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Title: Performance Evaluation of Fast Smith-Waterman Algorithm for Sequence Database Searches using CUDA GPU-Based Parallel Comp
Authors: Bustamam, Alhadi
Ardaneswari, Giannina
Tasman, Hengki
Lestari, Dian
Keywords: CUDA GPU Computing
Protein Database Searches
Smith-Waterman Sequence Alignment
Issue Date: May-2014
Publisher: Advanced Institute of Convergence Information Technology Research Center
Series/Report no.: Volume 5;Issue 2
Abstract: In bioinformatics, one of the gold-standard algorithms to compute the optimal similarity score between sequences in a sequence database searches is Smith-Waterman algorithm that uses dynamic programming. This algorithm has a quadratic time complexity which requires a long computation time for large-sized data. In this issue, parallel computing is essential for sequence database searches in order to reduce the running time and to increase the performance. In this paper, we discuss the parallel implementation performance of Smith-Waterman algorithm in GPU using CUDA C programming language with NVCC compiler on Linux environment. Furthermore, we assess the performance analysis using three parallelization models, including Inter-task Parallelization, Intratask Parallelization, and a combination of both models. Based on the simulation results, a combination of both models has better performance than the others. In addition the parallelization using combination of both models achieves an average speed-up of 313× and an average efficiency with a factor of 0.93.
ISSN: 2092-8637
Appears in Collections:Journal Collection

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