Highly efficient spatial data filtering in parallel using the opensource library CPPPO

Abstract

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®, 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. Program summaryProgram title: CPPPOCatalogue identifier: AFAQ_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AFAQ_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU Lesser General Public License, version 3No. of lines in distributed program, including test data, etc.: 1043965No. of bytes in distributed program, including test data, etc.: 11053655Distribution format: tar.gzProgramming language: C++, MPI, octave.Computer: Linux based clusters for HPC or workstations.Operating system: Linux based.Classification: 4.14, 6.5, 12.External routines: Qt5, hdf5-1.8.15, jsonlab, OpenFOAM/CFDEM, Octave/MatlabNature of problem:Development of closure models for momentum, species transport and heat transfer in fluid and fluid–particle systems using purely Eulerian or Euler–Lagrange simulators.Solution method:The CPPPO library contains routines to perform on-line (i.e., runtime) filtering and compute statistics on large parallel data sets.Running time:Performing a Favre averaging on a structured mesh of 1283 cells with a filter size of 643 cells using one Intel Xeon(R) E5-2650, requires approximately 4 h of computation.