Graph partitioning for dynamic, adaptive and multi-phase scientific simulations

Kirk Schloegel, George Karypis, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The efficient execution of scientific simulations on HPC systems requires a partitioning of the underlying mesh among the processors such that the load is balanced and the inter-processor communication is minimized. Graph partitioning algorithms have been applied with much success for this purpose. However, the parallelization of multi-phase and multi-physics computations poses new challenges that require fundamental advances in graph partitioning technology. In addition, most existing graph partitioning algorithms are not suited for the newer heterogeneous highperformance computing platforms. This talk will describe research efforts in our group that are focused on developing novel multi-constraint and multi-objective graph partitioning algorithms that can support the advancing state-ofthe-A rt in numerical simulation technologies. In addition, we will present our preliminary work on new partitioning algorithms that are well suited for heterogeneous architectures.

Original languageEnglish (US)
Title of host publicationProceedings - 2001 IEEE International Conference on Cluster Computing, CLUSTER 2001
EditorsDaniel S. Katz, Thomas Sterling, Mark Baker, Larry Bergman, Marcin Paprzycki, Rajkumar Buyya
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)0769511163
DOIs
StatePublished - 2001
Event2001 IEEE International Conference on Cluster Computing, CLUSTER 2001 - Newport Beach, United States
Duration: Oct 8 2001Oct 11 2001

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
Volume2001-October
ISSN (Print)1552-5244

Conference

Conference2001 IEEE International Conference on Cluster Computing, CLUSTER 2001
Country/TerritoryUnited States
CityNewport Beach
Period10/8/0110/11/01

Bibliographical note

Funding Information:
This work was supported by DOE contract number LLNL B347881, by NSF grants CCR-9972519, EIA-9986042, and ACI-9982274, by Army Research Office contracts DA/DAAG55-98-1-0441, by Army High Performance Computing Research Center cooperative agreement number DAAH04-95-2-0003/contract number DAAH04-95-C-0008, the content of which does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred. Additional support was provided by the IBM Partnership Award, and by the IBM SUR equipment grant. Access to computing facilities was provided by AHPCRC and the Minnesota Supercomputer Institute.

Publisher Copyright:
© 2001 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

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