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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 performance analysis of 64 threads based on different number of blocks.
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fig7: The performance analysis of 64 threads based on different number of blocks.

Mentions: Next, we take the query sequence, P86783, to investigate the influence of the number of blocks, as shown in Figure 7. The number of threads per block is set to 64. When there are more blocks, it means that the total number of threads in a grid is increased. Consequently, the number of subject sequences allocated to each thread is decreased. On the other hand, we cannot run more than one block at the same time on any streaming multiprocessor since each block required almost all the shared memory space in its resident streaming multiprocessor. As a result, increasing the number of blocks incurs higher overhead for context switching between blocks.


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 performance analysis of 64 threads based on different number of blocks.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: The performance analysis of 64 threads based on different number of blocks.
Mentions: Next, we take the query sequence, P86783, to investigate the influence of the number of blocks, as shown in Figure 7. The number of threads per block is set to 64. When there are more blocks, it means that the total number of threads in a grid is increased. Consequently, the number of subject sequences allocated to each thread is decreased. On the other hand, we cannot run more than one block at the same time on any streaming multiprocessor since each block required almost all the shared memory space in its resident streaming multiprocessor. As a result, increasing the number of blocks incurs higher overhead for context switching between blocks.

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.