Run-Time Power Modelling in Embedded GPUs with Dynamic Voltage and Frequency Scaling

Author:

Nunez-Yanez Jose1,Nikov Kris1,Eder Kerstin1,Hosseinabady Mohammad1

Affiliation:

1. Department of Electrical and Electronic Engineering, Bristol

Funder

H2020 European Research Council

Engineering and Physical Sciences Research Council

Royal Society

Publisher

ACM

Reference9 articles.

1. Power and Performance Characterization and Modeling of GPU-Accelerated Systems. In 2014 IEEE 28th International Parallel and Distributed Processing Symposium. 113--122 Power and Performance Characterization and Modeling of GPU-Accelerated Systems. In 2014 IEEE 28th International Parallel and Distributed Processing Symposium. 113--122 Y. Abe H. Sasaki S. Kato K. Inoue M. Edahiro M. Peres 2014 2014

2. Advances in Computers Elsevier 7--54. https://doi.org/10.1016/B978-0-12-396528-8.00002-X Advances in Computers 87 Bhavishya Goel Sally A. McKee Magnus Själander Chapter two - Techniques to Measure Model and Manage Power 2012

3. Xinxin Mei Qiang Wang and Xiaowen Chu. 2016. A Survey and Measurement Study of GPU DVFS on Energy Conservation. ArXiv abs/1610.01784 (2016). Xinxin Mei Qiang Wang and Xiaowen Chu. 2016. A Survey and Measurement Study of GPU DVFS on Energy Conservation. ArXiv abs/1610.01784 (2016).

4. Statistical power modeling of GPU kernels using performance counters

5. Krastin Nikov. 2017. BUILDMODEL. https://github.com/kranik/BUILDMODEL/tree/master/ARMPM_buildmodel. [Online; accessed 01-Aug-2017]. Krastin Nikov. 2017. BUILDMODEL. https://github.com/kranik/BUILDMODEL/tree/master/ARMPM_buildmodel. [Online; accessed 01-Aug-2017].

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