The huge amount of biological sequences accumulated in the world nucleotide and protein databases leads to the necessity of efficient tools for structural genomic and functional analysis. This scientific area requires powerful computing resources for exploring large sets of biological data. Multiple sequence alignment is an important method in the DNA and protein analysis, and is generally the alignment of three or more biological sequences of similar length. As a result of the processing, homology can be derived and the evolutionary relationships between the sequences can be explored. The goal of this paper is to propose parallelization and optimization of the multiple sequence alignment software MSA_BG in order to improve the performance, for the case study of the influenza virus sequences. The objective is code optimization, porting, scaling and performance evaluation of the parallel multiple sequence alignment software MSA_BG for Intel Xeon Phi (the MIC architecture). For this purpose a parallel multithreaded optimization including OpenMP has been implemented and verified. The experimental results show that the hybrid parallel implementation utilizing MPI and OpenMP provides considerably better performance than the original code.
Artificial Bee Colony, Bioinformatics, Hybrid Programming, High Performance Computing, Multiple Sequence Alignment, Parallel Programming, Performance
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Cite this paper
Plamenka Borovska, Veska Gancheva. (2018) Parallelization and Optimization of Multiple Biological Sequence Alignment Software Based on Social Behavior Model. International Journal of Computers, 3, 69-74
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