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Part of book or chapter of book . 2000 . Peer-reviewed
License: Springer TDM
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Parallel Multilevel Algorithms for Multi-constraint Graph Partitioning

Authors: Kirk Schloegel; George Karypis; Vipin Kumar;

Parallel Multilevel Algorithms for Multi-constraint Graph Partitioning

Abstract

Sequential multi-constraint graph partitioners have been developed to address the load balancing requirements of multi-phase simulations. The efficient execution of large multi-phase simulations on high performance parallel computers requires that the multi-constraint partitionings are computed in parallel. This paper presents a parallel formulation of a recently developed multi-constraint graph partitioning algorithm. We describe this algorithm and give experimental results conducted on a 128-processor Cray T3E. We show that our parallel algorithm is able to efficiently compute partitionings of similar edge-cuts as serial multi-constraint algorithms, and can scale to very large graphs. Our parallel multi-constraint graph partitioner is able to compute a three-constraint 128-way partitioning of a 7.5 million node graph in about 7 seconds on 128 processors of a Cray T3E.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
47
Top 10%
Top 10%
Average