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Semidefinite Programming for Graph Partitioning with Preferences in Data Distribution

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High Performance Computing for Computational Science — VECPAR 2002 (VECPAR 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2565))

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Abstract

Graph partitioning with preferences is one of the data distribution models for parallel computer, where partitioning and mapping are generatedto gether. It improves the overall throughput of message trafic by having communication restrictedto processors which are near each other, whenever possible. This model is obtained by associating to each vertex a value which reflects its net preference for being in one partition or another of the recursive bisection process. We have formulated a semidefinite programming relaxation for graph partitioning with preferences andimp lemented efficient subspace algorithm for this model. We numerically comparedou r new algorithm with a standardsem idefinite programming algorithm andsh ow that our subspace algorithm performs better.

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Authors and Affiliations

  1. The Department of Computer Science, The University of Iowa, 52242, Iowa City, IA, USA

    Suely Oliveira & Takako Soma

  2. The Department of Mathematics, The University of Iowa, 52242, Iowa City, IA, USA

    David Stewart

Authors
  1. Suely Oliveira
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  2. David Stewart
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  3. Takako Soma
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Editor information

Editors and Affiliations

  1. Faculdade de Engenharia da, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    José M. L. M. Palma  & A. Augusto Sousa  & 

  2. Department of Computer Science, University of Tennessee, 37996-1301, Knoxville, TN, USA

    Jack Dongarra

  3. Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camino de Vera, s/n, Apartado 22012, 46020, Valencia, Spain

    Vicente Hernández

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Oliveira, S., Stewart, D., Soma, T. (2003). Semidefinite Programming for Graph Partitioning with Preferences in Data Distribution. In: Palma, J.M.L.M., Sousa, A.A., Dongarra, J., Hernández, V. (eds) High Performance Computing for Computational Science — VECPAR 2002. VECPAR 2002. Lecture Notes in Computer Science, vol 2565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36569-9_48

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  • DOI: https://doi.org/10.1007/3-540-36569-9_48

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00852-1

  • Online ISBN: 978-3-540-36569-3

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