Matrix-free weighted quadrature for a computationally efficient isogeometric $k$-method

TL;DR

This paper introduces a matrix-free weighted quadrature approach for the isogeometric $k$-method, significantly enhancing computational efficiency and reducing memory use, especially at higher degrees, for solving elliptic problems.

Contribution

It proposes a novel matrix-free weighted quadrature strategy combined with an efficient preconditioner, enabling high-degree isogeometric $k$-methods to be computationally feasible and faster than traditional methods.

Findings

MF-WQ accelerates matrix operations and reduces memory consumption.

The method outperforms standard approaches even at low degrees.

High-degree $k$-methods become orders of magnitude faster with MF-WQ.

Abstract

The $k$-method is the isogeometric method based on splines (or NURBS, etc.) with maximum regularity. When implemented following the paradigms of classical finite element methods, the computational resources required by the $k-$method are prohibitive even for moderate degree. In order to address this issue, we propose a matrix-free strategy combined with weighted quadrature, which is an ad-hoc strategy to compute the integrals of the Galerkin system. Matrix-free weighted quadrature (MF-WQ) speeds up matrix operations, and, perhaps even more important, greatly reduces memory consumption. Our strategy also requires an efficient preconditioner for the linear system iterative solver. In this work we deal with an elliptic model problem, and adopt a preconditioner based on the Fast Diagonalization method, an old idea to solve Sylvester-like equations. Our numerical tests show that the isogeometric solver based on MF-WQ is faster than standard approaches (where the main cost is the matrix formation by standard Gaussian quadrature) even for low degree. But the main achievement is that, with MF-WQ, the $k$-method gets orders of magnitude faster by increasing the degree, given a target accuracy. Therefore, we are able to show the superiority, in terms of computational efficiency, of the high-degree $k$-method with respect to low-degree isogeometric discretizations. What we present here is applicable to more complex and realistic differential problems, but its effectiveness will depend on the preconditioner stage, which is as always problem-dependent. This situation is typical of modern high-order methods: the overall performance is mainly related to the quality of the preconditioner.