Publications

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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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Trilinos is an object-oriented software framework for the solution of large-scale, complex multi-physics engineering and scientific problems. While Trilinos was originally designed for scalable solutions of large problems, the fidelity needed by many simulations is significantly greater than what one could have envisioned two decades ago. When problem sizes exceed a billion elements even scalable applications and solver stacks require a complete revision. The second-generation Trilinos employs C++ templates in order to solve arbitrarily large problems. We present a case study of the integration of Trilinos with a low Mach fluids engineering application (SIERRA low Mach module/Nalu). Through the use of improved algorithms and better software engineering practices, we demonstrate good weak scaling for up to a nine billion element large eddy simulation (LES) problem on unstructured meshes with a 27 billion row matrix on 524,288 cores of an IBM Blue Gene/Q platform.

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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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