research-article

Evaluating integrated graphics processors for data center workloads

Authors:

Sangman Kim,

Indrajit Roy,

Vanish TalwarAuthors Info & Claims

HotPower '13: Proceedings of the Workshop on Power-Aware Computing and Systems

Article No.: 8, Pages 1 - 5

https://doi.org/10.1145/2525526.2525847

Published: 03 November 2013 Publication History

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Abstract

More than 90% of consumer computers use integrated graphics processors. In these processors, the CPU and the GPU share the same physical memory. Due to high density, good power efficiency, and low cost, integrated graphics processors are promising candidates for next-generation micro-servers and, hence, data-center workloads.

While discrete graphics processors have been extensively studied, there is very little work on characterizing integrated GPUs. This paper is a step towards understanding the power and performance of integrated GPUs. Our results reveal many architectural caveats that programmers need to be aware of to exploit integrated GPUs: memory contention between the CPU and GPU, workload dependent energy efficiency, and data transfer tradeoffs.

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Cited By

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Farooqui NRoy IChen YTalwar VBarik RLewis BShpeisman TSchwan K(2018)Accelerating Data Analytics on Integrated GPU Platforms via Runtime SpecializationInternational Journal of Parallel Programming10.1007/s10766-016-0482-x46:2(336-375)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10766-016-0482-x
Farooqui NRoy IChen YTalwar VSchwan KPalermo GFeo JTumeo AFranke H(2016)Accelerating graph applications on integrated GPU platforms via instrumentation-driven optimizationsProceedings of the ACM International Conference on Computing Frontiers10.1145/2903150.2903152(19-28)Online publication date: 16-May-2016
https://dl.acm.org/doi/10.1145/2903150.2903152
Barik RFarooqui NLewis BHu CShpeisman TFranke BWu YRastello F(2016)A black-box approach to energy-aware scheduling on integrated CPU-GPU systemsProceedings of the 2016 International Symposium on Code Generation and Optimization10.1145/2854038.2854052(70-81)Online publication date: 29-Feb-2016
https://dl.acm.org/doi/10.1145/2854038.2854052
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Evaluating integrated graphics processors for data center workloads

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HotPower '13: Proceedings of the Workshop on Power-Aware Computing and Systems

November 2013

66 pages

ISBN:9781450324588

DOI:10.1145/2525526

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

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Published: 03 November 2013

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SOSP '13: ACM SIGOPS 24th Symposium on Operating Systems Principles

November 3 - 6, 2013

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HotPower '13 Paper Acceptance Rate 13 of 38 submissions, 34%;

Overall Acceptance Rate 20 of 50 submissions, 40%

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Farooqui NRoy IChen YTalwar VBarik RLewis BShpeisman TSchwan K(2018)Accelerating Data Analytics on Integrated GPU Platforms via Runtime SpecializationInternational Journal of Parallel Programming10.1007/s10766-016-0482-x46:2(336-375)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10766-016-0482-x
Farooqui NRoy IChen YTalwar VSchwan KPalermo GFeo JTumeo AFranke H(2016)Accelerating graph applications on integrated GPU platforms via instrumentation-driven optimizationsProceedings of the ACM International Conference on Computing Frontiers10.1145/2903150.2903152(19-28)Online publication date: 16-May-2016
https://dl.acm.org/doi/10.1145/2903150.2903152
Barik RFarooqui NLewis BHu CShpeisman TFranke BWu YRastello F(2016)A black-box approach to energy-aware scheduling on integrated CPU-GPU systemsProceedings of the 2016 International Symposium on Code Generation and Optimization10.1145/2854038.2854052(70-81)Online publication date: 29-Feb-2016
https://dl.acm.org/doi/10.1145/2854038.2854052
Dayarathna MWen YFan R(2016)Data Center Energy Consumption Modeling: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2015.248118318:1(732-794)Online publication date: Sep-2017
https://doi.org/10.1109/COMST.2015.2481183
Kalidas RDaga MKrommydas KFeng W(2015)On the Performance, Energy, and Power of Data-Access Methods in Heterogeneous Computing SystemsProceedings of the 2015 IEEE International Parallel and Distributed Processing Symposium Workshop10.1109/IPDPSW.2015.131(871-879)Online publication date: 25-May-2015
https://dl.acm.org/doi/10.1109/IPDPSW.2015.131
Kim SBottleson JJin JBindu PSakhare SSpisak J(2015)Power Efficient MapReduce Workload Acceleration Using Integrated-GPUProceedings of the 2015 IEEE First International Conference on Big Data Computing Service and Applications10.1109/BigDataService.2015.12(162-169)Online publication date: 30-Mar-2015
https://dl.acm.org/doi/10.1109/BigDataService.2015.12

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Relational query coprocessing on graphics processors

Acceleration of cfd and data analysis using graphics processors

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