Performance and energy efficiency evaluation of heterogeneous systems for bioinformatics
Bioinformatics is one of the areas affected by current HPC problems due to the exponential growth of biological data in the last years and the increasing number of bioinformatics applications demanding HPC to meet performance requirements. One of these applications is sequence alignment, which is considered to be fundamental procedure in biological sciences. The alignment process compares two or more biological sequences and its purpose is to identify regions of similarity among them. The Smith-Waterman (SW) algorithm is a popular method for local sequence alignment that has been used as the basis for many subsequent algorithms, and is often employed as a benchmark when comparing different alignment techniques. However, due to the quadratic computational complexity of Smith-Waterman algorithm, several heuristics are used in practice that reduce the execution time but at the expense of not guaranteeing to discover the optimal local alignments. In order to process the ever increasing quantity of biological data with acceptable response times, it is necessary to develop new computational tools that are capable of accelerating key primitives and fundamental algorithms in an efficient manner from performance and energy consumption points of view. For that reason, this thesis considered, as general objective, evaluating performance and energy efficiency of HPC systems for accelerating Smith-Waterman biological sequence alignment.
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