research-article

Multi-variable Dynamic Power Management for the GPU Subsystem

Authors:

Pietro Mercati,

Michael Kishinevsky,

Francesco Paterna,

Tajana Šimunić RosingAuthors Info & Claims

DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017

Article No.: 2, Pages 1 - 6

https://doi.org/10.1145/3061639.3062288

Published: 18 June 2017 Publication History

Abstract

In this work, we present a control-theoretic algorithm to improve the energy efficiency of the GPU targeting deadline-driven graphics applications. Our algorithm dynamically controls multiple power knobs within the GPU (DVFS and number of active slices) that have different control time granularities. We developed a multi-rate predictive control to overcome the time granularity constraints in the control variables and reduce runtime overhead. To enable predictive control, we developed runtime analytical predictive models for performance and power of the GPU, that take input from hardware counters and temperature sensor readings. We evaluated our approach on the latest generation of Intel Core i5 platform. Our experimental results demonstrate significant average GPU energy savings of 25% compared to the state-of-the-art algorithm at negligible performance overhead.

References

[1]

{online} https://autohotkey.com/.

[2]

{online} http://venturebeat.com/2013/11/25/more-than-1-2-billion-people-are-playing-games/.

[3]

L. Benini et al. A survey of design techniques for system-level dynamic power management. IEEE TCAD, 8(3):299--316, 2000.

Digital Library

[4]

A. Chakrabarty et al. Support vector machine informed explicit nonlinear model predictive control using low-discrepancy sequences. IEEE Transactions on Automatic Control, PP(99):1--1, 2016.

[5]

G. F. Franklin et al. Digital Control of Dynamic Systems. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 3rd edition, 1997.

Digital Library

[6]

D. Kadjo et al. A control-theoretic approach for energy efficient CPU-GPU subsystem in mobile platforms. In DAC, pages 62:1--62:6, 2015.

Digital Library

[7]

P. Mercati et al. Workload and user experience-aware dynamic reliability management in multicore processors. In DAC, pages 1--6, 2013.

Digital Library

[8]

S. Mittal. Power management techniques for data centers: A survey. CoRR, abs/1404.6681, 2014.

[9]

J.-G. Park et al. Hicap: Hierarchical FSM-based dynamic integrated CPU-GPU frequency capping governor for energy-efficient mobile gaming. In ISLPED, pages 218--223, 2016.

Digital Library

[10]

A. Pathania et al. Integrated CPU-GPU power management for 3D mobile games. In DAC, pages 1--6, 2014.

Digital Library

[11]

A. Pathania et al. Power-performance modelling of mobile gaming workloads on heterogeneous MPSoCs. In DAC, pages 201:1--201:6, 2015.

Digital Library

[12]

I. Paul et al. Harmonia: Balancing compute and memory power in high-performance GPUs. In ISCA, pages 54--65, 2015.

Digital Library

[13]

A. Prakash et al. Improving mobile gaming performance through cooperative CPU-GPU thermal management. In DAC, pages 47:1--47:6, 2016.

Digital Library

[14]

R. Rao et al. Statistical analysis of subthreshold leakage current for VLSI circuits. IEEE Trans. Very Large Scale Integr. Syst., 12(2):131--139, 2004.

Digital Library

[15]

J. Rawlings et al. Model Predictive Control: Theory and Design. Nob Hill Pub., 2009.

[16]

J. Song et al. Fcm: Towards fine-grained GPU power management for closed source mobile games. In GLSVLSI, pages 353--356, 2016.

Digital Library

[17]

B. Sun et al. Texture-directed mobile GPU power management for closed-source games. In HPCC,CSS,ICESS, pages 348--354, 2014.

