
Summary: Computational methods based on the use of adaptively constructed nonuniform meshes reduce the amount of computation and storage necessary to perform many scientific calculations. The adaptive construction of such nonuniform meshes is an important part of these methods. We present a parallel algorithm for adaptive mesh refinement that is suitable for implementation on distributed-memory parallel computers. Experimental results obtained on the Intel DELTA are presented to demonstrate that for scientific computations involving the finite element method, the algorithm exhibits scalable performance and has a small run time in comparison with other aspects of the scientific computations examined. It is also shown that the algorithm has a fast expected running time under the parallel random access machine (PRAM) computation model.
sparse matrices, finite element method, parallel algorithms, Parallel numerical computation, distributed memory computers, Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs, Mesh generation, refinement, and adaptive methods for boundary value problems involving PDEs, unstructured mesh computation, Computational methods for sparse matrices, Complexity and performance of numerical algorithms, adaptive mesh refinement, performance
sparse matrices, finite element method, parallel algorithms, Parallel numerical computation, distributed memory computers, Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs, Mesh generation, refinement, and adaptive methods for boundary value problems involving PDEs, unstructured mesh computation, Computational methods for sparse matrices, Complexity and performance of numerical algorithms, adaptive mesh refinement, performance
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