Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

ProWATCh: A Proactive Cross-Layer Workload-Aware Temperature Management Framework for Low-Power Chip Multi-Processors

Published: 21 September 2015 Publication History

Abstract

With the increase in process variations and diversity in workloads, it is imperative to holistically explore optimization techniques for power and temperature from the circuit layer right up to the compiler/operating system (OS) layer. This article proposes one such holistic technique, called proactive workload aware temperature management framework for low-power chip multi-processors (ProWATCh). At the compiler level ProWATCh includes two techniques: (1) a novel compiler design for estimating the architectural parameters of a task at compile time; and (2) a model-based technique for dynamic estimation of architectural parameters at runtime. At the OS level ProWATCh integrates two techniques: (1) a workload- and temperature-aware process manager for dynamic distribution of tasks to different cores; and (2) a model predictive control-based task scheduler for generating the efficient sequence of task execution. At the circuit level ProWATCh implements either of two techniques: (1) a workload-aware voltage manager for dynamic supply and body bias voltage assignment for a given frequency in processors that support adaptive body bias (ABB); or (2) a workload-aware frequency governor for efficient assignment of upper and lower frequency bounds for frequency scaling in processors that do not support an ABB. Employing ProWATCh (with voltage manager) on an ABB-compatible 3D OpenSPARC architecture using MiBench benchmarks resulted in an average 18% (19ˆC) reduction in peak temperature. Evaluating ProWATCh on an existing quad-core Intel Corei7 processor with frequency governor alone (as the processor does not support an ABB interface) resulted in 10% (8ˆC) reduction in peak temperature when compared to what was obtained using the native Linux 3.0 completely fair scheduler (CFS). To study the effectiveness of the proposed framework across benchmark suites, ProWATCh was evaluated on a quad-core Intel Corei7 processor using CPU SPEC 2006 benchmarks which resulted in 7ˆC reduction in peak temperature as compared to the native Linux 3.0 CFS.

