@article {335,
	title = {Scalable molecular dynamics on CPU and GPU architectures with NAMD},
	journal = {J. Chem. Phys.},
	volume = {153},
	year = {2020},
	month = {07/2020},
	chapter = {044130},
	abstract = {<p>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 Charm++ 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 biomolec- ular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics\&nbsp;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; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchem- ical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward 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.</p>
},
	doi = {10.1063/5.0014475},
	author = {James C. Phillips and David J. Hardy and Julio D. C. Maia and John E. Stone and Jo{\~a}o V. Ribeiro and Rafael C. Bernardi and Ronak Buch and Giacomo Fiorin and J{\'e}r{\^o}me H{\'e}nin and Wei Jiang and Ryan McGreevy and Marcelo C. R. Melo and Brian K. Radak and Robert D. Skeel and Abhishek Singharoy and Yi Wang and Beno{\^\i}t Roux and Aleksei Aksimentiev and Zaida Luthey-Schulten and Laxmikant V. Kal{\'e} and Klaus Schulten and Christophe Chipot and Emad Tajkhorshid}
}
