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Throughput optimization of a multistage data visualisation pipeline
 Molnárová, Soňa ; Šmelko, Adam (advisor) ; Nečaský, Martin (referee)
Recent technological advances allowed vast increases of the size of datasets acquired by flow cytometry. With more cells and features captured, manual exploration and analysis of the data become challenging; multiple unsuper- vised methods were thus created to simplify the task. This thesis describes BlosSOM, a software that further improves the analysis possibilities by allow- ing interactive CUDA-accelerated supervision of the most common analysis approaches - dimensionality reduction, clustering, and annotation. The thesis overviews the features of BlosSOM implemented by the thesis author for the previous research project and details new developments, mainly a new dynamic workload balancing approach that allows BlosSOM to easily scale to very complex datasets while maintaining interactive framerate. To- gether with other improvements, BlosSOM is a production-ready software that offers all essential tools to analyze the flow cytometry data.
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