The performance of a large-scale, production-quality science and engineering application (‘app’) is often dominated by a small subset of the code. Even within that subset, computational and data access patterns are often repeated, so that an even smaller portion can represent the performance-impacting features. If application developers, parallel computing experts, and computer architects can together identify this representative subset and then develop a small mini-application (‘miniapp’) that can capture these primary performance characteristics, then this miniapp can be used to both improve the performance of the app as well as provide a tool for co-design for the high-performance computing community. However, a critical question is whether a miniapp can effectively capture key performance behavior of an app. This study provides a comparison of an implicit finite element semiconductor device modeling app on unstructured meshes with an implicit finite element miniapp on unstructured meshes. The goal is to assess whether the miniapp is predictive of the performance of the app. Finally, single compute node performance will be compared, as well as scaling up to 16,000 cores. Results indicate that the miniapp can be reasonably predictive of the performance characteristics of the app for a single iteration of the solver on a single compute node.
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?"
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.