1. High-order matrix-free incompressible flow solvers with GPU acceleration and low-order refined preconditioners
- Author
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Michael Franco, Julian Andrej, Will Pazner, and Jean-Sylvain Camier
- Subjects
General Computer Science ,Discretization ,Computer science ,Linear system ,MathematicsofComputing_NUMERICALANALYSIS ,General Engineering ,Numerical Analysis (math.NA) ,010103 numerical & computational mathematics ,01 natural sciences ,Finite element method ,Projection (linear algebra) ,Physics::Fluid Dynamics ,010101 applied mathematics ,symbols.namesake ,Matrix (mathematics) ,Incompressible flow ,Helmholtz free energy ,FOS: Mathematics ,symbols ,Applied mathematics ,Degree of a polynomial ,Mathematics - Numerical Analysis ,0101 mathematics ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
We present a matrix-free flow solver for high-order finite element discretizations of the incompressible Navier-Stokes and Stokes equations with GPU acceleration. For high polynomial degrees, assembling the matrix for the linear systems resulting from the finite element discretization can be prohibitively expensive, both in terms of computational complexity and memory. For this reason, it is necessary to develop matrix-free operators and preconditioners, which can be used to efficiently solve these linear systems without access to the matrix entries themselves. The matrix-free operator evaluations utilize GPU-accelerated sum-factorization techniques to minimize memory movement and maximize throughput. The preconditioners developed in this work are based on a low-order refined methodology with parallel subspace corrections. The saddle-point Stokes system is solved using block-preconditioning techniques, which are robust in mesh size, polynomial degree, time step, and viscosity. For the incompressible Navier-Stokes equations, we make use of projection (fractional step) methods, which require Helmholtz and Poisson solves at each time step. The performance of our flow solvers is assessed on several benchmark problems in two and three spatial dimensions., 20 pages, 11 figures
- Published
- 2020
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