Publikation

A GPU-based caching strategy for multi-material linear elastic FEM on regular grids

Christian Schlinkmann, Michael Roland, Christian Wolff, Patrick Trampert, Philipp Slusallek, Stefan Diebels, Tim Dahmen

In: PLOS ONE (PLOS) 15 10 Seiten 1-19 Public Library of Science 10/2020.

Abstrakt

In this study, we present a novel strategy to the method of finite elements (FEM) of linear elastic problems of very high resolution on graphic processing units (GPU). The approach exploits regularities in the system matrix that occur in regular hexahedral grids to achieve cache-friendly matrix-free FEM. The node-by-node method lies in the class of block-iterative Gauss-Seidel multigrid solvers. Our method significantly improves convergence times in cases where an ordered distribution of distinct materials is present in the dataset. The method was evaluated on three real world datasets: An aluminum-silicon (AlSi) alloy and a dual phase steel material sample, both captured by scanning electron tomography, and a clinical computed tomography (CT) scan of a tibia. The caching scheme leads to a speed-up factor of ×2-×4 compared to the same code without the caching scheme. Additionally, it facilitates the computation of high-resolution problems that cannot be computed otherwise due to memory consumption.

Projekte

Weitere Links

journal.pone.0240813.pdf (pdf, 1 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence