Abstract
To accelerate cache access and reduce the access time, the large number of data produced with different combined positions and many candidate sequences are distributed to the texture memory in GPUs when the modeling computation is used to solve in parallel the (l,d)-motif identification problem. The size of thread blocks in GPUs is set according to the size of data in combined positions, the best number of running threads in a thread block is found, and a cache-efficient parallel algorithm for identifying (l,d)-motifs in biosequences is designed by CPU and GPUs cooperative computing. The experimental results show that the proposed parallel algorithm can solve some (l,d)-motif identification instances of large size in less computation time and obtain good speedup and scalability.
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Acknowledgments
This work is supported in part by the National Nature Science Foundation of China under Grant No. 61462005, Nature Science Foundation of Guangxi under Grant No. 2014GXNSFAA118396 and 2014GXNSFAA118274, and Foundation of Guangdong Key Laboratory of Popular High Performance Computers, Shenzhen Key Laboratory of Service Computing and Applications under Grant No. SZU-GDPHPCL201414
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Zhong, C., Zhang, J., Hua, B., Yang, F., Liu, Z. (2016). Parallel Identifying (l,d)-Motifs in Biosequences Using CPU and GPU Computing. In: Zhu, D., Bereg, S. (eds) Frontiers in Algorithmics. FAW 2016. Lecture Notes in Computer Science(), vol 9711. Springer, Cham. https://doi.org/10.1007/978-3-319-39817-4_25
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DOI: https://doi.org/10.1007/978-3-319-39817-4_25
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