James C. Phillips, David J. Hardy, Julio D. C. Maia, John E. Stone,
João V. Ribeiro, Rafael C. Bernardi, Ronak Buch, Giacomo Fiorin,
Jérôme Hénin, Wei Jiang, Ryan McGreevy, Marcelo C. R. Melo,
Brian Radak, Robert D. Skeel, Abhishek Singharoy, Yi Wang, Benoît Roux,
Aleksei Aksimentiev, Zaida Luthey-Schulten, Laxmikant V. Kalé, Klaus
Schulten, Christophe Chipot, and Emad Tajkhorshid.
Scalable molecular dynamics on CPU and GPU architectures with
NAMD.
Journal of Chemical Physics, 153:044130, 2020.
(PMC: PMC7395834)
PHIL2020-ET
NAMD is a molecular dynamics program designed for high-performance
simulations of very large biological objects on CPU- and GPU-based
architectures. NAMD offers scalable
performance on petascale parallel supercomputers consisting of hundreds of
thousands of cores, as well as on inexpensive commodity clusters commonly
found in academic
environments. It is written in C++ and leans on parallel objects for
optimal performance on low-latency architectures. NAMD is a versatile,
multipurpose code that gathers
state-of-the-art algorithms to carry out simulations in apt thermodynamic
ensembles, using the widely popular CHARMM, AMBER, OPLS and GROMOS
biomolecular force fields.
Here, we review the main features of NAMD that allow both equilibrium and
enhanced-sampling molecular dynamics simulations with numerical efficiency.
We describe the
underlying concepts utilized by NAMD and their implementation, most notably
for handling long-range electrostatics, controlling the temperature, pressure
and pH, applying
external potentials on tailored grids, leveraging massively parallel resources in
multiple-copy simulations, as well as hybrid QM/MM descriptions. We detail the
variety of options
offered by NAMD for enhanced-sampling simulations aimed at determining
free-energy differences of either alchemical or geometrical transformations,
and outline their
applicability to specific problems. Last, we discuss the roadmap for the
development of NAMD and our current efforts towards achieving optimal
performance on GPU-based
architectures, for pushing back the limitations that have prevented biologically
realistic billion-atom objects to be fruitfully simulated, and for making large-
scale simulations less
expensive and easier to set up, run and analyze. NAMD is distributed free of
charge with its source code at www.ks.uiuc.edu.
