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Hierarchical Dynamic Thermal Management Method for High-Performance Many-Core Microprocessors

Authors:

Sheldon X.-D. Tan,

Zhenghong ZhangAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems (TODAES), Volume 22, Issue 1

Article No.: 1, Pages 1 - 21

https://doi.org/10.1145/2891409

Published: 10 August 2016 Publication History

Abstract

It is challenging to manage the thermal behavior of many-core microprocessors while still keeping them running at high performance since the control complexity increases as the core number increases. In this article, a novel hierarchical dynamic thermal management method is proposed to overcome this challenge. The new method employs model predictive control (MPC) with task migration and a DVFS scheme to ensure smooth control behavior and negligible computing performance sacrifice. In order to be scalable to many-core systems, the hierarchical control scheme is designed with two levels. At the lower level, the cores are spatially clustered into blocks, and local task migration is used to match current power distribution with the optimal distribution calculated by MPC. At the upper level, global task migration is used with the unmatched powers from the lower level. A modified iterative minimum cut algorithm is used to assist the task migration decision making if the power number is large at the upper level. Finally, DVFS is applied to regulate the remaining unmatched powers. Experiments show that the new method outperforms existing methods and is very scalable to manage many-core microprocessors with small performance degradation.

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Cited By

Yin LWang AZhu WGuo ALiu JTang MChen LZhang J(2024)A Stepwise Integration Separation of Variables Solver for Full-Chip Thermal Uncertainty AnalysisIEEE Transactions on Components, Packaging and Manufacturing Technology10.1109/TCPMT.2024.337410714:4(630-640)Online publication date: Apr-2024
https://doi.org/10.1109/TCPMT.2024.3374107
Vilarrubí MRegany DCamarasa JIbañez MRosell JBarrau J(2024)Numerical evaluation of doubly clamped self-adaptive fins acting as vortex generators inside micro heat sinks (MHS)International Journal of Heat and Mass Transfer10.1016/j.ijheatmasstransfer.2023.124931220(124931)Online publication date: Mar-2024
https://doi.org/10.1016/j.ijheatmasstransfer.2023.124931
Niknafs MEles PPeng Z(2023)Runtime Resource Management with Multiple-Step-Ahead Workload PredictionACM Transactions on Embedded Computing Systems10.1145/360521322:4(1-34)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3605213
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Index Terms

Hierarchical Dynamic Thermal Management Method for High-Performance Many-Core Microprocessors
1. Hardware
  1. Power and energy
    1. Thermal issues
      1. Temperature control

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Information & Contributors

Information

Published In

cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 22, Issue 1

January 2017

463 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/2948199

Editor:
Naehyuck Chang
Korea Advanced Institute of Science and Technology, Korea

Issue’s Table of Contents

Copyright © 2016 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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 10 August 2016

Accepted: 01 February 2016

Revised: 01 February 2016

Received: 01 August 2015

Published in TODAES Volume 22, Issue 1

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Funding Sources

Semiconductor Research Corporation
Open Foundation of State Key Laboratory of Electronic Thin Films and Integrated Devices
National Science Foundation
Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
National Natural Science Foundation of China

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Cited By

Yin LWang AZhu WGuo ALiu JTang MChen LZhang J(2024)A Stepwise Integration Separation of Variables Solver for Full-Chip Thermal Uncertainty AnalysisIEEE Transactions on Components, Packaging and Manufacturing Technology10.1109/TCPMT.2024.337410714:4(630-640)Online publication date: Apr-2024
https://doi.org/10.1109/TCPMT.2024.3374107
Vilarrubí MRegany DCamarasa JIbañez MRosell JBarrau J(2024)Numerical evaluation of doubly clamped self-adaptive fins acting as vortex generators inside micro heat sinks (MHS)International Journal of Heat and Mass Transfer10.1016/j.ijheatmasstransfer.2023.124931220(124931)Online publication date: Mar-2024
https://doi.org/10.1016/j.ijheatmasstransfer.2023.124931
Niknafs MEles PPeng Z(2023)Runtime Resource Management with Multiple-Step-Ahead Workload PredictionACM Transactions on Embedded Computing Systems10.1145/360521322:4(1-34)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3605213
Chen LLu JJin WTan S(2023)Fast Full-Chip Parametric Thermal Analysis Based on Enhanced Physics Enforced Neural Networks2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323696(1-8)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323696
Huang DPahlevan ACostero LZapater MAtienza D(2022)Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing ServersIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.315883241:12(5596-5609)Online publication date: Dec-2022
https://doi.org/10.1109/TCAD.2022.3158832
Zhang JSadiqbatcha SO'Dea MAmrouch HTan S(2022)Full-Chip Power Density and Thermal Map Characterization for Commercial Microprocessors Under Heat Sink CoolingIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.308808141:5(1453-1466)Online publication date: May-2022
https://doi.org/10.1109/TCAD.2021.3088081
Huang DPahlevan AZapater MAtienza D(2022)COCKTAIL: Multicore Co-Optimization Framework With Proactive Reliability ManagementIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.305895941:2(386-399)Online publication date: Feb-2022
https://doi.org/10.1109/TCAD.2021.3058959
Dalzotto Ada Silva Borges CRuaro MMoraes F(2022)Non-intrusive Monitoring Framework for NoC-based Many-Cores2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)10.1109/SBESC56799.2022.9965177(1-7)Online publication date: 21-Nov-2022
https://doi.org/10.1109/SBESC56799.2022.9965177
Sadiqbatcha SZhang JZhao HAmrouch HHenkel JTan S(2021)Post-Silicon Heat-Source Identification and Machine-Learning-Based Thermal Modeling Using Infrared Thermal ImagingIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.300754140:4(694-707)Online publication date: Apr-2021
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Mohammed MParaman NRahman AGhaleb FAl-Dhamari AMarsono M(2021)PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core SystemsIEEE Access10.1109/ACCESS.2021.31097179(124087-124099)Online publication date: 2021
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