diplomsko delo
Jani Bevk (Author), Uroš Lotrič (Mentor), Tomaž Curk (Co-mentor)

Abstract

Proteini sodelujejo v skoraj vseh procesih, ki se odvijajo v celicah živečih organizmov. Razumevanje njihovih funkcij je ključnega pomena za razumevanje bioloških procesov. Soodvisne spremembe v proteinih, ki se tekom njihove evolucije odvijajo, so tesno povezane z njihovo strukturo in funkcijo. Razvitih je bilo več različnih algoritmov za detekcijo soodvisnih sprememb, vsi pa so računsko zelo zahtevni. Eden izmed takšnih algoritmov je algoritem OMES, ki temelji na statističnem testu χ² in naključnem mešanju poravnav proteinskih zaporedij. Cilj diplomske naloge je paralelizacija in implementacija algoritma OMES na grafični procesni enoti z uporabo platforme CUDA. Grafične procesne enote so specializirani procesorji, ki danes najverjetneje nudijo najboljše razmerje med računsko močjo in ceno. V primerjavi z implementacijo na centralni procesni enoti smo dosegli stokratne pohitritve in porabili manj energije.

Keywords

analiza soodvisnih sprememb proteinov;OMES;splošnonamensko računanje na grafičnih procesnih enotah;CUDA;računalništvo;računalništvo in informatika;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [J. Bevk]
UDC: 004.42:575.224(043.2)
COBISS: 1536217283 Link will open in a new window
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Downloads: 384
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Other data

Secondary language: English
Secondary title: Correlated mutation analysis using graphics processing units
Secondary abstract: Proteins are involved in almost all processes that take part in cells of living organisms. Understanding of their function is important for the understanding of biological processes. Correlated mutations in proteins, which take place over the course of their evolution, are closely related to their structure and function. Several different algorithms for detection of correlated mutations have been developed, all very computationally intensive. One of such algorithms is the OMES algorithm, which is based on the statistical χ²-test and random shuffling of protein sequences. The aim of the thesis is paralelisation and implementation of the OMES algorithm on graphics processing units using the CUDA platform. Graphics processing units are specialized processors, which nowadays probably offer the best computing power to price ratio. Compared to the implementation on a central processing unit we achieved a 100-fold speedup and used less energy.
Secondary keywords: correlated mutation analysis;OMES;general-purpose computing on graphics processing units;CUDA;computer science;computer and information science;diploma;
File type: application/pdf
Type (COBISS): Bachelor thesis/paper
Study programme: 1000468
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 52 str.
ID: 8739667