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Cluster Computing

Erschienen in:

19.03.2021

ScalaParBiBit: scaling the binary biclustering in distributed-memory systems

verfasst von: Basilio B. Fraguela, Diego Andrade, Jorge González-Domínguez

Erschienen in: Cluster Computing | Ausgabe 3/2021

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Abstract

Biclustering is a data mining technique that allows us to find groups of rows and columns that are highly correlated in a 2D dataset. Although there exist several software applications to perform biclustering, most of them suffer from a high computational complexity which prevents their use in large datasets. In this work we present ScalaParBiBit, a parallel tool to find biclusters on binary data, quite common in many research fields such as text mining, marketing or bioinformatics. ScalaParBiBit takes advantage of the special characteristics of these binary datasets, as well as of an efficient parallel implementation and algorithm, to accelerate the biclustering procedure in distributed-memory systems. The experimental evaluation proves that our tool is significantly faster and more scalable that the state-of-the-art tool ParBiBit in a cluster with 32 nodes and 768 cores. Our tool together with its reference manual are freely available at https://github.com/fraguela/ScalaParBiBit.

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Titel: ScalaParBiBit: scaling the binary biclustering in distributed-memory systems
verfasst von: Basilio B. Fraguela
Diego Andrade
Jorge González-Domínguez
Publikationsdatum: 19.03.2021
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 3/2021
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-021-03261-z

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