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Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications

Journal of Parallel and Distributed Computing

Barrett, R.F.; Crozier, Paul C.; Doerfler, Douglas W.; Heroux, Michael A.; Lin, Paul L.; Thornquist, Heidi K.; Trucano, Timothy G.; Vaughan, Courtenay T.

Computational science and engineering application programs are typically large, complex, and dynamic, and are often constrained by distribution limitations. As a means of making tractable rapid explorations of scientific and engineering application programs in the context of new, emerging, and future computing architectures, a suite of "miniapps" has been created to serve as proxies for full scale applications. Each miniapp is designed to represent a key performance characteristic that does or is expected to significantly impact the runtime performance of an application program. In this paper we introduce a methodology for assessing the ability of these miniapps to effectively represent these performance issues. We applied this methodology to three miniapps, examining the linkage between them and an application they are intended to represent. Herein we evaluate the fidelity of that linkage. This work represents the initial steps required to begin to answer the question, "Under what conditions does a miniapp represent a key performance characteristic in a full app?"

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TYPE Journal Article YEAR 2015
ScopusOSTIDOI

Simulation information regarding Sandia National Laboratories trinity capability improvement metric

Agelastos, Anthony M.; Lin, Paul L.

Sandia National Laboratories, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory each selected a representative simulation code to be used as a performance benchmark for the Trinity Capability Improvement Metric. Sandia selected SIERRA Low Mach Module: Nalu, which is a uid dynamics code that solves many variable-density, acoustically incompressible problems of interest spanning from laminar to turbulent ow regimes, since it is fairly representative of implicit codes that have been developed under ASC. The simulations for this metric were performed on the Cielo Cray XE6 platform during dedicated application time and the chosen case utilized 131,072 Cielo cores to perform a canonical turbulent open jet simulation within an approximately 9-billion-elementunstructured- hexahedral computational mesh. This report will document some of the results from these simulations as well as provide instructions to perform these simulations for comparison.

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TYPE SAND Report YEAR 2013
DOIOSTI
Results 51–75 of 125
Results 51–75 of 125