Search arXivSearch

arXiv · 2509.19061

3D Blocking for Matrix-free Smoothers in 2D Variable-Viscosity Stokes Equations with Applications to Geodynamics

Abstract

We present the design, implementation, and evaluation of optimized matrix-free stencil kernels for multigrid smoothing in the incompressible Stokes equations with variable viscosity, motivated by geophysical flow problems. We investigate five smoother variants derived from different optimisation strategies: Red-Black Gauss-Seidel, Jacobi, fused Jacobi, blocked fused Jacobi, and a novel Jacobi smoother with RAS-type temporal blocking, a strategy that applies local iterations on overlapping tiles to improve cache reuse. To ensure correctness, we introduce an energy-based residual norm that balances velocity and pressure contributions, and validate all implementations using a high-contrast sinker benchmark representative of realistic geodynamic numerical models. Our performance study on NVIDIA GH200 Grace Hopper nodes of the ALPS supercomputer demonstrates that all smoothers scale well within a single NUMA domain, but the RAS-Jacobi smoother consistently achieves the best performance at higher core counts. It sustains over 90% weak-scaling efficiency up to 64 cores and delivers up to a threefold speedup compared to the C++ Jacobi baseline, owing to improved cache reuse and reduced memory traffic. These results show that temporal blocking, already employed in distributed-memory solvers to reduce communication, can also provide substantial benefits at the socket and NUMA level. This work highlights the importance of cache-aware stencil design for harnessing modern heterogeneous architectures and lays the groundwork for extending RAS-type temporal blocking strategies to three-dimensional problems and GPU accelerators.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Marcel Ferrari, Cyrill Püntener, Alexander Sotoudeh, Niklas Viebig. 2025-09-23. 3D Blocking for Matrix-free Smoothers in 2D Variable-Viscosity Stokes Equations with Applications to Geodynamics. https://arxiv.org/abs/2509.19061

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Bi-Hamiltonian in Semiflexible Polymers built upon Overdamping Process

Quantifying the interaction between a system of interest and its ambient conditions, the memory effect links the states of two distinct Hamiltonians: one for the target system and one for the environment. In this paper, we propose the diffusion process derived from the Smoluchowski equation that can derive the evolution process described by the memory effect integration in a non Markovian regime. The Smoluchowski picture, within the framework of stochastic thermodynamics, justifies a diffusion process incorporated into the equations of motion, and the result of the derivation enables a coarse-grained molecular dynamics simulation with the modified equation of motion to reproduce attenuation from collisions between single walled carbon nanotubes (SWCNTs) under far from equilibrium conditions. The results of the numerical experiments on the collision confirm that heat diffusion compensates for the correlated momentum arising from the memory effect between the two Hamiltonians in both equilibrium and far from equilibrium states.

physics.comp-ph

Translation of transient acoustic fields

A method is presented for the translation of acoustic field data from a source to a target region. Field data are represented as spherical harmonic expansions on spheres surrounding the source and target regions respectively and expansions are translated using a ``point and shoot'' method using the Kirchhoff--Helmholtz integral to carry out an axial translation from one sphere to the other. The principal motivation for the method is its use in a time-domain Fast Multipole Method, and test cases reflective of this application are presented. The method converges to six digits for appropriate values of parameters and for the values of $N$ considered here computational effort scales approximately as $N^{2}$ where $N$ is the order of spherical harmonic expansion for the field data. The method is causal and thus avoids artifacts generated in methods which are not based on intrinsically causal formulations.

physics.comp-ph

Learning continuous reaction paths for transition-state prediction

Transition states are defined by reaction pathways, yet most machine-learning methods predict them as isolated geometries. We introduce MARC-TS, a two-stage framework that learns a continuous, endpoint-conditioned path, queries it at any resolution and uses local path context to refine a transition-state candidate. We construct T1x-IRC-8K, a dataset of 8,209 reactions and 1,088,725 path-resolved geometries. On held-out reactions, the path model reduced complete-path error by 48.4% relative to endpoint interpolation, and the localizer achieved a mean aligned structural error of 0.127 Å. Quantum-chemical optimization and vibrational analysis yielded 405 frequency-confirmed first-order saddle-point candidates from 410 predictions. In a 100-reaction nudged elastic band comparison, learned-path initialization reached a joint geometry-and-force target for 66% of reactions, compared with 12% for geometric interpolation after 100 optimizer steps. By treating the path as a reusable representation rather than an auxiliary output, MARC-TS connects transition-state prediction, mechanistic interpretation and quantum-chemical refinement.

physics.comp-ph