Efficient and scalable parallel graph partitioning

The realization of efficient parallel graph partitioners requires the parallelization of the multi-level framework which is commonly used to improve the quality and speed of sequential partitioners. The two most critical issues in this framework are the coarsening phase, and the local refinement step performed in the uncoarsening phase. These two phases are difficult to parallelize, because the direct transposition in parallel of the matching algorithms used for coarsening, and of the inherently sequential Fiduccia-Mattheyses type algorithms traditionally used for local optimization, require much communication and synchronization, which hinder scalability. This paper describes new parallel algorithms which tackle these two issues: a simplified probabilistic matching algorithm, and a parallel banded diffusive algorithm, both of which are implemented in the PT-Scotch parallel graph partitioning software. Experimental results illustrate the efficiency and the scalability of these methods.

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