The paper describes our program implementation of Global Sequence alignments of DNA sequences using the Needleman-Wunsch algorithm. This modified Neeedlenan-Wunsch algorithm is designed to align multiple sequences in parallel with OpenMP. The algorithm supports different sequence sizes by scanning and equalizing them simultaneously. The results of our experiment are obtained by comparing Sars-CoV-2 fasta files in a versatile, speedy and convenient manner. The program is adapted for optimal performance when aligning Sars-CoV-2 sequences. We analyze program hotspots, multithreading execution and potential bottlenecks using the Intel vTune Profiler.

Global sequence alignment remains a fundamental technique in bioinformatics, yet its large-scale application is often limited by computational demands. In this work, we explore the impact of optimal global sequence alignment on sequence similarity by combining a GPU-accelerated version of the Needleman–Wunsch algorithm with Sentence-BERT (SBERT), a transformer-based embedding model. This setup allows us to perform exhaustive alignments across large datasets while also capturing semantic similarities that traditional scoring schemes may miss. By comparing alignment scores with SBERT-derived similarity metrics, we examine where classical alignment aligns with—or diverges from—contextual similarity. Our results show that while there is general agreement between the two approaches, notable exceptions highlight cases where semantic meaning extends beyond structural alignment. The combination of efficient global alignment and deep semantic modeling offers new insights into the relationship between sequence form and function, opening the door for more nuanced similarity measures in both biological and textual sequence analysis.