Improving the mapping of smith-waterman sequence database searches onto CUDA-enabled GPUS

Liang Tsung Huang, Chao Chin Wu, Lien Fu Lai, Yun Ju Li

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Sequence alignment lies at heart of the bioinformatics. The Smith-Waterman algorithm is one of the key sequence search algorithms and has gained popularity due to improved implementations and rapidly increasing compute power. Recently, the Smith-Waterman algorithm has been successfully mapped onto the emerging general-purpose graphics processing units (GPUs). In this paper, we focused on how to improve the mapping, especially for short query sequences, by better usage of shared memory. We performed and evaluated the proposed method on two different platforms (Tesla C1060 and Tesla K20) and compared it with two classic methods in CUDASW++. Further, the performance on different numbers of threads and blocks has been analyzed. The results showed that the proposed method significantly improves Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers.

Original languageEnglish
Article number185179
JournalBioMed Research International
Volume2015
DOIs
Publication statusPublished - 2015 Jan 1

Fingerprint

Databases
Sequence Alignment
Bioinformatics
Computational Biology
Data storage equipment
Graphics processing unit

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

Cite this

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abstract = "Sequence alignment lies at heart of the bioinformatics. The Smith-Waterman algorithm is one of the key sequence search algorithms and has gained popularity due to improved implementations and rapidly increasing compute power. Recently, the Smith-Waterman algorithm has been successfully mapped onto the emerging general-purpose graphics processing units (GPUs). In this paper, we focused on how to improve the mapping, especially for short query sequences, by better usage of shared memory. We performed and evaluated the proposed method on two different platforms (Tesla C1060 and Tesla K20) and compared it with two classic methods in CUDASW++. Further, the performance on different numbers of threads and blocks has been analyzed. The results showed that the proposed method significantly improves Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers.",
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Improving the mapping of smith-waterman sequence database searches onto CUDA-enabled GPUS. / Huang, Liang Tsung; Wu, Chao Chin; Lai, Lien Fu; Li, Yun Ju.

In: BioMed Research International, Vol. 2015, 185179, 01.01.2015.

Research output: Contribution to journalArticle

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