Reference : GPU-Accelerated Mahalanobis-Average Hierarchical Clustering Analysis
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/48101
GPU-Accelerated Mahalanobis-Average Hierarchical Clustering Analysis
English
Šmelko, Adam [Charles University in Prague > Department of Software Engineering]
Kratochvil, Miroslav mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core >]
Kruliš, Martin [Charles University in Prague > Department of Software Engineering]
Sieger, Tomáš [Czech Technical University in Prague > Department of Cybernetic]
Aug-2021
Lecture Notes in Computer Science
Springer
12820
580-595
Yes
No
International
0302-9743
1611-3349
Heidelberg
Germany
European Conference on Parallel Processing - Euro-Par 2021
from 30-08-2021 to 3-09-2021
[en] Clustering ; High-dimensional data ; GPU
[en] Hierarchical clustering is a common tool for simplification, exploration, and analysis of datasets in many areas of research.
For data originating in flow cytometry, a specific variant of agglomerative clustering based Mahalanobis-average linkage has been shown to produce results better than the common linkages.
However, the high complexity of computing the distance limits the applicability of the algorithm to datasets obtained from current equipment.
We propose an optimized, GPU-accelerated open-source implementation of the Mahalanobis-average hierarchical clustering that improves the algorithm performance by over two orders of magnitude, thus allowing it to scale to the large datasets.
We provide a detailed analysis of the optimizations and collected experimental results that are also portable to other hierarchical clustering algorithms; and demonstrate the use on realistic high-dimensional datasets.
Czech Science Foundation (GAČR) project 19-22071Y ; ELIXIR CZ LM2018131 (MEYS) ; Charles University grant SVV-260451 ; Czech Health Research Council (AZV) [NV18-08-00385]
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/48101
10.1007/978-3-030-85665-6_36
https://link.springer.com/chapter/10.1007%2F978-3-030-85665-6_36

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