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Russian Supercomputing Days

RuSCDays 2017: Supercomputing pp 363–375Cite as

The Architecture of Specialized GPU Clusters Used for Solving the Inverse Problems of 3D Low-Frequency Ultrasonic Tomography

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 793))

Abstract

This paper is dedicated to the development of the architecture of specialized GPU clusters that can be used as computing systems in medical ultrasound tomographic facilities that are currently being developed. The inverse problem of ultrasonic tomography is formulated as a coefficient inverse problem for a hyperbolic equation. An approximate solution is constructed using an iterative process of minimizing the residual functional between the measured and simulated wave fields. The algorithms used to solve the inverse problem are optimized for a GPU. The requirements for the architecture of a GPU cluster are formulated. The proposed architecture accelerates the reconstruction of ultrasonic tomographic images by 1000 times compared to what is achieved by a personal computer.

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Acknowledgements

This research was supported by Russian Science Foundation (project No. 17–11-01065). The study was carried out at the Lomonosov Moscow State University.

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Authors and Affiliations

  1. Lomonosov Moscow State University, Moscow, Russia

    Alexander Goncharsky & Sergey Seryozhnikov

Authors
  1. Alexander Goncharsky
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  2. Sergey Seryozhnikov
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Corresponding author

Correspondence to Sergey Seryozhnikov .

Editor information

Editors and Affiliations

  1. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Vladimir Voevodin

  2. Moscow State University, Moscow, Russia

    Sergey Sobolev

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Goncharsky, A., Seryozhnikov, S. (2017). The Architecture of Specialized GPU Clusters Used for Solving the Inverse Problems of 3D Low-Frequency Ultrasonic Tomography. In: Voevodin, V., Sobolev, S. (eds) Supercomputing. RuSCDays 2017. Communications in Computer and Information Science, vol 793. Springer, Cham. https://doi.org/10.1007/978-3-319-71255-0_29

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  • DOI: https://doi.org/10.1007/978-3-319-71255-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71254-3

  • Online ISBN: 978-3-319-71255-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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