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Accelerating Q-ary Sliding-Window Belief Propagation Algorithm with GPU

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IoT as a Service (IoTaaS 2019)

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Abstract

In this paper, we present a parallel Sliding-Window Belief Propagation algorithm to decode Q-ary Low-Density-Parity-Codes. The bottlenecks of sequential algorithm are carefully investigated. We use MATLAB platform to develop the parallel algorithm and run these bottlenecks simultaneously on thousands of threads of GPU. The experiment results show that our parallel algorithm achieves 2.3\(\times \) to 30.3\(\times \) speedup ratio than sequential algorithm.

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References

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Author information

Authors and Affiliations

  1. School of Information Engineering, Chang’an University, Xi’an, China

    Bowei Shan, Sihua Chen & Yong Fang

Authors
  1. Bowei Shan
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  2. Sihua Chen
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  3. Yong Fang
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Corresponding author

Correspondence to Bowei Shan .

Editor information

Editors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, China

    Bo Li

  2. Northwest University, Xi’an, China

    Jie Zheng

  3. Chang’an University, Xi’an, China

    Yong Fang

  4. Northwestern Polytechnical University, Xi’an, China

    Mao Yang

  5. Northwestern Polytechnical University, Xi’an, China

    Zhongjiang Yan

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Shan, B., Chen, S., Fang, Y. (2020). Accelerating Q-ary Sliding-Window Belief Propagation Algorithm with GPU. In: Li, B., Zheng, J., Fang, Y., Yang, M., Yan, Z. (eds) IoT as a Service. IoTaaS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-030-44751-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-44751-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44750-2

  • Online ISBN: 978-3-030-44751-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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