research-article

Maximizing Performance Under a Power Cap: A Comparison of Hardware, Software, and Hybrid Techniques

Authors:

Henry HoffmannAuthors Info & Claims

ACM SIGARCH Computer Architecture News, Volume 44, Issue 2

Pages 545 - 559

https://doi.org/10.1145/2980024.2872375

Published: 25 March 2016 Publication History

Abstract

Power and thermal dissipation constrain multicore performance scaling. Modern processors are built such that they could sustain damaging levels of power dissipation, creating a need for systems that can implement processor power caps. A particular challenge is developing systems that can maximize performance within a power cap, and approaches have been proposed in both software and hardware. Software approaches are flexible, allowing multiple hardware resources to be coordinated for maximum performance, but software is slow, requiring a long time to converge to the power target. In contrast, hardware power capping quickly converges to the the power cap, but only manages voltage and frequency, limiting its potential performance. In this work we propose PUPiL, a hybrid software/hardware power capping system. Unlike previous approaches, PUPiL combines hardware's fast reaction time with software's flexibility. We implement PUPiL on real Linux/x86 platform and compare it to Intel's commercial hardware power capping system for both single and multi-application workloads. We find PUPiL provides the same reaction time as Intel's hardware with significantly higher performance. On average, PUPiL outperforms hardware by from 1:18-2:4 depending on workload and power target. Thus, PUPiL provides a promising way to enforce power caps with greater performance than current state-of-the-art hardware-only approaches.

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da Silva Vde Lima ESchwarzrock JRossi FLuizelli MBeck ALorenzon A(2024)Synergistically Rebalancing the EDP of Container-Based Parallel ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.335735335:3(484-498)Online publication date: Mar-2024
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Ding JHoffmann HMohror KArnold DBadia R(2023)DPS: Adaptive Power Management for Overprovisioned SystemsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607091(1-14)Online publication date: 12-Nov-2023
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Index Terms

Maximizing Performance Under a Power Cap: A Comparison of Hardware, Software, and Hybrid Techniques
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Reconfigurable computing
  2. Real-time systems
    1. Real-time system architecture
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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Published In

cover image ACM SIGARCH Computer Architecture News

ACM SIGARCH Computer Architecture News Volume 44, Issue 2

ASPLOS'16

May 2016

774 pages

ISSN:0163-5964

DOI:10.1145/2980024

Editor:
Doug DeGroot
acm dot org

Issue’s Table of Contents

ASPLOS '16: Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems
March 2016
824 pages
ISBN:9781450340915
DOI:10.1145/2872362
General Chair:
Tom Conte
Georgia Tech, USA
,
Program Chair:
Yuanyuan Zhou
University of California, San Diego, USA

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Published: 25 March 2016

Published in SIGARCH Volume 44, Issue 2

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https://doi.org/10.1109/TPDS.2024.3357353
Ding JHoffmann HMohror KArnold DBadia R(2023)DPS: Adaptive Power Management for Overprovisioned SystemsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607091(1-14)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607091
Srikanthan SChakraborti SFerro PDwarkadas S(2022)MAPPER: Managing Application Performance via Parallel Efficiency Regulation*ACM Transactions on Architecture and Code Optimization10.1145/350176719:2(1-26)Online publication date: 24-Mar-2022
https://dl.acm.org/doi/10.1145/3501767
Koutsovasilis PAntonopoulos CBellas NLalis SPapadimitriou GChatzidimitriou AGizopoulos D(2022)The Impact of CPU Voltage Margins on Power-Constrained ExecutionIEEE Transactions on Sustainable Computing10.1109/TSUSC.2020.30451957:1(221-234)Online publication date: 1-Jan-2022
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Wang YZhang WHao MWang Z(2022)Online Power Management for Multi-Cores: A Reinforcement Learning Based ApproachIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.309227033:4(751-764)Online publication date: 1-Apr-2022
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Zhang CKumbhare AManousakis IZhang DMisra PAssis RWoolcock KMahalingam NWarrier BGauthier DKunnath LSolomon SMorales OFontoura MBianchini RMartínez JDuato JJohn L(2021)FlexProceedings of the 48th Annual International Symposium on Computer Architecture10.1109/ISCA52012.2021.00033(319-332)Online publication date: 14-Jun-2021
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Ciesielczyk TCabrera AOleksiak APiątek WWaligóra GAlmeida FBlanco V(2021)An approach to reduce energy consumption and performance losses on heterogeneous servers using power cappingJournal of Scheduling10.1007/s10951-020-00649-424:5(489-505)Online publication date: 1-Oct-2021
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