skip to main content
10.1145/2742854.2742857acmconferencesArticle/Chapter ViewAbstractPublication PagescfConference Proceedingsconference-collections
research-article

A significance-driven programming framework for energy-constrained approximate computing

Published: 06 May 2015 Publication History

Abstract

Approximate execution is a viable technique for energy-constrained environments, provided that applications have the mechanisms to produce outputs of the highest possible quality within the given energy budget.
We introduce a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows users to express the relative importance of computations for the quality of the end result, as well as minimum quality requirements. The significance-aware runtime system uses an application-specific analytical energy model to identify the degree of concurrency and approximation that maximizes quality while meeting user-specified energy constraints.
Evaluation on a dual-socket 8-core server shows that the proposed framework predicts the optimal configuration with high accuracy, enabling energy-constrained executions that result in significantly higher quality compared to loop perforation, a compiler approximation technique.

References

[1]
W. Baek and T. M. Chilimbi. Green: A framework for supporting energy-conscious programming using controlled approximation. In Proceedings of the 2010 ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI '10, pages 198--209, New York, NY, USA, 2010. ACM.
[2]
N. Bellas, S. M. Chai, M. Dwyer, and D. Linzmeier. Real-time fisheye lens distortion correction using automatically generated streaming accelerators. In Field Programmable Custom Computing Machines, 2009. FCCM'09. 17th IEEE Symposium on, pages 149--156. IEEE, 2009.
[3]
C. Bienia, S. Kumar, J. P. Singh, and K. Li. The parsec benchmark suite: Characterization and architectural implications. In Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques, PACT '08, pages 72--81, New York, NY, USA, 2008. ACM.
[4]
P. Gschwandtner, C. Chalios, D. Nikolopoulos, H. Vandierendonck, and T. Fahringer. On the potential of significance-driven execution for energy-aware hpc. Computer Science - Research and Development, pages 1--10, 2014.
[5]
S. Misailovic, D. Kim, and M. Rinard. Parallelizing sequential programs with statistical accuracy tests. ACM Trans. Embed. Comput. Syst., 12(2s): 88:1--88:26, May 2013.
[6]
OpenMP Architecture Review Board. OpenMP Application Program Interface (version 4.0). Technical report, July 2013.
[7]
A. Rahimi, A. Marongiu, P. Burgio, R. K. Gupta, and L. Benini. Variation-tolerant openmp tasking on tightly-coupled processor clusters. In Proceedings of the Conference on Design, Automation and Test in Europe, DATE '13, pages 541--546, San Jose, CA, USA, 2013. EDA Consortium.
[8]
A. Rahimi, A. Marongiu, R. K. Gupta, and L. Benini. A variability-aware openmp environment for efficient execution of accuracy-configurable computation on shared-fpu processor clusters. In Proceedings of the Ninth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS '13, pages 35:1--35:10, Piscataway, NJ, USA, 2013. IEEE Press.
[9]
M. Samadi, D. A. Jamshidi, J. Lee, and S. Mahlke. Paraprox: Pattern-based approximation for data parallel applications. In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '14, pages 35--50, New York, NY, USA, 2014. ACM.
[10]
M. Samadi, J. Lee, D. A. Jamshidi, A. Hormati, and S. Mahlke. Sage: Self-tuning approximation for graphics engines. In Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO-46, pages 13--24, New York, NY, USA, 2013. ACM.
[11]
A. Sampson, W. Dietl, E. Fortuna, D. Gnanapragasam, L. Ceze, and D. Grossman. Enerj: Approximate data types for safe and general low-power computation. In Proceedings of the 32Nd ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI '11, pages 164--174, New York, NY, USA, 2011. ACM.
[12]
F. Schmoll, A. Heinig, P. Marwedel, and M. Engel. Improving the fault resilience of an h.264 decoder using static analysis methods. ACM Trans. Embed. Comput. Syst., 13(1s): 31:1--31:27, Dec. 2013.
[13]
S. Sidiroglou-Douskos, S. Misailovic, H. Hoffmann, and M. Rinard. Managing performance vs. accuracy tradeoffs with loop perforation. In Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, ESEC/FSE '11, pages 124--134, New York, NY, USA, 2011. ACM.
[14]
A. Skodras, C. Christopoulos, and T. Ebrahimi. The jpeg 2000 still image compression standard. Signal Processing Magazine, IEEE, 18(5): 36--58, Sept. 2001.
[15]
J. Sloan, J. Sartori, and R. Kumar. On software design for stochastic processors. In Proceedings of the 49th Annual Design Automation Conference, DAC '12, pages 918--923, New York, NY, USA, 2012. ACM.
[16]
J. Treibig, G. Hager, and G. Wellein. Likwid: A lightweight performance-oriented tool suite for x86 multicore environments. In Parallel Processing Workshops (ICPPW), 2010 39th International Conference on, pages 207--216. IEEE, Sept. 2010.
[17]
G. Tzenakis, A. Papatriantafyllou, H. Vandierendonck, P. Pratikakis, and D. S. Nikolopoulos. Bddt: Block-level dynamic dependence analysis for task-based parallelism. In Advanced Parallel Processing Technologies, pages 17--31, 2013.
[18]
V. Vassiliadis, K. Parasyris, C. Chalios, C. D. Antonopoulos, S. Lalis, N. Bellas, H. Vandierendonck, and D. S. Nikolopoulos. A programming model and runtime system for significance-aware energy-efficient computing. CoRR, abs/1412.5150, 2014.
[19]
M. Vavalis and G. Sarailidis. Hybrid-numerical-PDE-solvers: Hybrid Elliptic PDE Solvers. http://dx.doi.org/10.5281/zenodo.11691, Sep 2014.
[20]
Q. Zhang, F. Yuan, R. Ye, and Q. Xu. Approxit: An approximate computing framework for iterative methods. In Proceedings of the The 51st Annual Design Automation Conference on Design Automation Conference, DAC '14, pages 97:1--97:6, New York, NY, USA, 2014. ACM.

