Application Modeling For Scalable Simulation Of Massively Parallel Systems

Keywords

Analytical models; Computational modeling; Data models; Hardware; Load modeling; Predictive models; Skeleton

Abstract

Macro-scale simulation has been advanced as one tool for application - architecture co-design to express operation of exascale systems. These simulations approximate the behavior of system components, trading off accuracy for increased evaluation speed. Application skeletons serve as the vehicle for these simulations, but they require accurately capturing the execution behavior of computation. The complexity of application codes, the heterogeneity of the platforms, and the increasing importance of simulating multiple performance metrics (e.g., execution time, energy) require new modeling techniques. We propose flexible statistical models to increase the fidelity of application simulation at scale. We present performance model validation for several exascale mini-applications that leverage a variety of parallel programming frameworks targeting heterogeneous architectures for both time and energy performance metrics. When paired with these statistical models, application skeletons were simulated on average 12.5 times faster than the original application incurring only 6.08% error, which is 12.5% faster and 33.7% more accurate than baseline models.

Publication Date

11-23-2015

Publication Title

Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015

Number of Pages

238-247

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/HPCC-CSS-ICESS.2015.286

Socpus ID

84961743341 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84961743341

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