Dynamic simulation parallel implementation

Molecular dynamics simulation package with various force field implementations, special support for AMBER. Parallel version and Xll trajectory viewer available. http //ganter.chemie.uni-dortmund.de/MOSCITO/... 

The implementation of molecular dynamics simulations on parallel computers needs a method that distributes over the processors both the evaluation of pair interactions and the integration of particle motions. The force terms involved in integrating the set of coupled differential equations (Newton s equations) characteristic of any MD simulation are typically nonlinear functions of the distance between pairs of atoms and may be either long-range or short-range. We use this attribute of the force terms in detailing the parallel algorithmic work conducted to date. 

This chapter highlights recent developments in auxiliary density functional theory (ADFT) and their implementation in deMon2k. The simplifications associated with ADFT permit an efficient parallel code structure that is suitable for research applications in the nano-regime with chemical accuracy. The presented Born-Oppenheimer molecular dynamics simulation shows that simulation times on the nanosecond time scale can be reached with ADFT. As the here presented applications show this opens new and exciting perspectives for computational chemistry and material simulations with first-principle methods. 

A reflection of the computational demands presented by MD simulations is the effort expended in the construction of special purpose hardware dedicated to performing MD calculations.A special purpose parallel computer, built at the IBM Almaden Research Center, for simulating classical many-body systems by MD, was described by Auerbach et al. The report emphasized motivation, design, and implementation, together with details of a new application to the dynamics of growth and form. 

The main characteristic of cellulcir automata is that each cell, which corresponds to a grid point in our model of the surface, is updated simultaneously. This allows for an efl cient implementation on massive parallel computers. It also facilitates the simulation of pattern formation, which is much harder to simulate with some asynchronous updating scheme as in dynamic Monte Carlo. [42] The question is how realistic a simultaneous update is, as a reaction seems to be a stochastic process. One has tried to incorporate this randomness by using so-called probabilistic cellular automata, in which updates are done with some probability. These cellular... 

The parallel execution of the dynamical ealeulations is already built in into SIM-BEX. In the present r ersion of the simulator the introduction of concurrency has been fairly simjjle due to the fad that oidy classical mechanics approaches are available. To the end of implementing also (juantum approaches we have carried out performance tests of the relevant quantum dynamics suites of codes. 

The extended simple point charge (SPC/E) model [59] is used. This model is known to give reasonably accurate values of static dielectric permittivity of liquid water at ambient conditions [60]. The MD simulations were performed for both H2O and D2O with the system size of 1024 particles at 220 K, 240 K, 267 K, 273 K, 300 K, and 355 K. The parallel molecular dynamics code for arbitrary molecular mixtures (DynaMix) is implemented by Lyubartsev and Laaksonen [61]. The simulations have been carried out on a Linux cluster built on the Tyan/Opteron 64 platform, which enables calculations of relatively long trajectories for a system of 1024 water molecules. The simulation run lengths depend on temperature and are in the range between 1 ns and 4 ns for the warmest and coldest simulation, respectively. As the initial condition was a cubic lattice, the equilibration time was chosen to be temperature dependent in the range from 200 ps at 355 Ktol ns at 200K. 

These are implemented in Aspen Dynamics by using Flowsheet Equations. Figure 8.28a shows the window that opens when Flowsheet is clicked in the Exploring-Simulation window. The parallel bars labeled Flowsheet are clicked and the Constraint-Flowsheet window shown in Figure 8.28b opens. The two required equations are entered using... 

Sivertsen and Djilali [67] developed a single-phase, non-isothermal 3D model which is implemented into a computational fluid dynamic code. The model allows parallel computing, thus making it practical to perform well-resolved simulations for large computational domains. The parallel solver allows them to use a large computational grid (total of 546000... 

---