Distributed-memory Parallel Algorithms for Matching and Coloring

Catalyurek, Umit; Dobrian, Florin; Gebremedhin, Assefaw H; Halappanavar, Mahantesh; Pothen, Alex

doi:10.1109/IPDPS.2011.360

Title: Distributed-memory Parallel Algorithms for Matching and Coloring

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Abstract

Graph matching and coloring constitute two fundamental classes of combinatorial problems having numerous established as well as emerging applications in computational science and engineering, high-performance computing, and informatics. We provide a snapshot of an on-going work on the design and implementation of new highly-scalable distributed-memory parallel algorithms for two prototypical problems from these classes, edge-weighted matching and distance-1 vertex coloring. Graph algorithms in general have low concurrency and poor data locality, making it challenging to achieve scalability on massively parallel machines. We overcome this challenge by employing a variety of techniques, including approximation, speculation and iteration, optimized communication, and randomization, in concert. We present preliminary results on weak and strong scalability studies conducted on an IBM Blue Gene/P machine employing up to tens of thousands of processors. The results show that the algorithms hold strong potential for computing at petascale.

Authors:: Catalyurek, Umit; Dobrian, Florin; Gebremedhin, Assefaw H; Halappanavar, Mahantesh; Pothen, Alex

Publication Date:: Tue May 31 00:00:00 EDT 2011

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1059212

Report Number(s):: PNNL-SA-77038

DOE Contract Number:: AC05-76RL01830

Resource Type:: Conference

Resource Relation:: Conference: IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW 2011), May 16-20, 2011 Anchorage, Alaska, 1971-1980

Country of Publication:: United States

Language:: English

Subject:: Combinatorial scientific computing; graph coloring; graph matching; parallel algorithms; petascale computing; distributed-memory architectures; MPI

Citation Formats


                    Catalyurek, Umit, Dobrian, Florin, Gebremedhin, Assefaw H, Halappanavar, Mahantesh, and Pothen, Alex. Distributed-memory Parallel Algorithms for Matching and Coloring.  United States: N. p., 2011. 
        Web.  doi:10.1109/IPDPS.2011.360.

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                    Catalyurek, Umit, Dobrian, Florin, Gebremedhin, Assefaw H, Halappanavar, Mahantesh, & Pothen, Alex. Distributed-memory Parallel Algorithms for Matching and Coloring.  United States.  https://doi.org/10.1109/IPDPS.2011.360

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                    Catalyurek, Umit, Dobrian, Florin, Gebremedhin, Assefaw H, Halappanavar, Mahantesh, and Pothen, Alex. 2011.  
        "Distributed-memory Parallel Algorithms for Matching and Coloring".  United States.  https://doi.org/10.1109/IPDPS.2011.360.

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@article{osti_1059212,

  title        = {Distributed-memory Parallel Algorithms for Matching and Coloring},

  author       = {Catalyurek, Umit and Dobrian, Florin and Gebremedhin, Assefaw H and Halappanavar, Mahantesh and Pothen, Alex},

  abstractNote = {Graph matching and coloring constitute two fundamental classes of combinatorial problems having numerous established as well as emerging applications in computational science and engineering, high-performance computing, and informatics. We provide a snapshot of an on-going work on the design and implementation of new highly-scalable distributed-memory parallel algorithms for two prototypical problems from these classes, edge-weighted matching and distance-1 vertex coloring. Graph algorithms in general have low concurrency and poor data locality, making it challenging to achieve scalability on massively parallel machines. We overcome this challenge by employing a variety of techniques, including approximation, speculation and iteration, optimized communication, and randomization, in concert. We present preliminary results on weak and strong scalability studies conducted on an IBM Blue Gene/P machine employing up to tens of thousands of processors. The results show that the algorithms hold strong potential for computing at petascale.},

  doi          = {10.1109/IPDPS.2011.360},

  url          = {https://www.osti.gov/biblio/1059212},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue May 31 00:00:00 EDT 2011},

  month        = {Tue May 31 00:00:00 EDT 2011}

}

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