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Multi-GPU Based Lattice Boltzmann Method for Hemodynamic Simulation in Patient-Specific Cerebral Aneurysm

Published online by Cambridge University Press:  30 April 2015

Changsheng Huang ,
Baochang Shi ,
Zhaoli Guo  and
Zhenhua Chai
Changsheng Huang
Affiliation:
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China
Baochang Shi*
Affiliation:
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China
Zhaoli Guo
Affiliation:
State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, China
Zhenhua Chai
Affiliation:
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China
*
*Corresponding author. Email addresses: [email protected] (C. Huang), [email protected] (Z. Guo), [email protected] (B. Shi), [email protected] (Z. Chai)
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Abstract

Conducting lattice Boltzmann method on GPU has been proved to be an effective manner to gain a significant performance benefit, thus the GPU or multi-GPU based lattice Boltzmann method is considered as a promising and competent candidate in the study of large-scale complex fluid flows. In this work, a multi-GPU based lattice Boltzmann algorithm coupled with the sparse lattice representation and message passing interface is presented. Some numerical tests are also carried out, and the results show that a parallel efficiency close to 90% can be achieved on a single-node cluster equipped with four GPU cards. Then the proposed algorithm is adopted to study the hemodynamics of patient-specific cerebral aneurysm with stent implanted. It is found that the stent can apparently reduce the aneurysmal inflow and improve the hemodynamic environment. This work also shows that the lattice Boltzmann method running on the GPU platform is a powerful tool to study the fluid mechanism within the aneurysms and enable us to better understand the pathogenesis and treatment of cerebral aneurysms.

Keywords

65Y0576Z0576M28Lattice Boltzmann methodGPU computingMPIcerebral aneurysmblood flow
Type
Research Article
Information
Communications in Computational Physics , Volume 17 , Issue 4 , April 2015 , pp. 960 - 974
Copyright
Copyright © Global-Science Press 2015 

