A matrix-free parallel two-level deflation preconditioner for the two-dimensional Helmholtz problems

TL;DR

This paper introduces a matrix-free, parallel two-level deflation preconditioner combined with the Complex Shifted Laplacian for efficient, scalable solution of 2D Helmholtz problems, achieving wave-number-independent convergence.

Contribution

It develops a novel matrix-free, parallel two-level deflation preconditioner integrated with CSLP, enhancing scalability and convergence for large-scale Helmholtz problems.

Findings

Achieves wave-number-independent convergence for medium wavenumbers.

Demonstrates satisfactory parallel scalability in numerical experiments.

Reduces memory consumption through matrix-free implementation.

Abstract

We propose a matrix-free parallel two-level-deflation preconditioner combined with the Complex Shifted Laplacian preconditioner(CSLP) for the two-dimensional Helmholtz problems. The Helmholtz equation is widely studied in seismic exploration, antennas, and medical imaging. It is one of the hardest problems to solve both in terms of accuracy and convergence, due to scalability issues of the numerical solvers. Motivated by the observation that for large wavenumbers, the eigenvalues of the CSLP-preconditioned system shift towards zero, deflation with multigrid vectors, and further high-order vectors were incorporated to obtain wave-number-independent convergence. For large-scale applications, high-performance parallel scalable methods are also indispensable. In our method, we consider the preconditioned Krylov subspace methods for solving the linear system obtained from finite-difference discretization. The CSLP preconditioner is approximated by one parallel geometric multigrid V-cycle. For the two-level deflation, the matrix-free Galerkin coarsening as well as high-order re-discretization approaches on the coarse grid are studied. The results of matrix-vector multiplications in Krylov subspace methods and the interpolation/restriction operators are implemented based on the finite-difference grids without constructing any coefficient matrix. These adjustments lead to direct improvements in terms of memory consumption. Numerical experiments of model problems show that wavenumber independence has been obtained for medium wavenumbers. The matrix-free parallel framework shows satisfactory weak and strong parallel scalability.