References

[1]
S. Adve, A. F. Harris, C. J. Hughes, D. L. Jones, R. H. Kravets, et al. 2002. The Illinois GRACE project: Global resource adaptation through cooperation. In Proceedings of the Workshop on Self-Healing, Adaptive and Self-MANaged Systems (SHAMAN'02).
[2]
N. L. Binkert, R. G. Dreslinski, L. R. Hsu, K. T. Lim, A. G. Saidi, and S. K. Reinhardt. 2006. The M5 simulator: Modeling networked systems. IEEE Micro 26, 4, 52--60.
[3]
E. F. Camacho and C. Bordons Alba. 2007. Model Predictive Control 2nd Ed. Springer.
[4]
T. Chantem, R. P. Dick, and X. S. Hu. 2008. Temperature-aware scheduling and assignment for hard real-time applications on MPSoCs. In Proceedings of the Design, Automation, and Test in Europe Conference (DATE'08). 288--293.
[5]
Y. Cheng, L. Zhang, Y. Han, and X. Li. 2011. Thermal-constrained task allocation for interconnect energy reduction in 3-D homogeneous MPSoCs. IEEE Trans. VLSI Syst. 21, 2, 239--249.
[6]
J. Choi, C.-Y. Cher, H. Franke, H. Hamann, A. Weger, and P. Bose. 2007. Thermal aware task scheduling at the system software level. In Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED'07). 213--218.
[7]
S. W. Chung and K. Skadron. 2006. A novel software solution for localized thermal problems. In Proceedings of the International Conference on Parallel and Distributed Processing and Applications (ISPA'06). 63--74.
[8]
J. Cong and B. Yuan. 2012. Energy efficient scheduling on heterogeneous multi-core architectures. In Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED'12).
[9]
A. K. Coskun, T. S. Rosing, K. Whisnant, and K. C. Gross. 2008a. Static and dynamic temperature aware scheduling for multiprocessor SoCs. IEEE Trans. VLSI Syst. 16, 9, 1127--1140.
[10]
A. K. Coskun, T. S. Rosing, and K. C. Gross. 2008b. Proactive temperature management in MPSoCs. In Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED'08). 165--170.
[11]
M. Cox, A. K. Singh, A. Kumar, and H. Corporaal. 2013. Thermal-aware mapping of streaming applications on 3D multi-processor systems. In Proceedings of the Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia'13). 11--20.
[12]
V. Gandhi, V. R. Devanathan, V. Visvanathan, M. Patnaik, and V. Kamakoti. 2013. Supply and body-bias voltage assignment based technique for power and temperature control on a chip at iso-performance conditions. J. Low Power Electron. 9, 2, 207--228.
[13]
K. C. Gross, K. Whisnant, and A. Urmanov. 2006. Electronic prognostics through continuous system telemetry. In Proceedings of the 60th Meeting of the Society for Machine Failure Prevention Technology (MFPT'06).
[14]
S. Herbert, S. Garg, and D. Marculescu. 2012. Exploiting process variability in voltage/frequency control. IEEE Trans. VLSI Syst. 20, 8, 1392--1404.
[15]
W. Huang, S. Ghosh, S. Velusamys, K. Sankaranarayanan, K. Skadron, and M. R. Stan. 2006. HotSpot: A compact thermal modeling methodology for early-stage VLSI design. IEEE Trans. VLSI Syst. 14, 5, 501--513.
[16]
W. Hung, G. M. Link, Y. Xie, N. Vijaykrishnan, and M. J. Irwin. 2006. Interconnect and thermal-aware floorplanning for 3D microprocessors. In Proceedings of the International Symposium on Quality Electronic Design (ISQED'06). 99--104.
[17]
Intel Core Processors Technical Resources. 2015. http://www.intel.com/content/www/us/en/processors/core/CoreTechnicalResources.html.
[18]
H. Khdr, T. Ebi, M. Shafique, H. Amrouch, and J. Henkel. 2014. mDTM: Multi-objective dynamic thermal management for on-chip systems. In Proceedings of the Design, Automation, and Test in Europe Conference (DATE'14).
[19]
P. Kumar, H. Yang, I. Bacivarov, and L. Thiele. 2014. COOLIP: Simple yet effective job allocation for distributed thermally-throttled processors. In Proceedings of the Design, Automation, and Test in Europe Conference (DATE'14).
[20]
S. Liu, J. Zhang, Q. Wu, and Q. Qui. 2010. Thermal-aware job allocation and scheduling for three dimensional chip multiprocessor. In Proceedings of the International Symposium on Quality Electronic Design (ISQED'10). 390--398.
[21]
A. Merkel and F. Bellosa. 2008. Task activity vectors: A new metric for temperature-aware scheduling. In Proceedings of the 3rd ACM SIGOPS/EuroSys European Conference on Computer Systems (EuroSys'08). 1--12.
[22]
C. A. Moritz, M. Krishna, I. Koren, and O. S. Unsal. 2005. US Patent 6934865 B2.
[23]
K. Nose and T. Sakurai. 2000. Optimization of VDD and VTH for low power and high speed applications. In Proceedings of the Asia and South Pacific Design Automation Conference (ASP-DAC'00). 469--474.
[24]
R. K. Pasumarthi, V. R. Devanathan, V. Visvanathan, S. Potluri, and V. Kamakoti. 2012. Thermal-safe dynamic test scheduling method using on-chip temperature sensors for 3D MPSoCs. J. Low Power Electron. 8, 5, 684--695.
[25]
S. Pinel, A. Marty, J. Tasselli, J.-P. Bailbe, E. Beyne, et al. 2002. Thermal modeling and management in ultrathin chip stack technology. IEEE Trans. Compon. Packag. Technol. 25, 2, 244--253.
[26]
M. D. Powell, A. Biswas, J. S. Emer, S. S. Mukherjee, B. R. Sheeikh, and S. Yardi. 2009. CAMP: A technique to estimate per-structure power at run-time using a few simple parameters. In Proceedings of the International Symposium on High Performance Computer Architecture (HPCA'09). 289--300.
[27]
A. Rahimi, L. Benini, and R. A. Gupta. 2013. Aging-aware compiler-directed VLIW assignment for GPGPU architectures. In Proceedings of the Design Automation Conference (DAC'13).
[28]
S. Sharifi, D. Krishnaswamy, and T. S. Rosing. 2013. PROMETHEUS: A proactive method for thermal management of heterogeneous MPSoCs. IEEE Trans. Comput.-Aided Des. Integr. Circ. Syst. 32, 7, 1110--1123.
[29]
H. Su, F. Liu, A. Devgan, E. Acar, and S. Nassif. 2003. Full chip leakage estimation considering power supply and temperature variations. In Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED'03). 78--83.
[30]
Texas Instruments. 2013. Adaptive body bias (ABB) on-chip LDO driver. http://lwn.net/Articles/549462/.
[31]
B. Vandevelde, M. Gonzalez, P. Limaye, P. Ratchev, and E. Beyne. 2007. Thermal cycling reliability of SnAgCu and SnPb solder joints: A comparison for several IC-packages. In Proceedings of the International Conference on Thermal and Mechanical Simulation and Experiments in Microelectronics and Microsystems (ESIME'07).
[32]
C. Yao, K. K. Saluja, and P. Ramanathan. 2011. Thermal-aware test scheduling using on-chip temperature sensors. In Proceedings of the International Conference on VLSI Design (VLSID'11). 376--381.
[33]
I. Yeo, C. C. Liu, and E. J. Kim. 2008. Predictive dynamic thermal management for multicore systems. In Proceedings of the Design Automation Conference (DAC'08). 734--749.
[34]
X. Zhou, J. Yang, Y. Xu, Y. Zhang, and J. Zhao. 2009. Thermal aware task scheduling for 3D multicore processors. IEEE Trans. Parallel Distrib. Syst. 21, 1, 60--71.
[35]
Z. Zhou, J. Yang, Y. Xu, Y. Zhang, and J. Zhao. 2010a. Thermal-aware task scheduling for 3D multicore processors. IEEE Trans. Parallel Distrib. Syst. 21, 1, 60--71.
[36]
X. Zhou, J. Yang, M. Chrobak, and Y. Zhang. 2010b. Performance-aware thermal management via task scheduling. ACM Trans. Archit. Code Optim. 7, 1.
[37]
S. Zhuravlev, J. C. Saez, S. Blagodurov, A. Fedorova, and M. Prieto. 2013. Survey of energy-cognizant scheduling techniques. IEEE Trans. Parallel Distrib. Syst. 24, 7, 1447--1464.

