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Multiscale Hemodynamics Using GPU Clusters

Published online by Cambridge University Press:  20 August 2015

Mauro Bisson ,
Massimo Bernaschi ,
Simone Melchionna ,
Sauro Succi  and
Efthimios Kaxiras
Mauro Bisson*
Affiliation:
Department of Computer Science, University of Rome “Sapienza”, Italy
Massimo Bernaschi*
Affiliation:
Istituto Applicazioni Calcolo, Consiglio Nazionale delle Ricerche, Rome, Italy
Simone Melchionna*
Affiliation:
Istituto Processi Chimico-Fisici, Consiglio Nazionale delle Ricerche, Rome, Italy Institute of Material Sciences and Engineering, École Polytechnique Fédérale de Lausanne, Switzerland
Sauro Succi*
Affiliation:
Istituto Applicazioni Calcolo, Consiglio Nazionale delle Ricerche, Rome, Italy Freiburg Institute for Advanced Studies, School of Soft Matter Research, Albertstr. 19, 79104 Freiburg, Germany
Efthimios Kaxiras*
Affiliation:
Institute of Material Sciences and Engineering, École Polytechnique Fédérale de Lausanne, Switzerland Department of Physics and School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
*
Corresponding author.Email:[email protected]
Email:[email protected]
Email:[email protected]
Email:[email protected]
Email:[email protected]
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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.

Keywords

02.70.NsMulti-GPU computinghemodynamicsmolecular dynamicsirregular domain
Type
Research Article
Information
Communications in Computational Physics , Volume 11 , Issue 1 , January 2012 , pp. 48 - 64
Copyright
Copyright © Global Science Press Limited 2012

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References

[1]Fyta, M., Melchionna, S., Kaxiras, E. and Succi, S., Multiscale coupling of molecular dynamics and hydrodynamics: applications to DNA translocation through a nanopore, Multiscale Modeling and Simulation, 5 (2006), 1156–1173.Google Scholar
[2]Fyta, M., Melchionna, S., Succi, S. and Kaxiras, E., Multiscale simulation of nanobiological flows, Comput. Sci. Eng., March/April, 2008.Google Scholar
[3]Benzi, R., Succi, S. and Vergassola, M., The lattice Boltzmann equation: theory and applications, Phys. Rep., 222(3) (1992), 145–197.Google Scholar
[4]Melchionna, S., Bernaschi, M., Succi, S., Kaxiras, E., Rybicki, F. J., Mitsouras, D., Coskun, A. U. and Feldman, C. L., Hydrokinetic approach to large-scale cardiovascular blood flow, Comput. Phys. Commun., 181 (2010), 462–472.Google Scholar
[5]Message Passing Interface Forum, MPI: A Message-Passing Interface Standard, 1994.Google Scholar
[6]Bisson, M., Bernaschi, M. and Melchionna, S., Parallel molecular dynamics with irregular domain decomposition, Commun. Comput. Phys., 10 (2011), 1071–1088.Google Scholar
[7]Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E. and Phillips, J. C., GPU Computing, Proc. IEEE, 96(5) (2008), 879–899.Google Scholar
[8]http://glaros.dtc.umn.edu/gkhome/metis/metis/overview.Google Scholar
[9]http://www.labri.fr/perso/pelegrin/scotch/.Google Scholar
[10]Succi, S., The Lattice Boltzmann Equation for Fluid Dynamics and Beyond, Oxford University Press, USA, 2001CrossRefGoogle Scholar
[11]Gay, J. G. and Berne, B. J., Modification of the overlap potential to mimic a linear site-site potential, J. Chem. Phys., 74 (1981), 3316–3319.CrossRefGoogle Scholar
[12]Dullweber, A., Leimkuhler, B. and McLachlan, R., Symplectic splitting methods for rigid body molecular dynamics, J. Chem. Phys., 107 (1997), 5840–5851.Google Scholar
[13]Bernaschi, M.et al., MUPHY: A parallel MUlti PHYsics/scale code for high performance bio-fluidic simulations, Comput. Phys. Commun., 180 (2009), 1495–1502.CrossRefGoogle Scholar
[14]http://www.ibm.com/systems/deepcomputing/bluegene/.Google Scholar
[15]Bernaschi, M., Fatica, M., Melchionna, S., Succi, S. and Kaxiras, E., A flexible high performance Lattice Boltzmann GPU code for the simulations of fluid flows in complex geometries, Concurrency Prac. Ex., DOI: 10.1002/cpe.1466 (2009).Google Scholar
[16]http://gpgpu.org/developer/cudpp.Google Scholar