Tag: Program Phases

SUSCOM’18 Paper: Program Power Profiling Based on Phase Behaviors

Program Power Profiling Based on Phase Behaviors, Xiaobin Ma, Zhihui Du, and Jason Liu. Sustainable Computing, Informatics and Systems, doi:10.1016/j.suscom.2018.05.001 – 17 May 2018. To appear. [preprint]

Abstract

Power profiling tools based on fast and accurate workload analysis can be useful for job scheduling and resource allocation aiming to optimize the power consumption of large-scale, high-performance computer systems. In this article, we propose a novel method for predicting the power consumption of a complete workload or application by extrapolating the power consumption of only a few code segments of the same application obtained from measurements. As such, it provides a fast and yet effective way for predicting the power consumption of the execution of both single and multi-threaded programs on arbitrary architectures without having to profile the entire program’s execution. The latter would be costly to obtain, especially if it is a long-running program. Our method employs a set of code analysis tools to capture the program’s phase behavior and then uses a multi-variable linear regression method to estimate the power consumption of the entire program. For validation, we select the SPEC 2006 benchmark suite and the NAS parallel benchmarks to evaluate the accuracy and effectiveness of our method. Experimental results on three generations of multicore processors show that our power profiling method achieves good accuracy in predicting program’s energy use with relatively small errors.

Bibtex

@Article{suscom18,
AUTHOR = {Ma, Xiaobin and Du, Zhihui and Liu, Jason},
TITLE = {Program Power Profiling Based on Phase Behaviors},
JOURNAL = {Sustainable Computing, Informatics and Systems},
URL = {https://doi.org/10.1016/j.suscom.2018.05.001},
DOI = {10.1016/j.suscom.2018.05.001}
}

REHPC’16 Paper: Program Power Profiling Based on Phases

Fast and Effective Power Profiling of Program Execution Based on Phase Behaviors, Xiaobin Ma, Zhihui Du and Jason Liu. In Proceedings of the 1st International Workshop on Resilience and/or Energy-Aware Techniques for High-Performance Computing (RE-HPC 2016), held in conjunction with the 7th International Green and Sustainable Computing Conference (IGSC 2016), November 2016. [paper]

Abstract

Power profiling tools based on fast and accurate workload analysis can be useful for job scheduling and resource allocation aiming to optimize the power consumption of large-scale high-performance computer systems. In this paper, we propose a novel method for predicting the power consumption of a complete workload or application by extrapolating the power consumption of only a few code segments of the same application obtained from measurement. As such, it provides a fast and yet effective way for predicting the power consumption of a single-threaded execution of a program on arbitrary architectures without having to profile the entire program’s execution. The latter would be costly to obtain, especially if it’s a long running program. Our method employs a set of code analysis tools to capture the program’s phase behavior and then adopts a multi-variable linear regression method to estimate the power consumption of the entire program. We use SPEC 2006 benchmark to evaluate the accuracy and effectiveness of our method. Experimental results show that our power profiling method achieves good accuracy in predicting program’s energy use with relatively small errors.

Bibtex

@INPROCEEDINGS{Ma2016:phases,
author={Xiaobin Ma and Zhihui Du and Jason Liu},
booktitle={Proceedings of the 7th International Green and Sustainable Computing Conference (IGSC)},
title={Fast and effective power profiling of program execution based on phase behaviors},
year={2016},
pages={1-8},
doi={10.1109/IGCC.2016.7892625},
month={Nov},}