Molecular modeling on GPUs

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 (as of 2022 ^[update] , up to 18,176 in the RTX 6000 Ada) working in parallel. Long before this event, the computational power of video cards was purely used to accelerate graphics calculations. The new features of these cards 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
• AutoDock – Molecular docking
• BigDFT Ab initio program based on wavelet
• BrianQC Quantum chemistry (HF and DFT) and molecular mechanics
• Blaze ligand-based virtual screening
• CHARMM – Molecular dynamics
• 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
• GPU4PySCF – GPU accelerated plugin package for PySCF
• GPUMD - A light weight general-purpose molecular dynamics code
• GROMACS on GPUs ^[11]
• HALMD – Highly Accelerated Large-scale MD package
• HOOMD-blue Archived 2011-11-11 at the Wayback Machine – 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
• RUMD - Roskilde University Molecular Dynamics ^[12]
• TeraChem – Quantum chemistry and ab initio Molecular Dynamics
• TINKER on GPUs. ^[13]
• 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 and PSI
• 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
• Exscalate4Cov large-scale virtual screening experiment

See also

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

References

1. Stone JE, Phillips JC, Freddolino PL, Hardy DJ, Trabuco LG, Schulten K (December 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. S2CID   15313533.
2. Yasuda K (August 2008). "Accelerating Density Functional Calculations with Graphics Processing Unit". Journal of Chemical Theory and Computation. 4 (8): 1230–1236. doi:10.1021/ct8001046. PMID   26631699.
3. Yasuda K (February 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. S2CID   8078401.
4. Vogt L, Olivares-Amaya R, Kermes S, Shao Y, Amador-Bedolla C, Aspuru-Guzik A (March 2008). "Accelerating resolution-of-the-identity second-order Møller-Plesset quantum chemistry calculations with graphical processing units" . The Journal of Physical Chemistry A. 112 (10): 2049–2057. Bibcode:2008JPCA..112.2049V. doi:10.1021/jp0776762. PMID   18229900. S2CID   4566211.
5. Ufimtsev IS, Martínez TJ (February 2008). "Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation". Journal of Chemical Theory and Computation. 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. S2CID   10225262.
7. Tornai GJ, Ladjánszki I, Rák Á, Kis G, Cserey G (October 2019). "Calculation of Quantum Chemical Two-Electron Integrals by Applying Compiler Technology on GPU". Journal of Chemical Theory and Computation. 15 (10): 5319–5331. doi:10.1021/acs.jctc.9b00560. PMID   31503475. S2CID   202555796.
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". Comput. Phys. Commun. 182 (5): 1120–1129. arXiv: 0912.3824 . Bibcode:2011CoPhC.182.1120C. doi:10.1016/j.cpc.2011.01.009. S2CID   7173093.
11. Yousif RH (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. Bailey N, Ingebrigtsen T, Hansen JS, Veldhorst A, Bøhling L, Lemarchand C, et al. (2017-12-14). "RUMD: A general purpose molecular dynamics package optimized to utilize GPU hardware down to a few thousand particles". SciPost Physics. 3 (6): 038. arXiv: 1506.05094 . Bibcode:2017ScPP....3...38B. doi: 10.21468/SciPostPhys.3.6.038 . ISSN   2542-4653. S2CID   43964588.
13. Harger M, Li D, Wang Z, Dalby K, Lagardère L, Piquemal JP, et al. (September 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.

External links

• More links for classical and quantum сhemistry on GPUs