Parallel Large-Scale Image Processing for Orthorectification

Changjin Im, Jae Heon Jeong, Chang Sung Jeong

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


Recently, as the amount of data that needs to be processed is getting larger, various techniques of parallel processing have been developed to deal with large-scale data efficiently and quickly. In this paper, we shall present fast parallel orthorectification algorithms for large-scale image data on various environments, and compare their performance in terms of speed-up and execution time. Our research consists of two parts: First, we implement parallel orthorectification algorithm on CPU multicore, Xeon-phi multicore and GPU. Second, we analyze these experiment results by comparing the performance of each algorithm. More specifically, we compare the performance of CPU multicore with Xeon-phi and GPU parallelization to find which one is more efficient as well as the performance of Xeon-phi multicore and GPU parallelization. We shall show that for the former, GPU parallelization is more efficient technique than CPU multicore parallelization, while for the latter, Xeon-phi multicore parallelization is more efficient technique than GPU parallelization. This is due to the data upload/download time on GPU. Even if we extend the experiment to infinite-scale, data upload/download time on GPU is still needed. Therefore, Xeon-phi multicore parallelization is better than GPU parallelization not only on the extended environment but also the infinite-scale environment.

Original languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781538654576
Publication statusPublished - 2019 Feb 22
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 2018 Oct 282018 Oct 31

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2018 IEEE Region 10 Conference, TENCON 2018
Country/TerritoryKorea, Republic of


  • CUDA
  • large-scale image
  • multi-GPU
  • multi-core
  • openmp
  • orthorectification
  • parallel
  • xeon-phi

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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