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Title: GPU-accelerated CellProfiler
Authors: Chakroun, Imen
Michiels, Nick 
Wuyts, Roel
Issue Date: 2018
Publisher: IEEE
Source: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE,
Abstract: CellProfiler excels at bridging the gap between advanced image analysis algorithms and scientists who lack computational expertise. It lacks however high performance capabilities needed for High Throughput Imaging experiments where workloads reach hundreds of TB of data and are computationally very demanding. In this work, we introduce a GPU-accelerated CellProfiler where the most time-consuming algorithmic steps are executed on Graphics Processing Units. Experiments on a benchmark dataset showed significant speedup over both single and multi-core CPU versions. The overall execution time was reduced from 9.83 Days to 31.64 Hours.
Notes: Chakroun, I (reprint author), IMEC, Exasci Life Lab, 75 Kapeldreef, B-3001 Leuven, Belgium.;;
Keywords: CellProfiler; High Throughput Imaging; Graphics Processing Units; High Performance Computing
Document URI:
ISBN: 9781538654880
DOI: 10.1109/BIBM.2018.8621271
ISI #: 000458654000057
Category: C1
Type: Proceedings Paper
Validations: ecoom 2020
vabb 2021
Appears in Collections:Research publications

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