AIR multigrid with GMRES polynomials (AIRG) and additive preconditioners for Boltzmann transport

We develop a reduction multigrid based on approximate ideal restriction (AIR) for use with asymmetric linear systems. We use fixed-order GMRES polynomials to approximate Aff−1 and we use these polynomials to build grid transfer operators and perform F-point smoothing. We can also apply a fixed sparsity to these polynomials to prevent fill-in. When applied in the streaming limit of the Boltzmann Transport Equation (BTE), with a P0 DG FEM angular discretisation and a low-memory spatial discretisation on unstructured grids, preconditioning an outer GMRES iteration with a single V-cycle of this “AIRG” multigrid outperforms the ℓAIR implementation in hypre, with two to three times less work. We measure one “work unit” as the the FLOPs required to compute a single matrix-vector product. We also measure the growth in work required to perform the setup/solve with space/angle refinement by computing f in O(nf), where n is the number of space/angle unknowns; We find that AIRG is very close to scalable. We find f=1.092 for the setup and f=1.003 for the solve, with growth from 83 work units to 84 in the solve across six levels of spatial refinement; in comparison, distance 2 ℓAIR with FCF-Jacobi smoothing gives f=1.1 in the solve, with growth from 112 work units to 219. Using AIRG and fixed-sparsity GMRES polynomials gives f=1.076 for the setup and f=1.018 for the solve. Three levels of angular refinement with AIRG and fixed-sparsity GMRES polynomials gives f=1.055 in the solve. We then present an iterative method designed for use with scattering which uses the additive combination of two preconditioners applied to the angular flux; a single AIRG V-cycle on the streaming/removal operator and a single V-cycle of BoomerAMG from hypre on a DSA-type operator. We find our iterative method is very close to scalable, with f=1.022 and f=1.02 in the solve with spatial and angular refinement, respectively. Similarly the memory use with space/angle refinement grows with a maximum of f=1.006.

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Publication Details

Journal
Journal of Computational Physics
Published
2024-08-08
DOI
https://doi.org/10.1016/j.jcp.2024.113342
Citations
3
Primary Topic
Advanced Numerical Methods in Computational Mathematics
Type
article
Field-Weighted Citation Impact
0.75

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article

AIR multigrid with GMRES polynomials (AIRG) and additive preconditioners for Boltzmann transport

Paul N. Smith, Steven Dargaville, Christopher C. Pain, R.P. Smedley‐Stevenson
3 citations
Journal of Computational Physics
Advanced Numerical Methods in Computational Mathematics
0.75
article

AIR multigrid with GMRES polynomials (AIRG) and additive preconditioners for Boltzmann transport

Paul N. Smith, Steven Dargaville, Christopher C. Pain, R.P. Smedley‐Stevenson
article en
3 citations

Abstract

We develop a reduction multigrid based on approximate ideal restriction (AIR) for use with asymmetric linear systems. We use fixed-order GMRES polynomials to approximate Aff−1 and we use these polynomials to build grid transfer operators and perform F-point smoothing. We can also apply a fixed sparsity to these polynomials to prevent fill-in. When applied in the streaming limit of the Boltzmann Transport Equation (BTE), with a P0 DG FEM angular discretisation and a low-memory spatial discretisation on unstructured grids, preconditioning an outer GMRES iteration with a single V-cycle of this “AIRG” multigrid outperforms the ℓAIR implementation in hypre, with two to three times less work. We measure one “work unit” as the the FLOPs required to compute a single matrix-vector product. We also measure the growth in work required to perform the setup/solve with space/angle refinement by computing f in O(nf), where n is the number of space/angle unknowns; We find that AIRG is very close to scalable. We find f=1.092 for the setup and f=1.003 for the solve, with growth from 83 work units to 84 in the solve across six levels of spatial refinement; in comparison, distance 2 ℓAIR with FCF-Jacobi smoothing gives f=1.1 in the solve, with growth from 112 work units to 219. Using AIRG and fixed-sparsity GMRES polynomials gives f=1.076 for the setup and f=1.018 for the solve. Three levels of angular refinement with AIRG and fixed-sparsity GMRES polynomials gives f=1.055 in the solve. We then present an iterative method designed for use with scattering which uses the additive combination of two preconditioners applied to the angular flux; a single AIRG V-cycle on the streaming/removal operator and a single V-cycle of BoomerAMG from hypre on a DSA-type operator. We find our iterative method is very close to scalable, with f=1.022 and f=1.02 in the solve with spatial and angular refinement, respectively. Similarly the memory use with space/angle refinement grows with a maximum of f=1.006.

Journal of Computational PhysicsVol. 518
AWE Nuclear Security Technologies (GB), Dorset Green Technology Park (GB), Imperial College London (GB)
Engineering and Physical Sciences Research Council
Affordable and clean energy
Openalex Percentile: Top 34%
Advanced Numerical Methods in Computational Mathematics
0.75
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