This is a list of notable discrete-event simulation software. From Wikipedia, the free encyclopedia. Wikipedia list article. Retrieved Retrieved ". JS - code. Categories : Simulation software Lists of software Scientific modeling Events computing. Hidden categories: Articles with short description Short description is different from Wikidata.
Namespaces Article Talk. Views Read Edit View history. Help Learn to edit Community portal Recent changes Upload file. Download as PDF Printable version. Add links. August 13, . August 28, . Care pathway simulator. A discrete event simulation program specifically designed for service industries e.
A simulation software platform to model and analyze virtually any manufacturing, material handling and logistics challenge. July 18, . September 6, . June 7, . A discrete event simulation software with a drag-and-drop interface for modeling simulations in 3D. January 27, . Combines system dynamics with aspects of discrete event simulation, embedded in a Monte Carlo framework. September 21, . A general purpose discrete event modeling tool that uses a drag and drop interface and the C programming language.
May 20, . MS4 Modeling Environment. A general purpose DEVS methodology based software environment for discrete event and hybrid models. July 23, . May 19, . Discrete event simulation software. On-The-Fly model changes while the simulation is running.
Visual interface with no coding environment. Includes VR and Physics engine. August 11, . September 14, . January 22, . Model-based system architecture exploration of electronics, embedded software and semiconductors based on timing, power consumption and functionality.Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
Sign In. PDES is a fine-grained parallel application whose performance and scalability is limited by communication latencies. Traditionally, PDES simulation kernels use message passing; often these simulators are written for distributed environments, and shared memory is used to optimize message passing among processes on the same machine. In this paper, we develop, characterize and optimize a thread-based version of a PDES simulator on three representative multi-core platforms.
The multi-threaded implementation eliminates multiple message copying and significantly minimizes synchronization delays.
We discover that the three platforms encounter substantially different bottlenecks because of their different architectures. We identify these bottlenecks and propose mechanisms to overcome them. Our results show that multi-threaded implementation improves the performance over an MPI-based version by up to a factor of 3 on the Core i7, 1. Article :. Date of Publication: 05 August DOI: Need Help?Exploitation of parallelism is the best technique to achieve the minimums time of simulation.
But parallelizing creates new problems in simulation. As we know that events are generated distributed fashion so the occurrence of errors can result the sequence in which to process the events compulsory indeterminate. In this research paper we try to present a model for analyzing inherent parallelism of simulation, with survey of existing techniques for parallel simulation perform.
Some innovations in these model are described, results in reliable evaluation of these techniques effectiveness. In this paper we are interested to study the performance of applications on the class of computing systems. We try to differentiate the following levels which are related to performance evaluation such as general abstract machine, application, language of simulation and simulator of discrete event.
Each lower level supports the upper level in simulation of discrete events. Efficiency of each level is determined partially by imposing some constraints to the simulator in this way. Particularly if performance figures are used iteratively for the application optimization, effectiveness of simulator play important role.
Huge discrete events simulation required a lot of time in case of sequential machines. Exploitation of parallelism is best technique to minimize the needed time of simulation. Main disadvantage of this type of simulation is the inherent complexity since the global notion cannot map easily on parallel computers.
To ensure the cause and effect relationships sophisticate clock synchronization algorithms needed which are correctly reproduced by simulator. The concept of parallel simulation was firstly suggest by K. M Chandy and Bryant  consist concepts of primary parallel simulation, deadlock problem deadlock resolution techniques and deadlock recovery. Other techniques were suggested by D. R Jefferson  which is based on virtual time concepts. Remaining research paper is organized as followings.
In section II we will introduce some discrete event simulation concepts. In section III we will discuss discrete event simulation from sequential to partial with their effectiveness and suggested parallel simulation methods. Finally we evaluate these methods and present some suggestions for future research work.
Simulation and modeling can be divided as the complex of activities which are associated to constructing model for real word system and simulate them on computer system.
It is necessary for each model should be on time based events. Simulation models can categorize according to their temporal behavior . A models is called continuous time model when flow of time smoothly and continuously. If time flow jumps after some particular time unit it is called discrete model.
Second type of model is based on sets range of models descriptive variables. The model is called continuous state mode if range of descriptive variable can be shown by real numbers. If model accepts only discrete values it is called discrete model. Continuous time base model can be further classified in discrete events and differential equations. If changes in state occur continuously and smoothly in time this model is call differential equation model is a continuous time continuous state model.
If state change occur at only finite points in time it is called discrete event model even if time flows continuously such as jump of time from one event to other and these events can be occur independently from each other. Idea of system and model of system, we already have discussed in classification of simulations.
These ideas required particular order for framework development of discrete event model of the system. Main ideas of simulation modeling are as follows. If we want to do the simulation in parallel, there are some basic questions which must be answered. How much advantage we can expect from doing things in parallel fashion?
