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.

References

[1]

George Almási, Cǎlin Caşcaval, and David A. Padua. 2002. Calculating Stack Distances Efficiently. In MSP. 37–43.

[2]

B. T. Bennett and V. J. Kruskal. 1975. LRU Stack Processing. IBM Journal of Research and Development 19, 4 (1975), 353–357.

Digital Library

[3]

E. Berg and E. Hagersten. 2004. StatCache: a probabilistic approach to efficient and accurate data locality analysis. In IEEE ISPASS, 2004. 20–27.

[4]

Daniel Byrne. 2018. A Survey of Miss-Ratio Curve Construction Techniques. CoRR abs/1804.01972(2018).

[5]

Asaf Cidon, Assaf Eisenman, Mohammad Alizadeh, and Sachin Katti. 2016. Cliffhanger: Scaling Performance Cliffs in Web Memory Caches. In NSDI. 379–392.

[6]

Thomas M. Conte, Mary Ann Hirsch, and W-MW Hwu. 1998. Combining trace sampling with single pass methods for efficient cache simulation. IEEE Trans. Comput. 47, 6 (1998), 714–720.

Digital Library

[7]

Chen Ding and Yutao Zhong. 2003. Predicting Whole-program Locality Through Reuse Distance Analysis. SIGPLAN Not. 38, 5 (May 2003), 245–257.

Digital Library

[8]

D. Eklov and E. Hagersten. 2010. StatStack: Efficient modeling of LRU caches. In IEEE ISPASS. 55–65.

[9]

L. He, Z. Yu, and H. Jin. 2012. FractalMRC: Online Cache Miss Rate Curve Prediction on Commodity Systems. In IEEE IPDPS. 1341–1351.

[10]

Xiameng Hu, Xiaolin Wang, Yechen Li, Lan Zhou, Yingwei Luo, Chen Ding, Song Jiang, and Zhenlin Wang. 2015. LAMA: Optimized Locality-aware Memory Allocation for Key-value Cache. In 2015 USENIX Annual Technical Conference (USENIX ATC 15). 57–69.

[11]

Xiameng Hu, Xiaolin Wang, Lan Zhou, Yingwei Luo, Chen Ding, and Zhenlin Wang. 2016. Kinetic Modeling of Data Eviction in Cache. In Proceedings of the 2016 USENIX Conference on Usenix Annual Technical Conference. 351–364.

Digital Library

[12]

Cliff Young Kaz Sato and David Patterson. 2017. An in-depth look at Google’s first Tensor Processing Unit (TPU). https://cloud.google.com/blog/products/gcp/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu

[13]

Yul H. Kim, Mark D. Hill, and David A. Wood. 1991. Implementing Stack Simulation for Highly-associative Memories. SIGMETRICS Perform. Eval. Rev. 19, 1 (April 1991), 212–213.

Digital Library

[14]

Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. 2018. The Case for Learned Index Structures. In SIGMOD(SIGMOD ’18). ACM, New York, NY, USA, 489–504.

[15]

R. L. Mattson, J. Gecsei, D. R. Slutz, and I. L. Traiger. 1970. Evaluation techniques for storage hierarchies. IBM Systems Journal 9, 2 (1970), 78–117.

Digital Library

[16]

Dushyanth Narayanan, Austin Donnelly, and Antony Rowstron. 2008. Write off-loading: Practical power management for enterprise storage. ACM TOS 4, 3 (2008), 10.

[17]

Qingpeng Niu, James Dinan, Qingda Lu, and Ponnuswamy Sadayappan. 2012. PARDA: A fast parallel reuse distance analysis algorithm. In IEEE IPDPS. 1284–1294.

[18]

Frank Olken. 1983. Efficient methods for calculating the success function of fixed space replacement polities. Performance Evaluation 3 (05 1983).

[19]

G. S. Paschos, G. Iosifidis, M. Tao, D. Towsley, and G. Caire. 2018. The Role of Caching in Future Communication Systems and Networks. IEEE Journal on Selected Areas in Communications 36, 6 (June 2018), 1111–1125.

Digital Library

[20]

Pavlos Petoumenos, Georgios Keramidas, Håkan Zeffer, Stefanos Kaxiras, and Erik Hagersten. 2006. Modeling Cache Sharing on Chip Multiprocessor Architectures. In IEEE IISWC. 160–171.

