Publications

Hu, Jonathan J.; Phipps, Eric T.

Abstract not provided.

Hu, Jonathan J.

Abstract not provided.

Tuminaro, Raymond S.; Hu, Jonathan J.; Gaidamour, Jeremie G.; Siefert, Christopher S.; Gerstenberger, Axel G.

Abstract not provided.

Ohm, Peter B.; Tuminaro, Raymond S.; Shadid, John N.; Hu, Jonathan J.; Wiesner, Tobias A.; Cyr, Eric C.

Abstract not provided.

Berger-Vergiat, Luc B.; Tuminaro, Raymond S.; Hu, Jonathan J.; Siefert, Christopher S.

Abstract not provided.

Wiesner, Tobias A.; Hu, Jonathan J.; Kelley, Brian K.; Siefert, Christopher S.

Abstract not provided.

Journal of Computational Physics

Tuminaro, Raymond S.; Hu, Jonathan J.

Abstract not provided.

Siefert, Christopher S.; Bochev, Pavel B.; Tuminaro, Raymond S.; Hu, Jonathan J.

Abstract not provided.

Siefert, Christopher S.; Bochev, Pavel B.; Hu, Jonathan J.; Tuminaro, Raymond S.

Abstract not provided.

Hu, Jonathan J.; Gaidamour, Jeremie G.; Siefert, Christopher S.; Tuminaro, Raymond S.

Abstract not provided.

Prokopenko, Andrey V.; Hu, Jonathan J.; Tuminaro, Raymond S.

Abstract not provided.

Lin, Paul L.; Shadid, John N.; Phillips, Edward G.; Hu, Jonathan J.; Cyr, Eric C.; Pawlowski, Roger P.; Tsuji, Paul T.; Sondak, David S.

Abstract not provided.

Tuminaro, Raymond S.; Bochev, Pavel B.; Hu, Jonathan J.

Abstract not provided.

Tuminaro, Raymond S.; Hu, Jonathan J.; Prokopenko, Andrey V.; Siefert, Christopher S.; Tsuji, Paul H.; Boman, Erik G.; Cyr, Eric C.; Lin, Paul L.; Shadid, John N.

Abstract not provided.

Ohm, Peter B.; Tuminaro, Raymond S.; Shadid, John N.; Hu, Jonathan J.; Wiesner, Tobias A.

Abstract not provided.

Ohm, Peter B.; Tuminaro, Raymond S.; Hu, Jonathan J.; Shadid, John N.; Cyr, Eric C.; Wiesner, Tobias A.

Abstract not provided.

Proposed for publication in SIAM Journal on Scientific Computing.

Bochev, Pavel B.; Tuminaro, Raymond S.; Hu, Jonathan J.

Abstract not provided.

Heroux, Michael A.; Kolda, Tamara G.; Long, Kevin R.; Hoekstra, Robert J.; Pawlowski, Roger P.; Phipps, Eric T.; Salinger, Andrew G.; Williams, Alan B.; Heroux, Michael A.; Hu, Jonathan J.; Lehoucq, Richard B.; Thornquist, Heidi K.; Tuminaro, Raymond S.; Willenbring, James M.; Bartlett, Roscoe B.; Howle, Victoria E.

The Trilinos Project is an effort to facilitate the design, development, integration and ongoing support of mathematical software libraries. In particular, our goal is to develop parallel solver algorithms and libraries within an object-oriented software framework for the solution of large-scale, complex multi-physics engineering and scientific applications. Our emphasis is on developing robust, scalable algorithms in a software framework, using abstract interfaces for flexible interoperability of components while providing a full-featured set of concrete classes that implement all abstract interfaces. Trilinos uses a two-level software structure designed around collections of packages. A Trilinos package is an integral unit usually developed by a small team of experts in a particular algorithms area such as algebraic preconditioners, nonlinear solvers, etc. Packages exist underneath the Trilinos top level, which provides a common look-and-feel, including configuration, documentation, licensing, and bug-tracking. Trilinos packages are primarily written in C++, but provide some C and Fortran user interface support. We provide an open architecture that allows easy integration with other solver packages and we deliver our software to the outside community via the Gnu Lesser General Public License (LGPL). This report provides an overview of Trilinos, discussing the objectives, history, current development and future plans of the project.

