Affiliation:
1. Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
Abstract
We introduce several parallel algorithms operating on a distributed forest of adaptive quadtrees/octrees. They are targeted at large-scale applications relying on data layouts that are more complex than required for standard finite elements, such as
hp
-adaptive Galerkin methods, particle tracking and semi-Lagrangian schemes, and in-situ post-processing and visualization. Specifically, we design algorithms to derive an adapted worker forest based on sparse data, to identify owner processes in a top-down search of remote objects, and to allow for variable process counts and per-element data sizes in partitioning and parallel file I/O. We demonstrate the algorithms’ usability and performance in the context of a particle tracking example that we scale to 21e9 particles and 64Ki MPI processes on the Juqueen supercomputer, and we describe the construction of a parallel assembly of variably sized spheres in space creating up to 768e9 elements on the Juwels supercomputer.
Funder
Bonn Hausdorff Center for Mathematics
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Initiative EXC
Publisher
Association for Computing Machinery (ACM)
Subject
Applied Mathematics,Software
Reference57 articles.
1. A Posteriori Error Estimation in Finite Element Analysis
2. Clelia Albrecht. 2016. Parallelization of Adaptive Gradient Augmented Level Set Methods. Master’s thesis. Rheinische Friedrich-Wilhelms-Universität Bonn. Clelia Albrecht. 2016. Parallelization of Adaptive Gradient Augmented Level Set Methods. Master’s thesis. Rheinische Friedrich-Wilhelms-Universität Bonn.
3. ParaView Catalyst
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