Richard M. Fujimoto

Parallel discrete event simulation

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.

MDDV: a mobility-centric data dissemination algorithm for vehicular networks

There has been increasing interest in the exploitation of advances in information technology in surface transportation systems. One trend is to exploit on-board sensing, computing and communication capabilities in vehicles, e.g., to augment and enhance existing intelligent transportation systems. A natural approach is to use vehicle-to-vehicle communications to disseminate information. In this paper, we propose MDDV, a mobility-centric approach for data dissemination in vehicular networks designed to operate efficiently and reliably despite the highly mobile, partitioned nature of these networks. MDDV is designed to exploit vehicle mobility for data dissemination, and combines the idea of opportunistic forwarding, trajectory based forwarding and geographical forwarding. We develop a generic mobile computing approach for designing localized algorithms in vehicular networks. Vehicles perform local operations based on their own knowledge while they collectively achieve a global behavior. We evaluate the performance of the MDDV algorithm using realistic simulation of the vehicle traffic in Atlanta area.

Parallel and distributed simulation

Parallel and distributed simulation is a field concerned with the execution of a simulation program on computing platforms containing multiple processors. This article focuses on the concurrent execution of discrete event simulation programs. The field has evolved and grown from its origins in the 1970s and 1980s and remains an active field of research to this day. An overview of parallel and distributed research is presented ranging from seminal work in the field to address problems such as synchronization to recent work in executing large-scale simulations on supercomputing platforms. Directions for future research in the field are explored.

Parallel Discrete Event Simulation

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.

GTW: a time warp system for shared memory multiprocessors

The design of the Georgia Tech Time Warp (GTW, version 2.0) executive for cache-coherent shared-memory multiprocessors is described. The programmers interface is presented. Several optimizations used to efficiently realize key functions such as event list manipulation, memory and buffer management, and message passing are discussed. An efficient algorithm for computing GVT on shared-memory multiprocessors is described. Measurements of a wireless personal communication services (PCS) network simulation indicate the GTW simulator is able to sustain performance as high as 335,000 committed events per second for this application on a 42-processor KSR-2 machine.

The Department of Defense High Level Architecture

The High Level Architecture (HLA) provides the specification of a common technical architecture for use across all classes of simulations in the US Department of Defense. It provides the structural basis for simulation interoperability. The baseline definition of the HLA includes (1) the HLA Rules, (2) the HLA Interface Specification, and (3) the HLA Object Model Template. This paper describes the motivations and processes used to develop the High Level Architecture and provides a technical description of key elements of the architecture and supporting software. Services defined in the interface specification for providing time management (TM) and data distribution management (DDM) for distributed simulations are described.

Large-scale network simulation: how big? how fast?

Parallel and distributed simulation tools are emerging that offer the ability to perform detailed, packet-level simulations of large-scale computer networks on an unprecedented scale. The state-of-the-art in large-scale network simulation is characterized quantitatively. For this purpose, a metric based on the number of packet transmissions that can be processed by a simulator per second of wallclock time (PTS) is used as a means to quantitatively assess packet-level network simulator performance. An approach to realizing scalable network simulations that leverages existing sequential simulation models and software is described. Results from a recent performance study are presented concerning large-scale network simulation on a variety of platforms ranging from workstations to cluster computers to supercomputers. These experiments include runs utilizing as many as 1536 processors yielding performance as high as 106 million PTS. The performance of packet-level simulations of web and ftp traffic, and denial of service attacks on networks containing millions of network nodes are briefly described, including a run demonstrating the ability to simulate a million web traffic flows in near real-time. New opportunities and research challenges to fully exploit this capability are discussed.

A generic framework for parallelization of network simulations

Discrete event simulation is widely used within the networking community for purposes such as demonstrating the validity of network protocols and architectures. Depending on the level of detail modeled within the simulation, the running time and memory requirements can be excessive. The goal of our research is to develop and demonstrate a practical, scalable approach to parallel and distributed simulation that will enable widespread reuse of sequential network simulation models and software. We focus on an approach to parallelization where an existing network simulator is used to build models of subnetworks that are composed to create simulations of larger networks. Changes to the original simulator care minimized, enabling the parallel simulator to easily track enhancements to the sequential version. We describe our lessons learned in applying this approach to the publicly available ns software package (McCanne and Floyd, 1997) and converting it to run in a parallel fashion on a network of workstations. This activity highlights a number of important problems, from the standpoint of how to parallelize an existing serial simulation model and achieving acceptable parallel performance.

Time Management in The High Level Architecture

Time management is required in simulations to ensure that temporal aspects of the system un der investigation are correctly reproduced by the simulation model. This paper describes the time management services that have been de fined in the High Level Architecture. The need for time management services is discussed, as well as design rationales that lead to the current definition of the HLA time management ser vices. These services are described, highlighting information that must flow between federates and the Runtime Infrastructure (RTI) software in order to efficiently implement time manage ment algorithms.

Parallel simulation today

This paper surveys topics that presently define the state of the art in parallel simulation. Included in the tutorial are discussions on new protocols, mathematical performance analysis, time parallelism, hardware support for parallel simulation, load balancing algorithms, and dynamic memory management for optimistic snchronization.