Scalable molecular dynamics on CPU and GPU architectures with NAMD

J. C. Phillips(University of Illinois Urbana-Champaign), David J. Hardy(University of Illinois Urbana-Champaign), Julio D. C. Maia(University of Illinois Urbana-Champaign), John E. Stone(University of Illinois Urbana-Champaign), João V. Ribeiro(University of Illinois Urbana-Champaign), Rafael C. Bernardi(University of Illinois Urbana-Champaign), Ronak Buch(University of Illinois Urbana-Champaign), Giacomo Fiorin(National Institutes of Health), Jérôme Hénin(Centre National de la Recherche Scientifique), Wei Jiang(Argonne National Laboratory), Ryan McGreevy(University of Illinois Urbana-Champaign), Marcelo C. R. Melo(University of Illinois Urbana-Champaign), Brian K. Radak(University of Illinois Urbana-Champaign), Robert D. Skeel(Arizona State University), Abhishek Singharoy(Arizona State University), Yi Wang(Chinese University of Hong Kong), Benoı̂t Roux(University of Chicago), Aleksei Aksimentiev(University of Illinois Urbana-Champaign), Zaida Luthey‐Schulten(University of Illinois Urbana-Champaign), Laxmikant V. Kalé(University of Illinois Urbana-Champaign), Klaus Schulten(University of Illinois Urbana-Champaign), Christophe Chipot(Centre National de la Recherche Scientifique), Emad Tajkhorshid(University of Illinois Urbana-Champaign)
The Journal of Chemical Physics
July 28, 2020
Cited by 3,215Open Access
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Abstract

NAMDis 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 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; 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 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 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.


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