More Web Proxy on the site http://driver.im/

Article

Accurate software performance estimation using domain classification and neural networks

Authors:

Márcio Seiji Oyamada,

Felipe Zschornack,

Flávio Rech WagnerAuthors Info & Claims

SBCCI '04: Proceedings of the 17th symposium on Integrated circuits and system design

Pages 175 - 180

https://doi.org/10.1145/1016568.1016617

Published: 04 September 2004 Publication History

Abstract

For the design of an embedded system, there is a variety of available processors, each one offering a different trade-off concerning factors such as performance and power consumption. High-level performance estimation of the embedded software implemented in a particular architecture is essential for a fast design space exploration, including the choice of the most appropriate processor. However, advanced architectures present many features, such as deep pipelines, branch prediction mechanisms and cache sizes, that have a non-linear impact on the execution time, which becomes hard to evaluate. In order to cope with this problem, this paper presents a neural network based approach for high-level performance estimation, which easily adapts to the non-linear behavior of the execution time in such advanced architectures. A method for automatic classification of applications is proposed, based on topological information extracted from the control flow graph of the application, enabling the utilization of domain-specific estimators and thus resulting in more accurate estimates. Practical experiments on a variety of benchmarks show estimation results with a mean error of 6.41% and a maximum error of 32%, which is more precise than previous work based on linear and non-linear approaches.

References

[1]

G. Bontempi and W. Kruijtzer. A Data Analysis Method for Software Performance Prediction. In Design, Automation and Test in Europe, pages 971--976. IEEE Computer Society Press, 2002.

Digital Library

[2]

A. Colin and I. Puaut. Worst Case Execution Time Analysis for a Processor with Branch Prediction. Journal of Real-Time Systems, 18(2-3):249--274, 2000.

Digital Library

[3]

J. Engblom, A. Ermedahl, and F. Stappert. A Worst-Case Execution-Time Analysis Tool Prototype for Embedded Real-Time Systems. In RTTOOLS'2001 - Workshop on Real-Time Tools, Aalborg, Denmark, 2001.

[4]

J. A. Freeman and D. M. Skapura. Neural Networks: Algorithms, Applications, and Programming Techniques. Addison-Wesley Publisher, 1992.

Digital Library

[5]

P. Giusto, G. Martin, and E. Harcourt. Reliable Estimation of Execution Time of Embedded Software. In Design, Automation and Test in Europe, pages 580--589. IEEE Computer Society Press, 2001.

Digital Library

[6]

GNU. GCC - GNU Compiler Collection. http://www.gnu.org

[7]

A. Hergenhan and W. Rosenstiel. Static Timing Analysis of Embedded Software on Advanced Processor Architectures. In Design, Automation and Test in Europe, pages 552--559. IEEE Computer Society Press, 2000.

Digital Library

[8]

M. Lajolo, M. Lazarescu, and A. Sangiovanni-Vincentelli. A Compilation-based Software Estimation Scheme for Hardware/Software Co-simulation. In Proceedings of the 7th International Workshop on Hardware/Software Codesign, pages 85--89. ACM Press, 1999.

Digital Library

[9]

X. Li, T. Mitra, and A. Roychoudhury. Accurate Timing Analysis by Modeling Caches, Speculation and their Interaction. In Design Automation Conference, pages 466--471. ACM Press, 2003.

Digital Library

[10]

Y.-T. S. Li and S. Malik. Performance Analysis of Embedded Software Using Implicit Path Enumeration. In Design Automation Conference, pages 456--461. ACM Press, 1995.

Digital Library

[11]

Y.-T. S. Li, S. Malik, and A. Wolfe. Performance Estimation of Embedded Software with Instruction Cache Modeling. In IEEE/ACM International Conference on Computer-Aided Design, pages 380--387. IEEE Computer Society Press, 1995.

Digital Library

[12]

S.-S. Lim, J. H. Han, J. Kim, and S. L. Min. A Worst Case Timing Analysis Technique for Multiple-Issue Machines. In IEEE Real-Time Systems Symposium, page 334. IEEE Computer Society Press, 1998.

Digital Library

[13]

Microlib. PowerPC 750 Simulator. http://www.microlib.org/G3/PowerPC750.php

[14]

E. Moser and W. Nebel. Case Study: System Model of Crane and Embedded Control. In Design, Automation and Test in Europe, page 721. IEEE Computer Society Press, 1999.

Digital Library

[15]

F. Stappert. WCET - Benchmarks. http://c-lab.de/home/en/download.html#wcet.

[16]

S. Thesing, J. Souyris, R. Heckmann, F. Randimbivololona, M. Langenbach, R. Wilhelm, and C. Ferdinand. An Abstract Interpretation-Based Timing Validation of Hard Real-Time Avionics. In: International Performance and Dependability Symposium (IPDS). IEEE Computer Society Press, June 2003.

[17]

F. Wolf and R. Ernst. Intervals in Software Execution Cost Analysis. In: International Symposium on System Synthesis, pages 130--135. ACM Press, 2000.

