I/O Post- and Co-Processing for High-Order Methods

Rasquin, Michel; Hillewaert, Koen; Bassi, Francesco; Colombo, Alessandro; Massa, Francesco Carlo; Rahier, Gilles; Martin, E. Dale; Renac, Florent

doi:10.1007/978-3-030-62048-6_9

Cited by 1 publication

(1 citation statement)

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“…We find that quadratic geometry is often sufficient for large deformation with quadratic and cubic solution spaces, and thus these methods can be used with existing meshing and visualization tools, though tailoring to p-version finite element efficiency [57] is beneficial. When cubic and higher order meshes are needed, one can use Gmsh [42] to generate arbitrary order meshes, but many popular mesh formats support at most second order elements and there is a need for improving data representation standards and postprocessing/visualization tools to better support high order geometry and solution fields [58]- [60].…”

Section: Discussionmentioning

confidence: 99%

Performance Portable Solid Mechanics via Matrix-Free $p$-Multigrid

Brown¹,

Barra²,

Beams³

et al. 2022

Preprint

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Finite element analysis of solid mechanics is a foundational tool of modern engineering, with low-order finite element methods and assembled sparse matrices representing the industry standard for implicit analysis. We use performance models and numerical experiments to demonstrate that highorder methods greatly reduce the costs to reach engineering tolerances while enabling effective use of GPUs. We demonstrate the reliability, efficiency, and scalability of matrix-free p-multigrid methods with algebraic multigrid coarse solvers through large deformation hyperelastic simulations of multiscale structures. We investigate accuracy, cost, and execution time on multi-node CPU and GPU systems for moderate to large models using AMD MI250X (OLCF Crusher), NVIDIA A100 (NERSC Perlmutter), and V100 (LLNL Lassen and OLCF Summit), resulting in order of magnitude efficiency improvements over a broad range of model properties and scales. We discuss efficient matrix-free representation of Jacobians and demonstrate how automatic differentiation enables rapid development of nonlinear material models without impacting debuggability and workflows targeting GPUs.

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Section: Discussionmentioning

confidence: 99%