Paper Title :Optimizing Parallel Scan Smith Waterman Algorithm On GPU
Author :Harsh Shukla, Monika Shah
Article Citation :Harsh Shukla ,Monika Shah ,
(2014 ) " Optimizing Parallel Scan Smith Waterman Algorithm On GPU " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 86-89,
Volume-2,Issue-9
Abstract : Smith-Waterman is a well-known local sequence alignment algorithm that is used for finding regions of maximum
similarity between two biological sequences and is known to be a highly compute intensive task. As it is based on dynamic
programming it guarantees optimal results. But Dynamic Programming has its own drawbacks such as heavy memory
consumption and significant amount of computations. Many academicians and researchers have tried variety of methods to
harness the large amount of computational capabilities provided by the GPU in order to make this algorithm run faster. This
paper proposes a version of Parallel Scan Smith-Waterman algorithm to improve performance of its phase-2. Here, we have
also compared and evaluated performance of proposed work with other approaches like anti-diagonal and blocked
anti-diagonal for both constant gap model and affine gap model and have observed remarkable performance gain.
Type : Research paper
Published : Volume-2,Issue-9
Copyright: © Institute of Research and Journals
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Published on 2014-09-01 |
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