research-article

Public Access

A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints

Authors:

John D. Lafferty, and

Henry HoffmannAuthors Info & Claims

ASPLOS '15: Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems

March 2015

Pages 267 - 281

https://doi.org/10.1145/2694344.2694373

Published: 14 March 2015 Publication History

Abstract

In many deployments, computer systems are underutilized -- meaning that applications have performance requirements that demand less than full system capacity. Ideally, we would take advantage of this under-utilization by allocating system resources so that the performance requirements are met and energy is minimized. This optimization problem is complicated by the fact that the performance and power consumption of various system configurations are often application -- or even input -- dependent. Thus, practically, minimizing energy for a performance constraint requires fast, accurate estimations of application-dependent performance and power tradeoffs. This paper investigates machine learning techniques that enable energy savings by learning Pareto-optimal power and performance tradeoffs. Specifically, we propose LEO, a probabilistic graphical model-based learning system that provides accurate online estimates of an application's power and performance as a function of system configuration. We compare LEO to (1) offline learning, (2) online learning, (3) a heuristic approach, and (4) the true optimal solution. We find that LEO produces the most accurate estimates and near optimal energy savings.

References

[1]

Jason Ansel, Maciej Pacula, Yee Lok Wong, Cy Chan, Marek Olszewski, Una-May O'Reilly, and Saman Amarasinghe. Sibling rivalry: online autotuning through local competitions. In CASES, 2012.

Digital Library

[2]

Jason Ansel, Yee Lok Wong, Cy Chan, Marek Olszewski, Alan Edelman, and Saman Amarasinghe. Language and compiler support for auto-tuning variable-accuracy algorithms. In CGO, 2011.

Digital Library

[3]

L.A Barroso and U. Holzle. The case for energy-proportional computing. Computer, 40(12):33--37, Dec 2007.

Digital Library

[4]

C. Bienia, S. Kumar, J. P. Singh, and K. Li. The PARSEC benchmark suite: Characterization and architectural implications. In PACT, 2008.

Digital Library

[5]

Ramazan Bitirgen, Engin Ipek, and Jose F. Martinez. Coordinated management of multiple interacting resources in chip multiprocessors: A machine learning approach. In MICRO, 2008.

Digital Library

[6]

S.P. Bradley, A.C. Hax, and T.L. Magnanti. Applied mathematical programming. Addison-Wesley Pub. Co., 1977.

[7]

Aaron Carroll and Gernot Heiser. Mobile multicores: Use them or waste them. In Proceedings of the Workshop on Power-Aware Computing and Systems, HotPower '13, pages 12:1--12:5, New York, NY, USA, 2013. ACM.

Digital Library

[8]

Shuai Che, Michael Boyer, Jiayuan Meng, David Tarjan, Jeremy W. Sheaffer, Sang-Ha Lee, and Kevin Skadron. Rodinia: A benchmark suite for heterogeneous computing. In IISWC, 2009.

Digital Library

[9]

Jian Chen and Lizy Kurian John. Predictive coordination of multiple on-chip resources for chip multiprocessors. In ICS, 2011.

Digital Library

[10]

Jian Chen, Lizy Kurian John, and Dimitris Kaseridis. Modeling program resource demand using inherent program characteristics. SIGMETRICS Perform. Eval. Rev., 39(1):1--12, June 2011.

Digital Library

[11]

Ryan Cochran, Can Hankendi, Ayse K. Coskun, and Sherief Reda. Pack & cap: adaptive dvfs and thread packing under power caps. In MICRO, 2011.

Digital Library

[12]

Qingyuan Deng, David Meisner, Abhishek Bhattacharjee, Thomas F Wenisch, and Ricardo Bianchini. Coscale: Coordinating cpu and memory system dvfs in server systems. In Microarchitecture (MICRO), 2012 45th Annual IEEE/ACM International Symposium on, pages 143--154. IEEE, 2012.

Digital Library

[13]

Petre Dini, Wolfgang Gentzsch, Mark Potts, Alexander Clemm, Mazin Yousif, and Andreas Polze. Internet, GRID, self-adaptability and beyond: Are we ready? Aug 2004.

