Skip to main content
Springer Nature Link
Log in

Accelerating the problem of microrheology in colloidal systems on a GPU

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Complex fluids are characterized with both solid and fluid properties by their elasticity and viscosity, or rheological behaviour. The flow of complex fluids is a focus of interest in a very wide range of applications in biophysics and soft matter, such as problems involving live cells, processing of plastic, glass, paints, foods, oil recovery and so on. Recently, microrheology has been developed as an accurate technique to obtain rheological properties in soft matter from the microscopic motion of colloidal tracers used as probes, either freely diffusing in the host medium (passive), or subjected to external forces (active). A drawback for the models that simulate these techniques is their high computational cost. Therefore, the use of high performance computing is mandatory to develop the microrheology models. In this work, a microrheology model based on simulations of a tracer in a bath of Brownian quasi-hard spheres is proposed and parallelized. The analysis of the results of the evaluation using different sizes of the problem shows that GPU programming is very appropriate to accelerate these kinds of models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. https://www.top500.org/lists/2016/06/.

  2. http://docs.nvidia.com/cuda/thrust/.

References

  1. Bolintineanu DS, Grest GS, Lechman JB, Pierce F, Plimpton SJ, Schunk PR (2014) Particle dynamics modeling methods for colloid suspensions. Comput Part Mech 1(3):321–356

    Article  Google Scholar 

  2. Brodtkorb AR, Hagen TR, Sætra ML (2013) Graphics processing unit (GPU) programming strategies and trends in GPU computing. J Parallel Distrib Comput 73(1):4–13

    Article  Google Scholar 

  3. Cicuta P, Donald A (2007) Microrheology: a review of the method and applications. Soft Matter 3:1449–1455

    Article  Google Scholar 

  4. Dhont J (1996) An introduction to dynamics of colloids. Studies in interface science. Elsevier, Amsterdam

    Google Scholar 

  5. Gazuz I, Puertas AM, Voigtmann T, Fuchs M (2009) Active and nonlinear microrheology in dense colloidal suspensions. Phys Rev Lett 102(24):248,302

    Article  Google Scholar 

  6. Gnann MV, Gazuz I, Puertas AM, Fuchs M, Voigtmann T (2011) Schematic models for active nonlinear microrheology. Soft Matter 7(4):1390–1396

    Article  Google Scholar 

  7. Hasimoto H (1959) On the periodic fundamental solutions of the Stokes equations and their application to viscous flow past a cubic array of spheres. J Fluid Mech 5:317–328

    Article  MathSciNet  MATH  Google Scholar 

  8. Hennessy JL, Patterson DA (2011) Computer architecture: a quantitative approach. Morgan Kaufmann, San Francisco

  9. Kopp M, Höfling F (2012) GPU-accelerated simulation of colloidal suspensions with direct hydrodynamic interactions. EPJ ST 210(1):101–117

    Google Scholar 

  10. NVIDIA: Nvidia’s next generation CUDA compute architecture: Fermi (2012). https://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf

  11. NVIDIA: Nvidia’s next generation CUDA compute architecture: Kepler gk110 (2012). https://www.nvidia.com/content/PDF/kepler/NVIDIA-Kepler-GK110-Architecture-Whitepaper.pdf

  12. NVIDIA Corporation: CUDA C PROGRAMMING GUIDE PG-02829-001_v7.5 (2015)

  13. Ortega G, Vázquez F, García I, Garzón EM (2014) FastSpMM: an efficient library for sparse matrix matrix product on GPUs. Comput J 57(7):968–979

  14. Paul W, Yoon DY (1995) Stochastic phase space dynamics with constraints for molecular systems. Phys Rev E 52:2076–2083

    Article  Google Scholar 

  15. Puertas AM, Voigtmann T (2014) Microrheology of colloidal systems. J Phys Condens Matter 26(24):243,101

  16. Schroer CFE, Heuer A (2015) Understanding the nonlinear dynamics of driven particles in supercooled liquids in terms of an effective temperature. J Chem Phys 143(22):224501

    Article  Google Scholar 

  17. Sololovskii R, Thachuk M, Patey G (2006) Tracer diffusion in hard sphere fluids: from molecular to hydrodynamic regimes. J Chem Phys 125:204,502

    Article  Google Scholar 

  18. Waigh TA (2016) Advances in the microrheology of complex fluids. Rep Prog Phys 79(7):074,601

    Article  Google Scholar 

  19. Winter D, Horbach J (2013) Non-linear active micro-rheology in a glass-forming soft-sphere mixture. J Chem Phys 138:12A512

    Article  Google Scholar 

  20. Winter D, Horbach J, Virnau P, Binder K (2012) Active nonlinear microrheology in a glass-forming yukawa fluid. Phys Rev Lett 108:028,303

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Group of Supercomputation-Algorithms, Department of Informatics, University of Almería, ceiA3, 04120, Almería, Spain

    G. Ortega & E. M. Garzón

  2. Group of Complex Fluids Physics, Department of Applied Physics, University of Almería, 04120, Almería, Spain

    A. M. Puertas

Authors
  1. G. Ortega
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. A. M. Puertas
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. E. M. Garzón
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to G. Ortega.

Additional information

This work has been supported by the Spanish Science and Technology Commission (CICYT) under contracts TIN2013-46957-C2-2-P, TIN2015-66680, FIS2015-69022-P and CAPAP-H5 network TIN2014-53522; Junta de Andalucia under contracts P11-TIC7176 and P12-TIC-301 in part financed by the European Regional Development Fund (ERDF) and the European HiPEAC Network of Excellence.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ortega, G., Puertas, A.M. & Garzón, E.M. Accelerating the problem of microrheology in colloidal systems on a GPU. J Supercomput 73, 370–383 (2017). https://doi.org/10.1007/s11227-016-1867-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-016-1867-8

Keywords