GPU-Accelerated Nested Schur-Complement Solvers for Atmospheric Flow Simulations Using Horizontally-Explicit, Vertically-Implicit Schemes and Discontinuous Galerkin Methods
Marco Artiano, Andrés Rueda-Ramírez, Oswald Knoth, Hendrik Ranocha, Gregor Gassner
Abstract
Horizontally explicit, vertically implicit (HEVI) time integration removes the acoustic time-step restriction imposed by fine vertical grids in nonhydrostatic atmospheric models, but requires fast solvers for the resulting vertical linear systems, especially on GPUs. We present GPU-accelerated nested Schur-complement solvers for these systems for a split-form discontinuous Galerkin spectral element method on Legendre-Gauss-Lobatto (LGL) nodes applied to the compressible Euler equations with gravity. In time, we use a Rosenbrock-W method, a linearly implicit Runge-Kutta scheme that retains its order and stability with approximate Jacobians. This allows a sparse vertical Jacobian that captures vertical acoustic waves and gravity on curvilinear meshes with orography and decouples into independent column systems. The solvers are nested: a global Schur complement (static condensation) exploits the LGL sparsity to condense each column onto a few element-edge unknowns, and a local Schur complement eliminates all variables but the vertical momentum within each element, exploiting that the pressure depends only on the potential-temperature density. We propose and analyze two condensation strategies, which lead to banded and block-tridiagonal reduced systems. The resulting HEVI schemes conserve total mass and potential-temperature density to round-off. The solvers are implemented for CPUs and GPUs in TrixiAtmo.jl, the atmospheric extension of Trixi.jl. We verify high-order convergence with linear inertia-gravity waves in a 2D channel and demonstrate robustness on the sphere with the baroclinic instability, a mountain-induced baroclinic instability, and the DCMIP-2025 gap-flow and vortex-shedding cases. For the baroclinic instability without orography, the scheme achieves 18.8 and 2.9 simulated years per day at 100 and 50 km resolution on one NVIDIA GH200 GPU in double precision.
Create a lesson
Related papers
Neural Network Preconditioning: A Case Study for the Solution of the Parametric Helmholtz Equation
Luc Giraud, Carola Kruse, Paul Mycek et al.
On the cyclic limiting behavior of the restarted Arnoldi algorithm for real symmetric matrices with restart length s=2
Vance Faber, Jarek Liesen, Jörg Liesen et al.
An algebraic multiscale framework for two-level overlapping Schwarz preconditioners
Filipe Cumaru, Hadi Hajibeygi, Cornelis Vuik et al.
Stable Matrix-Free Q-less QR via Preconditioning and Randomization
Oleg Balabanov, Maksim Melnichenko, Michael W. Mahoney
High-order uniformly accurate fully discrete schemes with long time near-conservations for two nonlinear coupled dispersive models in the nonrelativistic regime
Lina Wang, Jiyong Li, Bin Wang
CLNO: Conservative Local Neural Operator for Compressible Flow Prediction
Buxuan Wang, Rongzu Li, Qingsong Zou