Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters

By K. Bowers, Edmond Chow, Huafeng Xu, Ron O. Dror, Michael P. Eastwood, Brent A. Gregersen, John L. Klepeis, I. Kolossváry, Mark A. Moraes, Federico D. Sacerdoti and 3 more. International Conference on Software Composition, 2006

3,693 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/9c92838a8da9dd2bb8729f274a312fdc52d9f099

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. John D. Chodera
2. Klaus Schulten
3. Ping Tak Peter Tang
4. Alexandre M. J. J. Bonvin
5. Vijay S. Pande
6. Carlo Camilloni
7. Ron O. Dror
8. Michael R. Shirts

In Computer Networks and Communications

1. John D. Chodera
2. Szilárd Páll
3. Brian Towles
4. Vijay S. Pande
5. Giovanni Bussi

In Hardware and Architecture

1. John D. Chodera
2. Szilárd Páll
3. Vijay S. Pande
4. Cliff Young

Citations in the corpus

• Parallel reactive molecular dynamics: Numerical methods and algorithmic techniques (2012, methodology): “Handling range-limited non-bonded interactions have already been well-studied in literature [25, 24, 26].”
• Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer (2014, methodology): “For these systems, the best current software implementations of MD on commodity hardware or general-purpose supercomputers [6, 9, 21, 22, 36, 39] are limited to simulation rates of hundreds of nanoseconds of physical time per day of wall-clock time, even on the most powerful conventional…”
• PLUMED 2: New feathers for an old bird (2013, methodology): “Other software is available for analyzing existing trajectories [7, 20, 21, 22, 23, 24] or for biasing MD simulations [15, 9, 25, 26, 27, 7, 6].”
• Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0] (2022, background): “[393] DESMOND Umbrella sampling, Metadynamics, replica exchange [394] LAMMPS Harmonic restraints, applying external forces, replica exchange, parallel replica dynamics, temperature accelerated dynamics, original hyperdynamics, local hyperdynamics.”
• CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates (2019, methodology): “2015), LAMMPS (Plimpton 1995), Desmond (Bowers et al. 2006), OpenMM (Eastman et al.”
• Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development (2020, methodology): “Desmond together with the OPLS3 force field [25] was employed to simulate SIRT1 and SIRT3 in complex with either NAD+ or NADH and an acetyl-lysine substrate for 250–500 ns [94].”
• OpenMM 7: Rapid development of high performance algorithms for molecular dynamics (2016, methodology): “OpenMM does include modelling tools, but it also can directly read the file formats used by Amber [16], CHARMM [17], Gromacs [18], and Desmond [19].”
• CHARMM-GUI Membrane Builder for Complex Biological Membrane Simulations with Glycolipids and Lipoglycans. (2018, positive): “Importantly, Membrane Builder also provides well-validated simulation inputs for various MD programs 31 , such as CHARMM 32 , NAMD 33 , GROMACS 34 , AMBER 35 , GENESIS 36 , LAMMPS 37 , Desmond 38 , OpenMM 39 , and CHARMM/OpenMM 40 , allowing users to perform MD simulation with their familiar tool.”
• Developing a molecular dynamics force field for both folded and disordered protein states (2018, methodology): “Systems were initially equilibrated at 300 K and 1 bar for 1 ns using the Desmond software (44).”
• Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset (2016, methodology): “Journal of Computational Physics 117(1):1–19 [10] Bowers KJ, Chow DE, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE (2006) Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters.”
• CHARMM-GUI 10 Years for Biomolecular Modeling and Simulation (2016, methodology): “CHARMM-GUI has been supporting various MD engines such as CHARMM,([22]) NAMD,([23]) GROMACS,([24]) AMBER,([25]) OpenMM,([29]) and CHARMM/OpenMM,([30]) and was recently extended to support GENESIS,([26]) LAMMPS,([27]) and Desmond.([28]) While CHARMM,”
• Scalable molecular dynamics with NAMD (2005, methodology): “Similar parallelization schemes have been used in other recent MD packages such as Desmond [31] and Blue Matter [70], and in the scheme proposed by Snir [229].”