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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 block diagram of (a) CUDA-enabled GPUs and (b) the memory hierarchy.
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fig1: The block diagram of (a) CUDA-enabled GPUs and (b) the memory hierarchy.

Mentions: CUDA is a new language and development environment, allowing execution of general-purpose applications on NVIDIA's GPUs [28]. The hardware model is comprised of several highly threaded streaming multiprocessors (SMs), where each SM consists of a set of streaming processors (SPs), as shown in Figure 1(a). The computing system consists of a host that is a traditional CPU, also called host, and one or more GPUs, also called device, as shown in Figure 1(b).


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 block diagram of (a) CUDA-enabled GPUs and (b) the memory hierarchy.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: The block diagram of (a) CUDA-enabled GPUs and (b) the memory hierarchy.
Mentions: CUDA is a new language and development environment, allowing execution of general-purpose applications on NVIDIA's GPUs [28]. The hardware model is comprised of several highly threaded streaming multiprocessors (SMs), where each SM consists of a set of streaming processors (SPs), as shown in Figure 1(a). The computing system consists of a host that is a traditional CPU, also called host, and one or more GPUs, also called device, as shown in Figure 1(b).

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.