TY - CHAP
T1 - Application of Assembly of Finite Element Methods on Graphics Processors for Real-Time Elastodynamics
AU - Cecka, Cris
AU - Lew, Adrian
AU - Darve, Eric
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was partially supported by a research grant from the Academic Excellence Alliance program between King Abdullah University of Science and Technology and Stanford University. We also thank the Army High-Performance Computing and Research Center (AHPCRC) at Stanford for its support, as well as Juan-Pablo Samper-Mejia and Vivian Nguyen for their contribution during the 2010 AHPCRC Summer Institute.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2012
Y1 - 2012
N2 - This chapter discusses multiple strategies to perform general computations on unstructured grids, with specific application to the assembly of matrices in finite element methods (FEMs). It reviews and applies two methods for assembly of FEMs to produce and accelerate a FEM model for a nonlinear hyperelastic solid where the assembly, solution, update, and visualization stages are performed solely on the GPU, benefiting from speed-ups in each stage and avoiding costly GPUCPU transfers of data. For each method, the chapter discusses the NVIDIA GPU hardware's limiting resources, optimizations, key data structures, and dependence of the performance with respect to problem size, element size, and GPU hardware generation. Furthermore, this chapter informs potential users of the benefits of GPU technology, provides guidelines to help them implement their own FEM solutions, gives potential speed-ups that can be expected, and provides source code for reference. © 2012 Elsevier Inc. All rights reserved.
AB - This chapter discusses multiple strategies to perform general computations on unstructured grids, with specific application to the assembly of matrices in finite element methods (FEMs). It reviews and applies two methods for assembly of FEMs to produce and accelerate a FEM model for a nonlinear hyperelastic solid where the assembly, solution, update, and visualization stages are performed solely on the GPU, benefiting from speed-ups in each stage and avoiding costly GPUCPU transfers of data. For each method, the chapter discusses the NVIDIA GPU hardware's limiting resources, optimizations, key data structures, and dependence of the performance with respect to problem size, element size, and GPU hardware generation. Furthermore, this chapter informs potential users of the benefits of GPU technology, provides guidelines to help them implement their own FEM solutions, gives potential speed-ups that can be expected, and provides source code for reference. © 2012 Elsevier Inc. All rights reserved.
UR - http://hdl.handle.net/10754/597594
UR - https://linkinghub.elsevier.com/retrieve/pii/B9780123859631000162
UR - http://www.scopus.com/inward/record.url?scp=84882460891&partnerID=8YFLogxK
U2 - 10.1016/b978-0-12-385963-1.00016-2
DO - 10.1016/b978-0-12-385963-1.00016-2
M3 - Chapter
SN - 9780123859631
SP - 187
EP - 205
BT - GPU Computing Gems Jade Edition
PB - Elsevier BV
ER -