High-Performance Mesh Partitioning and Ghost Cell Generation for Visualization Software

Abstract

Post-processing large datasets efficiently in parallel requires good load balancing of geometry supplied to the visualization pipeline. When datasets are not pre-partitioned or cannot be read back from simulation output in well controlled pieces, it is necessary to perform a partitioning step before certain algorithms may be applied. Spatially sensitive operations such as resampling, smoothing or certain field advection/stencil algorithms require datasets/meshes to be contiguous and provide ghost cells so that artefacts do not occur at process boundaries where discontinuities occur. This paper presents an integration of the mesh partitioning library Zoltan, into the Visualization Toolkit framework, VTK and the parallel visualization tool ParaView. The implementation allows seamless generation of well partitioned datasets using a user provided weighting and a selection of ghost cell generation options. The algorithms, and results obtained with the partitioning classes are presented with representative use cases that show an order of magnitude increase in performance compared to the off-the-shelf partitioning available previously, improving performance and reducing memory consumption/duplication.