research-article

Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores

Authors:

Eduardo Weber Wächter,

Cédric de Bellefroid,

Karunakar Reddy Basireddy,

Amit Kumar Singh,

Bashir M. Al-Hashimi,

Geoff MerrettAuthors Info & Claims

IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Volume 27, Issue 6

Pages 1404 - 1415

https://doi.org/10.1109/TVLSI.2019.2896776

Published: 01 June 2019 Publication History

Abstract

Current multicore platforms contain different types of cores, organized in clusters (e.g., ARM’s big.LITTLE). These platforms deal with concurrently executing applications, having varying workload profiles and performance requirements. Runtime management is imperative for adapting to such performance requirements and workload variabilities and to increase energy and temperature efficiency. Temperature has also become a critical parameter since it affects reliability, power consumption, and performance and, hence, must be managed. This paper proposes an accurate temperature prediction scheme coupled with a runtime energy management approach to proactively avoid exceeding temperature thresholds while maintaining performance targets. Experiments show up to 20% energy savings while maintaining high-temperature averages and peaks below the threshold. Compared with state-of-the-art temperature predictors, this paper predicts 35% faster and reduces the mean absolute error from 3.25 to 1.15 °C for the evaluated applications’ scenarios.

References

[1]

A. M. Rahmani, M. H. Haghbayan, A. Miele, P. Liljeberg, A. Jantsch, and H. Tenhunen, “Reliability-aware runtime power management for many-core systems in the dark silicon era,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 25, no. 2, pp. 427–440, Feb. 2017.

Digital Library

[2]

Y. G. Kim, M. Kim, and S. W. Chung, “Enhancing energy efficiency of multimedia applications in heterogeneous mobile multi-core processors,” IEEE Trans. Comput., vol. 66, no. 11, pp. 1878–1889, Nov. 2017.

Digital Library

[3]

S. Yanget al., “Adaptive energy minimization of embedded heterogeneous systems using regression-based learning,” in Proc. Int. Workshop Power Timing Modeling, Optim. Simulation, Sep. 2015, pp. 103–110.

[4]

A. K. Coskun, T. S. Rosing, and K. Whisnant, “Temperature aware task scheduling in MPSoCs,” in Proc. EDAA, Apr. 2007, pp. 1–6.

[5]

A. Das, B. Al-Hashimi, and G. V. Merrett, “Adaptive and hierarchical runtime manager for energy-aware thermal management of embedded systems,” ACM Trans. Embedded Comput. Syst., vol. 15, no. 2, p. 24, 2016.

[6]

(2016). Exynos 5 Octa 5422. [Online]. Available: http://www.samsung.com/exynos/

[7]

A. Pathania, Q. Jiao, A. Prakash, and T. Mitra, “Integrated CPU-GPU power management for 3D mobile games,” in Proc. 51st ACM/EDAC/IEEE Design Autom. Conf. (DAC), Jun. 2014, pp. 1–6.

[8]

K. R. Basireddy, A. K. Singh, D. Biswas, G. V. Merrett, and B. M. Al-Hashimi, “Inter-cluster thread-to-core mapping and DVFS on heterogeneous multi-cores,” IEEE Trans. Multi-Scale Comput. Syst., vol. 4, no. 3, pp. 369–382, Jul. 2018.

[9]

K. R. Basireddy, A. Singh, G. V. Merrett, and B. M. Al-Hashimi, “ITMD: Run-time management of concurrent multi-threaded applications on heterogeneous multi-cores,” in Proc. Conf. Design, Autom. Test Eur. (DATE), Jan. 2017, p. 1.

[10]

C. Bienia, S. Kumar, J. P. Singh, and K. Li, “The PARSEC benchmark suite: Characterization and architectural implications,” Princeton Univ., Princeton, NJ, USA, Tech. Rep. TR-811-08, 2008.

Digital Library

[11]

A. Prakash, H. Amrouch, M. Shafique, T. Mitra, and J. Henkel, “Improving mobile gaming performance through cooperative CPU-GPU thermal management,” in Proc. Design Autom. Conf. (DAC), Jun. 2016, p. 47.

[12]

A. K. Coskun, T. S. Rosing, and K. Gross, “Utilizing predictors for efficient thermal management in multiprocessor SoCs,” IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 28, no. 10, pp. 1503–1516, Oct. 2009.

Digital Library

[13]

L. Ljung, System Identification: Theory for the User, 2nd ed. Upper Saddle River, NJ, USA: Prentice-Hal, 1999.

[14]

Y. Ge, Q. Qiu, and Q. Wu, “A multi-agent framework for thermal aware task migration in many-core systems,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 20, no. 10, pp. 1758–1771, Oct. 2010.

[15]

G. Singla, G. Kaur, A. K. Unver, and U. Y. Ogras, “Predictive dynamic thermal and power management for heterogeneous mobile platforms,” in Proc. Design Autom. Test Eur. Conf., Mar. 2015, pp. 960–965.

