Title
Initial Guesses For Sequences Of Linear Systems In A Gpu-Accelerated Incompressible Flow Solver
Abstract
We consider several methods for generating initial guesses when iteratively solving sequences of linear systems, showing that they can be implemented efficiently in GPU-accelerated PDE solvers, specifically solvers for incompressible flow. We propose new initial guess methods based on stabilized polynomial extrapolation and compare them to the projection method of Fischer [Comput. Methods Appl. Mech. Engrg., 163 (1998), pp. 193-204], showing that they are generally competitive with projection schemes despite requiring only half the storage and performing considerably less data movement and communication. Our implementations of these algorithms are freely available as part of the libParanumal collection of GPU-accelerated flow solvers.
Year
DOI
Venue
2021
10.1137/20M1368677
SIAM JOURNAL ON SCIENTIFIC COMPUTING
Keywords
DocType
Volume
initial guesses, iterative solvers, GPU-acceleration, partial differential equations, incompressible flow, projection, extrapolation, least-squares
Journal
43
Issue
ISSN
Citations 
4
1064-8275
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Anthony P. Austin100.34
Noel Chalmers210.70
T. Warburton319316.55