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Parallelization of the Kriging Algorithm in Stochastic Simulation with GPU Accelerators

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Geo-Informatics in Resource Management and Sustainable Ecosystem ( 2015, GRMSE 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 569))

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Abstract

3D realtime modeling places a heavy load on CPU. This paper presents a new method on 3D visualization in reservoir modeling system by using the computation power of modern programmable graphics hardware (GPU). The proposed scheme is devised to achieve parallel processing of massive reservoir logging data. By taking advantage of the GPU’s parallel processing capability, moreover, the performance of our scheme is discussed in comparison with that of the implementation entirely running on CPU. Experimental results clearly show that the proposed parallel processing can remarkably accelerate the data clustering task. Especially, although data-transferring from GPU to CPU is generally costly, acceleration by GPU is significant to save the total execution time of data-clustering, and significantly alleviates the computing load on CPU.

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Acknowledgment

Lin Liu thanks the support of Youth Fund of JiangXi Province (N0: GJJ14491).

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Authors and Affiliations

  1. Information Technology Institude, China University of Geosciences, No. 388 Lumo Road, Wuhan, People’s Republic of China

    Lin Liu & Chonglong Wu

  2. Software Institute, East China Institute of Technology, Nanchang, 430074, China

    Lin Liu & Zhibo Wang

  3. International School of Software, Wuhan University, Wuhan, 430079, China

    Zhibo Wang

Authors
  1. Lin Liu
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  2. Chonglong Wu
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  3. Zhibo Wang
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Corresponding author

Correspondence to Lin Liu .

Editor information

Editors and Affiliations

  1. Wuhan University , Wuhan, China

    Fuling Bian

  2. Eastern Michigan University , Ypsilanti, China

    Yichun Xie

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Liu, L., Wu, C., Wang, Z. (2016). Parallelization of the Kriging Algorithm in Stochastic Simulation with GPU Accelerators. In: Bian, F., Xie, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2015 2015. Communications in Computer and Information Science, vol 569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49155-3_19

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  • DOI: https://doi.org/10.1007/978-3-662-49155-3_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49154-6

  • Online ISBN: 978-3-662-49155-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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