Tag: Energy Models

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}
}

HPPAC’17 Paper: Energy-Aware Scheduling

When Good Enough Is Better: Energy-Aware Scheduling for Multicore Servers, Xinning Hui, Zhihui Dua, Jason Liu, Hongyang Sun, Yuxiong He, David A. Bader. In Proceedings of the 13th Workshop on High-Performance, Power-Aware Computing (HPPAC 2017), held in conjunction with 31st IEEE International Parallel and Distributed Processing Symposium (IPDPS 2017), May 2017. [paper]

Abstract

Power is a primary concern for mobile, cloud, and high-performance computing applications. Approximate computing refers to running applications to obtain results with tolerable errors under resource constraints, and it can be applied to balance energy consumption with service quality. In this paper, we propose a “Good Enough (GE)” scheduling algorithm that uses approximate computing to provide satisfactory QoS (Quality of Service) for interactive applications with significant energy savings. Given a user-specified quality level, the GE algorithm works in the AES (Aggressive Energy Saving) mode for the majority of the time, neglecting the low-quality portions of the workload. When the perceived quality falls below the required level, the algorithm switches to the BQ (Best Quality) mode with a compensation policy. To avoid core speed thrashing between the two modes, GE employs a hybrid power distribution scheme that uses the Equal-Sharing (ES) policy to distribute power among the cores when the workload is light (to save energy) and the Water-Filling (WF) policy when the workload is high (to improve quality). We conduct simulations to compare the performance of GE with existing scheduling algorithms. Results show that the proposed algorithm can provide large energy savings with satisfactory user experience.

Bibtex

@INPROCEEDINGS{ipdpsw17-approx,
author={X. Hui and Z. Du and J. Liu and H. Sun and Y. He and D. A. Bader},
booktitle={Proceedings of the 13th Workshop on High-Performance, Power-Aware Computing (HPPAC 2017), held in conjunction with 31st IEEE International Parallel and Distributed Processing Symposium (IPDPS 2017)},
title={When Good Enough Is Better: Energy-Aware Scheduling for Multicore Servers},
pages={984-993},
doi={10.1109/IPDPSW.2017.38},
month={May},
year={2017}
}

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},}