Publications

Burnham, Laurie B.; Warrender, Christina E.; Cole, Kerstan S.; Forsythe, James C.; Haass, Michael J.; Adams, Susan S.

Abstract not provided.

Keiter, Eric R.; Warrender, Christina E.; Mei, Ting M.; Russo, Thomas V.; Schiek, Richard S.; Thornquist, Heidi K.; Verley, Jason V.; Coffey, Todd S.; Pawlowski, Roger P.

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). This includes support for most popular parallel and serial computers. A differential-algebraic-equation (DAE) formulation, which better isolates the device model package from solver algorithms. This allows one to develop new types of analysis without requiring the implementation of analysis-specific device models. Device models that are specifically tailored to meet Sandias needs, including some radiationaware devices (for Sandia users only). Object-oriented code design and implementation using modern coding practices. Xyce is a parallel code in the most general sense of the phrase a message passing parallel implementation which allows it to run efficiently a wide range of computing platforms. These include serial, shared-memory and distributed-memory parallel platforms. Attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows.

Keiter, Eric R.; Mei, Ting M.; Russo, Thomas V.; Pawlowski, Roger P.; Schiek, Richard S.; Coffey, Todd S.; Thornquist, Heidi K.; Verley, Jason V.; Warrender, Christina E.

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users Guide [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users Guide [1] .

Aimone, James B.; Warrender, Christina E.; Trumbo, Derek T.

Over the last three years the Neurons to Algorithms (N2A) LDRD project teams has built infrastructure to discover computational structures in the brain. This consists of a modeling language, a tool that enables model development and simulation in that language, and initial connections with the Neuroinformatics community, a group working toward similar goals. The approach of N2A is to express large complex systems like the brain as populations of a discrete part types that have specific structural relationships with each other, along with internal and structural dynamics. Such an evolving mathematical system may be able to capture the essence of neural processing, and ultimately of thought itself. This final report is a cover for the actual products of the project: the N2A Language Specification, the N2A Application, and a journal paper summarizing our methods.

Russo, Thomas V.; Mei, Ting M.; Keiter, Eric R.; Schiek, Richard S.; Thornquist, Heidi K.; Verley, Jason V.; Coffey, Todd S.; Pawlowski, Roger P.; Warrender, Christina E.

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). This includes support for most popular parallel and serial computers. A differential-algebraic-equation (DAE) formulation, which better isolates the device model package from solver algorithms. This allows one to develop new types of analysis without requiring the implementation of analysis-specific device models. Device models that are specifically tailored to meet Sandias needs, including some radiationaware devices (for Sandia users only). Object-oriented code design and implementation using modern coding practices. Xyce is a parallel code in the most general sense of the phrase a message passing parallel implementation which allows it to run efficiently a wide range of computing platforms. These include serial, shared-memory and distributed-memory parallel platforms. Attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows.

Keiter, Eric R.; Mei, Ting M.; Russo, Thomas V.; Pawlowski, Roger P.; Schiek, Richard S.; Coffey, Todd S.; Thornquist, Heidi K.; Verley, Jason V.; Warrender, Christina E.

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users Guide [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users Guide [1] .

McClain, Jonathan T.; Rothganger, Fredrick R.; Trumbo, Derek T.; Warrender, Christina E.

Abstract not provided.

Aimone, James B.; Warrender, Christina E.; Trumbo, Derek T.

Abstract not provided.

Schiek, Richard S.; Mei, Ting M.; Rajamanickam, Sivasankaran R.; Keiter, Eric R.; Warrender, Christina E.; Aimone, James B.; Thornquist, Heidi K.; Russo, Thomas V.; Verley, Jason V.; Crossno, Patricia J.

Abstract not provided.

Schiek, Richard S.; Thornquist, Heidi K.; Warrender, Christina E.; Mei, Ting M.; Teeter, Corinne M.; Aimone, James B.

Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.

Trumbo, Derek T.; Warrender, Christina E.; Aimone, James B.; Teeter, Corinne M.; Rothganger, Fredrick R.

Abstract not provided.

Keiter, Eric R.; Warrender, Christina E.; Verley, Jason V.; Mei, Ting M.; Russo, Thomas V.; Pawlowski, Roger P.; Schiek, Richard S.; Santarelli, Keith R.; Coffey, Todd S.; Thornquist, Heidi K.

Abstract not provided.

Keiter, Eric R.; Warrender, Christina E.; Verley, Jason V.; Mei, Ting M.; Russo, Thomas V.; Pawlowski, Roger P.; Schiek, Richard S.; Santarelli, Keith R.; Coffey, Todd S.; Thornquist, Heidi K.

Abstract not provided.

Siirola, John D.; Tidwell, Vincent C.; Warrender, Christina E.; Morrow, James D.; Benz, Zachary O.

