Paper Info
Title
Adaptive performance prediction for integrated GPUs.
Abstract
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.
Year
DOI
Venue
2016
10.1145/2966986.2966997
ICCAD
Keywords
Field
DocType
adaptive performance prediction,integrated GPU,mobile processors,graphics applications,application throughput,power consumption,dynamic power management algorithms,performance sensitivity assessment,GPU frequency,performance impact,light-weight adaptive runtime performance model,frame processing time prediction,frame time sensitivity estimation,mobile platform,mean absolute percentage error,frame time sensitivity prediction
Graphics,Mean absolute percentage error,Adaptive performance,Efficient energy use,Computer science,Mobile processor,Real-time computing,Frame time,Time–frequency analysis,Throughput
Conference
ISSN
ISBN
Citations 
1933-7760
978-1-5090-3421-5
4
PageRank 
References 
Authors
0.42
9
7
Name
Order
Citations
PageRank
Ujjwal Das Gupta1376.59
joseph p campbell2366.76
Ümit Y. Ogras320315.03
Raid Ayoub447027.86
Michael Kishinevsky581467.81
Francesco Paterna6605.68
Suat Gumussoy77810.99