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In bioinformatics, alignments are commonly performed in genome and protein sequence analysis for gene identification and evolutionary similarities. There are several approaches for such analysis, each varying in accuracy and computational complexity. Smith-Waterman (SW) is by far the best algorithm for its accuracy in similarity scoring. However, execution time of this algorithm on general purpose processor based systems makes it impractical for use by life scientists. In this paper we take Smith-Waterman as a case study to explore the architectural features of Graphics Processing Units (GPUs) and evaluate the challenges the hardware architecture poses, as well as the software modifications needed to map the program architecture on to the GPU. We achieve a 23x speedup against the serial version of the SW algorithm. We further study the effect of memory organization and the instruction set architecture on …