Article

HaPPy: hyperthread-aware power profiling dynamically

Authors:

Stephane Eranian,

Jason MarsAuthors Info & Claims

USENIX ATC'14: Proceedings of the 2014 USENIX conference on USENIX Annual Technical Conference

Pages 211 - 218

Published: 19 June 2014 Publication History

Abstract

Quantifying the power consumption of individual applications co-running on a single server is a critical component for software-based power capping, scheduling, and provisioning techniques in modern datacenters. However, with the proliferation of hyperthreading in the last few generations of server-grade processor designs, the challenge of accurately and dynamically performing this power attribution to individual threads has been significantly exacerbated. Due to the sharing of core-level resources such as functional units, prior techniques are not suitable to attribute the power consumption between hyperthreads sharing a physical core.

In this paper, we present a runtime mechanism that quantifies and attributes power consumption to individual jobs at fine granularity. Specifically, we introduce a hyperthread-aware power model that differentiates between the states when both hardware threads of a core are in use, and when only one thread is in use. By capturing these two different states, we are able to accurately attribute power to each logical CPU in modern servers. We conducted experiments with several Google production workloads on an Intel Sandy Bridge server. Compared to prior hyperthread-oblivious model, HaPPy is substantially more accurate, reducing the prediction error from 20.5% to 7.5% on average and from 31.5% to 9.4% in the worst case.

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Ferroni MColmenares JHofmeyr SKubiatowicz JSantambrogio M(2018)Enabling power-awareness for the Xen hypervisorACM SIGBED Review10.1145/3199610.319961515:1(36-42)Online publication date: 20-Mar-2018
https://dl.acm.org/doi/10.1145/3199610.3199615
Guo LXu TXu MLiu XLin FOliveira RFelber PHu Y(2018)Power sandboxProceedings of the Thirteenth EuroSys Conference10.1145/3190508.3190533(1-15)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3190508.3190533
Khan KHirki MNiemi TNurminen JOu Z(2018)RAPL in ActionACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/31777543:2(1-26)Online publication date: 22-Mar-2018
https://dl.acm.org/doi/10.1145/3177754
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HaPPy: hyperthread-aware power profiling dynamically
1. General and reference
  1. Cross-computing tools and techniques
2. Social and professional topics
  1. Professional topics
    1. Computing profession

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Information

Published In

cover image Guide Proceedings

USENIX ATC'14: Proceedings of the 2014 USENIX conference on USENIX Annual Technical Conference

June 2014

512 pages

ISBN:9781931971102

Editors:
Garth Gibson
Carnegie Mellon University
,
Nickolai Zeldovich
Massachusetts Institute of Technology

Sponsors

VMware
NetApp
IBMR: IBM Research
Facebook: Facebook
HP: HP

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USENIX Association

United States

Publication History

Published: 19 June 2014

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Cited By

Ferroni MColmenares JHofmeyr SKubiatowicz JSantambrogio M(2018)Enabling power-awareness for the Xen hypervisorACM SIGBED Review10.1145/3199610.319961515:1(36-42)Online publication date: 20-Mar-2018
https://dl.acm.org/doi/10.1145/3199610.3199615
Guo LXu TXu MLiu XLin FOliveira RFelber PHu Y(2018)Power sandboxProceedings of the Thirteenth EuroSys Conference10.1145/3190508.3190533(1-15)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3190508.3190533
Khan KHirki MNiemi TNurminen JOu Z(2018)RAPL in ActionACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/31777543:2(1-26)Online publication date: 22-Mar-2018
https://dl.acm.org/doi/10.1145/3177754
Ha TSamejima MKomoda N(2017)Power and Performance Estimation for Fine-Grained Server Power Capping via Controlling Heterogeneous ApplicationsACM Transactions on Management Information Systems10.1145/30864498:4(1-19)Online publication date: 30-Aug-2017
https://dl.acm.org/doi/10.1145/3086449
Colmant MFelber PRouvoy RSeinturier L(2017)WattsKitProceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing10.1109/CCGRID.2017.27(514-523)Online publication date: 14-May-2017
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Dimakopoulou MEranian SKoziris NBambos NWest J(2016)Reliable and efficient performance monitoring in linuxProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.5555/3014904.3014950(1-13)Online publication date: 13-Nov-2016
https://dl.acm.org/doi/10.5555/3014904.3014950
Salinas-Hilburg JZapater MRisco-Martín JMoya JAyala JFanucci LTeich J(2016)Unsupervised power modeling of co-allocated workloads for energy efficiency in data centersProceedings of the 2016 Conference on Design, Automation & Test in Europe10.5555/2971808.2972121(1345-1350)Online publication date: 14-Mar-2016
https://dl.acm.org/doi/10.5555/2971808.2972121
Campanoni SHolloway GWei GBrooks DOlukotun KSmith AHundt RMars J(2015)HELIX-UPProceedings of the 13th Annual IEEE/ACM International Symposium on Code Generation and Optimization10.5555/2738600.2738630(235-245)Online publication date: 7-Feb-2015
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Li CHu YLiu LGu JSong MLiang XYuan JLi T(2015)Towards sustainable in-situ server systems in the big data eraACM SIGARCH Computer Architecture News10.1145/2872887.275038143:3S(14-26)Online publication date: 13-Jun-2015
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Li CHu YLiu LGu JSong MLiang XYuan JLi TMarr DAlbonesi D(2015)Towards sustainable in-situ server systems in the big data eraProceedings of the 42nd Annual International Symposium on Computer Architecture10.1145/2749469.2750381(14-26)Online publication date: 13-Jun-2015
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