research-article

Transferring Performance Prediction Models Across Different Hardware Platforms

Authors:

Jean-Christophe Petkovich,

Sebastian Fischmeister,

Krzysztof CzarneckiAuthors Info & Claims

ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering

Pages 39 - 50

https://doi.org/10.1145/3030207.3030216

Published: 17 April 2017 Publication History

Abstract

Many software systems provide configuration options relevant to users, which are often called features. Features influence functional properties of software systems as well as non-functional ones, such as performance and memory consumption. Researchers have successfully demonstrated the correlation between feature selection and performance. However, the generality of these performance models across different hardware platforms has not yet been evaluated.

We propose a technique for enhancing generality of performance models across different hardware environments using linear transformation. Empirical studies on three real-world software systems show that our approach is computationally efficient and can achieve high accuracy (less than 10% mean relative error) when predicting system performance across 23 different hardware platforms. Moreover, we investigate why the approach works by comparing performance distributions of systems and structure of performance models across different platforms.

References

[1]

NIST/SEMATECH e-Handbook of Statistical Methods. http://www.itl.nist.gov/div898/handbook/.

[2]

J. Antony. Design of Experiments for Engineers and Scientists. Butterworth-Heinemann, 2003.

[3]

S. Balsamo and M. Marzolla. Performance Evaluation of UML Software Architectures with Multiclass Queueing Network Models. In Proceedings of the 5th International Workshop on Software and Performance. ACM, 2005.

Digital Library

[4]

V. Cortellessa and R. Mirandola. PRIMA-UML: a performance validation incremental methodology on early UML diagrams. Science of Computer Programming, July 2002.

Digital Library

[5]

J. Guo, K. Czarnecki, S. Apel, N. Siegmund, and A. Wasowski. Variability-aware performance prediction: A statistical learning approach. In Proc. ASE. IEEE, 2013.

Digital Library

[6]

K. Hoste, A. Phansalkar, L. Eeckhout, A. Georges, L. K. John, and K. De Bosschere. Performance prediction based on inherent program similarity. In Proceedings of the 15th International Conference on Parallel Architectures and Compilation Techniques, PACT '06, pages 114--122, New York, NY, USA, 2006. ACM.

Digital Library

[7]

F. Hutter, L. Xu, H. H. Hoos, and K. Leyton-Brown. Algorithm runtime prediction: Methods & evaluation. Artificial Intelligence, 206(0):79--111, Jan. 2014.

Digital Library

[8]

F. Hutter, L. Xu, H. H. Hoos, and K. Leyton-Brown. Algorithm runtime prediction: Methods & evaluation (extended abstract). In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), July 2015.

[9]

M. Kowal, M. Tschaikowski, M. Tribastone, and I. Schaefer. Scaling Size and Parameter Spaces in Variability-Aware Software Performance Models (T). In 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2015.

Digital Library

[10]

D. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, 2008.

Digital Library

[11]

J. C. Petkovich, A. Oliveira, Y. Zhang, T. Reidemeister, and S. Fischmeister. DataMill: A Distributed Heterogeneous Infrastructure For Robust Experimentation. Software: Practice and Experience, pages n/a--n/a, 2015.

[12]

W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C (2nd Ed.): The Art of Scientific Computing. Cambridge Univ. Press, 1992.

Digital Library

[13]

B. D. Ripley. Stochastic simulation, volume 316. John Wiley & Sons, 2009.

[14]

I. Sobol and Y. Levitan. A pseudo-random number generator for personal computers. Computers and Mathematics with Applications, 37(4--5):33--40, 1999.

[15]

SQLite. SQLite. https://www.sqlite.org/. Accessed April. 15th, 2016.

[16]

The Tukaani Project. XZ Utils. http://tukaani.org/xz/. Accessed April. 17th, 2016.

[17]

E. Thereska, B. Doebel, A. X. Zheng, and P. Nobel. Practical performance models for complex, popular applications. SIGMETRICS Perform. Eval. Rev., 38(1):1--12, June 2010.

Digital Library

[18]

P. Valov, J. Guo, and K. Czarnecki. Empirical comparison of regression methods for variability-aware performance prediction. In Proceedings of the 19th International Conference on Software Product Line, SPLC '15, pages 186--190, New York, NY, USA, 2015. ACM.

Digital Library

[19]

VideoLAN Organization. x264, the best H.264/AVC encoder. http://www.videolan.org/developers/x264.html. Accessed April. 15th, 2016.

[20]

D. Westermann, J. Happe, R. Krebs, and R. Farahbod. Automated inference of goal-oriented performance prediction functions. In Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, ASE 2012, pages 190--199, New York, NY, USA, 2012. ACM.

Digital Library

[21]

Y. Zhang, J. Guo, E. Blais, and K. Czarnecki. Performance prediction of configurable software systems by fourier learning. In Proceedings of the International Conference on Automated Software Engineering (ASE), 2015.