Cited By

View all
  • (2023)Advanced Encryption Standard-Based Encryption for Secured Transmission of Data in Cognitive Radio with Multi-channelsInventive Systems and Control10.1007/978-981-99-1624-5_6(77-93)Online publication date: 15-Jun-2023
  • (2021)Transmission State Prediction from MAC in Cognitive Radio via Optimized Deep Learning ArchitectureInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142152012135:09(2152012)Online publication date: 29-May-2021
  • (2020)Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data CenterIEEE Transactions on Green Communications and Networking10.1109/TGCN.2020.29870634:2(577-592)Online publication date: Jun-2020
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Journal on Emerging Technologies in Computing Systems
ACM Journal on Emerging Technologies in Computing Systems  Volume 12, Issue 3
Special Issue on Cross-Layer System Design and Regular Papers
September 2015
207 pages
ISSN:1550-4832
EISSN:1550-4840
DOI:10.1145/2828988
Issue’s Table of Contents
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

Publication History

Published: 21 September 2015
Accepted: 01 March 2015
Revised: 01 February 2015
Received: 01 June 2014
Published in JETC Volume 12, Issue 3

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. 3D chip multi-processor
  2. Thermal and power management
  3. adaptive body bias
  4. delay and power modeling
  5. finite horizon control
  6. frequency governors
  7. model predictive control
  8. task allocation
  9. temperature-ware scheduling
  10. voltage assignment

Qualifiers

  • Research-article
  • Research
  • Refereed

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 19 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2023)Advanced Encryption Standard-Based Encryption for Secured Transmission of Data in Cognitive Radio with Multi-channelsInventive Systems and Control10.1007/978-981-99-1624-5_6(77-93)Online publication date: 15-Jun-2023
  • (2021)Transmission State Prediction from MAC in Cognitive Radio via Optimized Deep Learning ArchitectureInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142152012135:09(2152012)Online publication date: 29-May-2021
  • (2020)Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data CenterIEEE Transactions on Green Communications and Networking10.1109/TGCN.2020.29870634:2(577-592)Online publication date: Jun-2020
  • (2016)ProMACComputer Communications10.1016/j.comcom.2016.05.01293:C(27-38)Online publication date: 1-Nov-2016

View Options

Login options

Full Access

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