Rough assessment of GPU capabilities for parallel PCC-based biclustering method applied to microarray data sets

• Autorzy: Orzechowski P. , Boryczko K.

Identyfikatory

DOI

10.1515/bams-2015-0033

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

biclustering; data mining; graphics processing unit; GPU; OpenCL; Parallel algorithms

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2015
• Tom: Vol. 11, no. 4
• Strony: 243--248
• Opis fizyczny: Bibliogr. 20 poz., wykr.

Twórcy

autor

Orzechowski P.

• 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

autor

Boryczko K.

• Faculty of Computer Science, Department of Computer Science, Electronics, and Telecommunications, AGH University of Science and Technology, Cracow, Poland

Bibliografia

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