NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This paper, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Next, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, e.g., the Tcl scripting language. Finally, the paper provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.

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NAMD is a molecular dynamics program designed for high-performance simulations of very large biological objects on central processing unit (CPU)-and graphics processing unit (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, 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 GPUbased 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.

NAMD is a molecular dynamics program designed for high performance simulations of large biomolecular systems on parallel computers. An object-oriented design imple mented using C++ facilitates the incorporation of new algorithms into the program. NAMD uses spatial decom position coupled with a multithreaded, message-driven design, which is shown to scale efficiently to multiple processors. Also, NAMD incorporates the distributed par allel multipole tree algorithm for full electrostatic force evaluation in O( N) time. NAMD can be connected via a communication system to a molecular graphics program in order to provide an interactive modeling tool for viewing and modifying a running simulation. The application of NAMD to a protein-water system of 32,867 atoms illus trates the performance of NAMD.

Over the past few years resilience has became a major issue for high-performance computing (HPC) systems, in particular in the perspective of large petascale systems and future exascale systems. These systems will typically gather from half a million to several millions of central processing unit (CPU) cores running up to a billion threads. From the current knowledge and observations of existing large systems, it is anticipated that exascale systems will experience various kind of faults many times per day. It is also anticipated that the current approach for resilience, which relies on automatic or application level checkpoint/ restart, will not work because the time for checkpointing and restarting will exceed the mean time to failure of a full system. This set of projections leaves the community of fault tolerance for HPC systems with a difficult challenge: finding new approaches, which are possibly radically disruptive, to run applications until their normal termination, despite the essentially unstable nature of exascale systems. Yet, the community has only five to six years to solve the problem. This white paper synthesizes the motivations, observations and research issues considered as determinant of several complimentary experts of HPC in applications, programming models, distributed systems and system management.

NAMD is a fully featured, production molecular dynamics program for high performance simulation of large biomolecular systems. We have previously, at SC2000, presented scaling results for simulations with cutoff electrostatics on up to 2048 processors of the ASCI Red machine, achieved with an object-based hybrid force and spatial decomposition scheme and an aggressive measurement-based predictive load balancing framework. We extend this work by demonstrating similar scaling on the much faster processors of the PSC Lemieux Alpha cluster, and for simulations employing efficient (order N log N) particle mesh Ewald full electrostatics. This unprecedented scalability in a biomolecular simulation code has been attained through latency tolerance, adaptation to multiprocessor nodes, and the direct use of the Quadrics Elan library in place of MPI by the Charm++/Converse parallel runtime system.

CHANGA is an N-body cosmology simulation application implemented using CHARM++. In this paper, we present the parallel design of CHANGA and address many challenges arising due to the high dynamic ranges of clustered datasets. We propose optimizations based on adaptive techniques. We evaluate the performance of CHANGA on highly clustered datasets: a z ∼ 0 snapshot of a 2 billion particle realization of a 25 Mpc volume, and a 52 million particle multi-resolution realization of a dwarf galaxy. For the 25 Mpc volume, we show strong scaling on up to 128K cores of Blue Waters. We also demonstrate scaling up to 128K cores of a multi-stepping run of the 2 billion particle simulation. While the scaling of the multi-stepping run is not as good as single stepping, the throughput at 128K cores is greater by a factor of 2. We also demonstrate strong scaling on up to 512K cores of Blue Waters for two large, uniform datasets with 12 and 24 billion particles.

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