<![CDATA[Deoxyribonucleic acid (DNA) is composed of nucleotide chains containing nitrogenous bases, phosphate groups, and pentose sugars, with variations primarily occurring in the sequence of nitrogenous bases. The analysis of DNA sequences often employs the Smith-Waterman algorithm for sequence alignment, a fundamental technique in bioinformatics. This research evaluates the performance of the Smith-Waterman algorithm across varying sequence lengths (10, 102, 103, and 104) using both serial and parallel implementations with the OpenMPI library. The study focuses on measuring execution times and speedup on 4, 6, 10, 12, and 24 cores. Results indicate that while execution times increase with longer sequences, parallelization significantly reduces processing time for sequences longer than 102. However, smaller sequences exhibit higher overhead on shorter lengths. The findings underscore the importance of efficient parallel programming and task allocation strategies in optimizing computational performance for DNA sequence analysis.]]>
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