Molecular modeling on GPUs

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Ionic liquid simulation on GPU (Abalone)

Molecular modeling on GPU is the technique of using a graphics processing unit (GPU) for molecular simulations.[1]

In 2007, NVIDIA introduced video cards that could be used not only to show graphics but also for scientific calculations. These cards include many arithmetic units (currently up to 1536) working in parallel. Long before this event, the computational power of video cards was purely used to accelerate graphics calculations. What was new is that NVIDIA made it possible to develop parallel programs in a high-level language (CUDA). This technology substantially simplified programming by enabling programs to be written in C/C++. More recently, OpenCL allows GPU acceleration in a platform-independent manner.

Quantum chemistry calculations[2][3][4][5][6] and molecular mechanics simulations[7][8][9] (molecular modeling in terms of classical mechanics) are among beneficial applications of this technology. The video cards can accelerate the calculations tens of times, so a PC with such a card has the power similar to that of a cluster of workstations based on common processors.

GPU accelerated molecular modelling software[edit]



  • OpenMM – an API for accelerating molecular dynamics on GPUs, v1.0 provides GPU-accelerated version of GROMACS
  • mdcore – an Open-source platform-independent library for molecular dynamics simulations on modern shared-memory parallel architectures.

Distributed computing projects[edit]

See also[edit]


  1. ^ John E. Stone, James C. Phillips, Peter L. Freddolino, David J. Hardy 1, Leonardo G. Trabuco, Klaus Schulten (2007). "Accelerating molecular modeling applications with graphics processors". Journal of Computational Chemistry 28 (16): 2618–2640. doi:10.1002/jcc.20829. PMID 17894371. 
  2. ^ Koji Yasuda (2008). "Accelerating Density Functional Calculations with Graphics Processing Unit". J. Chem. Theory Comput. 4 (8): 1230–1236. doi:10.1021/ct8001046. 
  3. ^ Koji Yasuda (2008). "Two-electron integral evaluation on the graphics processor unit". Journal of Computational Chemistry 29 (3): 334–342. doi:10.1002/jcc.20779. PMID 17614340. 
  4. ^ Leslie Vogt, Roberto Olivares-Amaya, Sean Kermes, Yihan Shao, Carlos Amador-Bedolla and Alán Aspuru-Guzik (2008). "Accelerating Resolution-of-the-Identity Second-Order Møller−Plesset Quantum Chemistry Calculations with Graphical Processing Units". J. Phys. Chem. A 112 (10): 2049–2057. doi:10.1021/jp0776762. PMID 18229900. 
  5. ^ Ivan S. Ufimtsev and Todd J. Martinez (2008). "Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation". J. Chem. Theo. Comp. 4 (2): 222–231. doi:10.1021/ct700268q. 
  6. ^ Ivan S. Ufimtsev and Todd J. Martinez (2008). "Graphical Processing Units for Quantum Chemistry". Comp. Sci. Eng. 10 (6): 26–34. doi:10.1109/MCSE.2008.148. 
  7. ^ Joshua A. Anderson, Chris D. Lorenz, A. Travesset (2008). "General Purpose Molecular Dynamics Simulations Fully Implemented on Graphics Processing Units". Journal of Computational Physics 227 (10): 5342–5359. Bibcode:2008JCoPh.227.5342A. doi:10.1016/ 
  8. ^ Christopher I. Rodrigues, David J. Hardy, John E. Stone, Klaus Schulten, and Wen-Mei W. Hwu. (2008). "GPU acceleration of cutoff pair potentials for molecular modeling applications.". In CF'08: Proceedings of the 2008 conference on Computing frontiers, New York, NY, USA: 273–282. 
  9. ^ Peter H. Colberg, Felix Höfling (2011). "Highly accelerated simulations of glassy dynamics using GPUs: Caveats on limited floating-point precision". Comp. Phys. Comm. 182 (5): 1120–1129. arXiv:0912.3824. Bibcode:2011CoPhC.182.1120C. doi:10.1016/j.cpc.2011.01.009. 

External links[edit]