JLiu WZhang LTian J(2024)Enhanced In-Network Caching for Deep Learning in Edge NetworksElectronics10.3390/electronics1323463213:23(4632)Online publication date: 24-Nov-2024
https://doi.org/10.3390/electronics13234632
Xiao JXiang YWang XLuo YPimentel AWang ZGallivan KNikolopoulos DBeivide RGallopoulos E(2023)FLORIA: A Fast and Featherlight Approach for Predicting Cache PerformanceProceedings of the 37th International Conference on Supercomputing10.1145/3577193.3593740(25-36)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1145/3577193.3593740
Xiang YWang XHuang ZWang ZLuo YWang ZOliveira RFelber PHu Y(2018)DCAPSProceedings of the Thirteenth EuroSys Conference10.1145/3190508.3190511(1-15)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3190508.3190511
Hu XWang XZhou LLuo YWang ZDing CYe C(2018)Fast Miss Ratio Curve Modeling for Storage CacheACM Transactions on Storage10.1145/318575114:2(1-34)Online publication date: 12-Apr-2018
https://dl.acm.org/doi/10.1145/3185751

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