[21]

Moinuddin K. Qureshi and Yale N. Patt. 2006. Utility-Based Cache Partitioning: A Low-Overhead, High-Performance, Runtime Mechanism to Partition Shared Caches. In International Symposium on Microarchitecture. 423–432.

[22]

Trausti Saemundsson, Hjortur Bjornsson, Gregory Chockler, and Ymir Vigfusson. 2014. Dynamic Performance Profiling of Cloud Caches. In SOCC. 28:1–28:14.

[23]

D. L. Schuff, M. Kulkarni, and V. S. Pai. 2010. Accelerating multicore reuse distance analysis with sampling and parallelization. In PACT. 53–63.

[24]

Xipeng Shen, Jonathan Shaw, Brian Meeker, and Chen Ding. 2007. Locality Approximation Using Time. SIGPLAN Not. 42, 1 (Jan. 2007), 55–61.

Digital Library

[25]

Rabin A. Sugumar and Santosh G. Abraham. 1991. Efficient Simulation of Multiple Cache Configurations using Binomial Trees. Technical Report.

[26]

David K. Tam, Reza Azimi, Livio B. Soares, and Michael Stumm. 2009. RapidMRC: Approximating L2 Miss Rate Curves on Commodity Systems for Online Optimizations. SIGPLAN Not. 44, 3, 121–132.

Digital Library

[27]

Y. C. Tay and Min Zou. 2006. A Page Fault Equation for Modeling the Effect of Memory Size. Perform. Eval. 63, 2 (Feb. 2006), 99–130.

Digital Library

[28]

Dominique Thiébaut. 1989. On the Fractal Dimension of Computer Programs and Its Application to the Prediction of the Cache Miss Ratio. IEEE Trans. Comput. 38, 7 (July 1989), 1012–1026.

Digital Library

[29]

Carl A. Waldspurger, Nohhyun Park, Alexander Garthwaite, and Irfan Ahmad. 2015. Efficient MRC Construction with SHARDS. In FAST. 95–110.

[30]

Jake Wires, Stephen Ingram, Zachary Drudi, Nicholas J. A. Harvey, and Andrew Warfield. 2014. Characterizing Storage Workloads with Counter Stacks. In OSDI. 335–349.

[31]

Liang Yuan, Chen Ding, Wesley Smith, Peter Denning, and Yunquan Zhang. 2019. A Relational Theory of Locality. ACM Trans. Archit. Code Optim.(2019).

[32]

Yutao Zhong and Wentao Chang. 2008. Sampling-based Program Locality Approximation. In ISMM. 91–100.

[33]

Yutao Zhong, Xipeng Shen, and Chen Ding. 2009. Program Locality Analysis Using Reuse Distance. ACM Trans. Program. Lang. Syst. 31, 6, Article 20 (Aug. 2009), 39 pages.

Digital Library

[34]

Pin Zhou, Vivek Pandey, Jagadeesan Sundaresan, Anand Raghuraman, Yuanyuan Zhou, and Sanjeev Kumar. 2004. Dynamic Tracking of Page Miss Ratio Curve for Memory Management. SIGOPS Oper. Syst. Rev. 38, 5 (Oct. 2004), 177–188.

Digital Library

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

Index Terms

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

Index terms have been assigned to the content through auto-classification.

Recommendations

Estimating cache misses and locality using stack distances
ICS '03: Proceedings of the 17th annual international conference on Supercomputing

Cache behavior modeling is an important part of modern optimizing compilers. In this paper we present a method to estimate the number of cache misses, at compile time, using a machine independent model based on stack algorithms. Our algorithm computes ...
Using the first-level cache stack distance histograms to predict multi-level LRU cache misses

For cache analytical modeling, the stack distance theory is widely utilized to predict LRU-cache behaviors. Typically, the stack distance histogram collecting is implemented by profiling memory references. However, the profiled memory references merely ...
Computing the Fréchet Gap Distance
Abstract
Measuring the similarity of two polygonal curves is a fundamental computational task. Among alternatives, the Fréchet distance is one of the most well-studied similarity measures. Informally, the Fréchet distance is described as the minimum leash ... $^{}$ $^{}^{}$

Comments

Information & Contributors

Information

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

Copyright © 2020 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]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Research
Refereed limited

Conference

WWW '20

Sponsor:

SIGWEB

WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
234
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)2

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

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

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents