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Parallel Aligning Multiple Metabolic Pathways on Hybrid CPU and GPU Architectures

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Parallel Architecture, Algorithm and Programming (PAAP 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 729))

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Abstract

Metabolic pathway alignment remains an important tool in systems biology, and has become even more important with the growing mass of metabolic pathway data. However, the process of aligning multiple metabolic pathways scale poorly with either the number of pathways or with the size of pathways, and no attempts have been made to exploit the parallelism of the pathway alignments to improve the efficiency. This paper proposes a parallel metabolic pathway alignment method called PMMPA. In PMMPA, we design a commonly used parallel algorithm for the computation of reaction (node) similarity in GPU, and implement a parallel strategy for aligning multiple metabolic pathways in multi-core CPU. The experimental results show that this parallel alignment implementation achieves at most 300 times faster than the single-threaded version, the parallel implementation of aligning metabolic pathways on the hybrid CPU and GPU architecture is promising in improving the efficiency.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China under Grant No. 61462005, Nature Science Foundation of Guangxi under Grant No. 2014GXNSFAA118396, Foundation of Guangdong Key Laboratory of Popular High Performance Computers, Shenzhen Key Laboratory of Service Computing and Applications under Grant No. SZU-GDPHPCL201414, and Data Science of Guangxi Higher Education Key Laboratory.

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Authors and Affiliations

  1. School of Computer and Electronics and Information, Guangxi University, Nanning, China

    Yiran Huang, Cheng Zhong, Jinxiong Zhang & Jun Liu

  2. Guangxi Universities Key Laboratory of Parallel and Distributed Computing, Nanning, China

    Yiran Huang, Cheng Zhong, Jinxiong Zhang & Jun Liu

  3. Guangdong Key Laboratory of Popular High Performance Computers, Shenzhen Key Laboratory of Service Computing and Applications, Shenzhen, China

    Yiran Huang, Cheng Zhong & Jun Liu

  4. Data Science of Guangxi Higher Education Key Laboratory, Guangxi Teachers Education University, Nanning, China

    Ye Li

Authors
  1. Yiran Huang
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  2. Cheng Zhong
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  3. Jinxiong Zhang
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  4. Ye Li
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  5. Jun Liu
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Corresponding author

Correspondence to Cheng Zhong .

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Editors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China

    Guoliang Chen

  2. Sun Yat-sen University, Guangzhou, Guangdong, China

    Hong Shen

  3. Hainan University, Haikou, Hainan, China

    Mingrui Chen

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Huang, Y., Zhong, C., Zhang, J., Li, Y., Liu, J. (2017). Parallel Aligning Multiple Metabolic Pathways on Hybrid CPU and GPU Architectures. In: Chen, G., Shen, H., Chen, M. (eds) Parallel Architecture, Algorithm and Programming. PAAP 2017. Communications in Computer and Information Science, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-6442-5_46

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  • DOI: https://doi.org/10.1007/978-981-10-6442-5_46

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6441-8

  • Online ISBN: 978-981-10-6442-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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