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Accelerated Parallel Based Distance Calculations for Live-Cell Time-Lapse Images

  • Hui-Jun Cheng
  • Chun-Yuan LinEmail author
  • Chun-Chien Mao
Conference paper
  • 502 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1013)

Abstract

Live-cell time-lapse images with particles generated by experiments are useful for observing results, even for proposing novel hypotheses. By identifying particles and cells as objects from these images and then calculating measures from them, such as the distances, they can be quantized for the relationship of particles and cells. However, this work is very time-consuming when calculating the distances among a large number of images. Hence, a very important issue will be presented here in order to accelerate the calculations. In this paper, we will propose parallel algorithms for calculating particle-cell distances, abbreviate to a PCD problem. Two parallel PCD algorithms, called PPCDOMP and PPCDCUDA, will be developed by using OpenMP and CUDA. After the experimental tests, the PPCDOMP with 16 CPU threads achieves 11.7 times by comparing with the PCD algorithm in a single thread; however, the PPCDCUDA with 256 GPU threads per thread block only achieves 3.2 times. Therefore, the PPCDOMP algorithm is suitable for analyzing live-cell time-lapse images with particles based on the shared memory environments.

Keywords

Live-cell time-lapse images Particle-cell distance GPU OpenMP CUDA 

Notes

Acknowledgments

Part of this work was supported by the Ministry of Science and Technology under the grant MOST 107-2221-E-182-063-MY2. This work also was supported by the Higher Education Sprout Project funded by the Ministry of Science and Technology and Ministry of Education in Taiwan. The authors would like to thank the anonymous reviewers and experts discussed with us in the past.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hui-Jun Cheng
    • 1
  • Chun-Yuan Lin
    • 1
    • 2
    • 3
    • 4
    Email author
  • Chun-Chien Mao
    • 2
  1. 1.AI Innovation Research CenterChang Gung UniversityTaoyuanTaiwan
  2. 2.Department of Computer Science and Information EngineeringChang Gung UniversityTaoyuanTaiwan
  3. 3.Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial HospitalTaoyuanTaiwan
  4. 4.Brain Research CenterNational Tsing Hua UniversityHsinchuTaiwan

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