[14]

Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla, and Michael F. P. O'Boyle. A predictive model for dynamic microarchitectural adaptivity control. In MICRO, 2010.

Digital Library

[15]

Bradley Efron and Carl Morris. Data analysis using stein's estimator and its generalizations. Journal of the American Statistical Association, 70(350):311--319, 1975.

[16]

Antonio Filieri, Henry Hoffmann, and Martina Maggio. Automated design of self-adaptive software with control-theoretical formal guarantees. In ICSE, 2014.

Digital Library

[17]

J. Flinn and M. Satyanarayanan. Energy-aware adaptation for mobile applications. In SOSP, 1999.

Digital Library

[18]

Jason Flinn and M. Satyanarayanan. Managing battery lifetime with energy-aware adaptation. ACM Trans. Comp. Syst., 22(2), May 2004.

Digital Library

[19]

Andrew Gelman, John B Carlin, Hal S Stern, David B Dunson, Aki Vehtari, and Donald B Rubin. Bayesian data analysis. CRC press, 2013.

[20]

W. Gentzsch, K. Iwano, D. Johnston-Watt, M.A. Minhas, and M. Yousif. Self-adaptable autonomic computing systems: An industry view. In Proceedings of the 16th International Workshop on Database and Expert Systems Applications, pages 201--205, Aug 2005.

[21]

Henry Hoffmann. Racing vs. pacing to idle: A comparison of heuristics for energy-aware resource allocation. In HotPower, 2013.

Digital Library

[22]

Henry Hoffmann, Jonathan Eastep, Marco D. Santambrogio, Jason E. Miller, and Anant Agarwal. Application heartbeats: a generic interface for specifying program performance and goals in autonomous computing environments. In ICAC, 2010.

Digital Library

[23]

Henry Hoffmann, Jim Holt, George Kurian, Eric Lau, Martina Maggio, Jason E. Miller, Sabrina M. Neuman, Mahmut Sinangil, Yildiz Sinangil, Anant Agarwal, Anantha P. Chandrakasan, and Srinivas Devadas. Self-aware computing in the angstrom processor. In DAC, 2012.

Digital Library

[24]

Henry Hoffmann, Martina Maggio, Marco D. Santambrogio, Alberto Leva, and Anant Agarwal. A generalized software framework for accurate and efficient managment of performance goals. In EMSOFT, 2013.

Digital Library

[25]

Henry Hoffmann, Stelios Sidiroglou, Michael Carbin, Sasa Misailovic, Anant Agarwal, and Martin Rinard. Dynamic knobs for responsive power-aware computing. In ASPLOS, 2011.

Digital Library

[26]

T. Horvath, T. Abdelzaher, K. Skadron, and Xue Liu. Dynamic voltage scaling in multitier web servers with end-to-end delay control. Computers, IEEE Transactions on, 56(4), 2007.

Digital Library

[27]

Connor Imes, David H. K. Kim, Martina Maggio, and Henry Hoffmann. Poet: A portable approach to minimizing energy under soft real-time constraints. In RTAS, 2015.

[28]

Engin Ipek, Onur Mutlu, José F. Martínez, and Rich Caruana. Self-optimizing memory controllers: A reinforcement learning approach. In ISCA, 2008.

Digital Library

[29]

J.O. Kephart. Research challenges of autonomic computing. In ICSE, 2005.

Digital Library

[30]

Minyoung Kim, Mark-Oliver Stehr, Carolyn Talcott, Nikil Dutt, and Nalini Venkatasubramanian. xtune: A formal methodology for cross-layer tuning of mobile embedded systems. ACM Trans. Embed. Comput. Syst., 11(4), January 2013.

Digital Library

[31]

Robert Laddaga. Guest editor's introduction: Creating robust software through self-adaptation. IEEE Intelligent Systems, 14, 1999.

Digital Library

[32]

Etienne Le Sueur and Gernot Heiser. Slow down or sleep, that is the question. In Proceedings of the 2011 USENIX Annual Technical Conference, Portland, OR, USA, June 2011.

Digital Library

[33]

B.C. Lee, J. Collins, Hong Wang, and D. Brooks. Cpr: Composable performance regression for scalable multiprocessor models. In MICRO, 2008.

