The modeling of solids is most naturally placed within a Lagrangian framework because it requires constitutive models which depend on knowledge of the original material orientations and subsequent deformations. Detailed kinematic information is needed to ensure material frame indifference which is captured through the deformation gradient F. Such information can be tracked easily in a Lagrangian code. Unfortunately, not all problems can be easily modeled using Lagrangian concepts due to severe distortions in the underlying motion. Either a Lagrangian/Eulerian or a pure Eulerian modeling framework must be introduced. We discuss and contrast several Lagrangian/Eulerian approaches for keeping track of the details of material kinematics.
Shared libraries have become ubiquitous and are used to achieve great resource efficiencies on many platforms. The same properties that enable efficiencies on time-shared computers and convenience on small clusters prove to be great obstacles to scalability on large clusters and High Performance Computing platforms. In addition, Light Weight operating systems such as Catamount have historically not supported the use of shared libraries specifically because they hinder scalability. In this report we will outline the methods of supporting shared libraries on High Performance Computing platforms using Light Weight kernels that we investigated. The considerations necessary to evaluate utility in this area are many and sometimes conflicting. While our initial path forward has been determined based on this evaluation we consider this effort ongoing and remain prepared to re-evaluate any technology that might provide a scalable solution. This report is an evaluation of a range of possible methods of supporting dynamically linked executables on capability class1 High Performance Computing platforms. Efforts are ongoing and extensive testing at scale is necessary to evaluate performance. While performance is a critical driving factor, supporting whatever method is used in a production environment is an equally important and challenging task.
The development continues for Finite State Abstraction (FSA) methods to enable Impacts Analysis (IA) for cyber attack against power grid control systems. Building upon previous work, we successfully demonstrated the addition of Bounded Model Checking (BMC) to the FSA method, which constrains grid conditions to reasonable behavior. The new FSA feature was successfully implemented and tested. FSA is an important part of IA for the power grid, complementing steady-state approaches. It enables the simultaneous evaluation of myriad dynamic trajectories for the system, which in turn facilitates IA for whole ranges of system conditions simultaneously. Given the potentially wide range and subtle nature of potential control system attacks, this is a promising research approach. In this report, we will explain the addition of BMC to the previous FSA work and some testing/simulation upon the implemented code using a two-bus test system. The current FSA approach and code allow the calculation of the acceptability of power grid conditions post-cyber attack (over a given time horizon and for a specific grid topology). Future work will enable analysis spanning various topologies (to account for switching events), as well as an understanding of the cyber attack stimuli that can lead to undesirable grid conditions.
Application performance is determined by a combination of many choices: hardware platform, runtime environment, languages and compilers used, algorithm choice and implementation, and more. In this complicated environment, we find that the use of mini-applications - small self-contained proxies for real applications - is an excellent approach for rapidly exploring the parameter space of all these choices. Furthermore, use of mini-applications enriches the interaction between application, library and computer system developers by providing explicit functioning software and concrete performance results that lead to detailed, focused discussions of design trade-offs, algorithm choices and runtime performance issues. In this paper we discuss a collection of mini-applications and demonstrate how we use them to analyze and improve application performance on new and future computer platforms.