Parallel hypergraph partitioning for scientific computing.
- Ohio State University, Columbus
- Utrecht University, The Netherlands
Graph partitioning is often used for load balancing in parallel computing, but it is known that hypergraph partitioning has several advantages. First, hypergraphs more accurately model communication volume, and second, they are more expressive and can better represent nonsymmetric problems. Hypergraph partitioning is particularly suited to parallel sparse matrix-vector multiplication, a common kernel in scientific computing. We present a parallel software package for hypergraph (and sparse matrix) partitioning developed at Sandia National Labs. The algorithm is a variation on multilevel partitioning. Our parallel implementation is novel in that it uses a two-dimensional data distribution among processors. We present empirical results that show our parallel implementation achieves good speedup on several large problems (up to 33 million nonzeros) with up to 64 processors on a Linux cluster.
- Research Organization:
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 968387
- Report Number(s):
- SAND2005-4327C; TRN: US200924%%409
- Resource Relation:
- Conference: Proposed for presentation at the International Workshop on Combinatorial Scientific Computing held June 21-23, 2005 in Toulouse, France.
- Country of Publication:
- United States
- Language:
- English
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