TY - GEN
T1 - Adaptive performance prediction for integrated GPUs
AU - Gupta, Ujjwal
AU - Campbell, Joseph
AU - Ogras, Umit
AU - Ayoub, Raid
AU - Kishinevsky, Michael
AU - Paterna, Francesco
AU - Gumussoy, Suat
N1 - Funding Information:
This work was supported partially by Strategic CAD Labs, Intel Corporation and National Science Foundation under Grant No. CNS-1526562.
Publisher Copyright:
© 2016 ACM.
PY - 2016/11/7
Y1 - 2016/11/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85000963046&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85000963046&partnerID=8YFLogxK
U2 - 10.1145/2966986.2966997
DO - 10.1145/2966986.2966997
M3 - Conference contribution
AN - SCOPUS:85000963046
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
BT - 2016 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016
Y2 - 7 November 2016 through 10 November 2016
ER -