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Highly efficient spatial data filtering in parallel using the opensource library CPPPO
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PRE
AI
DOI：10.1016/j.cpc.2016.05.026.png)
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Abstract
En 中文
CPPPO is a compilation of parallel data processing routines developed with the aim to create a library for scale bridging (i.e. connecting different scales by mean of closure models) in a multi-scale approach. CPPPO features a number of parallel filtering algorithms designed for use with structured and unstructured Eulerian meshes, as well as Lagrangian data sets. In addition, data can be processed on the fly, allowing the collection of relevant statistics without saving individual snapshots of the simulation state. Our library is provided with an interface to the widely-used CFD solver OpenFOAM (R), and can be easily connected to any other software package via interface modules. Also, we introduce a novel, extremely efficient approach to parallel data filtering, and show that our algorithms scale super-linearly on multi-core clusters. Furthermore, we provide a guideline for choosing the optimal Eulerian cell selection algorithm depending on the number of CPU cores used. Finally, we demonstrate the accuracy and the parallel scalability of CPPPO in a showcase focusing on heat and mass transfer from a dense bed of particles.
Keywords:
Multi-scale
Closure models
Parallel filtering
Post processing
MPI
Journal
IF:
3.4
Papers:
1.2W
Citations:
4.0W
Organization
Cited Papers
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