Digital Library

Cited By

Mai GHe ZYu GChen ZChen P(2024)CTuner: Automatic NoSQL Database Tuning with Causal Reinforcement LearningProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3674809(269-278)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3674809
Chen TLi M(2024)Adapting Multi-objectivized Software Configuration TuningProceedings of the ACM on Software Engineering10.1145/36437511:FSE(539-561)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643751
Gong JChen T(2024)Predicting Configuration Performance in Multiple Environments with Sequential Meta-LearningProceedings of the ACM on Software Engineering10.1145/36437431:FSE(359-382)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643743
Show More Cited By

Index Terms

Transferring Performance Prediction Models Across Different Hardware Platforms
1. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance

Recommendations

Transferring Pareto Frontiers across Heterogeneous Hardware Environments
ICPE '20: Proceedings of the ACM/SPEC International Conference on Performance Engineering

Software systems provide user-relevant configuration options called features. Features affect functional and non-functional system properties, whereas selections of features represent system configurations. A subset of configuration space forms a Pareto ...
Analytical Performance Estimation for Large-Scale Reconfigurable Dataflow Platforms
Next-generation high-performance computing platforms will handle extreme data- and compute-intensive problems that are intractable with today’s technology. A promising path in achieving the next leap in high-performance computing is to embrace ...
Temporal Performance Modelling of Serverless Computing Platforms
WoSC '20: Proceedings of the 2020 Sixth International Workshop on Serverless Computing

Analytical performance models have been shown very efficient in analyzing, predicting, and improving the performance of distributed computing systems. However, there is a lack of rigorous analytical models for analyzing the transient behaviour of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering

April 2017

450 pages

ISBN:9781450344043

DOI:10.1145/3030207

General Chairs:
Walter Binder
Università della Svizzera Italiana, Switzerland
,
Vittorio Cortellessa
Università dell'Aquila, Italy
,
Program Chairs:
Anne Koziolek
Karlsruhe Institute of Technology, Germany
,
Evgenia Smirni
College of William & Mary, USA
,
Meikel Poess
Oracle Corporation, USA

Copyright © 2017 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

SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGSOFT: ACM Special Interest Group on Software Engineering
SPEC: SPEC Research Group

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Natural Sciences and Engineering Research Council of Canada
Shanghai Municipal Natural Science Foundation
Pratt & Whitney Canada

Conference

ICPE '17

Sponsor:

ICPE '17: ACM/SPEC International Conference on Performance Engineering

April 22 - 26, 2017

L'Aquila, Italy

Acceptance Rates

ICPE '17 Paper Acceptance Rate 27 of 83 submissions, 33%;

Overall Acceptance Rate 252 of 851 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

47
Total Citations
View Citations
311
Total Downloads

Downloads (Last 12 months)40
Downloads (Last 6 weeks)5

Reflects downloads up to 07 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mai GHe ZYu GChen ZChen P(2024)CTuner: Automatic NoSQL Database Tuning with Causal Reinforcement LearningProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3674809(269-278)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3674809
Chen TLi M(2024)Adapting Multi-objectivized Software Configuration TuningProceedings of the ACM on Software Engineering10.1145/36437511:FSE(539-561)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643751
Gong JChen T(2024)Predicting Configuration Performance in Multiple Environments with Sequential Meta-LearningProceedings of the ACM on Software Engineering10.1145/36437431:FSE(359-382)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643743
Acher MCombemale BRandrianaina GJezequel J(2024)Embracing Deep Variability For Reproducibility and ReplicabilityProceedings of the 2nd ACM Conference on Reproducibility and Replicability10.1145/3641525.3663621(30-35)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3641525.3663621
Chen PChen TLi M(2024)MMO: Meta Multi-Objectivization for Software Configuration TuningIEEE Transactions on Software Engineering10.1109/TSE.2024.338891050:6(1478-1504)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3388910
Nakamori TMatsuura SMiyazaki TNakazono SSato THoshino TKawashima H(2024)ChimeraTL: Transfer Learning in DBMS with Fewer Samples2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW61823.2024.00046(310-316)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDEW61823.2024.00046
Dey ADhakal AIslam TYeom JPatki TNichols DMovsesyan ABhatele A(2024)Relative Performance Prediction Using Few-Shot Learning2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00278(1764-1769)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00278
Lesoil LSpieker HGotlieb AAcher MTemple PBlouin AJézéquel J(2024)Learning input-aware performance models of configurable systemsJournal of Systems and Software10.1016/j.jss.2023.111883208:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.jss.2023.111883
Acher MMartinez J(2023)Generative AI for Reengineering Variants into Software Product LinesProceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B10.1145/3579028.3609016(57-66)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1145/3579028.3609016
Acher MDuarte JJézéquel J(2023)On Programming Variability with Large Language Model-based AssistantProceedings of the 27th ACM International Systems and Software Product Line Conference - Volume A10.1145/3579027.3608972(8-14)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1145/3579027.3608972
Show More Cited By

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.

Media

Figures

Other

Tables

View Table of Contents