Graphics processing unit accelerated one-dimensional blood flow computation in the human arterial tree

Authors

  • Lucian Itu,

    Corresponding author
    1. Automatics and Information Technology, Transilvania University of Brasov, Str. Politehnicii nr. 1, Brasov 500024, Romania
    2. Siemens Corporate Technology, Siemens Corporation, Bulevardul Eroilor Nr. 3A, Brasov 500007, Romania
    • Correspondence to: Lucian Itu, Automatics and Information Technology, Transilvania University of Brasov, Str. Politehnicii nr. 1, Brasov 500024, Romania.

      E-mail: lucian.itu@unitbv.ro

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  • Puneet Sharma,

    1. Imaging and Computer Vision, Siemens Corporation, Corporate Technology, 755 College Road East, Princeton, NJ, 08540, USA
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  • Ali Kamen,

    1. Imaging and Computer Vision, Siemens Corporation, Corporate Technology, 755 College Road East, Princeton, NJ, 08540, USA
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  • Constantin Suciu,

    1. Automatics and Information Technology, Transilvania University of Brasov, Str. Politehnicii nr. 1, Brasov 500024, Romania
    2. Siemens Corporate Technology, Siemens Corporation, Bulevardul Eroilor Nr. 3A, Brasov 500007, Romania
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  • Dorin Comaniciu

    1. Imaging and Computer Vision, Siemens Corporation, Corporate Technology, 755 College Road East, Princeton, NJ, 08540, USA
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SUMMARY

One-dimensional blood flow models have been used extensively for computing pressure and flow waveforms in the human arterial circulation. We propose an improved numerical implementation based on a graphics processing unit (GPU) for the acceleration of the execution time of one-dimensional model. A novel parallel hybrid CPU–GPU algorithm with compact copy operations (PHCGCC) and a parallel GPU only (PGO) algorithm are developed, which are compared against previously introduced PHCG versions, a single-threaded CPU only algorithm and a multi-threaded CPU only algorithm. Different second-order numerical schemes (Lax–Wendroff and Taylor series) are evaluated for the numerical solution of one-dimensional model, and the computational setups include physiologically motivated non-periodic (Windkessel) and periodic boundary conditions (BC) (structured tree) and elastic and viscoelastic wall laws. Both the PHCGCC and the PGO implementations improved the execution time significantly. The speed-up values over the single-threaded CPU only implementation range from 5.26 to 8.10 × , whereas the speed-up values over the multi-threaded CPU only implementation range from 1.84 to 4.02 × . The PHCGCC algorithm performs best for an elastic wall law with non-periodic BC and for viscoelastic wall laws, whereas the PGO algorithm performs best for an elastic wall law with periodic BC. Copyright © 2013 John Wiley & Sons, Ltd.

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