Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/27671
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. imen.chakroun@imec.be; nick.michiels@uhasselt.be; roel.wuyts@imec.be | Keywords: | CellProfiler; High Throughput Imaging; Graphics Processing Units; High Performance Computing | Document URI: | http://hdl.handle.net/1942/27671 | 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 |
Files in This Item:
File | Description | Size | Format | |
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08621271.pdf Restricted Access | Published version | 94.58 kB | Adobe PDF | View/Open Request a copy |
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