Parallel tree algorithms for AMR and non-standard data access
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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 applications appear in various contexts, examples being the hp-adaptive discontinuous Galerkin method, element-based particle tracking, 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.
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