Tag: HPC Modeling

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},}

PADS’16 Paper: Integrated Interconnect Model

An Integrated Interconnection Network Model for Large-Scale Performance Prediction, Kishwar Ahmed, Mohammad Obaida, Jason Liu, Stephan Eidenbenz, Nandakishore Santhi, and Guillaume Chapuis. In Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS 2016), May 2016. [paper]

Abstract

Interconnection network is a critical component of high- performance computing architecture and application co-design. For many scientific applications, the increasing communication complexity poses a serious concern as it may hinder the scaling properties of these applications on novel architectures. It is apparent that a scalable, efficient, and accurate interconnect model would be essential for performance evaluation studies. In this paper, we present an interconnect model for predicting the performance of large-scale applications on high-performance architectures. In particular, we present a sufficiently detailed interconnect model for Cray’s Gemini 3-D torus network. The model has been integrated with an implementation of the Message-Passing Interface (MPI) that can mimic most of its functions with packet-level accuracy on the target platform. Extensive experiments show that our integrated model provides good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance.

Bibtex

@inproceedings{Ahmed2016:interconnect,
 author = {Ahmed, Kishwar and Obaida, Mohammad and Liu, Jason and Eidenbenz, Stephan and Santhi, Nandakishore and Chapuis, Guillaume},
 title = {An Integrated Interconnection Network Model for Large-Scale Performance Prediction},
 booktitle = {Proceedings of the 2016 Annual ACM Conference on SIGSIM Principles of Advanced Discrete Simulation},
 series = {SIGSIM-PADS '16},
 year = {2016},
 isbn = {978-1-4503-3742-7},
 location = {Banff, Alberta, Canada},
 pages = {177--187},
 numpages = {11},
 url = {http://doi.acm.org/10.1145/2901378.2901396},
 doi = {10.1145/2901378.2901396},
 acmid = {2901396},
 publisher = {ACM},
 address = {New York, NY, USA},
}