What is the inherent simulation parallelism? How well did we perform this once the job is done?He received the Ph. He has been an active researcher in the parallel and distributed simulation community sinceand has published numerous technical papers on this subject.
His publications include three books and several award-winning articles on parallel and distributed simulation. He has given several keynote addresses and tutorials on parallel and distributed simulation at leading conferences.
He has served on the organizing committees for several leading conferences in the parallel and distributed simulation area. Discrete event simulation is a widely used approach to model systems such as communication networks, manufacturing systems, and urban infrastructures. Parallel discrete event simulation PDES is concerned with the concurrent execution of a single simulation run on high performance computing platforms. Many impressive successes have been reported to date, yet PDES is still not the method of choice by the vast majority of practitioners when attacking large-scale discrete event simulation problems.
Further, new, massively parallel high performance computing platforms and cloud computing environments have appeared in recent years that present new challenges to effectively exploiting this technology.
I will briefly review the parallel discrete event simulation field beginning from seminal work to address the so-called synchronization problem. I will review some of the successes that have been achieved to date illustrating the potential offered by this technology.
I will then discuss key impediments that have prevented the technology from achieving widespread adoption by the general modeling and simulation community. I will discuss recent work highlighting issues concerning the parallel simulation of large scale-free networks and exploitation of contemporary and emerging computing platforms in order to highlight key research problems that remain to be addressed.
October 12, AM PG5: Speaker Bio Dr. Description Discrete event simulation is a widely used approach to model systems such as communication networks, manufacturing systems, and urban infrastructures. Recent Posts Got my first job!A discrete-event simulation DES models the operation of a system as a discrete sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the system. Both forms of DES contrast with continuous simulation in which the system state is changed continuously over time on the basis of a set of differential equations defining the rates of change of state variables.
A common exercise in learning how to build discrete-event simulations is to model a queuesuch as customers arriving at a bank to be served by a teller. In this example, the system entities are Customer-queue and Tellers. The system events are Customer-Arrival and Customer-Departure.
The event of Teller-Begins-Service can be part of the logic of the arrival and departure events. The system states, which are changed by these events, are Number-of-Customers-in-the-Queue an integer from 0 to n and Teller-Status busy or idle. The random variables that need to be characterized to model this system stochastically are Customer-Interarrival-Time and Teller-Service-Time.
An agent-based framework for performance modeling of an optimistic parallel discrete event simulator is another example for a discrete event simulation. In addition to the logic of what happens when system events occur, discrete event simulations include the following:. A system state is a set of variables that captures the salient properties of the system to be studied. The state trajectory over time S t can be mathematically represented by a step function whose value can change whenever an event occurs.
The simulation must keep track of the current simulation time, in whatever measurement units are suitable for the system being modeled. In discrete-event simulations, as opposed to continuous simulations, time 'hops' because events are instantaneous — the clock skips to the next event start time as the simulation proceeds. The simulation maintains at least one list of simulation events. This is sometimes called the pending event set because it lists events that are pending as a result of previously simulated event but have yet to be simulated themselves.
An event is described by the time at which it occurs and a type, indicating the code that will be used to simulate that event. It is common for the event code to be parametrized, in which case, the event description also contains parameters to the event code. When events are instantaneous, activities that extend over time are modeled as sequences of events. Some simulation frameworks allow the time of an event to be specified as an interval, giving the start time and the end time of each event.
Single-threaded simulation engines based on instantaneous events have just one current event. In contrast, multi-threaded simulation engines and simulation engines supporting an interval-based event model may have multiple current events.
In both cases, there are significant problems with synchronization between current events. The pending event set is typically organized as a priority queuesorted by event time. Various priority queue implementations have been studied in the context of discrete event simulation  ; alternatives studied have included splay treesskip listscalendar queues and ladder queues.
Typically, events are scheduled dynamically as the simulation proceeds. The simulation needs to generate random variables of various kinds, depending on the system model. This is accomplished by one or more Pseudorandom number generators. The use of pseudo-random numbers as opposed to true random numbers is a benefit should a simulation need a rerun with exactly the same behavior.
One of the problems with the random number distributions used in discrete-event simulation is that the steady-state distributions of event times may not be known in advance. As a result, the initial set of events placed into the pending event set will not have arrival times representative of the steady-state distribution. This problem is typically solved by bootstrapping the simulation model.
Parallel Discrete Event Simulation: Opportunities and Challenges
Only a limited effort is made to assign realistic times to the initial set of pending events. These events, however, schedule additional events, and with time, the distribution of event times approaches its steady state. This is called bootstrapping the simulation model.
In gathering statistics from the running model, it is important to either disregard events that occur before the steady state is reached or to run the simulation for long enough that the bootstrapping behavior is overwhelmed by steady-state behavior.She constructed a 12 day vacation that was a trip of a lifetime for us.
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List of discrete event simulation software
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