Tag: Program Analysis

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}
}

SIGSIM-PADS’18 Paper: Parallel Application Performance Prediction

Parallel Application Performance Prediction Using Analysis Based Models and HPC Simulations, Mohammad Abu Obaida, Jason Liu, Gopinath Chennupati, Nandakishore Santhi, and Stephan Eidenbenz. In Proceedings of the 2018 SIGSIM Principles of Advanced Discrete Simulation (SIGSIM-PADS’18), May 2018. [paper]

Abstract

Parallel application performance models provide valuable insight about the performance in real systems. Capable tools providing fast, accurate, and comprehensive prediction and evaluation of high-performance computing (HPC) applications and system architectures have important value. This paper presents PyPassT, an analysis based modeling framework built on static program analysis and integrated simulation of target HPC architectures. More specifically, the framework analyzes application source code written in C with OpenACC directives and transforms it into an application model describing its computation and communication behavior (including CPU and GPU workloads, memory accesses, and message-passing transactions). The application model is then executed on a simulated HPC architecture for performance analysis. Preliminary experiments demonstrate that the proposed framework can represent the runtime behavior of benchmark applications with good accuracy.

Bibtex

@inproceedings{pads18-hpcpred,
title = {Parallel Application Performance Prediction Using Analysis Based Models and HPC Simulations},
author = {Mohammad Abu Obaida and Jason Liu and Gopinath Chennupati and Nandakishore Santhi and Stephan Eidenbenz},
booktitle = {Proceedings of the 2018 SIGSIM Principles of Advanced Discrete Simulation (SIGSIM-PADS’18)},
pages = {49--59},
month = {May},
year = {2018},
doi = {10.1145/3200921.3200937}
}

Slides