DynamO: A free O(N) general event-driven molecular-dynamics simulator

Molecular-dynamics algorithms for systems of particles interacting through discrete or "hard" potentials are fundamentally different to the methods for continuous or "soft" potential systems. Although many software packages have been developed for continuous potential systems, software for discrete potential systems based on event-driven algorithms are relatively scarce and specialized. We present DynamO, a general event-driven simulation package which displays the optimal O(N) asymptotic scaling of the computational cost with the number of particles N, rather than the O(N log(N)) scaling found in most standard algorithms. DynamO provides reference implementations of the best available event-driven algorithms. These techniques allow the rapid simulation of both complex and large (>10^6 particles) systems for long times. The performance of the program is benchmarked for elastic hard sphere systems, homogeneous cooling and sheared inelastic hard spheres, and equilibrium Lennard-Jones fluids. This software and its documentation are distributed under the GNU General Public license and can be freely downloaded from http://marcusbannerman.co.uk/dynamo

An analysis of internal/external event ordering strategies for COTS distributed simulation

Distributed simulation is a technique that is used to link together several models so that they can work together (or interoperate) as a single model. The High Level Architecture (HLA) (IEEE 1516.2000) is the de facto standard that defines the technology for this interoperation. The creation of a distributed simulation of models developed in COTS Simulation Packages (CSPs) is of interest. The motivation is to attempt to reduce lead times of simulation projects by reusing models that have already been developed. This paper discusses one of the issues involved in distributed simulation with CSPs. This is the issue of synchronising data sent between models with the simulation of a model by a CSP, the so-called external/internal event ordering problem. The motivation is that the particular algorithm employed can represent a significant overhead on performance

Optimized Gillespie algorithms for the simulation of Markovian epidemic processes on large and heterogeneous networks

Numerical simulation of continuous-time Markovian processes is an essential and widely applied tool in the investigation of epidemic spreading on complex networks. Due to the high heterogeneity of the connectivity structure through which epidemics is transmitted, efficient and accurate implementations of generic epidemic processes are not trivial and deviations from statistically exact prescriptions can lead to uncontrolled biases. Based on the Gillespie algorithm (GA), in which only steps that change the state are considered, we develop numerical recipes and describe their computer implementations for statistically exact and computationally efficient simulations of generic Markovian epidemic processes aiming at highly heterogeneous and large networks. The central point of the recipes investigated here is to include phantom processes, that do not change the states but do count for time increments. We compare the efficiencies for the susceptible-infected-susceptible, contact process and susceptible-infected-recovered models, that are particular cases of a generic model considered here. We numerically confirm that the simulation outcomes of the optimized algorithms are statistically indistinguishable from the original GA and can be several orders of magnitude more efficient.Comment: 12 pages, 9 figure

Shawn: A new approach to simulating wireless sensor networks

We consider the simulation of wireless sensor networks (WSN) using a new approach. We present Shawn, an open-source discrete-event simulator that has considerable differences to all other existing simulators. Shawn is very powerful in simulating large scale networks with an abstract point of view. It is, to the best of our knowledge, the first simulator to support generic high-level algorithms as well as distributed protocols on exactly the same underlying networks.Comment: 10 pages, 2 figures, 2 tables, Latex, to appear in Design, Analysis, and Simulation of Distributed Systems 200

Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks

Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling.Comment: Minor changes and updates to reference