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Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs.

Huang LT, Wu CC, Lai LF, Li YJ - Biomed Res Int (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Informatics, Tzu Chi University, Hualien 970, Taiwan.

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.

No MeSH data available.


The pseudocode of the scoring function in our proposed method.
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pseudo1: The pseudocode of the scoring function in our proposed method.

Mentions: Since the query profile packs the scores of every four continuous residues and saves the packed scores on texture memory, we can get four scores whenever texture memory is accessed. The fetched four scores have to be saved in four registers because these scores are for the calculation of the four consecutive cells on the same column inside a tile. Consequently, P should be a multiple of four. The pseudo code of our method is shown in Pseudocode 1, where P and K are set to four to reduce the pressure on the register requirement.


Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs.

Huang LT, Wu CC, Lai LF, Li YJ - Biomed Res Int (2015)

The pseudocode of the scoring function in our proposed method.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4538332&req=5

pseudo1: The pseudocode of the scoring function in our proposed method.
Mentions: Since the query profile packs the scores of every four continuous residues and saves the packed scores on texture memory, we can get four scores whenever texture memory is accessed. The fetched four scores have to be saved in four registers because these scores are for the calculation of the four consecutive cells on the same column inside a tile. Consequently, P should be a multiple of four. The pseudo code of our method is shown in Pseudocode 1, where P and K are set to four to reduce the pressure on the register requirement.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Informatics, Tzu Chi University, Hualien 970, Taiwan.

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.

No MeSH data available.