Digital Library

[18]

M. Viredaz et al. Energy management on handheld devices. Queue, 1(7):44--52, 2003.

Digital Library

[19]

H. Zhang et al. Sampling-based explicit model predictive control for output tracking. In CDC, pages 4722--4727, 2016.

Cited By

Yang YXie CLiu LLeong PSong S(2024)Efficient Radius Search for Adaptive Foveal Sizing Mechanism in Collaborative Foveated Rendering FrameworkIEEE Transactions on Mobile Computing10.1109/TMC.2023.3277577(1-13)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3277577
Jang HLee SPark HLee W(2024)Development of a Low-Power Touch-Based Lifelogging ProcessorIEEE Transactions on Circuits and Systems II: Express Briefs10.1109/TCSII.2024.336841071:8(3910-3914)Online publication date: Aug-2024
https://doi.org/10.1109/TCSII.2024.3368410
Choi EPark JLee KLee JHan KLee W(2024)Day-night architecture: Development of an ultra-low power RISC-V processor for wearable anomaly detectionJournal of Systems Architecture10.1016/j.sysarc.2024.103161(103161)Online publication date: May-2024
https://doi.org/10.1016/j.sysarc.2024.103161
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Index Terms

Multi-variable Dynamic Power Management for the GPU Subsystem
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
      1. Firmware
    2. System on a chip
2. Hardware
  1. Power and energy
    1. Power estimation and optimization
      1. Chip-level power issues

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cover image ACM Conferences

DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017

June 2017

533 pages

ISBN:9781450349277

DOI:10.1145/3061639

Copyright © 2017 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]

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EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

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SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2017

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DAC '17

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DAC '17: The 54th Annual Design Automation Conference 2017

June 18 - 22, 2017

TX, Austin, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Yang YXie CLiu LLeong PSong S(2024)Efficient Radius Search for Adaptive Foveal Sizing Mechanism in Collaborative Foveated Rendering FrameworkIEEE Transactions on Mobile Computing10.1109/TMC.2023.3277577(1-13)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3277577
Jang HLee SPark HLee W(2024)Development of a Low-Power Touch-Based Lifelogging ProcessorIEEE Transactions on Circuits and Systems II: Express Briefs10.1109/TCSII.2024.336841071:8(3910-3914)Online publication date: Aug-2024
https://doi.org/10.1109/TCSII.2024.3368410
Choi EPark JLee KLee JHan KLee W(2024)Day-night architecture: Development of an ultra-low power RISC-V processor for wearable anomaly detectionJournal of Systems Architecture10.1016/j.sysarc.2024.103161(103161)Online publication date: May-2024
https://doi.org/10.1016/j.sysarc.2024.103161
Choi YPark SJeon SHa RCha H(2022)Optimizing Energy Consumption of Mobile GamesIEEE Transactions on Mobile Computing10.1109/TMC.2021.305838121:10(3744-3756)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TMC.2021.3058381
Ma YZhou JChantem TDick RWang SHu X(2020)Improving Reliability of Soft Real-Time Embedded Systems on Integrated CPU and GPU PlatformsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.294068139:10(2218-2229)Online publication date: Oct-2020
https://doi.org/10.1109/TCAD.2019.2940681
Al-hayanni MRafiev AXia FShafik RRomanovsky AYakovlev A(2020)PARMA: Parallelization-Aware Run-Time Management for Energy-Efficient Many-Core SystemsIEEE Transactions on Computers10.1109/TC.2020.297578769:10(1507-1518)Online publication date: 1-Oct-2020
https://doi.org/10.1109/TC.2020.2975787
Choi YPark SCha HSong JKim MLane NBalan R(2019)Graphics-aware Power Governing for Mobile DevicesProceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services10.1145/3307334.3326075(469-481)Online publication date: 12-Jun-2019
https://dl.acm.org/doi/10.1145/3307334.3326075
Majzoub SSaleh RAshraf ITaouil MHamdioui S(2019)Energy Optimization for Large-Scale 3D Manycores in the Dark-Silicon EraIEEE Access10.1109/ACCESS.2019.29004777(33115-33129)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2900477
Zou ALeng JHe XZu YGill CReddi VZhang XOskin MInoue K(2018)Voltage-stacked GPUsProceedings of the 51st Annual IEEE/ACM International Symposium on Microarchitecture10.1109/MICRO.2018.00039(390-402)Online publication date: 20-Oct-2018
https://dl.acm.org/doi/10.1109/MICRO.2018.00039
Hsieh YChen M(2018)Toward Green Computing: Striking the Trade-Off between Memory Usage and Energy Consumption of Sequential Pattern Mining on GPU2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)10.1109/AIKE.2018.00033(152-155)Online publication date: Sep-2018
https://doi.org/10.1109/AIKE.2018.00033

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