Parallel Multilevel k-way Partitioning Scheme for Irregular Graphs

In this paper we present a parallel formulation of a multilevel k-way graph partitioning algorithm. The multilevel k-way partitioning algorithm reduces the size of the graph by collapsing vertices and edges (coarsening phase), finds a k-way partition of the smaller graph, and then it constructs a k-...

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Vydané v:Supercomputing '96 conference proceedings : the International Conference on High Performance Computing and Communications : November 17-22, 1996, Pittsburgh, PA s. 35
Hlavní autori: Karypis, G., Kumar, V.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 1996
Predmet:
Computer science
Concurrent computing
Contracts
Finite element methods
Kernighan-Lin Heuristic
Military computing
Multilevel Partitioning Methods
Parallel algorithms
Parallel Graph Partitioning
Parallel Sparse Matrix Algorithms
Partitioning algorithms
Sparse matrices
Spectral Partitioning Methods
Testing
Transportation
ISBN:0897918541, 9780897918541
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Shrnutí:In this paper we present a parallel formulation of a multilevel k-way graph partitioning algorithm. The multilevel k-way partitioning algorithm reduces the size of the graph by collapsing vertices and edges (coarsening phase), finds a k-way partition of the smaller graph, and then it constructs a k-way partition for the original graph by projecting and refining the partition to successively finer graphs (uncoarsening phase). A key innovative feature of our parallel formulation is that it utilizes graph coloring to effectively parallelize both the coarsening and the refinement during the uncoarsening phase. Our algorithm is able to achieve a high degree of concurrency, while maintaining the high quality partitions produced by the serial algorithm. We test our scheme on a large number of graphs from finite element methods, and transportation domains. Our parallel formulation on Cray T3D, produces high quality 128-way partitions on 128 processors in a little over two seconds, for graphs with a million vertices. Thus our parallel algorithm makes it possible to perform dynamic graph partition in adaptive computations without compromising quality.
ISBN:0897918541
9780897918541
DOI:10.1109/SUPERC.1996.183537