Szczegóły publikacji
Opis bibliograficzny
Rough assessment of GPU capabilities for parallel PCC-based biclustering method applied to microarray data sets / Patryk ORZECHOWSKI, Krzysztof BORYCZKO // Bio-Algorithms and Med-Systems / Jagiellonian University. Medical College ; ISSN 1895-9091. — 2015 — vol. 11 iss. 4, s. 243–248. — Bibliogr. s. 247–248, Abstr. — Publikacja dostępna online od: 2015-12-02
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 95836 |
|---|---|
| Data dodania do BaDAP | 2016-01-29 |
| DOI | 10.1515/bams-2015-0033 |
| Rok publikacji | 2015 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Bio-Algorithms and Med-Systems |
Abstract
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.