. In this paper we discuss an application of network computing in the area of stochastic simulation. We focus on main programming issues associated with designing of the latest version of AKAROA2, a simulation package in which network computing is applied in a practical and user-friendly way. This implemention is based on Multiple Replications In Parallel (MRIP) scenario of distributed simulation, in which multiple computers of a network operate as concurrent simulation engines generating statistically equivalent simulation output data, and submitting them to global data analysers responsible for analysis of the final results and for stopping the simulation. The MRIP scenario can achieve speedup equal to the number of processors used. Keywords: distributed computing, distributed simulation, stochastic simulation 1 Introduction The number of computer networks, and computers in these networks, has been growing exponentially. This has already led to creation of vast computin...

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Abstract. Quantitative stochastic simulation as a tool used by engineers of various disciplines for studying and evaluating performance of various systems suffers from the fact that sound simulation studies requires very long runlength to obtain the results with the accuracy. In this paper we look at traditional approaches to distributed quantitative stochastic simulation and propose a new scenario, named Multiple Replications in Paralel Time Streams (MRIP), that solve the problem in an efficient way. An implementation of MRIP in a simulation package AKAROA has been also described. AKAROA accepts ordinary (nonparallel) simulation models and creates fully automatically the environment required for running MRIP on workstations of a local area network. Presented results show that MRIP offers linear speedup of simulation. Limitations of this scenario for running distributed quantitative stochastic simulation are also discussed. 1.

this paper describes our approach from the system point of view. The programming interface is described in detail in the next section, following which the design and salient implementation aspects are discussed. Representative results from a few simulation systems are then reported, and the concluding section discusses some of the critical issues in such an approach, the implications for applications other than stochastic simulation, and ongoing and future work

Local or wide-area connected workstation cluster-based computation systems are inherently failure-prone, particularly for long running computations. In this work we introduce a variety of features for failure resilience in the EcliPSe system for replicative applications. Key characteristics of fault-tolerant EcliPSe are ease of use, low statesaving costs, system scalability and good performance. 1 INTRODUCTION Cluster computing, a low-cost alternative to supercomputers, involves the use of workstation clusters to solve compute-intensive problems with solutions that are amenable to distribution [15]. In recent years, this mode of computation has grown to envelop an increasing number of applications, mainly for scientific problems. At the present time, heterogeneous workstation clusters are not ideal replacements for supercomputers, mainly because of their low interconnection bandwidth and reliability. Today's relatively low speed networks and communication protocols, not really design...

Concurrent computing on networked collections of computer systems is rapidly evolving into a viable technology that is attractive from the economic, performance, and availability perspectives. Several software infrastructures that support such heterogeneous network-based concurrent computing have evolved, and are in use for productionquality high-performance computing. In this paper, we describe such a system, and present our experiences with its use for massively concurrent computing in the application domain of polymer physics. The application involves stochastic simulation of polymer chains for measuring scale-invariant phenomena at critical disorder. The parallelization is achieved through the EcliPSe toolkit, and conducted on a flexible, treestructured virtual machine made up of arbitrary and heterogeneous computing nodes dispersed across the country. These nodes cooperate to perform the simulation and pool results together in real time at a central node which initiates the parall...

In recent years, encouraged by today's fast workstations and by software systems designed to transform workstation clusters into parallel programming environments, network of workstations have been increasingly used as computational engines. Networked workstations, however, are not ideal replacements for supercomputers, because of the low interconnection capacity provided by current local area networks. In this paper, we present an application using the EcliPSe toolkit, a system for replication-based parallel processing in heterogeneous environments. Although primarily designed for replicative applications (that generally do not require large amounts of communication), EcliPSe can be used for more general forms of message-passing parallel processing. We describe the use of the toolkit in the parallelization of a cluster labeling algorithm. The algorithm is designed so that it uses some of EcliPSe's features to reduce communication overhead, making the algorithm suitable for execution o...

Parallel simulation methods can be used to reduce the execution time of the simulations of complex systems. In this context, we apply the method of the replications in parallel in order to reduce the execution time of the models. The particular technique consists of starting a fixed number of replications that are maintained in execution until a stop criterion is achieved. Different stopping criteria can be used in order to finish the simulation. These criteria must be robust in order to provide results according the applied statistical theory. The results obtained with two habitual criteria applied to a simple simulation model show that it is necessary to provide more elaborated stopping criteria of the simulation in order to obtain the adequate coverage. In order to reduce the detected problems we propose a new stopping criterion that increments the coverage of the true mean. The analysis of performance shows that, obviously, this better coverage has the cost of more simulation time, but it is important to get results statistically correct. 1.

. Distributed and parallel simulation has been a popular research topic in recent years. The research has primarily concentrated on correctness and speedup of distributed simulation. The statistical output analysis that is an essential part of simulating stochastic systems has attained only small attention. Parallel simulation is often mentioned as an attractive alternative for steady-state simulations. However, empirical results are not widely reported. We describe an implementation of an method that combines independent simultaneous replications and the spectral method. We also present experimental results are based on 12 simulation models executed in a network of Sun SPARCstations. CR Categories and Subject Descriptors: I.6.6 [Simulation and Modeling]: Simulation Output Analysis --- sequential estimation. I.6.8 [Simulation and Modeling]: Types of Simulation --- parallel. General Terms: Algorithms, Experimentation, Performance. Additional Key Words and Phrases: Parallel simulation...