HILUCSI: Simple, Robust, and Fast Multilevel ILU for Large-Scale Saddle-Point Problems from PDEs

TL;DR

HILUCSI is a new multilevel ILU preconditioner designed to be simple, robust, and fast for large-scale saddle-point problems from PDEs, improving robustness and efficiency over previous methods.

Contribution

It introduces a hierarchical ILU with mixed preprocessing and inverse-based dropping, enhancing robustness and scalability for indefinite and large-scale PDE systems.

Findings

Outperforms ILUPACK and other solvers on benchmark problems.

Improves robustness for indefinite and saddle-point systems.

Achieves scalable performance on systems with millions of unknowns.

Abstract

Incomplete factorization is a widely used preconditioning technique for Krylov subspace methods for solving large-scale sparse linear systems. Its multilevel variants, such as ILUPACK, are more robust for many symmetric or unsymmetric linear systems than the traditional, single-level incomplete LU (or ILU) techniques. However, the previous multilevel ILU techniques still lacked robustness and efficiency for some large-scale saddle-point problems, which often arise from systems of partial differential equations (PDEs). We introduce HILUCSI, or Hierarchical Incomplete LU-Crout with Scalability-oriented and Inverse-based dropping. As a multilevel preconditioner, HILUCSI statically and dynamically permutes individual rows and columns to the next level for deferred factorization. Unlike ILUPACK, HILUCSI applies symmetric preprocessing techniques at the top levels but always uses unsymmetric preprocessing and unsymmetric factorization at the coarser levels. The deferring combined with mixed preprocessing enabled a unified treatment for nearly or partially symmetric systems, and simplified the implementation by avoiding mixed $1\times 1$ and $2\times 2$ pivots for symmetric indefinite systems. We show that this combination improves robustness for indefinite systems without compromising efficiency. Furthermore, to enable superior efficiency for large-scale systems with millions or more unknowns, HILUCSI introduces a scalability-oriented dropping in conjunction with a variant of inverse-based dropping. We demonstrate the effectiveness of HILUCSI for dozens of benchmark problems, including those from the mixed formulation of the Poisson equation, Stokes equations, and Navier-Stokes equations. We also compare its performance with ILUPACK, the supernodal ILUTP in SuperLU, and multithreaded direct solvers in PARDISO and MUMPS.