Decrease timetosolution through improved linearsystem setup and solve
Abstract
The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MWscale 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, multiturbine wind farm simulations will require exascaleclass resources. The primary code in the ExaWind project is Nalu, which is an unstructuredgrid solver for the acousticallyincompressible NavierStokes equations, and mass continuity is maintained through pressure projection. The model consists of the masscontinuity Poissontype equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linearsystem setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as reinitialization of matrices and recomputation of preconditioners is required at every time step We describe in this report our efforts to decrease the setup and solution time for the masscontinuity Poisson system with respect to the benchmark timing results reported in FY18 Q1. In particular, we investigate improving and evaluating two types ofmore »
 Authors:

 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 National Renewable Energy Lab. (NREL), Golden, CO (United States)
 Publication Date:
 Research Org.:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); National Renewable Energy Lab. (NREL), Golden, CO (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
 OSTI Identifier:
 1458348
 Report Number(s):
 SAND20186759R
664816
 DOE Contract Number:
 AC0494AL85000; 17SC20SC; ACO205CH11231; NA0003525; AC3608G028308
 Resource Type:
 Technical Report
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; 17 WIND ENERGY
Citation Formats
Hu, Jonathan J., Thomas, Stephen, Dohrmann, Clark R., Ananthan, Shreyas, Domino, Stefan P., Williams, Alan B., and Sprague, Michael. Decrease timetosolution through improved linearsystem setup and solve. United States: N. p., 2018.
Web. doi:10.2172/1458348.
Hu, Jonathan J., Thomas, Stephen, Dohrmann, Clark R., Ananthan, Shreyas, Domino, Stefan P., Williams, Alan B., & Sprague, Michael. Decrease timetosolution through improved linearsystem setup and solve. United States. https://doi.org/10.2172/1458348
Hu, Jonathan J., Thomas, Stephen, Dohrmann, Clark R., Ananthan, Shreyas, Domino, Stefan P., Williams, Alan B., and Sprague, Michael. 2018.
"Decrease timetosolution through improved linearsystem setup and solve". United States. https://doi.org/10.2172/1458348. https://www.osti.gov/servlets/purl/1458348.
@article{osti_1458348,
title = {Decrease timetosolution through improved linearsystem setup and solve},
author = {Hu, Jonathan J. and Thomas, Stephen and Dohrmann, Clark R. and Ananthan, Shreyas and Domino, Stefan P. and Williams, Alan B. and Sprague, Michael},
abstractNote = {The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MWscale 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, multiturbine wind farm simulations will require exascaleclass resources. The primary code in the ExaWind project is Nalu, which is an unstructuredgrid solver for the acousticallyincompressible NavierStokes equations, and mass continuity is maintained through pressure projection. The model consists of the masscontinuity Poissontype equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linearsystem setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as reinitialization of matrices and recomputation of preconditioners is required at every time step We describe in this report our efforts to decrease the setup and solution time for the masscontinuity 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 RugeStfiben AMG (CAMG) and smoothedaggregation AMG (SAAMG), which are implemented in the Hypre and Trilinos/MueLu software stacks, respectively. Preconditioner performance was optimized through existing capabilities and settings.},
doi = {10.2172/1458348},
url = {https://www.osti.gov/biblio/1458348},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {6}
}