Cited By

View all
  • (2023)Approximate Computing: Hardware and Software Techniques, Tools and Their ApplicationsJournal of Circuits, Systems and Computers10.1142/S021812662430001033:04Online publication date: 20-Sep-2023
  • (2022)Towards an Approximation-Aware Computational Workflow Framework for Accelerating Large-Scale Discovery TasksProceedings of the 2022 Workshop on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems10.1145/3524053.3542746(7-14)Online publication date: 25-Jul-2022
  • (2021)HPACProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476216(1-14)Online publication date: 14-Nov-2021
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CF '15: Proceedings of the 12th ACM International Conference on Computing Frontiers
May 2015
413 pages
ISBN:9781450333580
DOI:10.1145/2742854
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: 06 May 2015

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. approximate computing
  2. energy efficiency
  3. modeling
  4. significance

Qualifiers

  • Research-article

Funding Sources

  • European Social Fund and Greek national resources
  • UK Engineering and Physical Sciences Research Council

Conference

CF'15
Sponsor:
CF'15: Computing Frontiers Conference
May 18 - 21, 2015
Ischia, Italy

Acceptance Rates

CF '15 Paper Acceptance Rate 33 of 96 submissions, 34%;
Overall Acceptance Rate 273 of 785 submissions, 35%

Upcoming Conference

CF '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 06 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Approximate Computing: Hardware and Software Techniques, Tools and Their ApplicationsJournal of Circuits, Systems and Computers10.1142/S021812662430001033:04Online publication date: 20-Sep-2023
  • (2022)Towards an Approximation-Aware Computational Workflow Framework for Accelerating Large-Scale Discovery TasksProceedings of the 2022 Workshop on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems10.1145/3524053.3542746(7-14)Online publication date: 25-Jul-2022
  • (2021)HPACProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476216(1-14)Online publication date: 14-Nov-2021
  • (2018)Energy-Quality Scalable Integrated Circuits and Systems: Continuing Energy Scaling in the Twilight of Moore’s LawIEEE Journal on Emerging and Selected Topics in Circuits and Systems10.1109/JETCAS.2018.28814618:4(653-678)Online publication date: Dec-2018
  • (2017)Significance-Aware Program Execution on Unreliable HardwareACM Transactions on Architecture and Code Optimization10.1145/305898014:2(1-25)Online publication date: 28-Apr-2017
  • (2016)Towards automatic significance analysis for approximate computingProceedings of the 2016 International Symposium on Code Generation and Optimization10.1145/2854038.2854058(182-193)Online publication date: 29-Feb-2016

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media