Continuous technological innovation is enveloping all areas of Information Technology. Among the many, one of the main objectives of this evolution remains process optimization. In recent years, the advent of new hardware technologies, such as Graphical Processing Units (GPUs), has made this type of optimization more and more feasible, introducing in practice a change in the development paradigm. This change has forced a switch from sequential implementation, typical of Central Processing Units (CPU), to a parallel implementation. This type of approach is particularly efficient given the presence, within this hardware, of a large number of microprocessors, called threads. In the field of bioinformatics, in particular, this paradigm shift allows to obtain great results in speeding up the execution of algorithms typically expensive at the computational level, bringing the decrease in the time of execution of DNA analysis from several months to a few hours. The objective of this thesis project is to carry out a study in order to theorize, implement and test techniques for the optimization of algorithms for DNA analysis. In detail, the project is part of a series of projects for the optimization of a particular analysis tool: the Germline Pipeline. Previous projects to this one have focused on the phase of Alignment and Sorting of reads, while this one has as focus the Variant Calling. The development phase included the acceleration of the Pari-HMM Forward Algorithm (PFA). The algorithm has been analyzed in all its components and, following a new logic of parallel implementation, a new version has been developed that would allow, returning the same results, to decrease considerably the execution time. The analysis of the obtained results, compared both with a CPU version of the algorithm and with other known state-of-the-art results, has confirmed how it is possible to obtain extremely encouraging results through the use of graphical hardware. In addition, he pointed out that it is not necessary to use very expensive hardware in order to execute some algorithms in reasonable time, emphasizing how the assembly of hybrid architectures, combined with GPU-optimized algorithms can lead not only to a gain in terms of execution time but also to a reduction in expenses for the hardware needed to perform the analysis. On this basis, it is recommended that researchers take advantage of these types of technologies in order to optimize execution time. Future research could be aimed at completing the optimization of the algorithms that form the Germline Pipeline, followed by a subsequent integration with the work of this thesis project in order to compare the whole analysis process with other GPU approaches and find the most efficient approach.