Digital Library

Cited By

Kumar V(2022)Estimation of an Early WCET Using Different Machine Learning ApproachesAdvances on P2P, Parallel, Grid, Cloud and Internet Computing10.1007/978-3-031-19945-5_30(297-307)Online publication date: 18-Oct-2022
https://doi.org/10.1007/978-3-031-19945-5_30
Kumar V(2021)Deep Neural Network Approach to Estimate Early Worst-Case Execution Time2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)10.1109/DASC52595.2021.9594326(1-8)Online publication date: 3-Oct-2021
https://doi.org/10.1109/DASC52595.2021.9594326
Kim JHa S(2020)Fast Performance Estimation and Design Space Exploration of SSD Using AI TechniquesEmbedded Computer Systems: Architectures, Modeling, and Simulation10.1007/978-3-030-60939-9_1(1-17)Online publication date: 7-Oct-2020
https://doi.org/10.1007/978-3-030-60939-9_1
Show More Cited By

Index Terms

Accurate software performance estimation using domain classification and neural networks
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

Recommendations

Applying neural networks to performance estimation of embedded software

High-level performance estimation of embedded software implemented in a particular processor is essential for a fast design space exploration, when the designer needs to evaluate different processor architectures (and their different versions) and also ...
Performance estimation in a simultaneous multithreading processor
MASCOTS '96: Proceedings of the 4th International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems

We present a model to estimate the performance of dynamically interleaving instruction streams in super-scalar architectures. Instructions executed per cycle (IPC) are calculated from simple descriptions of the workload and hardware. We compare ...
Machine Learning Classification to Effort Estimation for Embedded Software Development Projects

This paper discusses the effect of classification in estimating the amount of effort in man-days associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SBCCI '04: Proceedings of the 17th symposium on Integrated circuits and system design

September 2004

296 pages

ISBN:1581139470

DOI:10.1145/1016568

General Chair:
Edna Natividade da Silva Barros
Federal University of Pernambuco, Brazil
,
Program Chairs:
Flàvio Rech Wagner
Federal University of Rio Grande do Sul, Brazil
,
Luigi Carro
Federal University of Rio Grande do Sul, Brazil
,
Franz Josef Rammig
University of Paderborn, Germany

Copyright © 2004 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 September 2004

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

SBCCI04

Sponsor:

SBCCI04: 17th Symposium on Integrated Circuits and System Design

September 7 - 11, 2004

Pernambuco, Brazil

Acceptance Rates

Overall Acceptance Rate 133 of 347 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
494
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kumar V(2022)Estimation of an Early WCET Using Different Machine Learning ApproachesAdvances on P2P, Parallel, Grid, Cloud and Internet Computing10.1007/978-3-031-19945-5_30(297-307)Online publication date: 18-Oct-2022
https://doi.org/10.1007/978-3-031-19945-5_30
Kumar V(2021)Deep Neural Network Approach to Estimate Early Worst-Case Execution Time2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)10.1109/DASC52595.2021.9594326(1-8)Online publication date: 3-Oct-2021
https://doi.org/10.1109/DASC52595.2021.9594326
Kim JHa S(2020)Fast Performance Estimation and Design Space Exploration of SSD Using AI TechniquesEmbedded Computer Systems: Architectures, Modeling, and Simulation10.1007/978-3-030-60939-9_1(1-17)Online publication date: 7-Oct-2020
https://doi.org/10.1007/978-3-030-60939-9_1
Huybrechts TCassimon AMercelis SHellinckx P(2018)Introduction of Deep Neural Network in Hybrid WCET AnalysisAdvances on P2P, Parallel, Grid, Cloud and Internet Computing10.1007/978-3-030-02607-3_38(415-425)Online publication date: 17-Oct-2018
https://doi.org/10.1007/978-3-030-02607-3_38
Tetzlaff DGlesner S(2013)Intelligent prediction of execution times2013 Second International Conference on Informatics & Applications (ICIA)10.1109/ICoIA.2013.6650262(234-239)Online publication date: Sep-2013
https://doi.org/10.1109/ICoIA.2013.6650262
Franke B(2012)Statistical Performance Modeling in Functional Instruction Set SimulatorsACM Transactions on Embedded Computing Systems10.1145/2180887.218089911S:1(1-22)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1145/2180887.2180899
Bohm IFranke BTopham N(2010)Cycle-accurate performance modelling in an ultra-fast just-in-time dynamic binary translation instruction set simulator2010 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation10.1109/ICSAMOS.2010.5642102(1-10)Online publication date: Jul-2010
https://doi.org/10.1109/ICSAMOS.2010.5642102
Powell DFranke BRosenstiel WWakabayashi K(2009)Using continuous statistical machine learning to enable high-speed performance prediction in hybrid instruction-/cycle-accurate instruction set simulatorsProceedings of the 7th IEEE/ACM international conference on Hardware/software codesign and system synthesis10.1145/1629435.1629478(315-324)Online publication date: 11-Oct-2009
https://dl.acm.org/doi/10.1145/1629435.1629478
Wang ZSanchez AHerkersdorf AAvritzer AWeyuker EWoodside M(2008)SciSimProceedings of the 7th international workshop on Software and performance10.1145/1383559.1383565(33-42)Online publication date: 23-Jun-2008
https://dl.acm.org/doi/10.1145/1383559.1383565
Franke BFalk H(2008)Fast cycle-approximate instruction set simulationProceedings of the 11th international workshop on Software & compilers for embedded systems10.1145/1361096.1361109(69-78)Online publication date: 13-Mar-2008
https://dl.acm.org/doi/10.1145/1361096.1361109
Show More Cited By

View Options

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