Bettencourt, Matthew T.; Kramer, Richard M.; Cartwright, Keith C.; Phillips, Edward G.; Ober, Curtis C.; Pawlowski, Roger P.; Swan, Matthew S.; Kalashnikova, Irina; Phipps, Eric T.; Conde, Sidafa C.; Cyr, Eric C.; Ulmer, Craig D.; Kordenbrock, Todd H.; Levy, Scott L.; Templet, Gary J.; Hu, Jonathan J.; Lin, Paul L.; Glusa, Christian A.; Siefert, Christopher S.; Glass, Micheal W.

This report documents the outcome from the ASC ATDM Level 2 Milestone 6358: Assess Status of Next Generation Components and Physics Models in EMPIRE. This Milestone is an assessment of the EMPIRE (ElectroMagnetic Plasma In Realistic Environments) application and three software components. The assessment focuses on the electromagnetic and electrostatic particle-in-cell solu- tions for EMPIRE and its associated solver, time integration, and checkpoint-restart components. This information provides a clear understanding of the current status of the EMPIRE application and will help to guide future work in FY19 in order to ready the application for the ASC ATDM L 1 Milestone in FY20. It is clear from this assessment that performance of the linear solver will have to be a focus in FY19.

Lin, Paul L.; Shadid, John N.; Hu, Jonathan J.; Pawlowski, Roger P.; Cyr, Eric C.; Prokopenko, Andrey V.

Abstract not provided.

Robinson, Allen C.; Mosso, Stewart J.; Siefert, Christopher S.; Hu, Jonathan J.

Abstract not provided.

Hu, Jonathan J.; Thomas, Stephen T.; Dohrmann, Clark R.; Ananthan, Shreyas A.; Domino, Stefan P.; Williams, Alan B.; Sprague, Michael S.

The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary code in the ExaWind project is Nalu, which is an unstructured-grid solver for the acoustically-incompressible Navier-Stokes equations, and mass continuity is maintained through pressure projection. The model consists of the mass-continuity Poisson-type equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as re-initialization of matrices and re-computation of preconditioners is required at every time step We describe in this report our efforts to decrease the setup and solution time for the mass-continuity Poisson system with respect to the benchmark timing results reported in FY18 Q1. In particular, we investigate improving and evaluating two types of algebraic multigrid (AMG) preconditioners: Classical Ruge-Stfiben AMG (C-AMG) and smoothed-aggregation AMG (SA-AMG), which are implemented in the Hypre and Trilinos/MueLu software stacks, respectively. Preconditioner performance was optimized through existing capabilities and settings.

Hu, Jonathan J.; Thomas, Stephen T.; Dohrmann, Clark R.; Ananthan, Shreyas A.; Domino, Stefan P.; Williams, Alan B.; Sprague, Michael S.

The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources.

Adcock, Christiane A.; Ananthan, Shreyas A.; Berget-Vergiat, Luc B.; Brazell, Michael B.; Brunhart-Lupo, Nicholas B.; Hu, Jonathan J.; Knaus, Robert C.; Melvin, Jeremy M.; Moser, Bob M.; Mullowney, Paul M.; Rood, Jon R.; Sharma, Ashesh S.; Thomas, Stephen T.; Vijayakumar, Ganesh V.; Williams, Alan B.; Wilson, Robert V.; Yamazaki, Ichitaro Y.; Sprague, Michael S.

The goal of the ExaWind project is to enable predictive simulations of wind farms comprised of many megawatt-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, capturing the thin boundary layers, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources.

Bidadi, Shreyas B.; Brazell, Michael B.; Brunhart-Lupo, Nicholas B.; Henry de Frahan, Marc T.; Lee, Dong H.; Hu, Jonathan J.; Melvin, Jeremy M.; Mullowney, Paul M.; Vijayakumar, Ganesh V.; Moser, Robert D.; Rood, Jon R.; Sakievich, Philip S.; Sharma, Ashesh S.; Williams, Alan B.; Sprague, Michael A.

The goal of the ExaWind project is to enable predictive simulations of wind farms comprised of many megawatt-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, capturing the thin boundary layers, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources.