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A GPU-based caching strategy for multi-material linear elastic FEM on regular grids
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598514/ https://www.ncbi.nlm.nih.gov/pubmed/33125404 http://dx.doi.org/10.1371/journal.pone.0240813 |
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author | Schlinkmann, Christian Roland, Michael Wolff, Christian Trampert, Patrick Slusallek, Philipp Diebels, Stefan Dahmen, Tim |
author_facet | Schlinkmann, Christian Roland, Michael Wolff, Christian Trampert, Patrick Slusallek, Philipp Diebels, Stefan Dahmen, Tim |
author_sort | Schlinkmann, Christian |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7598514 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-75985142020-11-03 A GPU-based caching strategy for multi-material linear elastic FEM on regular grids Schlinkmann, Christian Roland, Michael Wolff, Christian Trampert, Patrick Slusallek, Philipp Diebels, Stefan Dahmen, Tim PLoS One Research Article 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. Public Library of Science 2020-10-30 /pmc/articles/PMC7598514/ /pubmed/33125404 http://dx.doi.org/10.1371/journal.pone.0240813 Text en © 2020 Schlinkmann et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Schlinkmann, Christian Roland, Michael Wolff, Christian Trampert, Patrick Slusallek, Philipp Diebels, Stefan Dahmen, Tim A GPU-based caching strategy for multi-material linear elastic FEM on regular grids |
title | A GPU-based caching strategy for multi-material linear elastic FEM on regular grids |
title_full | A GPU-based caching strategy for multi-material linear elastic FEM on regular grids |
title_fullStr | A GPU-based caching strategy for multi-material linear elastic FEM on regular grids |
title_full_unstemmed | A GPU-based caching strategy for multi-material linear elastic FEM on regular grids |
title_short | A GPU-based caching strategy for multi-material linear elastic FEM on regular grids |
title_sort | gpu-based caching strategy for multi-material linear elastic fem on regular grids |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598514/ https://www.ncbi.nlm.nih.gov/pubmed/33125404 http://dx.doi.org/10.1371/journal.pone.0240813 |
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