Dense Affinity Propagation on Clusters of GPUs

  • Marcin Kurdziel
  • Krzysztof Boryczko
Conference paper

DOI: 10.1007/978-3-642-31464-3_61

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7203)
Cite this paper as:
Kurdziel M., Boryczko K. (2012) Dense Affinity Propagation on Clusters of GPUs. In: Wyrzykowski R., Dongarra J., Karczewski K., Waśniewski J. (eds) Parallel Processing and Applied Mathematics. PPAM 2011. Lecture Notes in Computer Science, vol 7203. Springer, Berlin, Heidelberg

Abstract

This article focuses on implementation of Affinity Propagation, a state of the art method for finding exemplars in sets of patterns, on clusters of Graphical Processing Units. When finding exemplars in dense, non-metric data Affinity Propagation has O(n2) memory complexity. This limits the size of problems that can fit in the Graphical Processing Unit memory. We show, however, that dense Affinity Propagation can be distributed on multiple Graphical Processing Units with low communication-to-computation ratio. By exploiting this favorable communication pattern we propose an implementation which can find exemplars in large, dense data sets efficiently, even when run over slow interconnect.

Keywords

Affinity Propagation multi-GPU implementation clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marcin Kurdziel
    • 1
  • Krzysztof Boryczko
    • 1
  1. 1.Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of Computer ScienceAGH University of Science and TechnologyKrakowPoland

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