On the performance of molecular dynamics applications on current high-end systems

Abstract

The effective exploitation of current high performance computing (HPC) platforms in molecular simulation relies on the ability of the present generation of parallel molecular dynamics code to make effective utilisation of these platforms and their components, including CPUs and memory. In this paper, we investigate the efficiency and scaling of a series of popular molecular dynamics codes on the UK's national HPC resources, an IBM p690+ cluster and an SGI Altix 3700. Focusing primarily on the AMBER, DL_POLY and NAMD simulation codes, we demonstrate the major performance and scalability advantages that arise through a distributed, rather than a replicated data approach.

Understanding the influence of structural hierarchy and its coupling with chemical environment on the strength of idealized tropocollagen–hydroxyapatite biomaterials

“…All simulations are performed using NAMD package as it can be scaled well to very large processor counts (Hein et al, 2005). A combination of CHARMM22 force field (Ponder and Case (2003), and an inorganic force field reported earlier for HAP (Hauptmann et al, 2003;Bhowmik et al, 2007a), is used to model the interatomic interactions.…”

Geometric analysis characterizes molecular rigidity in generic and non-generic protein configurations

“…Computationally integrating these data sources has proved challenging [8, 9]. Molecular dynamics simulations can yield atomically detailed trajectories, but rely on imperfect force-fields and often demand specialized hardware [17] and algorithms to examine long, biologically relevant time scales or larger molecules [25]. By contrast, non-deterministic conformational sampling-based algorithms, such as kinematics-based methods, can provide high-level insights into conformational ensembles at spatiotemporal scales beyond the reach of molecular dynamics simulations [9, 10, 29].…”

Molecular Basis of Peptide Recognition by the TCR: Affinity Differences Calculated Using Large Scale Computing

“…In this study, we used NAMD (16), a highly scalable massively parallel MD code (17), running on 256 processors of Pittsburgh Supercomputing Center's LeMieux, the most powerful supercomputer system in the world for open research. Our simulations show very substantial acceleration relative to single processor runs, reducing the time needed to simulate a nanosecond of a system of ϳ100,000 atoms to 10 h. It required ϳ75,000 CPU hours and 35 gigabytes of disk space for all the simulations.…”