Mixed Precision GMRES-based Iterative Refinement with Recycling

TL;DR

This paper introduces a mixed precision iterative refinement method for solving linear systems that incorporates Krylov subspace recycling to accelerate GMRES solves across refinement steps, improving efficiency especially for large or complex problems.

Contribution

It presents a novel integration of Krylov subspace recycling into mixed precision GMRES-based iterative refinement, enhancing computational efficiency for solving linear systems.

Findings

Recycling accelerates GMRES convergence in iterative refinement.

The method performs well on dense, Toeplitz, and real-world problems.

Numerical experiments confirm improved efficiency with recycling.

Abstract

With the emergence of mixed precision capabilities in hardware, iterative refinement schemes for solving linear systems $Ax=b$ have recently been revisited and reanalyzed in the context of three or more precisions. These new analyses show that under certain constraints on condition number, the LU factorization of the matrix can be computed in low precision without affecting the final accuracy. Another promising technique is GMRES-based iterative refinement, which, in contrast to the standard approach, use GMRES preconditioned by the low-precision triangular factors to solve for the approximate solution update in each refinement step. This more accurate solution method extends the range of problems which can be solved with a given combination of precisions. However, in certain settings, GMRES may require too many iterations per refinement step, making it potentially more expensive than simply recomputing the LU factors in a higher precision. Krylov subspace recycling is a well-known technique for reusing information across sequential invocations of a Krylov subspace method on systems with the same or a slowly changing coefficient matrix. In this work, we incorporate the idea of Krylov subspace recycling into a mixed precision GMRES-based iterative refinement solver. The insight is that in each refinement step, we call preconditioned GMRES on a linear system with the same coefficient matrix $A$, with only the right-hand side changing. In this way, the GMRES solves in subsequent refinement steps can be accelerated by recycling information obtained from the first step. We perform extensive numerical experiments on various random dense problems, Toeplitz problems (prolate matrices), and problems from real applications, which confirm the benefits of the recycling approach.