scispace - formally typeset

Topic

Simulation software

About: Simulation software is a(n) research topic. Over the lifetime, 7872 publication(s) have been published within this topic receiving 73733 citation(s).
Papers
More filters

Journal ArticleDOI
Richard M. Fujimoto1Institutions (1)
TL;DR: This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes and introduces interesting synchronization problems that are at the heart of the PDES problem.
Abstract: Parallel discrete event simulation (PDES), sometimes called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises from the fact that large simulations in engineering, computer science, economics, and military applications, to mention a few, consume enormous amounts of time on sequential machines. From an academic point of view, parallel simulation is interesting because it represents a problem domain that often contains substantial amounts of parallelism (e.g., see [59]), yet paradoxically, is surprisingly difficult to parallelize in practice. A sufficiently general solution to the PDES problem may lead to new insights in parallel computation as a whole. Historically, the irregular, data-dependent nature of PDES programs has identified it as an application where vectorization techniques using supercomputer hardware provide little benefit [14].A discrete event simulation model assumes the system being simulated only changes state at discrete points in simulated time. The simulation model jumps from one state to another upon the occurrence of an event. For example, a simulator of a store-and-forward communication network might include state variables to indicate the length of message queues, the status of communication links (busy or idle), etc. Typical events might include arrival of a message at some node in the network, forwarding a message to another network node, component failures, etc.We are especially concerned with the simulation of asynchronous systems where events are not synchronized by a global clock, but rather, occur at irregular time intervals. For these systems, few simulator events occur at any single point in simulated time; therefore parallelization techniques based on lock-step execution using a global simulation clock perform poorly or require assumptions in the timing model that may compromise the fidelity of the simulation. Concurrent execution of events at different points in simulated time is required, but as we shall soon see, this introduces interesting synchronization problems that are at the heart of the PDES problem.This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes. For completeness, we conclude this section by mentioning other approaches to exploiting parallelism in simulation problems.Comfort and Shepard et al. have proposed using dedicated functional units to implement specific sequential simulation functions, (e.g., event list manipulation and random number generation [20, 23, 47]). This method can provide only a limited amount of speedup, however. Zhang, Zeigler, and Concepcion use the hierarchical decomposition of the simulation model to allow an event consisting of several subevents to be processed concurrently [21, 98]. A third alternative is to execute independent, sequential simulation programs on different processors [11, 39]. This replicated trials approach is useful if the simulation is largely stochastic and one is performing long simulation runs to reduce variance, or if one is attempting to simulate a specific simulation problem across a large number of different parameter settings. However, one drawback with this approach is that each processor must contain sufficient memory to hold the entire simulation. Furthermore, this approach is less suitable in a design environment where results of one experiment are used to determine the experiment that should be performed next because one must wait for a sequential execution to be completed before results are obtained.

1,595 citations


Journal ArticleDOI
Georges Aad1, Brad Abbott1, Jalal Abdallah1, A. A. Abdelalim1  +2582 moreInstitutions (23)
TL;DR: The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for large-scale production of events on the LHC Computing Grid, including supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors.
Abstract: The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for large-scale production of events on the LHC Computing Grid. This simulation requires many components, from the generators that simulate particle collisions, through packages simulating the response of the various detectors and triggers. All of these components come together under the ATLAS simulation infrastructure. In this paper, that infrastructure is discussed, including that supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors. Also described are the tools allowing the software validation, performance testing, and the validation of the simulated output against known physics processes.

1,363 citations


Proceedings ArticleDOI
Richard M. Fujimoto1Institutions (1)
01 Oct 1989
TL;DR: This tutorial surveys the state of the art in executing discrete event simulation programs on a parallel computer, and focuses attention on asynchronous simulation programs where few events occur at any single point in simulated time.
Abstract: This tutorial surveys the state of the art in executing discrete event simulation programs on a parallel computer. Specifically, we will focus attention on asynchronous simulation programs where few events occur at any single point in simulated time, necessitating the concurrent execution of events occurring at different points in time. We first describe the parallel discrete event simulation problem, and examine why it so difficult. We review several simulation strategies that have been proposed, and discuss the underlying ideas on which they are based. We critique existing approaches in order to clarify their respective strengths and weaknesses.

1,188 citations


Proceedings ArticleDOI
01 Jan 2013
TL;DR: A versatile, scalable, yet powerful general-purpose robot simulation framework called V-REP, which allows for direct incorporation of various control techniques and renders simulations and simulation models more accessible to a general-public, by reducing the simulation model deployment complexity.
Abstract: From exploring planets to cleaning homes, the reach and versatility of robotics is vast. The integration of actuation, sensing and control makes robotics systems powerful, but complicates their simulation. This paper introduces a versatile, scalable, yet powerful general-purpose robot simulation framework called V-REP. The paper discusses the utility of a portable and flexible simulation framework that allows for direct incorporation of various control techniques. This renders simulations and simulation models more accessible to a general-public, by reducing the simulation model deployment complexity. It also increases productivity by offering built-in and ready-to-use functionalities, as well as a multitude of programming approaches. This allows for a multitude of applications including rapid algorithm development, system verification, rapid prototyping, and deployment for cases such as safety/remote monitoring, training and education, hardware control, and factory automation simulation.

995 citations


Book
15 Jun 1994
TL;DR: This book provides an introduction and comprehensive reference to modeling and simulation techniques using computers and emphasizes applications in economics and the environmental sciences and contains a disk with simulation software (SIMPAS) and 50 system models.
Abstract: From the Publisher: This book provides an introduction and comprehensive reference to modeling and simulation techniques using computers. It emphasizes applications in economics and the environmental sciences and contains a disk with simulation software (SIMPAS) and 50 system models.

991 citations


Network Information
Related Topics (5)
Software

130.5K papers, 2M citations

86% related
Control theory

299.6K papers, 3.1M citations

83% related
Artificial neural network

207K papers, 4.5M citations

82% related
Fuzzy logic

151.2K papers, 2.3M citations

81% related
Optimization problem

96.4K papers, 2.1M citations

80% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20222
2021252
2020348
2019395
2018402
2017387