GPU acceleration of an image characterization algorithm for document similarity analysis

Guochun Shi, Volodymyr Kindratenko, Rob Kooper, Peter Bajcsy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper aims to provide decision support for selecting software and hardware architecture for content-based document comparison. We evaluate Java, C, CUDA C and OpenCL implementations of an image characterization algorithm used for content-based document comparison on a CPU and NVIDIA and AMD graphics processing units (GPUs). Based on our experimental results, we conclude that the original Java implementation of the image characterization algorithm running on a CPU-based architecture can be accelerated by a factor of 6 if the Java code is re-implemented in C, or by a factor of almost 16 if the Java code is re-implemented in CUDA C and run on NVIDIA GTX 480 GPU hardware. We also provide a power efficiency analysis.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 9th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2011
Pages209-216
Number of pages8
DOIs
StatePublished - Dec 1 2011
Event9th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2011 - Sharm El-Sheikh, Egypt
Duration: Dec 27 2011Dec 30 2011

Publication series

NameProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
ISSN (Print)2161-5322
ISSN (Electronic)2161-5330

Other

Other9th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2011
Country/TerritoryEgypt
CitySharm El-Sheikh
Period12/27/1112/30/11

Keywords

  • GPU
  • content-based document comparison
  • non-parametric probability density function estimation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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