Publications

Salinger, Andrew G.

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Salinger, Andrew G.

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Gao, Xujiao G.; Nielsen, Erik N.; Muller, Richard P.; Young, Ralph W.; Salinger, Andrew G.; Kalashnikova, Irina

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Salinger, Andrew G.

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Proposed for publication in Communications in Computational Physics.

Salinger, Andrew G.; Phipps, Eric T.

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Salinger, Andrew G.

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Phipps, Eric T.; Pawlowski, Roger P.; Salinger, Andrew G.

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Muller, Richard P.; Lu, Tzu-Ming L.; Tracy, Lisa A.; Ten Eyck, Gregory A.; Lilly, Michael L.; Carroll, Malcolm; Nielsen, Erik N.; Gao, Xujiao G.; Young, Ralph W.; Salinger, Andrew G.; Kalashnikova, Irina; Rahman, Rajib R.; Bishop, Nathaniel B.; Carr, Stephen M.

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Gao, Xujiao G.; Nielsen, Erik N.; Muller, Richard P.; Salinger, Andrew G.; Young, Ralph W.; Carroll, Malcolm

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Salinger, Andrew G.

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Computational Electronics (IWCE), 2012 15th International Workshop on

Gao, Xujiao G.; Nielsen, Erik N.; Muller, Richard P.; Young, Ralph W.; Salinger, Andrew G.; Carroll, Malcolm

We present the Quantum Computer Aided Design (QCAD) simulator that targets modeling quantum devices, particularly Si double quantum dots (DQDs) developed for quantum computing. The simulator core includes Poisson, Schrodinger, and Configuration Interaction solvers which can be run individually or combined self-consistently. The simulator is built upon Sandia-developed Trilinos and Albany components, and is interfaced with the Dakota optimization tool. It is being developed for seamless integration, high flexibility and throughput, and is intended to be open source. The QCAD tool has been used to simulate a large number of fabricated silicon DQDs and has provided fast feedback for design comparison and optimization.

Nielsen, Erik N.; Young, Ralph W.; Muller, Richard P.; Salinger, Andrew G.; Carroll, Malcolm

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Nielsen, Erik N.; Salinger, Andrew G.; Muller, Richard P.; Young, Ralph W.

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Scientific Programming

Pawlowski, Roger P.; Phipps, Eric T.; Salinger, Andrew G.

An approach for incorporating embedded simulation and analysis capabilities in complex simulation codes through template-based generic programming is presented. This approach relies on templating and operator overloading within the C++ language to transform a given calculation into one that can compute a variety of additional quantities that are necessary for many state-of-the-art simulation and analysis algorithms. An approach for incorporating these ideas into complex simulation codes through general graph-based assembly is also presented. These ideas have been implemented within a set of packages in the Trilinos framework and are demonstrated on a simple problem from chemical engineering. © 2012 - IOS Press and the authors. All rights reserved.

Gao, Xujiao G.; Nielsen, Erik N.; Young, Ralph W.; Salinger, Andrew G.; Muller, Richard P.

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Salinger, Andrew G.

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Phipps, Eric T.; Pawlowski, Roger P.; Salinger, Andrew G.

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Pawlowski, Roger P.; Phipps, Eric T.; Salinger, Andrew G.

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Parks, Michael L.; Littlewood, David J.; Salinger, Andrew G.

This report details efforts to deploy Agile Components for rapid development of a peridynamics code, Peridigm. The goal of Agile Components is to enable the efficient development of production-quality software by providing a well-defined, unifying interface to a powerful set of component-based software. Specifically, Agile Components facilitate interoperability among packages within the Trilinos Project, including data management, time integration, uncertainty quantification, and optimization. Development of the Peridigm code served as a testbed for Agile Components and resulted in a number of recommendations for future development. Agile Components successfully enabled rapid integration of Trilinos packages into Peridigm. A cost of this approach, however, was a set of restrictions on Peridigm's architecture which impacted the ability to track history-dependent material data, dynamically modify the model discretization, and interject user-defined routines into the time integration algorithm. These restrictions resulted in modifications to the Agile Components approach, as implemented in Peridigm, and in a set of recommendations for future Agile Components development. Specific recommendations include improved handling of material states, a more flexible flow control model, and improved documentation. A demonstration mini-application, SimpleODE, was developed at the onset of this project and is offered as a potential supplement to Agile Components documentation.

Owen, Steven J.; Coffey, Todd S.; Salinger, Andrew G.

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Muller, Richard P.; Nielsen, Erik N.; Gao, Xujiao G.; Salinger, Andrew G.; Young, Ralph W.; Verley, Jason V.; Carroll, Malcolm

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Owen, Steven J.; Salinger, Andrew G.; Coffey, Todd S.

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Ostien, Jakob O.; Salinger, Andrew G.; Mota, Alejandro M.; Littlewood, David J.; Littlewood, David J.

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Salinger, Andrew G.; Owen, Steven J.; Pawlowski, Roger P.; Phipps, Eric T.; Siefert, Christopher S.; Staten, Matthew L.

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Salinger, Andrew G.; Pawlowski, Roger P.

Dynamical systems theory provides a powerful framework for understanding the behavior of complex evolving systems. However applying these ideas to large-scale dynamical systems such as discretizations of multi-dimensional PDEs is challenging. Such systems can easily give rise to problems with billions of dynamical variables, requiring specialized numerical algorithms implemented on high performance computing architectures with thousands of processors. This talk will describe LOCA, the Library of Continuation Algorithms, a suite of scalable continuation and bifurcation tools optimized for these types of systems that is part of the Trilinos software collection. In particular, we will describe continuation and bifurcation analysis techniques designed for large-scale dynamical systems that are based on specialized parallel linear algebra methods for solving augmented linear systems. We will also discuss several other Trilinos tools providing nonlinear solvers (NOX), eigensolvers (Anasazi), iterative linear solvers (AztecOO and Belos), preconditioners (Ifpack, ML, Amesos) and parallel linear algebra data structures (Epetra and Tpetra) that LOCA can leverage for efficient and scalable analysis of large-scale dynamical systems.