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High-Performance Cluster Estimation Using Many-Core Models

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Frontier and Innovation in Future Computing and Communications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 301))

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Abstract

This paper presents implementation of high-performance subtractive clustering on a single instruction multiple data (SIMD) many-core processor. Since there is no general consensus that which grain size of the many-core processor provides the maximum performance, this paper explores the effects of varying the number of processing elements (PEs) and different amount of memories by introducing image data-per-processing element (IDPE) variation as a design variable, which is the amount of image data directly mapped to each PE. In this study, five PE configurations (IDPEs = 16, 64, 256, 1,024, and 4,096) are used for evaluating the system performance in terms of execution time and system utilization. In addition, this paper compares the proposed approach with a CPU-based implementation to show the potential for the improved performance of the proposed approach. Experimental results show that the proposed approach achieves 16.73× speedup at PEs = 4,096 over the CPU-based implementation.

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References

  1. Qing Y, Dongxu Z, Feng T (2010) An initialization method for fuzzy C-means algorithm using subtractive clustering. In: International conference on intelligent networks and intelligent systems, Shenyang, pp 393–396

    Google Scholar 

  2. Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278

    Article  MathSciNet  Google Scholar 

  3. Park IK, Singhal N, Lee MH, Cho S, Kim CW (2011) Design and performance evaluation of image processing algorithms on GPUs. IEEE Trans Parallel Distrib Syst 22(1):91–104

    Article  Google Scholar 

  4. Xiao S, Feng W–C (2010) Inter-block GPU communication via fast barrier synchronization. In: IEEE international symposium on parallel and distributed processing, Paris, pp 1–12

    Google Scholar 

  5. Feng W–C, Xiao S (2010) To GPU synchronize or not GPU synchronize? In: IEEE international symposium on circuits and systems, Paris, pp 3801–3804

    Google Scholar 

  6. Woo D-H, Lee H-S (2008) Extending Amdahl’s law for energy-efficient computing in the many-core era. IEEE Comput 41(12):24–31

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. NRF-2013R1A2A2A05004566).

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Authors and Affiliations

  1. School of Electrical Engineering, University of Ulsan, Ulsan, South Korea

    Junsang Seo, Myeongsu Kang & Jong-Myon Kim

  2. School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea

    Cheol-Hong Kim

Authors
  1. Junsang Seo
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  2. Myeongsu Kang
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  3. Cheol-Hong Kim
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  4. Jong-Myon Kim
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Corresponding author

Correspondence to Jong-Myon Kim .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science & and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

  2. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Zomaya

  3. Humanitas College, Kyung Hee University, Seoul, Korea, Republic of (South Korea)

    Hwa-Young Jeong

  4. Computer Science & Software Engineering, Monmouth University, W. Long Branch, New Jersey, USA

    Mohammad Obaidat

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© 2014 Springer Science+Business Media Dordrecht

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Seo, J., Kang, M., Kim, CH., Kim, JM. (2014). High-Performance Cluster Estimation Using Many-Core Models. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_24

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  • DOI: https://doi.org/10.1007/978-94-017-8798-7_24

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-8797-0

  • Online ISBN: 978-94-017-8798-7

  • eBook Packages: EngineeringEngineering (R0)

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