GPU‐based Polynomial Finite Element Matrix Assembly for Simplex Meshes
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Summary
This paper presents a matrix assembly technique for arbitrary polynomial order finite element simulations on simplex meshes for graphics processing units (GPU) that avoids the need for an intermediate sparse matrix and performs assembly directly into the final, GPU‐optimized data structure.
- Type
- article
- Published
- 2018-10-01
- Cited by
- 10
- References
- 38
- Access
- Open access
- OpenAlex
- https://openalex.org/W2898468574
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53087398
Keywords
Computer science, Polygon mesh, Simplex, Finite element method, Matrix (chemical analysis)
References
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- The Basis Refinement Method
- Codimensional surface tension flow on simplicial complexes
- Developing a scalable hybrid MPI/OpenMP unstructured finite element model
- Scalable and efficient implementation of 3d unstructured meshes computation: a case study on matrix assembly
- Porting a high-order finite-element earthquake modeling application to NVIDIA graphics cards using CUDA
- The h, p and h-p version of the finite element method: basis theory and applications
- Achievements and some unsolved problems of the finite element method
- Interactive deformable models with quadratic bases in Bernstein–Bézier-form
- Improving the Performance of the Sparse Matrix Vector Product with GPUs
- Finite Element Algorithms and Data Structures on Graphical Processing Units
- Efficient GPU Data Structures and Methods to Solve Sparse Linear Systems in Dynamics Applications
- Codimensional non-Newtonian fluids
- Assembly of finite element methods on graphics processors
- GPU accelerated fast FEM deformation simulation
- Comparison of different higher order finite element schemes for the simulation of Lamb waves
Cited by
- Joint Schedule and Layout Autotuning for Sparse Matrices with Compound Entries on GPUs
- Analysis of Schedule and Layout Tuning for Sparse Matrices With Compound Entries on GPUs
- OLBVH: octree linear bounding volume hierarchy for volumetric meshes
- Generative Machine Learning for Resource-Aware 5G and IoT Systems
- TEdit: A Distributed Tetrahedral Mesh Editor with Immediate Simulation Feedback
- DeepPhysics: A physics aware deep learning framework for real‐time simulation
- Fast harmonic tetrahedral mesh optimization
- Quick Clusters: A GPU-Parallel Partitioning for Efficient Path Tracing of Unstructured Volumetric Grids
- Massively Parallel Adaptive Collapsing of Edges for Unstructured Tetrahedral Meshes
- Interacting with FEM Simulated Tubes in AR
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