Digital Library

[34]

Benjamin C. Lee and David Brooks. Efficiency trends and limits from comprehensive microarchitectural adaptivity. In ASPLOS, 2008.

Digital Library

[35]

Benjamin C. Lee and David M. Brooks. Accurate and efficient regression modeling for microarchitectural performance and power prediction. In ASPLOS, 2006.

Digital Library

[36]

Baochun Li and K. Nahrstedt. A control-based middleware framework for quality-of-service adaptations. IEEE Journal on Selected Areas in Communications, 17(9), 1999.

Digital Library

[37]

J. Li and J.F. Martinez. Dynamic power-performance adaptation of parallel computation on chip multiprocessors. In HPCA, 2006.

[38]

C. Lu, Y. Lu, T.F. Abdelzaher, J.A. Stankovic, and S.H. Son. Feedback control architecture and design methodology for service delay guarantees in web servers. IEEE TPDS, 17(9):1014--1027, September 2006.

Digital Library

[39]

Martina Maggio, Henry Hoffmann, Marco D. Santambrogio an d Anant Agarwal, and Alberto Leva. Power optimization in embedded systems via feedback control of resource allocation. IEEE Transactions on Control Systems Technology (to appear).

[40]

Martina Maggio, Henry Hoffmann, Alessandro V. Papadopoulos, Jacopo Panerati, Marco D. Santambrogio, Anant Agarwal, and Alberto Leva. Comparison of decision-making strategies for self-optimization in autonomic computing systems. ACM Trans. Auton. Adapt. Syst., 7(4):36:1--36:32, December 2012.

Digital Library

[41]

David Meisner, Christopher M. Sadler, Luiz Andre Barroso, Wolf-Dietrich Weber, and Thomas F. Wenisch. Power management of online data-intensive services. ISCA, 2011.

Digital Library

[42]

Carl N Morris. Parametric empirical bayes inference: theory and applications. Journal of the American Statistical Association, 78(381):47--55, 1983.

[43]

R. Narayanan, B. Ozisikyilmaz, J. Zambreno, G. Memik, and A. Choudhary. Minebench: A benchmark suite for data mining workloads. In IISWC, 2006.

[44]

Paula Petrica, Adam M. Izraelevitz, David H. Albonesi, and Christine A. Shoemaker. Flicker: A dynamically adaptive architecture for power limited multicore systems. In ISCA, 2013.

Digital Library

[45]

Dmitry Ponomarev, Gurhan Kucuk, and Kanad Ghose. Re- ducing power requirements of instruction scheduling through dynamic allocation of multiple datapath resources. In MICRO, 2001.

Digital Library

[46]

R. Raghavendra, P. Ranganathan, V Talwar, Z. Wang, and X. Zhu. No "power" struggles: coordinated multi-level power management for the data center. In ASPLOS, 2008.

Digital Library

[47]

R. Rajkumar, C. Lee, J. Lehoczky, and Dan Siewiorek. A resource allocation model for qos management. In RTSS, 1997.

Digital Library

[48]

Arjun Roy, Stephen M. Rumble, Ryan Stutsman, Philip Levis, David Mazieres, and Nickolai Zeldovich. Energy management in mobile devices with the cinder operating system. In EuroSys, 2011.

Digital Library

[49]

Mazeiar Salehie and Ladan Tahvildari. Self-adaptive software: Landscape and research challenges. ACM Trans. Auton. Adapt. Syst., 4(2):1--42, 2009.

Digital Library

[50]

David C. Snowdon, Etienne Le Sueur, Stefan M. Petters, and Gernot Heiser. Koala: A platform for os-level power management. In EuroSys, 2009.

Digital Library

[51]

Michal Sojka, Pavel Písa, Dario Faggioli, Tommaso Cucinotta, Fabio Checconi, Zdenek Hanzalek, and Giuseppe Lipari. Modular software architecture for flexible reservation mechanisms on heterogeneous resources. Journal of Systems Architecture, 57(4), 2011.

Digital Library

[52]

Srinath Sridharan, Gagan Gupta, and Gurindar S. Sohi. Holistic run-time parallelism management for time and energy efficiency. In ICS, 2013.

