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Thermal monitoring mechanisms for chip multiprocessors

Authors:

Seda Ogrenci Memik,

Rajarshi MukherjeeAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 5, Issue 2

Article No.: 9, Pages 1 - 33

https://doi.org/10.1145/1400112.1400114

Published: 03 September 2008 Publication History

Abstract

With large-scale integration and increasing power densities, thermal management has become an important tool to maintain performance and reliability in modern process technologies. In the core of dynamic thermal management schemes lies accurate reading of on-die temperatures. Therefore, careful planning and embedding of thermal monitoring mechanisms into high-performance systems becomes crucial. In this paper, we propose three techniques to create sensor infrastructures for monitoring the maximum temperature on a multicore system. Initially, we extend a nonuniform sensor placement methodology proposed in the literature to handle chip multiprocessors (CMPs) and show its limitations. We then analyze a grid-based approach where the sensors are placed on a static grid covering each core and show that the sensor readings can differ from the actual maximum core temperature by as much as 12.6°C when using 16 sensors per core. Also, as large as 10.6% of the thermal emergencies are not captured using the same number of sensors. Based on this observation, we first develop an interpolation scheme, which estimates the maximum core temperature through interpolation of the readings collected at the static grid points. We show that the interpolation scheme improves the measurement accuracy and emergency coverage compared to grid-based placement when using the same number of sensors. Second, we present a dynamic scheme where only a subset of the sensor readings is collected to predict the maximum temperature of each core. Our results indicate that, we can reduce the number of active sensors by as much as 50%, while maintaining similar measurement accuracy and emergency coverage compared to the case where the entire sensor set on the grid is sampled at all times.

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Cited By

Khan LIsanaka SLiou F(2024)FPGA-Based Sensors for Distributed Digital Manufacturing Systems: A State-of-the-Art ReviewSensors10.3390/s2423770924:23(7709)Online publication date: 2-Dec-2024
https://doi.org/10.3390/s24237709
Touati DOukaira AHassan AAli MLakhssassi ASavaria Y(2023)Accurate On-Chip Thermal Peak Detection Based on Heuristic Algorithms and Embedded Temperature SensorsElectronics10.3390/electronics1213297812:13(2978)Online publication date: 6-Jul-2023
https://doi.org/10.3390/electronics12132978
Khadirsharbiyani SKotra JRao KKandemir M(2022)Data ConvectionProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35080276:1(1-25)Online publication date: 28-Feb-2022
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Index Terms

Thermal monitoring mechanisms for chip multiprocessors
1. Applied computing
  1. Computers in other domains
    1. Personal computers and PC applications
      1. Microcomputers

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Published In

cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 5, Issue 2

August 2008

124 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/1400112

Issue’s Table of Contents

Copyright © 2008 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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Association for Computing Machinery

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Publication History

Published: 03 September 2008

Revised: 01 August 2007

Received: 01 March 2007

Accepted: 01 January 2007

Published in TACO Volume 5, Issue 2

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Cited By

Khan LIsanaka SLiou F(2024)FPGA-Based Sensors for Distributed Digital Manufacturing Systems: A State-of-the-Art ReviewSensors10.3390/s2423770924:23(7709)Online publication date: 2-Dec-2024
https://doi.org/10.3390/s24237709
Touati DOukaira AHassan AAli MLakhssassi ASavaria Y(2023)Accurate On-Chip Thermal Peak Detection Based on Heuristic Algorithms and Embedded Temperature SensorsElectronics10.3390/electronics1213297812:13(2978)Online publication date: 6-Jul-2023
https://doi.org/10.3390/electronics12132978
Khadirsharbiyani SKotra JRao KKandemir M(2022)Data ConvectionProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35080276:1(1-25)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3508027
Wang XWang SJiang YSingh AYang MHuang L(2022)Combating Stealthy Thermal Covert Channel Attack With Its Thermal Signal Transmitted in Direct Sequence Spread SpectrumIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319734441:11(4064-4075)Online publication date: Nov-2022
https://doi.org/10.1109/TCAD.2022.3197344
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
Zambrano BGarzon EStrangio SIannaccone GLanuzza M(2022)A 0.6V–1.8V Compact Temperature Sensor With 0.24 °C Resolution, ±1.4 °C Inaccuracy and 1.06nJ per ConversionIEEE Sensors Journal10.1109/JSEN.2022.317110622:12(11480-11488)Online publication date: 15-Jun-2022
https://doi.org/10.1109/JSEN.2022.3171106
Chen KChen C(2022)Entropy-based Thermal Sensor Allocation for Temperature-aware Multi-core Platforms2022 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS48785.2022.9937996(2534-2537)Online publication date: 28-May-2022
https://doi.org/10.1109/ISCAS48785.2022.9937996
Posobkiewicz KGórecki K(2021)Influence of Selected Factors on Thermal Parameters of the Components of Forced Cooling Systems of Electronic DevicesElectronics10.3390/electronics1003034010:3(340)Online publication date: 1-Feb-2021
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Silva AWeber IMartins AMoraes F(2021)Hardware Accelerator for Runtime Temperature Estimation in Many-CoresIEEE Design & Test10.1109/MDAT.2021.306891438:4(62-69)Online publication date: Aug-2021
https://doi.org/10.1109/MDAT.2021.3068914
An TIm YYoo JCho YLee HHeo Y(2021)Methodology for Accurate Hotspot Prediction by Upscale Spatiotemporal Resolution of Temperature Sensing using a Power Meter and Resistor-Capacitor Thermal Model2021 20th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (iTherm)10.1109/ITherm51669.2021.9503200(130-138)Online publication date: 1-Jun-2021
https://doi.org/10.1109/ITherm51669.2021.9503200
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