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Simulation, especially discrete event simulation (DES), is used in a variety of disciplines where numerical methods are difficult or impossible to apply. One problem with this method is that a sufficiently detailed simulation may take hours or days to execute, and multiple runs may be needed in order to generate the desired results. Parallel discrete event simulation (PDES) has been explored for many years as a method to decrease the time taken to execute a simulation. Many protocols have been developed which work well for particular types of simulations, but perform poorly when used for other types of simulations. Often it is difficult to know a priori whether a particular protocol is appropriate for a given problem. In this work, an adaptive synchronization method (ASM) is developed which works well on an entire spectrum of problems. The ASM determines, using an artificial neural network (ANN), the likelihood that a particular event is safe to process.
Discrete-event simulation has long been an integral part of the design process of complex engineering systems and the modelling of natural phenomena. Many of the systems that we seek to understand or control can be modelled as digital systems. In a digital model, we view the system at discrete instants of time, in effect taking snapshots of the system at these instants. For example, in a computer network simulation an event can be the sending of a message from one node to another node while in a VLSI logic simulation, the arrival of a signal at a gate may be viewed as an event. Digital systems such as computer systems are naturally susceptible to this approach. However, a variety of other systems may also be modelled this way. These include transportation systems such as air-traffic control systems, epidemiological models such as the spreading of a virus, and military war-gaming models. This book is representative of the advances in this field.
Exponential growth in computer technology, both in terms of individual CPUs and parallel technologies over the past decades has triggered rapid progress in large scale simulations. However, despite these achievements it has become clear that many conventional state-of-the-art techniques are ill-equipped to tackle problems that inherently involve multiple scales in configuration space. Our difficulty is that conventional ("time driven" or "time stepped") techniques update all parts of simulation space (fields, particles) synchronously, i.e. at time intervals assumed to be the same throughout the global computation domain or at best varying on a sub-domain basis (in adaptive mesh refinement algorithms). Using a serial electrostatic model, it was recently shown that discrete event techniques can lead to more than two orders of magnitude speedup compared to the time-stepped approach. In this research, the focus is on the extension of this technique to parallel architectures, using parallel discrete event simulation. Previous research in parallel discrete event simulations of scientific phenomena has been limited This thesis outlines a technique for converting a time-stepped simulation in the scientific domain into an equivalent parallel discrete event model. As a candidate simulation, an electromagnetic hybrid plasma simulation is considered. The experiments and analysis show the trade-offs on performance by varying the following factors: the simulations model characteristics (e.g. lookahead), applications load balancing, and accuracy of simulation results. The experiments are performed on a high performance cluster, using a conservative synchronization mechanism. Initial performance results are encouraging, demonstrating very good parallel speedup for large-scale model configurations containing tens of thousands of cells. Overheads for inter-processor communication remain a challenge for smaller computations.
This set of technical books contains all the information presented at the 1995 International Conference on Parallel Processing. This conference, held August 14 - 18, featured over 100 lectures from more than 300 contributors, and included three panel sessions and three keynote addresses. The international authorship includes experts from around the globe, from Texas to Tokyo, from Leiden to London. Compiled by faculty at the University of Illinois and sponsored by Penn State University, these Proceedings are a comprehensive look at all that's new in the field of parallel processing.
Active networking is an exciting new paradigm in digital networking that has the potential to revolutionize the manner in which communication takes place. It is an emerging technology, one in which new ideas are constantly being formulated and new topics of research are springing up even as this book is being written. This technology is very likely to appeal to a broad spectrum of users from academia and industry. Therefore, this book was written in a way that enables all these groups to understand the impact of active networking in their sphere of interest. Information services managers, network administrators, and e-commerce developers would like to know the potential benefits of the new technology to their businesses, networks, and applications. The book introduces the basic active networking paradigm and its potential impacts on the future of information handling in general and on communications in particular. This is useful for forward-looking businesses that wish to actively participate in the development of active networks and ensure a head start in the integration of the technology in their future products, be they applications or networks. Areas in which active networking is likely to make significant impact are identified, and the reader is pointed to any related ongoing research efforts in the area. The book also provides a deeper insight into the active networking model for students and researchers, who seek challenging topics that define or extend frontiers of the technology. It describes basic components of the model, explains some of the terms used by the active networking community, and provides the reader with taxonomy of the research being conducted at the time this book was written. Current efforts are classified based on typical research areas such as mobility, security, and management. The intent is to introduce the serious reader to the background regarding some of the models adopted by the community, to outline outstanding issues concerning active networking, and to provide a snapshot of the fast-changing landscape in active networking research. Management is a very important issue in active networks because of its open nature. The latter half of the book explains the architectural concepts of a model for managing active networks and the motivation for a reference model that addresses limitations of the current network management framework by leveraging the powerful features of active networking to develop an integrated framework. It also describes a novel application enabled by active network technology called the Active Virtual Network Management Prediction (AVNMP) algorithm. AVNMP is a pro-active management system; in other words, it provides the ability to solve a potential problem before it impacts the system by modeling network devices within the network itself and running that model ahead of real time.