[16]

G. Bhat, G. Singla, A. K. Unver, and U. Y. Ogras, “Algorithmic optimization of thermal and power management for heterogeneous mobile platforms,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 26, no. 3, pp. 544–557, Mar. 2018.

Digital Library

[17]

N. Peters, D. Füß, S. Park, and S. Chakraborty, “Frame-based and thread-based power management for mobile games on HMP platforms,” in Proc. IEEE 34th Int. Conf. Comput. Design (ICCD), Oct. 2016, pp. 169–176.

[18]

S. Pagani, H. Khdr, J.-J. Chen, M. Shafique, M. Li, and J. Henkel, “Thermal safe power (TSP): Efficient power budgeting for heterogeneous manycore systems in dark silicon,” IEEE Trans. Comput., vol. 66, no. 1, pp. 147–162, Jan. 2017.

Digital Library

[19]

G. Bhat, S. Gumussoy, and U. Y. Ogras, “Power-temperature stability and safety analysis for multiprocessor systems,” ACM Trans. Embed. Comput. Syst., vol. 16, no. 5s, pp. 145:1–145:19, 2017. [Online]. Available: http://doi.acm.org/10.1145/3126567

[20]

A. Weissel and F. Bellosa, “Process cruise control: Event-driven clock scaling for dynamic power management,” in Proc. Int. Conf. Compil., Archit., Synthesis Embedded Syst. (CASES), 2002, pp. 238–246.

[21]

L. C. Singleton, C. Poellabauer, and K. Schwan, “Monitoring of cache miss rates for accurate dynamic voltage and frequency scaling,” Proc. SPIE, vol. 5680, pp. 121–125, Jan. 2005.

[22]

V. Spiliopoulos, G. Keramidas, S. Kaxiras, and K. Efstathiou, “Power-performance adaptation in Intel core i7,” in Proc. 2nd Workshop Comput. Archit. Oper. Syst. Co-Design, 2011, pp. 1–10.

[23]

A. Nabina and J. L. Nunez-Yanez, “Adaptive voltage scaling in a dynamically reconfigurable FPGA-based platform,” ACM Trans. Reconfigurable Technol. Syst., vol. 5, no. 4, p. 20, 2012.

[24]

A. K. Singh, C. Leech, K. R. Basireddy, B. M. Al-Hashimi, and G. V. Merrett, “Learning-based run-time power and energy management of multi/many-core systems: Current and future trends,” J. Low Power Electron., vol. 13, no. 3, pp. 310–325, Sep. 2017.

[25]

R. A. Shafik, S. Yang, A. Das, L. A. Maeda-Nunez, G. V. Merrett, and B. M. Al-Hashimi, “Learning transfer-based adaptive energy minimization in embedded systems,” IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 35, no. 6, pp. 877–890, Aug. 2016.

Digital Library

[26]

R. Cochran, C. Hankendi, A. K. Coskun, and S. Reda, “Pack & cap: Adaptive DVFS and thread packing under power caps,” in Proc. 44th Annu. IEEE/ACM Int. Symp. Microarchitecture, Dec. 2011, pp. 175–185.

[27]

H. Sasaki, S. Imamura, and K. Inoue, “Coordinated power-performance optimization in manycores,” in Proc. 22nd Int. Conf. Parallel Archit. Compilation Techn. (PACT), Oct. 2013, pp. 51–61.

[28]

K. V. Craeynest, A. Jaleel, L. Eeckhout, P. Narvaez, and J. Emer, “Scheduling heterogeneous multi-cores through performance impact estimation (PIE),” ACM SIGARCH Comput. Archit. News, vol. 40, no. 3, pp. 213–224, 2012.

[29]

A. Aalsaud, R. Shafik, A. Rafiev, F. Xia, S. Yang, and A. Yakovlev, “Power–aware performance adaptation of concurrent applications in heterogeneous many-core systems,” in Proc. Int. Symp. Low Power Electron. Design, 2016, pp. 368–373.

[30]

J. Ma, G. Yan, Y. Han, and X. Li, “An analytical framework for estimating scale-out and scale-up power efficiency of heterogeneous manycores,” IEEE Trans. Comput., vol. 65, no. 2, pp. 367–381, Feb. 2016.

Digital Library

[31]

E. Del Sozzo, G. C. Durelli, E. Trainiti, A. Miele, M. D. Santambrogio, and C. Bolchini, “Workload-aware power optimization strategy for asymmetric multiprocessors,” in Proc. Design, Autom. Test Eur. Conf. Exhib. (DATE), Mar. 2016, pp. 531–534.

[32]

B. Donyanavard, T. Mück, S. Sarma, and N. Dutt, “SPARTA: Runtime task allocation for energy efficient heterogeneous manycores,” in Proc. 11th IEEE/ACM/IFIP Int. Conf. Hardw./Softw. Codesign Syst. Synthesis, Oct. 2016, pp. 1–10.

[33]

S. C. Woo, M. Ohara, E. Torrie, J. P. Singh, and A. Gupta, “The SPLASH-2 programs: Characterization and methodological considerations,” in Proc. 22nd Annu. Int. Symp. Comput. Archit., 1995, pp. 24–36.