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References

[1]Foutrakis, G. N., Yonas, H., Sclabassi, R. J., Saccular aneurysm formation in curved and bifurcating arteries, Am. J. Neuroradiol., 20 (7)(1999), 1309–1317.Google Scholar
[2]Stuhne, G. R., Steinman, D. A., Finite-element modeling of the hemodynamics of stented aneurysms, J. Biomech. Eng., 126 (2004), 382–387.Google Scholar
[3]Radaelli, A., Augsburger, L., Cebral, J., Ohta, M., Rüfenacht, D., Balossino, R., Benndorf, G., Hose, D., Marzo, A., Metcalfe, R., et al., Reproducibility of haemodynamical simulations in a subject-specific stented aneurysm model – A report on the virtual intracranial stenting challenge 2007, J. Biomech., 41 (10) (2008), 2069–2081.CrossRefGoogle Scholar
[4]Appanaboyina, S., Mut, F., Löhner, R., Putman, C., Cebral, J., Simulation of intracranial aneurysm stenting: Techniques and challenges, Comput. Method. Appl. Mech. Engr., 198 (45–46) (2009), 3567–3582.Google Scholar
[5]Cebral, J. R., Mut, F., Sforza, D., Löhner, R., Scrivano, E., Lylyk, P., Putman, C., Clinical application of image-based cfd for cerebral aneurysms, Int. J. Numer. Meth. Bio., 27 (7) (2011), 977–992.CrossRefGoogle ScholarPubMed
[6]Cebral, J., Mut, F., Raschi, M., Scrivano, E., Ceratto, R., Lylyk, P., Putman, C., Aneurysm rupture following treatment with flow-diverting stents: Computational hemodynamics analysis of treatment, Am. J. Neuroradiol., 32 (1), (2011) 27–33.Google Scholar
[7]Benzi, R., Succi, S., Vergassola, M., The lattice Boltzmann equation: Theory and applications, Phys. Rep., 222 (3) (1992) 145–197.Google Scholar
[8]Chen, S., Doolen, G.D, Lattice Boltzmann method for fluid flows, Annu. Rev. Fluid Mech., 30 (1998) 329–364.Google Scholar
[9]Succi, S., The Lattice Boltzmann Equation for Fluid Dynamics and Beyond, Oxford University Press, Oxford, 2001.Google Scholar
[10]Hirabayashi, M., Ohta, M., Rüfenacht, D. A., Chopard, B., Characterization of flow reduction properties in an aneurysm due to a stent, Phys. Rev. E, 68 (2) (2003), 021918.Google Scholar
[11]Chopard, B., Ouared, R., Rüfenacht, D. A., A lattice boltzmann simulation of clotting in stented aneursysms and comparison with velocity or shear rate reductions, Math. Comput. Simulat., 72 (2–6) (2006), 108–112.Google Scholar
[12]Bernsdorf, J., Wang, D., Non-newtonian blood flow simulation in cerebral aneurysms, Comput. Math. Appl., 58 (5) (2009), 1024–1029.Google Scholar
[13]He, X., Duckwiler, G., Valentino, D.J., Lattice Boltzmann simulation of cerebral artery hemo-dynamics, Comput. Fluids, 38 (4) (2009), 789–796.Google Scholar
[14]Kim, Y. H., Xu, X., Lee, J. S., The effect of stent porosity and strut shape on saccular aneurysm and its numerical analysis with lattice boltzmann method, Ann. Biomed. Eng., 38 (7) (2010), 2274–2292.Google Scholar
[15]Huang, C., Chai, Z., Shi, B., Non-Newtonian effect on hemodynamic characteristics of blood flow in stented cerebral aneurysm, Commun. Comput. Phys., 13 (3) (2013), 916–928.Google Scholar
[16]Weichert, F., Walczak, L., Fisseler, D., Opfermann, T., Razzaq, M., Münster, R., Turek, S., Grunwald, I., Roth, C., Veith, C., Wagner, M., Simulation of intra-aneurysmal blood flow by using different numerical methods, Comput. Math. Methods Med., 2013 (2013), doi: 10.1155/2013/527654.Google Scholar
[17]Tölke, J., Krafczyk, M., TeraFLOP computing on a desktop PC with GPUs for 3D CFD, Int. J. Comput. Fluid D., 22 (7) (2008), 443–456.Google Scholar
[18]Kuznik, F., Obrecht, C., Rusaouen, G., Roux, J.-J., LBM based flow simulation using GPU computing processor, Comput. Math. Appl., 59 (7) (2010), 2380–2392.Google Scholar
[19]Obrecht, C., Kuznik, F., Tourancheau, B., Roux, J.-J., The The LMA project: Multi-GPU implementation of the lattice Boltzmann method, Int. J. High Perform. C., 25 (3) (2011), 295–303.Google Scholar
[20]Obrecht, C., Kuznik, F., Tourancheau, B., Roux, J.-J., Multi-GPU implementation of the lattice Boltzmann method, Comput. Math. Appl., 65 (2) (2013), 252–261.CrossRefGoogle Scholar
[21]Toelke, J., De Prisco, G., Mu, Y., Lattice Boltzmann multi-phase simulations in porous media using multiple GPUs, AGU Fall Meeting Abstracts (2011).Google Scholar
[22]Xiong, Q. G., Li, B., Xu, J., Fang, X. J., Wang, X. W., Wang, L. M., He, X. F., Ge, W., Efficient parallel implementation of the lattice Boltzmann method on large clusters of graphic processing units, Chinese Sci. Bull., 57 (7) (2012), 707–715.Google Scholar
[23]Schulz, M., Krafczyk, M., Tölke, J., Rank, E., Parallelization strategies and efficiency of cfd computations in complex geometries using lattice boltzmann methods on high-performance computers, in: High Performance Scientific and Engineering Computing, Springer, 2002, 115–122.Google Scholar
[24]He, X., Luo, L.-S., Theory of the lattice Boltzmann method: From the Boltzmann equation to the lattice Boltzmann equation, Phys. Rev. E, 56 (6) (1997): 6811.Google Scholar
[25]Qian, Y., d’Humières, D., Lallemand, P., Lattice BGK model for Navier-Stokes equation, Europhys. Lett., 17 (6) (1992), 479–484.Google Scholar
[26]He, X., Zou, Q., Luo, L.-S., Dembo, M., Analytic solutions of simple flows and analysis of nonslip boundary conditions for the lattice Boltzmann BGK model, J. Stat. Phys., 87 (1) (1997), 115–136.Google Scholar
[27]Guo, Z. L., Zheng, C. G., Shi, B. C., An extrapolation method for boundary conditions in lattice Boltzmann method, Phys. Fluids 14 (6) (2002) 2007–2010.Google Scholar
[28]Chai, Z. H., Shi, B. C., Zheng, L., Simulating high Reynolds number flow in two-dimensional lid-driven cavity by multi-relaxation-time lattice Boltzmann method, Chin. Phys. 15 (8) (2006) 1855–1863.Google Scholar
[29]Obrecht, C., Kuznik, F., Tourancheau, B., Roux, J. J., Global memory access modelling for efficient implementation of the lattice boltzmann method on graphics processing units, in: High Performance Computing for Computational Science – CVECPAR2010, in: Lecture Notes in Computer Science, Springer, 2010.Google Scholar
[30]Pan, C., Prins, J. F., Miller, C. T., A high-performance lattice boltzmann implementation to model flow in porous media, Comput. Phys. Commun., 158 (2) (2004), 89–105.Google Scholar
[31]Axner, L., Bernsdorf, J., Zeiser, T., Lammers, P., Linxweiler, J., Hoekstra, A. G., Performance evaluation of a parallel sparse lattice boltzmann solver, J. Comput. Phys., 227 (10) (2008), 4895–4911.CrossRefGoogle Scholar
[32]Bernaschi, M., Fatica, M., Melchionna, S., Succi, S., Kaxiras, E., A flexible high-performance lattice Boltzmann GPU code for the simulations of fluid flows in complex geometries, Concurr. Comput.: Pract. Exp., 22 (1) (2010), 1–14.CrossRefGoogle Scholar
[33]Bernaschi, M., Bisson, M., Fatica, M., Melchionna, S., Succi, S., Petaflop hydrokinetic simulations of complex flows on massive GPU clusters, Comput. Phys. Commun., 184 (2) (2013), 329–341.CrossRefGoogle Scholar
[34] NVIDIA, NVIDIA CUDA Compute Unified Device Architecture: Programming Guide (Version 3.2).Google Scholar
[35]Cebral, J. R., Castro, M. A., Burgess, J. E., Pergolizzi, R. S., Sheridan, M. J., Putman, C. M., Characterization of cerebral aneurysms for assessing risk of rupture by using patient-specific computational hemodynamics models, Am. J. Neuroradiol., 26 (10) (2005), 2550–2559.Google Scholar