Implementation of CUDA GPU-based parallel computing on Smith-Waterman algorithm to sequence database searches

Alhadi B., Gianinna Ardaneswari, Dian Lestari

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

Abstract

In bioinformatics, one of the goldstandard 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 of Smith-Waterman algorithm in GPU using CUDA C programming language with NVCC compiler on Linux environment. Furthermore, we run the performance analysis using three parallelization models, including Inter-task Parallelization, Intra-task 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 313x and an average efficiency with a factor of 0.93.

Original languageEnglish
Pages137-142
Number of pages6
DOIs
Publication statusPublished - 1 Jan 2013
Event2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013 - Bali, Indonesia
Duration: 28 Sep 201329 Sep 2013

Conference

Conference2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013
Country/TerritoryIndonesia
CityBali
Period28/09/1329/09/13

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