A fully generalized over operator with applications to image composition in parallel visualization for big data science

Dongliang Chu, Chase Qishi Wu, Zongmin Wang, Yongqiang Wang

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

2 Scopus citations

Abstract

The over operator is commonly used for α-blending in various visualization techniques. In the current form, it is a binary operator and must respect the restriction of order dependency, hence posing a significant performance limit. This paper proposes a fully generalized version of this operator. Compared with its predecessor, the fully generalized over operator is not only n-operator compatible but also any-order friendly. To demonstrate the advantages of the proposed operator, we apply it to the asynchronous and order-dependent image composition problem in parallel visualization for big data science and further parallelize it for performance improvement. We conduct theoretical analyses to establish the performance superiority of the proposed over operator in comparison with its original form, which is further validated by extensive experimental results in the context of real-life scientific visualization.

Original languageEnglish (US)
Title of host publication2014 20th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2014 - Proceedings
PublisherIEEE Computer Society
Pages560-567
Number of pages8
ISBN (Electronic)9781479976157
DOIs
StatePublished - 2014
Externally publishedYes
Event20th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2014 - Hsinchu, Taiwan, Province of China
Duration: Dec 16 2014Dec 19 2014

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
Volume2015-April
ISSN (Print)1521-9097

Other

Other20th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2014
Country/TerritoryTaiwan, Province of China
CityHsinchu
Period12/16/1412/19/14

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture

Keywords

  • Parallel visualization
  • big data
  • image composition

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