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

• Abalone – Molecular Dynamics (Benchmark)
• ACEMD on GPUs since 2009 Benchmark
• AMBER on GPUs version
• Ascalaph on GPUs version – Ascalaph Liquid GPU
• BigDFT Ab initio program based on wavelet
• BrianQC Quantum chemistry (HF and DFT) and molecular mechanics
• Blaze ligand-based virtual screening
• CP2K Ab initio molecular dynamics
• Desmond (software) on GPUs, workstations, and clusters
• Firefly (formerly PC GAMESS)
• FastROCS
• GOMC – GPU Optimized Monte Carlo simulation engine
• GPIUTMD – Graphical processors for Many-Particle Dynamics
• GROMACS on GPUs [11]
• HALMD – Highly Accelerated Large-scale MD package
• HOOMD-blue – Highly Optimized Object-oriented Many-particle Dynamics—Blue Edition
• LAMMPS on GPUs version – lammps for accelerators
• LIO DFT-Based GPU optimized code -
• Octopus has support for OpenCL.
• oxDNA – DNA and RNA coarse-grained simulations on GPUs
• PWmat – Plane-Wave Density Functional Theory simulations
• TeraChem – Quantum chemistry and ab initio Molecular Dynamics
• TINKER on GPUs.[12]
• VMD & NAMD on GPUs versions
• YASARA runs MD simulations on all GPUs using OpenCL.

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

• GPUGRID distributed supercomputing infrastructure
• Folding@home distributed computing project

See also

• GPU
• GPU cluster
• GPGPU
• Molecular design software
• List of quantum chemistry and solid state physics software
• Comparison of software for molecular mechanics modeling
• Comparison of nucleic acid simulation software
• Molecule editor
• Folding@home
• Simulated reality

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)

External links

• More links for classical and quantum сhemistry on GPUs