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The massively parallel computing using graphical processing unit (GPU), which based on tens of thousands of parallel threats within hundreds of GPU's streaming processors, has gained broad popularity and attracted researchers in a wide range of application areas from finance, computer aided engineering, computational fluid dynamics, game physics, numerics, science, medical imaging, life science, and...
Local Sequence alignment is an important task for bioinformatics. The most widely used algorithm is Smith-Waterman has a quadratic time complexity which is time consuming especially in large biological database search. Many attempts were made to accelerate Smith-Waterman using parallel architecture. In this paper a parallel implementation of Smith Waterman algorithm will be presented. This implementation...
The graphics processing unit (GPU) has evolved from being a fixed-function processor with programmable stages into a programmable processor with many fixed-function components that deliver massive parallelism. By modifying the GPU's stream processor to support “general-purpose computation” on the GPU (GPGPU), applications that perform massive vector operations can realize many orders-of-magnitude...
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