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Evaluating integrated graphics processors for data center workloads

Published: 03 November 2013 Publication History

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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  • (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
  • (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
  • (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
  • Show More Cited By

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cover image ACM Conferences
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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Published: 03 November 2013

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Overall Acceptance Rate 20 of 50 submissions, 40%

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Cited By

View all
  • (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
  • (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
  • (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
  • (2016)Data Center Energy Consumption Modeling: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2015.248118318:1(732-794)Online publication date: Sep-2017
  • (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
  • (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

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