Digital Library

[53]

Q. Sun, G. Dai, and W. Pan. LPV model and its application in web server performance control. In ICCSSE, 2008.

Digital Library

[54]

Vibhore Vardhan, Wanghong Yuan, Albert F. Harris III, Sarita V. Adve, Robin Kravets, Klara Nahrstedt, Daniel Grobe Sachs, and Douglas L. Jones. Grace-2: integrating fine-grained application adaptation with global adaptation for saving energy. IJES, 4(2), 2009.

[55]

Jonathan A. Winter, David H. Albonesi, and Christine A. Shoemaker. Scalable thread scheduling and global power management for heterogeneous many-core architectures. In PACT, 2010.

Digital Library

[56]

CF Jeff Wu. On the convergence properties of the em algorithm. The Annals of statistics, pages 95--103, 1983.

[57]

Qiang Wu, Philo Juang, Margaret Martonosi, and Douglas W. Clark. Formal online methods for voltage/frequency control in multiple clock domain microprocessors. In ASPLOS, 2004.

Digital Library

[58]

Weidan Wu and Benjamin C Lee. Inferred models for dynamic and sparse hardware-software spaces. In Microarchitecture (MICRO), 2012 45th Annual IEEE/ACM International Symposium on, pages 413--424. IEEE, 2012.

Digital Library

[59]

Joshua J. Yi, David J. Lilja, and Douglas M. Hawkins. A statistically rigorous approach for improving simulation methodology. In HPCA, 2003.

Digital Library

[60]

Kai Yu, Volker Tresp, and Anton Schwaighofer. Learning gaussian processes from multiple tasks. In Proceedings of the 22nd international conference on Machine learning, pages 1012--1019. ACM, 2005.

Digital Library

[61]

R. Zhang, C. Lu, T.F. Abdelzaher, and J.A. Stankovic. Controlware: A middleware architecture for feedback control of software performance. In ICDCS, 2002.

Digital Library

[62]

Xiao Zhang, Rongrong Zhong, Sandhya Dwarkadas, and Kai Shen. A flexible framework for throttling-enabled multicore management (temm). In ICPP, 2012.

Digital Library

Cited By

Chen WWang YXu YGao CHan YZhang L(2022)Amphis: Managing Reconfigurable Processor Architectures With Generative Adversarial LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319798041:11(3993-4003)Online publication date: Nov-2022
https://doi.org/10.1109/TCAD.2022.3197980
Shamsa EKanduri ALiljeberg PRahmani A(2022)Concurrent Application Bias Scheduling for Energy Efficiency of Heterogeneous Multi-Core PlatformsIEEE Transactions on Computers10.1109/TC.2021.306155871:4(743-755)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TC.2021.3061558
Bateni SLiu CGavrilovska AZadok E(2020)NeuOSProceedings of the 2020 USENIX Conference on Usenix Annual Technical Conference10.5555/3489146.3489171(371-385)Online publication date: 15-Jul-2020
https://dl.acm.org/doi/10.5555/3489146.3489171
Show More Cited By

Index Terms

A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

Recommendations

A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints
ASPLOS '15

In many deployments, computer systems are underutilized -- meaning that applications have performance requirements that demand less than full system capacity. Ideally, we would take advantage of this under-utilization by allocating system resources so ...
Read More
A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints
ASPLOS'15

In many deployments, computer systems are underutilized -- meaning that applications have performance requirements that demand less than full system capacity. Ideally, we would take advantage of this under-utilization by allocating system resources so ...
Read More
Minimizing energy under performance constraints on embedded platforms: resource allocation heuristics for homogeneous and single-ISA heterogeneous multi-cores
Special Issue on the 4th Embedded Operating Systems Workshop (EWiLi 2014)

This paper explores the problem of energy optimization in embedded platforms. Specifically, it studies resource allocation strategies for meeting performance constraints with minimal energy consumption. We present a comparison of solutions for both ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ASPLOS '15: Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems

March 2015

720 pages

ISBN:9781450328357

DOI:10.1145/2694344

General Chairs:
Ozcan Ozturk
Bilkent University, Turkey
,
Kemal Ebcioglu
Global Supercomputing, USA
,
Program Chair:
Sandhya Dwarkadas
University of Rochester, USA

