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Speeding Up Network Layout and Centrality Measures for Social Computing Goals

  • Puneet Sharma
  • Udayan Khurana
  • Ben Shneiderman
  • Max Scharrenbroich
  • John Locke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6589)

Abstract

This paper presents strategies for speeding up calculation of graph metrics and layout by exploiting the parallel architecture of modern day Graphics Processing Units (GPU), specifically Compute Unified Device Architecture (CUDA) by Nvidia. Graph centrality metrics like Eigenvector, Betweenness, Page Rank and layout algorithms like Fruchterman − Rheingold are essential components of Social Network Analysis (SNA). With the growth in adoption of SNA in different domains and increasing availability of huge networked datasets for analysis, social network analysts require faster tools that are also scalable. Our results, using NodeXL, show up to 802 times speedup for a Fruchterman-Rheingold graph layout and up to 17,972 times speedup for Eigenvector centrality metric calculations on a 240 core CUDA-capable GPU.

Keywords

Social Computing Social Network Analysis CUDA 

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Puneet Sharma
    • 1
    • 2
  • Udayan Khurana
    • 1
    • 2
  • Ben Shneiderman
    • 1
    • 2
  • Max Scharrenbroich
    • 3
  • John Locke
    • 1
  1. 1.Computer Science DepartmentUniversity of MarylandCollege ParkUSA
  2. 2.Human-Computer Interaction LabUniversity of MarylandCollege ParkUSA
  3. 3.Department of MathematicsUniversity of MarylandCollege ParkUSA

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