A comparison of tetrahedron quality measures
Finite Elements in Analysis and Design
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Multilevel diffusion schemes for repartitioning of adaptive meshes
Journal of Parallel and Distributed Computing - Special issue on dynamic load balancing
Estimating the tensor of curvature of a surface from a polyhedral approximation
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
On the inherent weakness of conditional synchronization primitives
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
Sparse parallel Delaunay mesh refinement
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Parallel unstructured mesh generation by an advancing front method
Mathematics and Computers in Simulation
Fast viscoelastic behavior with thin features
ACM SIGGRAPH 2008 papers
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The automatic generation of 3D finite element meshes (FEM) is still a bottleneck for the simulation of large fluid dynamic problems. Although today there are several algorithms that can generate good meshes without user intervention, in cases where the geometry changes during the calculation and thousands of meshes must be constructed, the computational cost of this process can exceed the cost of the FEM. There has been a lot of work in FEM parallelization and the algorithms work well in different parallel architectures, but at present there has not been much success in the parallelization of mesh generation methods. This paper will present a massive parallelization scheme for re-meshing with tetrahedral elements using the local modification algorithm. This method is frequently used to improve the quality of elements once the mesh has been generated, but we will show it can also be applied as a regeneration process, starting with the distorted and invalid mesh of the previous step. The parallelization is carried out using OpenCL and OpenMP in order to test the method in a multiple CPU architecture and also in Graphics Processing Units (GPUs). Finally we present the speedup and quality results obtained in meshes with hundreds of thousands of elements and different parallel APIs.