68,681 research outputs found
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
Programming agent-based demographic models with cross-state and message-exchange dependencies: A study with speculative PDES and automatic load-sharing
Agent-based modeling and simulation is a versatile and promising methodology to capture complex interactions among entities and their surrounding environment. A great advantage is its ability to model phenomena at a macro scale by exploiting simpler descriptions at a micro level. It has been proven effective in many fields, and it is rapidly becoming a de-facto standard in the study of population dynamics. In this article we study programmability and performance aspects of the last-generation ROOT-Sim speculative PDES environment for multi/many-core shared-memory architectures. ROOT-Sim transparently offers a programming model where interactions can be based on both explicit message passing and in-place state accesses. We introduce programming guidelines for systematic exploitation of these facilities in agent-based simulations, and we study the effects on performance of an innovative load-sharing policy targeting these types of dependencies. An experimental assessment with synthetic and real-world applications is provided, to assess the validity of our proposal
Load-Sharing Policies in Parallel Simulation of Agent-Based Demographic Models
Execution parallelism in agent-Based Simulation (ABS) allows to deal with complex/large-scale models. This raises the need for runtime environments able to fully exploit hardware parallelism, while jointly offering ABS-suited programming abstractions. In this paper, we target last-generation Parallel Discrete Event Simulation (PDES) platforms for multicore systems. We discuss a programming model to support both implicit (in-place access) and explicit (message passing) interactions across concurrent Logical Processes (LPs). We discuss different load-sharing policies combining event rate and implicit/explicit LPs’ interactions.
We present a performance study conducted on a synthetic test case, representative of a class of agent-based models
SimInf: An R package for Data-driven Stochastic Disease Spread Simulations
We present the R package SimInf which provides an efficient and very flexible
framework to conduct data-driven epidemiological modeling in realistic large
scale disease spread simulations. The framework integrates infection dynamics
in subpopulations as continuous-time Markov chains using the Gillespie
stochastic simulation algorithm and incorporates available data such as births,
deaths and movements as scheduled events at predefined time-points. Using C
code for the numerical solvers and OpenMP to divide work over multiple
processors ensures high performance when simulating a sample outcome. One of
our design goal was to make SimInf extendable and enable usage of the numerical
solvers from other R extension packages in order to facilitate complex
epidemiological research. In this paper, we provide a technical description of
the framework and demonstrate its use on some basic examples. We also discuss
how to specify and extend the framework with user-defined models.Comment: The manual has been updated to the latest version of SimInf (v6.0.0).
41 pages, 16 figure
SimpactCyan 1.0 : an open-source simulator for individual-based models in HIV epidemiology with R and Python interfaces
SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic “intervention” event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework
The parallel replica method for simulating long trajectories of Markov chains
The parallel replica dynamics, originally developed by A.F. Voter,
efficiently simulates very long trajectories of metastable Langevin dynamics.
We present an analogous algorithm for discrete time Markov processes. Such
Markov processes naturally arise, for example, from the time discretization of
a continuous time stochastic dynamics. Appealing to properties of
quasistationary distributions, we show that our algorithm reproduces exactly
(in some limiting regime) the law of the original trajectory, coarsened over
the metastable states.Comment: 13 pages, 6 figure
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