and

We present a cloning mechanism that enables the evaluation of multiple simulated futures. Performance of the mechanism is analyzed and evaluated experimentally on a shared memory multiprocessor. A running parallel discrete event simulation is dynamically cloned at decision points to explore different execution paths concurrently. In this way, what-if and alternative scenario analysis can be performed in applications such as gaming or tactical and strategic battle management. A construct called virtual logical processes avoids repeating common computations among clones and improves efficiency. The advantages of cloning are preserved regardless of the number of clones (or execution paths). Our performance results with a commercial air traffic control simulation demonstrate that cloning can significantly reduce the time required to compute multiple alternate futures

Scalability of parallel simulation cloning

In previous work we presented an algorithm for cloning parallel simulations that enables multiple simulated execution paths to be explored simultaneously. The method is targeted for parallel discrete event simulators that provide the simulation application developer a logical process (LP) execution model. The cloning algorithm gains efficiency by cloning logical processes only as necessary. In this work we examine the scalability of cloning in detail. Specifically, we examine how the number of clones impacts the performance of cloning as we vary the “size ” of the simulation problem.

Georgia Tech Time Warp (GTW Version 3.1) Programmer's Manual for Distributed Network of Workstations

This manual gives an introduction to writing parallel discrete event simulation programs for the Georgia Tech Time Warp (GTW) system (version 3.1). Time Warp is a synchronization mechanism for parallel discrete event simulation programs. GTW is a Time Warp simulation kernel implemented on distributed network of uniprocessor and shared memory multiprocessor machines

DISCRETE EVENT INFRASTRUCTURE

The PDES literature offers a rich set of techniques for distributed and efficient simulation. However, there is a growing need for simulators that support agent-based applications, and PDES systems are not always well suited for these applications. Example agent-based applications include simulation of biological systems such as ants and bees, multi-robot systems and battlefield simulations. The robotics research community has developed agent-based simulators that provide useful APIs for agent applications. However, such simulators have performance limitations, and they do not scale well. Our approach is to provide middleware between an agent-based API and a PDES simulation kernel. The result is a simulation system that offers an agentbased API for the programmer to a high performance PDES system. Here we describe our design and initial implementation of SASSY, the Scalable Agents Simulation System. We describe our initial implementation and compare the design with related approaches.