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Parallelizing Affinity Propagation Using Graphics Processing Units for Spatial Cluster Analysis over Big Geospatial Data

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Advances in Geocomputation

Part of the book series: Advances in Geographic Information Science ((AGIS))

Abstract

Introduced in 2007, affinity propagation (AP) is a relatively new machine learning algorithm for unsupervised classification that has seldom been applied in geospatial applications. One bottleneck is that AP could hardly handle large data, and a serial computer program would take a long time to complete an AP calculation. New multicore and manycore computer architectures, combined with application accelerators, show promise for achieving scalable geocomputation by exploiting task and data levels of parallelism. This chapter introduces our recent progress in parallelizing the AP algorithm on a graphics processing unit (GPU) for spatial cluster analysis, the potential of the proposed solution to process big geospatial data, and its broader impact for the GIScience community.

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Notes

  1. 1.

    Sample AP datasets: http://www.psi.toronto.edu/affinitypropagation/vsh/.

  2. 2.

    Source: http://genes.toronto.edu/affinitypropagation/faq.html.

  3. 3.

    Source: http://www.psi.toronto.edu/index.php?q=affinity%20propagation.

  4. 4.

    Source: http://www.psi.toronto.edu/affinitypropagation/apcluster_unsupported.txt.

  5. 5.

    PySAL: https://geodacenter.asu.edu/pysal.

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Acknowledgments

This research was partially supported by the National Science Foundation (NSF) through the award NSF SMA-1416509 “IBSS: Spatiotemporal Modeling of Human Dynamics across Social Media and Social Networks” and the National Institutes of Health (NIH) through the award NIH 1R21CA182874-01 “Reducing Physician Distribution Uncertainty in Spatial Accessibility Research.” Any opinions, findings, recommendations, or conclusions expressed in this material are those of the author and do not necessarily reflect the views of the NSF or NIH.

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Authors and Affiliations

  1. Department of Geosciences, University of Arkansas, 216 Gearhart Hall, Fayetteville, AR, 72701, USA

    Xuan Shi

Authors
  1. Xuan Shi
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Corresponding author

Correspondence to Xuan Shi .

Editor information

Editors and Affiliations

  1. School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, Texas, USA

    Daniel A. Griffith

  2. School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, Texas, USA

    Yongwan Chun

  3. School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, Texas, USA

    Denis J. Dean

Appendices

Appendix 1

CUDA/GPU program that reflects the preceding work flow for AP implementation in comparison with the original serial C program

Appendix 2

The host program and device program in CUDA/GPU code

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Shi, X. (2017). Parallelizing Affinity Propagation Using Graphics Processing Units for Spatial Cluster Analysis over Big Geospatial Data. In: Griffith, D., Chun, Y., Dean, D. (eds) Advances in Geocomputation. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-22786-3_32

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