Multiscale Hemodynamics Using GPU Clusters

Multiscale Hemodynamics Using GPU Clusters

Year:    2012

Communications in Computational Physics, Vol. 11 (2012), Iss. 1 : pp. 48–64

Abstract

The parallel implementation of MUPHY, a concurrent multiscale code for large-scale hemodynamic simulations in anatomically realistic geometries, for multi-GPU platforms is presented. Performance tests show excellent results, with a nearly linear parallel speed-up on up to 32GPUs and a more than tenfold GPU/CPU acceleration, all across the range of GPUs. The basic MUPHY scheme combines a hydrokinetic (Lattice Boltzmann) representation of the blood plasma, with a Particle Dynamics treatment of suspended biological bodies, such as red blood cells. To the best of our knowledge, this represents the first effort in the direction of laying down general design principles for multiscale/physics parallel Particle Dynamics applications in non-ideal geometries. This configures the present multi-GPU version of MUPHY as one of the first examples of a high-performance parallel code for multiscale/physics biofluidic applications in realistically complex geometries.

You do not have full access to this article.

Already a Subscriber? Sign in as an individual or via your institution

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.210910.250311a

Communications in Computational Physics, Vol. 11 (2012), Iss. 1 : pp. 48–64

Published online:    2012-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    17

Keywords:   

  1. A Method for Simulating the Dynamics of Rarefied Gas Based on Lattice Boltzmann Equations and the BGK Equation

    Ilyin, O. V.

    Computational Mathematics and Mathematical Physics, Vol. 58 (2018), Iss. 11 P.1817

    https://doi.org/10.1134/S0965542518110052 [Citations: 3]
  2. A distributed parallel multiple-relaxation-time lattice Boltzmann method on general-purpose graphics processing units for the rapid and scalable computation of absolute permeability from high-resolution 3D micro-CT images

    Alpak, F. O. | Gray, F. | Saxena, N. | Dietderich, J. | Hofmann, R. | Berg, S.

    Computational Geosciences, Vol. 22 (2018), Iss. 3 P.815

    https://doi.org/10.1007/s10596-018-9727-7 [Citations: 37]
  3. Validation of a lattice Boltzmann method implementation for a 3D transient fluid flow in an intracranial aneurysm geometry

    Závodszky, Gábor | Paál, György

    International Journal of Heat and Fluid Flow, Vol. 44 (2013), Iss. P.276

    https://doi.org/10.1016/j.ijheatfluidflow.2013.06.008 [Citations: 26]
  4. Analysis and Applications of Lattice Boltzmann Simulations

    Lattice Boltzmann Method for Sparse Geometries

    Tomczak, Tadeusz

    2018

    https://doi.org/10.4018/978-1-5225-4760-0.ch005 [Citations: 0]
  5. The application of the Lattice Boltzmann method to the one-dimensional modeling of pulse waves in elastic vessels

    Ilyin, Oleg

    Wave Motion, Vol. 95 (2020), Iss. P.102533

    https://doi.org/10.1016/j.wavemoti.2020.102533 [Citations: 4]
  6. Towards a generalised GPU/CPU shallow-flow modelling tool

    Smith, Luke S. | Liang, Qiuhua

    Computers & Fluids, Vol. 88 (2013), Iss. P.334

    https://doi.org/10.1016/j.compfluid.2013.09.018 [Citations: 84]
  7. Computational explorations at the physics-chemistry-biology interface

    Melchionna, Simone | Sterpone, Fabio | Succi, Sauro

    International Journal of Molecular Biology, Vol. 3 (2018), Iss. 2

    https://doi.org/10.15406/ijmboa.2018.03.00054 [Citations: 0]
  8. Multiscale Fluid Mechanics and Modeling

    Chen, Shiyi | Wang, Moran | Xia, Zhenhua

    Procedia IUTAM, Vol. 10 (2014), Iss. P.100

    https://doi.org/10.1016/j.piutam.2014.01.012 [Citations: 16]
  9. Physically based visual simulation of the Lattice Boltzmann method on the GPU: a survey

    Navarro-Hinojosa, Octavio | Ruiz-Loza, Sergio | Alencastre-Miranda, Moisés

    The Journal of Supercomputing, Vol. 74 (2018), Iss. 7 P.3441

    https://doi.org/10.1007/s11227-018-2392-8 [Citations: 15]
  10. The application of the Lattice Boltzmann method to the one-dimensional modeling of blood flow in elastic vessels

    Ilyin, Oleg

    Journal of Physics: Conference Series, Vol. 1163 (2019), Iss. P.012024

    https://doi.org/10.1088/1742-6596/1163/1/012024 [Citations: 0]
  11. The modeling of nonlinear pulse waves in elastic vessels using the Lattice Boltzmann method

    Ilyin, O. V.

    Computer Research and Modeling, Vol. 11 (2019), Iss. 4 P.707

    https://doi.org/10.20537/2076-7633-2019-11-4-707-722 [Citations: 1]
  12. Analysis of GPU Data Access Patterns on Complex Geometries for the D3Q19 Lattice Boltzmann Algorithm

    Herschlag, Gregory | Lee, Seyong | Vetter, Jeffrey S. | Randles, Amanda

    IEEE Transactions on Parallel and Distributed Systems, Vol. 32 (2021), Iss. 10 P.2400

    https://doi.org/10.1109/TPDS.2021.3061895 [Citations: 9]
  13. Euro-Par 2012 Parallel Processing

    Optimized Hybrid Parallel Lattice Boltzmann Fluid Flow Simulations on Complex Geometries

    Fietz, Jonas | Krause, Mathias J. | Schulz, Christian | Sanders, Peter | Heuveline, Vincent

    2012

    https://doi.org/10.1007/978-3-642-32820-6_81 [Citations: 18]
  14. GPU Data Access on Complex Geometries for D3Q19 Lattice Boltzmann Method

    Herschlag, Gregory | Lee, Seyong | Vetter, Jeffrey S. | Randles, Amanda

    2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), (2018), P.825

    https://doi.org/10.1109/IPDPS.2018.00092 [Citations: 19]