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GfxDoctor: A Holistic Graphics Energy Profiler for Mobile Devices

Authors:

Y. Charlie HuAuthors Info & Claims

EuroSys '17: Proceedings of the Twelfth European Conference on Computer Systems

Pages 359 - 373

https://doi.org/10.1145/3064176.3064206

Published: 23 April 2017 Publication History

Abstract

Graphics is one of the major energy drain sources in smartphone apps. To optimize the app graphics energy, however, developers face the challenge of highly complex graphics rendering process, which involves multiple system layers including the app, the framework, the GPU, and the asynchronous interactions among them. Current diagnostic tools can profile the resource usage from certain layers, but fall short in stitching together profiling information across all the layers which is needed to provide developers with the visual effect-energy tradeoff at the app source-code level.

In this paper, we design and implement a holistic graphics energy diagnosis tool, GfxDoctor1, that helps developers to systematically diagnose energy inefficiencies in app graphics at the app source-code level, by precisely quantifying (1) the visual effect of each UI update, and (2) the aggregate energy drain spent in traversing the entire frame rendering stack due to each UI update. GfxDoctor overcomes three challenges faced in deriving per-UI-update visual effect and energy accounting, asynchrony across system layers, UI update batching, and "black-box" GPU, with two key techniques -- lightweight view-frame-ID-based information flow tracking, and OpenGL record-and-replay plus frame diffing. We show the effectiveness of GfxDoctor by profiling a randomly sampled set of 30 popular Android apps which reveals three types of graphics energy bugs happening in 8 out of the 30 apps. Removing these bugs reduces the app energy drain by 46% to 90%.

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Cited By

Arouj AAbdelmoniem AAlhilal AYou LWang C(2024)Resource-Aware Split Federated Learning for Edge Intelligence2024 IEEE 3rd Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML)10.1109/SenSys-ML62579.2024.00008(15-20)Online publication date: 13-May-2024
https://doi.org/10.1109/SenSys-ML62579.2024.00008
Arouj AAbdelmoniem A(2024)Towards energy-aware Federated Learning via collaborative computing approachComputer Communications10.1016/j.comcom.2024.04.012Online publication date: Apr-2024
https://doi.org/10.1016/j.comcom.2024.04.012
Tu XMallik AChen DHan KAltintas OWang HXie JSha KBanerjee SChen J(2023)Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge DevicesProceedings of the Eighth ACM/IEEE Symposium on Edge Computing10.1145/3583740.3628442(80-93)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3583740.3628442
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cover image ACM Conferences

EuroSys '17: Proceedings of the Twelfth European Conference on Computer Systems

April 2017

648 pages

ISBN:9781450349383

DOI:10.1145/3064176

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 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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Published: 23 April 2017

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EuroSys '17

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EuroSys '17: Twelfth EuroSys Conference 2017

April 23 - 26, 2017

Belgrade, Serbia

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Overall Acceptance Rate 241 of 1,308 submissions, 18%

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Twentieth European Conference on Computer Systems

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Cited By

Arouj AAbdelmoniem AAlhilal AYou LWang C(2024)Resource-Aware Split Federated Learning for Edge Intelligence2024 IEEE 3rd Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML)10.1109/SenSys-ML62579.2024.00008(15-20)Online publication date: 13-May-2024
https://doi.org/10.1109/SenSys-ML62579.2024.00008
Arouj AAbdelmoniem A(2024)Towards energy-aware Federated Learning via collaborative computing approachComputer Communications10.1016/j.comcom.2024.04.012Online publication date: Apr-2024
https://doi.org/10.1016/j.comcom.2024.04.012
Tu XMallik AChen DHan KAltintas OWang HXie JSha KBanerjee SChen J(2023)Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge DevicesProceedings of the Eighth ACM/IEEE Symposium on Edge Computing10.1145/3583740.3628442(80-93)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3583740.3628442
Xu QDavis JHu YJindal A(2022)An Empirical Study on the Impact of Deep Parameters on Mobile App Energy Usage2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER53432.2022.00103(844-855)Online publication date: Mar-2022
https://doi.org/10.1109/SANER53432.2022.00103
Ghoshal MDash PKong ZXu QHu YKoutsonikolas DLi Y(2022)Can 5G mmWave Support Multi-user AR?Passive and Active Measurement10.1007/978-3-030-98785-5_8(180-196)Online publication date: 22-Mar-2022
https://doi.org/10.1007/978-3-030-98785-5_8
Ahn WHong CHan KChoi SOh JLim S(2021)Scrolling-Aware Rendering to Reduce Frame Rates on SmartphonesElectronics10.3390/electronics1017217710:17(2177)Online publication date: 6-Sep-2021
https://doi.org/10.3390/electronics10172177
Kim YWu C(2021)AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated LearningMICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3466752.3480129(183-198)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3466752.3480129
Hu YLiu SHuang PBahar IHerlihy MWitchel ELebeck A(2019)A Case for Lease-Based, Utilitarian Resource Management on Mobile DevicesProceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3297858.3304057(301-315)Online publication date: 4-Apr-2019
https://dl.acm.org/doi/10.1145/3297858.3304057
Ghaleb T(2019)Software Energy Measurement at Different Levels of Granularity2019 International Conference on Computer and Information Sciences (ICCIS)10.1109/ICCISci.2019.8716456(1-6)Online publication date: Apr-2019
https://doi.org/10.1109/ICCISci.2019.8716456
Chen BLi XZhou XZhu Z(2019)Delayed Wake-Up Mechanism Under Suspend Mode of SmartphoneMethionine Dependence of Cancer and Aging10.1007/978-3-030-12981-1_24(346-355)Online publication date: 7-Feb-2019
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