µsik — A Micro-Kernel for Parallel/Distributed Simulation Systems

Perumalla, Kalyan S.

doi:10.1109/pads.2005.1

Cited by 87 publications

(58 citation statements)

References 27 publications

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“…There are several run-time support libraries for running parallel discrete-event simulations such as Root-Sim (Alessandro Pellegrini & Quaglia, 2014), ROSS (Carothers, Bauer, & Pearce, 2002), Warped (Martin, McBrayer, & Wilsey, 1996) or sik (Perumalla, 2005). As a case study, we chose sik simulation library for parallel discrete-event simulation (Onggo, 2008(Onggo, , 2010.…”

Section: A Solution For Parallel Demographic Simulation: Yades Frameworkmentioning

confidence: 99%

“…As a case study, we chose sik simulation library for parallel discrete-event simulation (Onggo, 2008(Onggo, , 2010. It has been used in multiple projects and has shown a good performance in the classical benchmark Phold (Perumalla, 2005). In sik, models adopt the process interaction world-view; hence a simulation model is formed by a set of interacting (logical) processes (LP) hosted on each processor.…”

Section: A Solution For Parallel Demographic Simulation: Yades Frameworkmentioning

confidence: 99%

“…The detailed implementation of Yades' physical and logical processes and the design of the web-user interface to approach social scientists and modelers can be seen in the Appendix. Perumalla (2005Perumalla ( , 2007 has carried out a number of experiments to evaluate the performance of µsik simulation library. Hence, we do not repeat it in this paper.…”

Section: Logical Processes Communicate Through Events With the Standamentioning

confidence: 99%

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Approaching parallel computing to simulating population dynamics in demography

et al. 2016

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Agent-based modelling and simulation is a promising methodology that can be applied in the study of population dynamics. The main advantage of this technique is that it allows representing the particularities of the individuals that are modeled along with the interactions that take place among them and their environment. Hence, classical numerical simulation approaches are less adequate for reproducing complex dynamics. Nowadays, there is a rise of interest on using distributed computing to perform large-scale simulation of social systems. However, the inherent complexity of this type of applications is challenging and requires the study of possible solutions from the parallel computing perspective (e.g., how to deal with fine grain or irregular workload). In this paper, we discuss the particularities of simulating populating dynamics by using parallel discrete event simulation methodologies. To illustrate our approach, we present a possible solution to make transparent the use of parallel simulation for modeling demographic systems: Yades tool. In Yades, modelers can easily define models that describe different demographic processes with a web user interface and transparently run them on any computer architecture environment thanks to its demographic simulation library and code generator. Therefore, transparency is provided by by two means: the provision of a web user interface where modelers and policy makers can specify their agent-based models with the tools they are familiar with, and the automatic generation of the simulation code that can be executed in any platform (cluster or supercomputer). A study is conducted to evaluate the performance of our solution in a High Performance Computing environment. The main benefit of this outline is that our findings can be generalized to problems with similar characteristics to our demographic simulation model.

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Section: A Solution For Parallel Demographic Simulation: Yades Frameworkmentioning

confidence: 99%

Section: A Solution For Parallel Demographic Simulation: Yades Frameworkmentioning

confidence: 99%

Section: Logical Processes Communicate Through Events With the Standamentioning

confidence: 99%

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Approaching parallel computing to simulating population dynamics in demography

et al. 2016

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“…Examples of such environments are: YADDES (Yet Another Distributed Discrete Event Simulator) [129], SPEEDES (Synchronous Parallel Environment for Emulation and Discrete Event Simulation) [130], WARPED [131], HLA (High-Level Architecture) [132], WarpIV [133], Parsec (Parallel Simulation Environment for Complex Systems) [139], POSE (Parallel Object-oriented Simulation Environment) [140], JAMES II (JAva-based Multipurpose Environment for Simulation) [141], lsik [134], and Unified Framework [142].…”

Section: Parallel and Distributed Environmentsmentioning

confidence: 99%

Synchronization methods in parallel and distributed discrete-event simulation

Jafer

Liu

Wainer

2013

Simulation Modelling Practice and Theory

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a b s t r a c tThis work attempts to provide insight into the problem of executing discrete event simulation in a distributed fashion. The article serves as the state of the art in Parallel DiscreteEvent Simulation (PDES) by surveying existing algorithms and analyzing the merits and drawbacks of various techniques. We discuss the main characteristics of existing synchronization methods for parallel and distributed discrete event simulation. The two major categories of synchronization protocols, namely conservative and optimistic, are introduced and various approaches within each category are presented. We also present the latest efforts towards PDES on emerging platforms such as heterogeneous multicore processors, Web services, as well as Grid and Cloud environment.Ó 2012 Elsevier B.V. All rights reserved. Parallel discrete-event simulationWith the computing power and advanced software available today, modeling and simulation has become a cost-effective tool for detailed analysis of a broad array of natural and artificial systems. Parallel and distributed simulation is widely accepted as the technology of choice to speed up large-scale discrete-event simulation and to promote reusability and interoperability of simulation components. Parallel Discrete-Event Simulation (PDES) has received increasing interest as simulations become more time consuming and geographically distributed. A rich literature has already been developed in the last three decades, taking advantage of the increasing availability of highly parallel and distributed computing platforms. It has been several years since the last survey by Perumalla [5] and a refreshed review of the latest developments would be needed for us to ponder new research initiatives, especially on emerging platforms such as many-core processors, Internetscale simulation environments, and cloud-based virtualized infrastructures.In parallel and distributed simulations, the entire simulation task is divided into a set of smaller subtasks with each executed on a different processor or node. Hence, the simulation system is viewed as a collection of concurrent processes, each modeling a different part of the physical system and executing on a dedicated processor in a sequential fashion. These processes communicate with each other by exchanging time-stamped event messages. An event refers to an update to simulation system state at a specific simulation time instant. Throughout the simulation, events arrive at destination processes, and depending on the delivery ordering system of the simulation, they are processed differently. The two commonly used orderings are (1) receive-order and (2) timestamp-order. With the first type, events are delivered to the destination processes when they arrive at the destination. On the other hand, with the timestamp-order, events are delivered in non-decreasing order of their timestamp, requiring runtime checks and buffering to ensure such ordering.1569-190X/$ -see front matter Ó

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“…ClusterSim supports visual modeling and simulation of clusters and their workloads for performance analysis. µsik (Perumalla, 2005) is a micro-kernel for PDES that supports both conservative and optimistic parallel simulations.…”

Section: Related Workmentioning

confidence: 99%

SimCore: A Library for Rapid Development of Large Scale Parallel Simulations

Thulasidasan

Kroc

Eidenbenz

2012

Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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Abstract:We present the SimCore parallel simulation library, an object-oriented framework for developing parallel distributed discrete-event simulation applications, implemented in C++ with a Python front-end. SimCore is designed to scale to thousands of processors but is simple enough to use for application programmers, an outcome of its fast C++ core and message passing routines integrated with the full expressive power of Python. We discuss the design philosophy of SimCore including the software architecture and the C++/Python interface implementation that allows applications to be written in pure Python or a hybrid of Python and C++ or even pure C++. We also provide real world examples of the scalability and briefly describe a few diverse applications that have been deployed using SimCore.

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µsik — A Micro-Kernel for Parallel/Distributed Simulation Systems

Abstract: We present a novel micro-kernel

Cited by 87 publications

References 27 publications

Approaching parallel computing to simulating population dynamics in demography

Approaching parallel computing to simulating population dynamics in demography

Synchronization methods in parallel and distributed discrete-event simulation

SimCore: A Library for Rapid Development of Large Scale Parallel Simulations

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