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Bio-Algorithms and Med-Systems

Editor-in-Chief: Roterman-Konieczna , Irena

CiteScore 2018: 0.29

SCImago Journal Rank (SJR) 2018: 0.129
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Rough assessment of GPU capabilities for parallel PCC-based biclustering method applied to microarray data sets

Patryk Orzechowski
  • Corresponding author
  • Faculty of Electrical Engineering, Department of Automatics and Bioengineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza Av. 30, 30-059 Cracow, Poland
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Krzysztof Boryczko
  • Faculty of Computer Science, Department of Computer Science, Electronics, and Telecommunications, AGH University of Science and Technology, Cracow, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2015-12-02 | DOI: https://doi.org/10.1515/bams-2015-0033


Parallel computing architectures are proven to significantly shorten computation time for different clustering algorithms. Nonetheless, some characteristics of the architecture limit the application of graphics processing units (GPUs) for biclustering task, whose function is to find focal similarities within the data. This might be one of the reasons why there have not been many biclustering algorithms proposed so far. In this article, we verify if there is any potential for application of complex biclustering calculations (CPU+GPU). We introduce minimax with Pearson correlation – a complex biclustering method. The algorithm utilizes Pearson’s correlation to determine similarity between rows of input matrix. We present two implementations of the algorithm, sequential and parallel, which are dedicated for heterogeneous environments. We verify the weak scaling efficiency to assess if a heterogeneous architecture may successfully shorten heavy biclustering computation time.

Keywords: biclustering; data mining; graphics processing unit (GPU); OpenCL; parallel algorithms


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About the article

Corresponding author: Patryk Orzechowski, Faculty of Electrical Engineering, Department of Automatics and Bioengineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza Av. 30, 30-059 Cracow, Poland, E-mail:

Received: 2015-09-25

Accepted: 2015-10-20

Published Online: 2015-12-02

Published in Print: 2015-12-01

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: This research was funded by the Polish National Science Center (Narodowe Centrum Nauki, grant no. 2013/11/N/ST6/03204). This research was supported in part by PL-Grid Infrastructure.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

Citation Information: Bio-Algorithms and Med-Systems, Volume 11, Issue 4, Pages 243–248, ISSN (Online) 1896-530X, ISSN (Print) 1895-9091, DOI: https://doi.org/10.1515/bams-2015-0033.

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