Parallel Graph Partitioning and Sparse Matrix Ordering Library Version 4 . 0

By George Karypis, K. Schloegel. 2003

495 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/46fe89ee18398d47d89c7abb91755ad52345cf0d

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Citations in the corpus

An overview of SuperLU: Algorithms, implementation, and user interface (2003, methodology): “We plan to apply ParMeTiS[Karypis et al. 2003] to the graph of AT + A to obtain both a sparsity-preserving ordering and a separator tree.”
Highly scalable parallel domain decomposition methods with an application to biomechanics (2010, methodology): “We discretize the problem using linear tetrahedra and decompose it either regularly or using the ParMetis [20] graph partitioner.”
Parallel hypergraph partitioning for scientific computing (2006, unknown): “1 [14] (parallel graph partitioner), PaToH 3.”
Sourcebook of parallel computing (2003, methodology): “This partitioning can be used to redistribute the graph prior to performing parallel multilevel (or spectral) partitioning [547].”
Evaluating the performance of parallel subsurface simulators: An illustrative example with PFLOTRAN (2014, methodology): “PFLOTRAN’s unstructured grid implementation relies upon PETSc’s interface to graph partitioning software such as ParMETIS [ Karypis and Schloegel , 2011] and PETSc data structures such as Index Sets, VecScatters, LocalToGlobalMappings to create parallel vectors and matrices and appropriate scatter/g”
An unstructured-grid, finite-volume, nonhydrostatic, parallel coastal ocean simulator (2006, methodology): “Significant computational performance gains are attained with the use of the ParME- TIS libraries (Karypis et al., 1998), which enable load balancing while minimizing overhead incurred by interprocessor communication.”