ACM SIGPLAN Notices Volume 50, Issue 4
ASPLOS '15
April 2015
676 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2775054
Editor:
Andy Gill
University of Kansas, Lawrence, KS
Issue’s Table of Contents
ACM SIGARCH Computer Architecture News Volume 43, Issue 1
ASPLOS'15
March 2015
676 pages
ISSN:0163-5964
DOI:10.1145/2786763
Editor:
Doug DeGroot
acm dot org
Issue’s Table of Contents

Copyright © 2015 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

In-Cooperation

SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tag

probabilistic graphical models

Qualifiers

Research-article

Funding Sources

ONR
DARPA
DOE
NSF grant

Conference

ASPLOS '15

Sponsor:

ASPLOS '15: Architectural Support for Programming Languages and Operating Systems

March 14 - 18, 2015

Istanbul, Turkey

Acceptance Rates

ASPLOS '15 Paper Acceptance Rate 48 of 287 submissions, 17%;

Overall Acceptance Rate 535 of 2,713 submissions, 20%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

72
Total Citations
View Citations
1,814
Total Downloads

Downloads (Last 12 months)155
Downloads (Last 6 weeks)22

Other Metrics

View Author Metrics

Citations

Cited By

Chen WWang YXu YGao CHan YZhang L(2022)Amphis: Managing Reconfigurable Processor Architectures With Generative Adversarial LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319798041:11(3993-4003)Online publication date: Nov-2022
https://doi.org/10.1109/TCAD.2022.3197980
Shamsa EKanduri ALiljeberg PRahmani A(2022)Concurrent Application Bias Scheduling for Energy Efficiency of Heterogeneous Multi-Core PlatformsIEEE Transactions on Computers10.1109/TC.2021.306155871:4(743-755)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TC.2021.3061558
Bateni SLiu CGavrilovska AZadok E(2020)NeuOSProceedings of the 2020 USENIX Conference on Usenix Annual Technical Conference10.5555/3489146.3489171(371-385)Online publication date: 15-Jul-2020
https://dl.acm.org/doi/10.5555/3489146.3489171
Wan CSantriaji MRogers EHoffmann HMaire MLu SGavrilovska AZadok E(2020)ALERTProceedings of the 2020 USENIX Conference on Usenix Annual Technical Conference10.5555/3489146.3489170(353-369)Online publication date: 15-Jul-2020
https://dl.acm.org/doi/10.5555/3489146.3489170
Xydis SChristoforidis ESoudris DLi Z(2020)DDOTProceedings of the 57th ACM/EDAC/IEEE Design Automation Conference10.5555/3437539.3437636(1-6)Online publication date: 20-Jul-2020
https://dl.acm.org/doi/10.5555/3437539.3437636
Damaskinos GGuerraoui RKermarrec ANitu VPatra RTaiani F(2020)FLeetProceedings of the 21st International Middleware Conference10.1145/3423211.3425685(163-177)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1145/3423211.3425685
Hoffmann HJantsch ADutt N(2020)Embodied Self-Aware Computing SystemsProceedings of the IEEE10.1109/JPROC.2020.2977054108:7(1027-1046)Online publication date: Jul-2020
https://doi.org/10.1109/JPROC.2020.2977054
Zou PLP ABarker KGe R(2020)Indicator-Directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)10.1109/CCGrid49817.2020.00-37(559-568)Online publication date: May-2020
https://doi.org/10.1109/CCGrid49817.2020.00-37
Zhang HHoffmann HTaufer MBalaji PPeña A(2019)PoDDProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3295500.3356174(1-23)Online publication date: 17-Nov-2019
https://dl.acm.org/doi/10.1145/3295500.3356174
Zhang XXiao XHe LMa YHuang YLiu XXu WLiu C(2019)PIFA: An Intelligent Phase Identification and Frequency Adjustment Framework for Time-Sensitive Mobile Computing2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)10.1109/RTAS.2019.00013(54-64)Online publication date: Apr-2019
https://doi.org/10.1109/RTAS.2019.00013
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

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

Media

Figures

Other

Tables

View Table of Contents