Accelerated Parallel Based Distance Calculations for Live-Cell Time-Lapse Images
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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.
KeywordsLive-cell time-lapse images Particle-cell distance GPU OpenMP CUDA
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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