Abstract
This chapter describes a data-parallel, distributed-memory algorithm for identifying and labeling the connected sub-meshes within a three-dimensional mesh. The identification task is challenging in a distributed-memory setting because connectivity is transitive and the cells composing a sub-mesh may span many processors. The algorithm employs a multi-stage application of the Union-find algorithm and a spatial partitioning scheme to efficiently merge information across processors and to produce a global labeling of connected sub-meshes. Marking each vertex with its corresponding sub-mesh label allows mesh features to be isolated based on topology, enabling important analysis capabilities. The algorithm performs well in parallel; results are presented from a weak scaling study with concurrency levels up to 2,197 cores and meshes containing over two billion cells. This chapter is an extension of previous work by Harrison et al. (Data-parallel mesh connected components labeling and analysis. In: EuroGraphics Symposium on Parallel Graphics and Visualization (EGPGV), pp. 131–140, April 2011). It contains significant algorithmic improvements over the previous version, improved exploration of key bottlenecks in the algorithm, and improved clarity of presentation.
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Acknowledgements
This work was supported by the Director, Office of Advanced Scientific Computing Research, Office of Science, of the U.S. Department of Energy under Contract DE-AC02-05CH11231, was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, and was also supported in part by the National Science Foundation, grants OCI-0906379 and OCI-0751397.
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Harrison, C., Weiler, J., Bleile, R., Gaither, K., Childs, H. (2015). A Distributed-Memory Algorithm for Connected Components Labeling of Simulation Data. In: Bennett, J., Vivodtzev, F., Pascucci, V. (eds) Topological and Statistical Methods for Complex Data. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44900-4_1
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