Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model

doi:https://doi.org/10.2172/1399438

Title: Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model

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Abstract

A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. The algorithm, named cuPPA, is custom-designed for single instruction multiple data (SIMD) style of parallel processing supported by modern processors such as graphical processing units (GPUs). To the best of our knowledge, our algorithm is the first to exploit GPUs, and also the fastest implementation available today, to generate scale free networks using the preferential attachment model. A detailed performance study is presented to understand the scalability and runtime characteristics of the cuPPA algorithm. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, cuPPA generates a scale free network of a billion edges in less than 2 seconds.

Authors:

Perumalla, Kalyan S. ^[1]; Alam, Maksudul ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:: 2017-10-01

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1399438

Report Number(s):: ORNL/TM-2017/486

DOE Contract Number:: AC05-00OR22725

Resource Type:: Technical Report

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Perumalla, Kalyan S., and Alam, Maksudul. Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model.  United States: N. p., 2017. 
        Web.  doi:10.2172/1399438.

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                    Perumalla, Kalyan S., & Alam, Maksudul. Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model.  United States.  https://doi.org/10.2172/1399438

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                    Perumalla, Kalyan S., and Alam, Maksudul. Sun .  
        "Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model".  United States.  https://doi.org/10.2172/1399438.  https://www.osti.gov/servlets/purl/1399438.

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@article{osti_1399438,

  title        = {Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model},

  author       = {Perumalla, Kalyan S. and Alam, Maksudul},

  abstractNote = {A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. The algorithm, named cuPPA, is custom-designed for single instruction multiple data (SIMD) style of parallel processing supported by modern processors such as graphical processing units (GPUs). To the best of our knowledge, our algorithm is the first to exploit GPUs, and also the fastest implementation available today, to generate scale free networks using the preferential attachment model. A detailed performance study is presented to understand the scalability and runtime characteristics of the cuPPA algorithm. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, cuPPA generates a scale free network of a billion edges in less than 2 seconds.},

  doi          = {10.2172/1399438},

  url          = {https://www.osti.gov/biblio/1399438},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2017},

  month        = {10}

}

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