Advances in Domain Mapping of Massively Parallel Scientific Computations

doi:10.2172/1331498

Title: Advances in Domain Mapping of Massively Parallel Scientific Computations

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Abstract

One of the most important concerns in parallel computing is the proper distribution of workload across processors. For most scientific applications on massively parallel machines, the best approach to this distribution is to employ data parallelism; that is, to break the datastructures supporting a computation into pieces and then to assign those pieces to different processors. Collectively, these partitioning and assignment tasks comprise the domain mapping problem.

Authors:

Leland, Robert W. ^[1]; Hendrickson, Bruce A. ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Publication Date:: 2015-10-01

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1331498

Report Number(s):: SAND2015-8747R
615285

DOE Contract Number:: AC04-94AL85000

Resource Type:: Technical Report

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Leland, Robert W., and Hendrickson, Bruce A. Advances in Domain Mapping of Massively Parallel Scientific Computations.  United States: N. p., 2015. 
        Web.  doi:10.2172/1331498.

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                    Leland, Robert W., & Hendrickson, Bruce A. Advances in Domain Mapping of Massively Parallel Scientific Computations.  United States.  doi:10.2172/1331498.

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                    Leland, Robert W., and Hendrickson, Bruce A. Thu .  
        "Advances in Domain Mapping of Massively Parallel Scientific Computations".  United States.  doi:10.2172/1331498.  https://www.osti.gov/servlets/purl/1331498.

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@article{osti_1331498,

  title        = {Advances in Domain Mapping of Massively Parallel Scientific Computations},

  author       = {Leland, Robert W. and Hendrickson, Bruce A.},

  abstractNote = {One of the most important concerns in parallel computing is the proper distribution of workload across processors. For most scientific applications on massively parallel machines, the best approach to this distribution is to employ data parallelism; that is, to break the datastructures supporting a computation into pieces and then to assign those pieces to different processors. Collectively, these partitioning and assignment tasks comprise the domain mapping problem.},

  doi          = {10.2172/1331498},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2015},

  month        = {10}

}

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