Adaptive performance prediction for integrated GPUs

Ujjwal Gupta, Joseph Campbell, Umit Ogras, Raid Ayoub, Michael Kishinevsky, Francesco Paterna, Suat Gumussoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations


Integrated GPUs have become an indispensable component of mobile processors due to the increasing popularity of graphics applications. The GPU frequency is a key factor both in application throughput and mobile processor power consumption under graphics workloads. Therefore, dynamic power management algorithms have to assess the performance sensitivity to the GPU frequency accurately. Since the impact of the GPU frequency on performance varies rapidly over time, there is a need for online performance models that can adapt to varying workloads. This paper presents a light-weight adaptive runtime performance model that predicts the frame processing time. We use this model to estimate the frame time sensitivity to the GPU frequency. Our experiments on a mobile platform running common GPU benchmarks show that the mean absolute percentage error in frame time and frame time sensitivity prediction are 3.8% and 3.9%, respectively.

Original languageEnglish (US)
Title of host publication2016 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450344661
StatePublished - Nov 7 2016
Event35th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016 - Austin, United States
Duration: Nov 7 2016Nov 10 2016

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152


Other35th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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