TY - CHAP

T1 - Parallel adaptive solvers in compressible petsc-fun3d simulations

AU - Bhowmick, S.

AU - Kaushik, D.

AU - McInnes, L.

AU - Norris, B.

AU - Raghavan, P.

N1 - Funding Information:
This work was supported in part by the National Science Foundation through grants ACI-0102537, CCR-0075792, CCF-035334, CCF-0444345, ECS-0102345 and EIA-022191 and by the Mathematical, Information, and Computational Sciences Division subprogram of the Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy, under Contract W-31-109-ENG-38 and DE-AC03-76SF00098.

PY - 2006

Y1 - 2006

N2 - The chapter presents a polyalgorithmic technique for adaptively selecting the linear solver method to match the numeric properties of linear systems as they evolve during the course of nonlinear iterations. The approach combines more robust but more costly methods when needed in particularly challenging phases of solution, with cheaper, though less powerful, methods in other phases. The chapter demonstrates that this adaptive, polyalgorithmic approach leads to improvements in overall simulation time, is easily parallelized, and is scalable in the context of this large-scale computational fluid dynamics application. This approach reduced overall execution time by using cheaper, though less powerful, linear solvers for relatively easy linear systems and then switching to more robust but more costly methods for more difficult linear systems. The results demonstrate that adaptive solvers can be implemented easily in a multiprocessor environment and are scalable. The chapter investigates adaptive solvers in problem domains and considers more adaptive approaches, including a polynomial heuristic where the trends of the indicators can be estimated by fitting a function to known data points. The chapter also combines adaptive heuristics with high-performance component infrastructure for performance monitoring and analysis.

AB - The chapter presents a polyalgorithmic technique for adaptively selecting the linear solver method to match the numeric properties of linear systems as they evolve during the course of nonlinear iterations. The approach combines more robust but more costly methods when needed in particularly challenging phases of solution, with cheaper, though less powerful, methods in other phases. The chapter demonstrates that this adaptive, polyalgorithmic approach leads to improvements in overall simulation time, is easily parallelized, and is scalable in the context of this large-scale computational fluid dynamics application. This approach reduced overall execution time by using cheaper, though less powerful, linear solvers for relatively easy linear systems and then switching to more robust but more costly methods for more difficult linear systems. The results demonstrate that adaptive solvers can be implemented easily in a multiprocessor environment and are scalable. The chapter investigates adaptive solvers in problem domains and considers more adaptive approaches, including a polynomial heuristic where the trends of the indicators can be estimated by fitting a function to known data points. The chapter also combines adaptive heuristics with high-performance component infrastructure for performance monitoring and analysis.

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U2 - 10.1016/B978-044452206-1/50033-1

DO - 10.1016/B978-044452206-1/50033-1

M3 - Chapter

AN - SCOPUS:84882497048

SN - 9780444522061

SP - 277

EP - 284

BT - Parallel Computational Fluid Dynamics 2005

PB - Elsevier

ER -