Publications

Results 51–100 of 149

Search results

Jump to search filters

On the scalability of the Albany/FELIX first-order stokes approximation ice sheet solver for large-scale simulations of the Greenland and Antarctic ice sheets

Procedia Computer Science

Tezaur, Irina K.; Tuminaro, Raymond S.; Perego, Mauro; Salinger, Andrew G.; Price, Stephen F.

We examine the scalability of the recently developed Albany/FELIX finite-element based code for the first-order Stokes momentum balance equations for ice flow. We focus our analysis on the performance of two possible preconditioners for the iterative solution of the sparse linear systems that arise from the discretization of the governing equations: (1) a preconditioner based on the incomplete LU (ILU) factorization, and (2) a recently-developed algebraic multigrid (AMG) preconditioner, constructed using the idea of semi-coarsening. A strong scalability study on a realistic, high resolution Greenland ice sheet problem reveals that, for a given number of processor cores, the AMG preconditioner results in faster linear solve times but the ILU preconditioner exhibits better scalability. A weak scalability study is performed on a realistic, moderate resolution Antarctic ice sheet problem, a substantial fraction of which contains floating ice shelves, making it fundamentally different from the Greenland ice sheet problem. Here, we show that as the problem size increases, the performance of the ILU preconditioner deteriorates whereas the AMG preconditioner maintains scalability. This is because the linear systems are extremely ill-conditioned in the presence of floating ice shelves, and the ill-conditioning has a greater negative effect on the ILU preconditioner than on the AMG preconditioner.

More Details
TYPE Conference Poster YEAR 2015
OSTIScopus

QCAD Simulation and Optimization of Semiconductor Double Quantum Dots

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

We present the Quantum Computer Aided Design (QCAD) simulator that targets modeling quantum devices, particularly silicon double quantum dots (DQDs) developed for quantum qubits. The simulator has three differentiating features: (i) its core contains nonlinear Poisson, effective mass Schrodinger, and Configuration Interaction solvers that have massively parallel capability for high simulation throughput, and can be run individually or combined self-consistently for 1D/2D/3D quantum devices; (ii) the core solvers show superior convergence even at near-zero-Kelvin temperatures, which is critical for modeling quantum computing devices; (iii) it couples with an optimization engine Dakota that enables optimization of gate voltages in DQDs for multiple desired targets. The Poisson solver includes MaxwellBoltzmann and Fermi-Dirac statistics, supports Dirichlet, Neumann, interface charge, and Robin boundary conditions, and includes the effect of dopant incomplete ionization. The solver has shown robust nonlinear convergence even in the milli-Kelvin temperature range, and has been extensively used to quickly obtain the semiclassical electrostatic potential in DQD devices. The self-consistent Schrodinger-Poisson solver has achieved robust and monotonic convergence behavior for 1D/2D/3D quantum devices at very low temperatures by using a predictor-correct iteration scheme. The QCAD simulator enables the calculation of dot-to-gate capacitances, and comparison with experiment and between solvers. It is observed that computed capacitances are in the right ballpark when compared to experiment, and quantum confinement increases capacitance when the number of electrons is fixed in a quantum dot. In addition, the coupling of QCAD with Dakota allows to rapidly identify which device layouts are more likely leading to few-electron quantum dots. Very efficient QCAD simulations on a large number of fabricated and proposed Si DQDs have made it possible to provide fast feedback for design comparison and optimization.

More Details
TYPE SAND Report YEAR 2013
DOIOSTI

Component-Based Scientific Application Development

Salinger, Andrew G.

Over the past few years, we have defined and gone a long ways towards implementing a component-based strategy for building scientific application codes. We have asserted that this approach offers significant advantages over a model of writing project-based application codes. There are now several technical and programmatic successes that validate these claims. Not only are there net benefits to code projects that follow this strategy, but also the most striking gains are for the long-term impact and productivity of our computational science organizations.

More Details
TYPE SAND Report YEAR 2012
DOIOSTI

The QCAD framework for quantum device modeling

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

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

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.

More Details
TYPE Journal Article YEAR 2012
OSTIDOI

Automating embedded analysis capabilities and managing software complexity in multiphysics simulation, Part I: Template-based generic programming

Scientific Programming

Pawlowski, Roger; 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.

More Details
TYPE Journal Article YEAR 2012
ScopusOSTIDOI
Results 51–100 of 149
Results 51–100 of 149