Participatory modeling has become an important tool in facilitating resource decision making and dispute resolution. Approaches to modeling that are commonly used in this context often do not adequately account for important human factors. Current techniques provide insights into how certain human activities and variables affect resource outcomes; however, they do not directly simulate the complex variables that shape how, why, and under what conditions different human agents behave in ways that affect resources and human interactions related to them. Current approaches also do not adequately reveal how the effects of individual decisions scale up to have systemic level effects in complex resource systems. This lack of integration prevents the development of more robust models to support decision making and dispute resolution processes. Development of integrated tools is further hampered by the fact that collection of primary data for decision-making modeling is costly and time consuming. This project seeks to develop a new approach to resource modeling that incorporates both technical and behavioral modeling techniques into a single decision-making architecture. The modeling platform is enhanced by use of traditional and advanced processes and tools for expedited data capture. Specific objectives of the project are: (1) Develop a proof of concept for a new technical approach to resource modeling that combines the computational techniques of system dynamics and agent based modeling, (2) Develop an iterative, participatory modeling process supported with traditional and advance data capture techniques that may be utilized to facilitate decision making, dispute resolution, and collaborative learning processes, and (3) Examine potential applications of this technology and process. The development of this decision support architecture included both the engineering of the technology and the development of a participatory method to build and apply the technology. Stakeholder interaction with the model and associated data capture was facilitated through two very different modes of engagement, one a standard interface involving radio buttons, slider bars, graphs and plots, while the other utilized an immersive serious gaming interface. The decision support architecture developed through this project was piloted in the Middle Rio Grande Basin to examine how these tools might be utilized to promote enhanced understanding and decision-making in the context of complex water resource management issues. Potential applications of this architecture and its capacity to lead to enhanced understanding and decision-making was assessed through qualitative interviews with study participants who represented key stakeholders in the basin.

Schiek, Richard S.; Warrender, Christina E.; Thornquist, Heidi K.; Mei, Ting M.; Keiter, Eric R.; Russo, Thomas V.

Abstract not provided.

Rothganger, Fredrick R.; Warrender, Christina E.; Speed, Ann S.; Rohrer, Brandon R.; Naugle, Asmeret B.; Trumbo, Derek T.

Abstract not provided.

Warrender, Christina E.; Thornquist, Heidi K.; Mei, Ting M.; Keiter, Eric R.; Russo, Thomas V.

Abstract not provided.

Keiter, Eric R.; Warrender, Christina E.; Mei, Ting M.; Russo, Thomas V.; Pawlowski, Roger P.; Schiek, Richard S.; Santarelli, Keith R.; Coffey, Todd S.; Thornquist, Heidi K.

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users Guide. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users Guide. The Xyce Parallel Electronic Simulator has been written to support, in a rigorous manner, the simulation needs of the Sandia National Laboratories electrical designers. It is targeted specifically to run on large-scale parallel computing platforms but also runs well on a variety of architectures including single processor workstations. It also aims to support a variety of devices and models specific to Sandia needs. This document is intended to complement the Xyce Users Guide. It contains comprehensive, detailed information about a number of topics pertinent to the usage of Xyce. Included in this document is a netlist reference for the input-file commands and elements supported within Xyce; a command line reference, which describes the available command line arguments for Xyce; and quick-references for users of other circuit codes, such as Orcad's PSpice and Sandia's ChileSPICE.

Keiter, Eric R.; Warrender, Christina E.; Mei, Ting M.; Russo, Thomas V.; Pawlowski, Roger P.; Schiek, Richard S.; Santarelli, Keith R.; Coffey, Todd S.; Thornquist, Heidi K.

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: (1) Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). Note that this includes support for most popular parallel and serial computers; (2) Improved performance for all numerical kernels (e.g., time integrator, nonlinear and linear solvers) through state-of-the-art algorithms and novel techniques. (3) Device models which are specifically tailored to meet Sandia's needs, including some radiation-aware devices (for Sandia users only); and (4) Object-oriented code design and implementation using modern coding practices that ensure that the Xyce Parallel Electronic Simulator will be maintainable and extensible far into the future. Xyce is a parallel code in the most general sense of the phrase - a message passing parallel implementation - which allows it to run efficiently on the widest possible number of computing platforms. These include serial, shared-memory and distributed-memory parallel as well as heterogeneous platforms. Careful attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows. The development of Xyce provides a platform for computational research and development aimed specifically at the needs of the Laboratory. With Xyce, Sandia has an 'in-house' capability with which both new electrical (e.g., device model development) and algorithmic (e.g., faster time-integration methods, parallel solver algorithms) research and development can be performed. As a result, Xyce is a unique electrical simulation capability, designed to meet the unique needs of the laboratory.

Schiek, Richard S.; Warrender, Christina E.

Abstract not provided.

Speed, Ann S.; Warrender, Christina E.

In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

Speed, Ann S.; Warrender, Christina E.

In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

Behavioral and Brain Sciences

Verzi, Stephen J.; Wagner, John S.; Warrender, Christina E.

Abstract not provided.

Doser, Adele D.; Speed, Ann S.; Warrender, Christina E.

In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

Behavioral Brain Science

Speed, Ann S.; Verzi, Stephen J.; Wagner, John S.; Warrender, Christina E.

Abstract not provided.