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The Optimization and Parallelization of Two-Dimensional Zigzag Scanning on the Matrix

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14257))

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Abstract

With the expansion of applications, such as image processing, scientific computing, numerical simulation, biomedicine, social network and so on, enormous quantities of data need to be crunched in order to get the valuable parts and discard redundant ones. For those data represented as two-dimensional digital matrix, two alternative schemes by scanning the elements of the matrix in zigzag route have been proposed. Fixed-point mode and navigation mode used in the proposed schemes are introduced. Performance comparison between the two schemes and previous works are analyzed. The experimental results show that our proposed schemes perform well with large scales of matrices. Moreover, the design of parallel programming based on our proposed scheme has been given. Finally, we have discussed the efficiency of parallelization. The speedup we get is from 3.413 to 3.996, which is stably growth with the scales of matrices.

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Acknowledgments

This work is supported financially by the National Natural Science Foundation of China (NSFC) under grant 61471306 and 61672438. The Natural Science Foundation of Sichuan Province under grant 2022NSFSC0548 and 2023NSFSC 1966, Smart Education Research Fund of Southwest University of Science and Technology under grant 22ZHJYZD02 and 22SXB004, the Education and Teaching Research Project of Sichuan Provincial Education Department under grant JG2021-1414, and the Key R&D Projects of Sichuan Province under grant 2020YFS0360.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Mianyang, 621010, Sichuan, China

    Ling Li, Yaobin Wang, Lijuan Peng, Yuming Feng, Ning Liu & **aolin Jia

  2. Key Laboratory of Testing Technology for Manufacturing Process in Ministry of Education, Mianyang, 621010, Sichuan, China

    Ling Li, Yaobin Wang, Lijuan Peng, Yuming Feng, Ning Liu & **aolin Jia

  3. Mobile Internet of Things and Radio Frequency Identification Technology Key Laboratory of Mianyang (MIOT&RFID), Mianyang, 621010, Sichuan, China

    Ling Li, Yaobin Wang, Lijuan Peng, Yuming Feng, Ning Liu & **aolin Jia

  4. School of Economic and Management, Southwest University of Science and Technology, Mianyang, 621010, Sichuan, China

    Guangwei Li

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  1. Ling Li
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  2. Yaobin Wang
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  3. Lijuan Peng
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  4. Yuming Feng
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  6. Guangwei Li
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  7. **aolin Jia
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Corresponding author

Correspondence to Yaobin Wang .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Li, L. et al. (2023). The Optimization and Parallelization of Two-Dimensional Zigzag Scanning on the Matrix. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_15

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  • DOI: https://doi.org/10.1007/978-3-031-44216-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44215-5

  • Online ISBN: 978-3-031-44216-2

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