Dealing with bioinformatics tasks require the use of various gene and protein sequence databases the size of which are growing rapidly with the enhancement of technology. Larger databases provide an opportunity for a better and more thorough understanding of biological processes. However the use of large databases significantly increases the run time of bioinformatics algorithms. Information technology offers a variety of methods for reducing software run time. The increased computing capacity of computer environments used to realize algorithms allow for a widespread and effective use of these methods in solving bioinformatics tasks.
The aim of the dissertation is to analyse the possibility of using the parallel programming methods offered by the supercomputer of the Budapest University of Technology and Economics (SUPERMAN) for the execution of bioinformatics algorithms.
The study gives an overview on the prevalent methods for bioinformatics tasks, the parallel programming architectures and paradigms, coupled by some specific algorithms used for solving bioinformatics tasks, then describes the implemented parallel versions of these algorithms and finally it discloses the test results of running these algorithms in different hardware environments.