Abstract not found

Simulation is introduced in terms of its different forms and uses, but the focus on discrete event modeling for systems analysis is dominant as it has been during the evolution of the technique within operations research and the management sciences. This evolutionary trace of over almost fifty years notes the importance of bidirectional influences with computer science, probability and statistics, and mathematics. No area within the scope of operations research and the management sciences has been affected more by advances in computing technology than simulation. This assertion is affirmed in the review of progress in those technical areas that collectively define the art and science of simulation. A holistic description of the field must include the roles of professional societies, conferences and symposia, and publications. The closing citation of a scientific value judgment from over 30 years in the past hopefully provides a stimulus for contemplating what lies ahead in the next 50 years.

A discrete-event simulation environment, called GMSim, based on the generalized semi-Markov process (GSMP) framework is described. The tool is completely generic and extendible by Tcl script programming. Application specific components are developed in an objected-oriented setting by C++ programming in combination with M4 macro processing. Components are conveniently integrated by run-time linking. The strong links to the underlying mathematical GSMP description is favorable in two respects. First, qualitative results from a body of theory is readily available. Next, the structured view leads to an efficient implementation.

In the last three decades a considerable amount of research has been pursued in the efficient implementation of the pending event set (PES) associated with discrete-event simulation. The reason is simple: a fast event management has a very crucial impact in the total running time of both sequential and parallel simulations. This report focuses on this problem by studying the empirical performance of a number of solutions to the PES implementation in which we include a complete binary tree described in [26], 1 Introduction The PES is defined as the set of all the events generated during a discrete-event simulation and whose occurrence have not been simulated yet. In order to determine the next event to take place, it is necessary to extract the event with the least time from the PES. We call this operation extract-min. On the other hand, the occurrence of any event during the simulation can produce the insertion of new pending or future events in the PES; insert operation. These two b...

The development of a discrete-event simulation tool, called GMSim, based on the generalized semi-Markov process (GSMP) formalism is described. The GSMP representation comprises both analysis and simulation in a unied framework. This paper focuses on the simulation aspect and how to deal with a combinatorially exploding state space. A compositional GSMP modeling methodology is proposed, which in turn combined with an object-oriented programming approach. A key feature of the resulting tool is the close resemblance with the underlying mathematical structure. This facilitates coherent modeling and also an efficient implementation. The tool is completely generic and extendible by Tcl script programming. Application specific components are developed by C++ programming in combination with M4 macro processing.

this paper we study the performance of the very first tournament based complete binary tree. We focus on discrete-event simulation and our results show that this unknown predecessor of heaps can be a more efficient alternative to the fastest pending event set implementations reported in the literature. We also extend the idea of binary tournaments to a (2; L)-tournament structure which exhibits the property of delaying the processing of events with larger timestamps whilst it keeps similar theoretical performance bounds to the native (2; 1)-structure or CBT. This property can be certainly useful in systems where many pending events are expected to be deleted or rescheduled during the simulation. 2 Tournament trees

This paper describes the system, our experiences with its implementation, and its applicability to simulation modeling. We report on performance measurements of different implementations of the simulation scheduler, and of different algorithms for simulating service disciplines

The bulk-synchronous parallel (BSP) model of computing has been proposed to enable the development of portable software which achieves scalable performance across diverse parallel architectures. A number of applications of computing science have been demonstrated to be efficiently supported by the BSP model in practice.

The widespread use of sequential simulation in large scale parameter studies means that large cost savings can be made by improving the performance of these simulators. Sequential discrete event simulation systems usually employ a central event list to manage future events. This is a priority queue ordered by event timestamps. Many different priority queue algorithms have been developed with the aim of improving simulator performance. Researchers developing asynchronous conservative parallel discrete event simulations have reported exceptional performance for their systems running sequentially in certain cases. This paper compares the performance of simulations using a selection of high performance central event list implementations to that achieved using techniques borrowed from the parallel simulation community. Theoretical and empirical analysis of the algorithms is presented demonstrating the range of performance that can be achieved, and the benefits of employing parallel simulation techniques in a sequential execution environment.

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.