Molecular modeling on GPUs

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

Ionic liquid simulation on GPU (Abalone)

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 (as of 2016, up to 3,584 in Tesla P100) 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 application programming interface (API) named CUDA. This technology substantially simplified programming by enabling programs to be written in C/C++. More recently, OpenCL allows cross-platform GPU acceleration.

Quantum chemistry calculations[2][3][4][5][6][7] and molecular mechanics simulations[8][9][10] (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

Programs

API
  • BrianQC – has an open C level API for quantum chemistry simulations on GPUs, provides GPU-accelerated version of Q-Chem
  • 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
gollark: I guess you could just have `unsafe`/`average_c` blocks for that.
gollark: Pointer arithmetic, for one thing?
gollark: Rust was designed with ownership to start with and still can't satisfactorily chëck everything. C code is typically a horrible minefield of unsafe everything so a borrow checker could have problems.
gollark: > someone, quick! a borrow checker for C<@356107472269869058> Isn't that impossible without stupidly high false positive rates and/or missing many unsound things?
gollark: Maybe I can just expand the theming UI and ship a "monospace and black" theme.

See also

References
  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. CiteSeerX 10.1.1.466.3823. doi:10.1002/jcc.20829. PMID 17894371.CS1 maint: multiple names: authors list (link)
  2. Koji Yasuda (2008). "Accelerating Density Functional Calculations with Graphics Processing Unit". J. Chem. Theory Comput. 4 (8): 1230–1236. doi:10.1021/ct8001046. PMID 26631699.
  3. Koji Yasuda (2008). "Two-electron integral evaluation on the graphics processor unit". Journal of Computational Chemistry. 29 (3): 334–342. CiteSeerX 10.1.1.498.364. doi:10.1002/jcc.20779. PMID 17614340.
  4. Leslie Vogt; Roberto Olivares-Amaya; Sean Kermes; Yihan Shao; Carlos Amador-Bedolla; 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. Bibcode:2008JPCA..112.2049V. doi:10.1021/jp0776762. PMID 18229900.
  5. Ivan S. Ufimtsev & 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. PMID 26620654.
  6. Ivan S. Ufimtsev & Todd J. Martinez (2008). "Graphical Processing Units for Quantum Chemistry". Computing in Science & Engineering. 10 (6): 26–34. Bibcode:2008CSE....10f..26U. doi:10.1109/MCSE.2008.148.
  7. Gábor J. Tornai; István Ladjánszki; Ádám Rák; Gergely Kis & György Cserey (2019). "Calculation of quantum chemical two-electron integrals by applying compiler technology on GPU". J. Chem. Theo. Comp. 15 (10): 5319–5331. doi:10.1021/acs.jctc.9b00560. PMID 31503475.
  8. 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. CiteSeerX 10.1.1.552.2883. doi:10.1016/j.jcp.2008.01.047.
  9. Christopher I. Rodrigues; David J. Hardy; John E. Stone; Klaus Schulten & 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.
  10. 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.
  11. Yousif, Ragheed Hussam (2020). "Exploring the Molecular Interactions between Neoculin and the Human Sweet Taste Receptors through Computational Approaches" (PDF). Sains Malaysiana. 49 (3): 517–525. doi:10.17576/jsm-2020-4903-06.
  12. M. Harger, D. Li, Z. Wang, K. Dalby, L. Lagardère, J.-P. Piquemal, J. Ponder, P. Ren (2017). "Tinker-OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs". Journal of Computational Chemistry. 38 (23): 2047–2055. doi:10.1002/jcc.24853. PMC 5539969. PMID 28600826.CS1 maint: multiple names: authors list (link)
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