[34]

K. Yu, D. Han, C. Youn, S. Hwang, and J. Lee, “Power-aware task scheduling for big. LITTLE mobile processor,” in Proc. Int. SoC Design Conf. (ISOCC), Nov. 2013, pp. 208–212.

[35]

V. Pallipadi and A. Starikovskiy, “The ondemand governor,” in Proc. Linux Symp., vol. 2, 2006, pp. 215–230.

[36]

The Linux Governors. Accessed: Aug. 19, 2017. [Online]. Available: https://www.kernel.org/doc/Documentation/cpu-freq/governors.txt

[37]

F. Zanini, C. N. Jones, D. Atienza, and G. De Micheli, “Multicore thermal management using approximate explicit model predictive control,” in Proc. Int. Symp. Circuits Syst. (ISCAS), May 2010, pp. 3321–3324.

Cited By

Narang GDeshwal AAyoub RKishinevsky MRao Doppa JPande P(2023)Dynamic Power Management in Large Manycore Systems: A Learning-to-Search FrameworkACM Transactions on Design Automation of Electronic Systems10.1145/360350128:5(1-21)Online publication date: 8-Sep-2023
https://dl.acm.org/doi/10.1145/3603501
Chen KLiao YChen CWang L(2023)Adaptive Machine Learning-Based Proactive Thermal Management for NoC SystemsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2023.328296931:8(1114-1127)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TVLSI.2023.3282969
Sharma YMoulik S(2022)HEATACM SIGAPP Applied Computing Review10.1145/3558053.355805622:2(34-43)Online publication date: 17-Aug-2022
https://dl.acm.org/doi/10.1145/3558053.3558056
Show More Cited By

Index Terms

Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores

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

Recommendations

Predictive dynamic thermal management for multicore systems
DAC '08: Proceedings of the 45th annual Design Automation Conference

Recently, processor power density has been increasing at an alarming rate resulting in high on-chip temperature. Higher temperature increases current leakage and causes poor reliability. In this paper, we propose a Predictive Dynamic Thermal Management (...
Improving energy efficiency of multi-threaded applications using heterogeneous CMOS-TFET multicores
ISLPED '11: Proceedings of the 17th IEEE/ACM international symposium on Low-power electronics and design

Energy-Delay-Product-aware DVFS is a widely-used technique that improves energy efficiency by dynamically adjusting the frequencies of cores. Further, for multithreaded applications, barrier-aware DVFS is a method that can dynamically tune the ...
Coordinated energy management in heterogeneous processors
SC13 --The International Conference for High Performance Computing, Networking, Storage and Analysis

This paper examines energy management in a heterogeneous processor consisting of an integrated CPU--GPU for high-performance computing HPC applications. Energy management for HPC applications is challenged by their uncompromising performance ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Very Large Scale Integration (VLSI) Systems

IEEE Transactions on Very Large Scale Integration (VLSI) Systems Volume 27, Issue 6

June 2019

246 pages

ISSN:1063-8210

Issue’s Table of Contents

1063-8210 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 June 2019

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Narang GDeshwal AAyoub RKishinevsky MRao Doppa JPande P(2023)Dynamic Power Management in Large Manycore Systems: A Learning-to-Search FrameworkACM Transactions on Design Automation of Electronic Systems10.1145/360350128:5(1-21)Online publication date: 8-Sep-2023
https://dl.acm.org/doi/10.1145/3603501
Chen KLiao YChen CWang L(2023)Adaptive Machine Learning-Based Proactive Thermal Management for NoC SystemsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2023.328296931:8(1114-1127)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TVLSI.2023.3282969
Sharma YMoulik S(2022)HEATACM SIGAPP Applied Computing Review10.1145/3558053.355805622:2(34-43)Online publication date: 17-Aug-2022
https://dl.acm.org/doi/10.1145/3558053.3558056
Sharma YMoulik SHong JBures MPark JCerny T(2022)CETASProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507079(501-509)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3477314.3507079
Chen KTang HWu CChen C(2022)Thermal Sensor Placement for Multicore Systems Based on Low-Complex Compressive Sensing TheoryIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.314347641:11(5100-5111)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1109/TCAD.2022.3143476
Sharma YChakraborty SMoulik S(2022)ETA-HP: an energy and temperature-aware real-time scheduler for heterogeneous platformsThe Journal of Supercomputing10.1007/s11227-021-04257-778:8(1-25)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s11227-021-04257-7
Zhang TSeo MDonyanavard BDutt NKurdahi F(2021)Predicting Failures in Embedded Systems Using Long Short-Term InferenceIEEE Embedded Systems Letters10.1109/LES.2020.300736113:3(85-89)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1109/LES.2020.3007361
Revathi NSumathi G(2021)Multistep temperature prediction for proactive thermal management on chip multiprocessorsThe Journal of Supercomputing10.1007/s11227-020-03611-577:8(8967-8994)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11227-020-03611-5

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents