Optimistic Parallel Discrete Event Simulations of Physical Systems Using Reverse Computation
Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation
Discrete-event Execution Alternatives on General Purpose Graphical Processing Units (GPGPUs)
Proceedings of the 20th Workshop on Principles of Advanced and Distributed Simulation
Self-adaptive time integration of flux-conservative equations with sources
Journal of Computational Physics
Constructing multi-point discrete event integration schemes
WSC '05 Proceedings of the 37th conference on Winter simulation
On the stability and performance of discrete event methods for simulating continuous systems
Journal of Computational Physics
Exploiting the Concept of Activity for Dynamic Reconfiguration of Distributed Simulation
DS-RT '07 Proceedings of the 11th IEEE International Symposium on Distributed Simulation and Real-Time Applications
DAG-guided parallel asynchronous variational integrators with super-elements
Proceedings of the 2007 Summer Computer Simulation Conference
A formal framework for stochastic DEVS modeling and simulation
Proceedings of the 2008 Spring simulation multiconference
On constructing optimistic simulation algorithms for the discrete event system specification
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Tuned and wildly asynchronous stencil kernels for hybrid CPU/GPU systems
Proceedings of the 23rd international conference on Supercomputing
Continuity and change (activity) are fundamentally related in DEVS simulation of continuous systems
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
Scalable simulation of electromagnetic hybrid codes
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
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Recent advances in discrete event modeling of continuous systems have emphasized the need for high performance simulation engines. This need is particularly acute when discrete event methods are applied to the numerical solution of partial differential equations. Accurate approximations can require thousands, or even millions, of cells. The corresponding requirements for memory and computing power can readily exceed what is available on a single processor computer. Discrete event simulations are characterized by asynchronous and irregular, random, or data dependent behavior. This makes parallel algorithm design particularly challenging. Known parallel discrete event simulation algorithms have been developed in terms of event and process oriented world views. In contrast to this, the Discrete Event System Specification (DEVS) forms the foundation of research into discrete event approximations of continuous systems. While event and process oriented models can be expressed in terms of the DEVS modeling formalism, there are DEVS models that do not seem to have an equivalent representation in the event or process oriented world views. This leaves open the question of how existing parallel discrete event simulation algorithms must be adapted in order to simulate DEVS models. In this dissertation, discrete simulation algorithms are built up from the basic definition of a discrete event system. The parallel algorithms that are developed through this approach are shown to operate correctly. To conclude this study, these algorithms are applied to producing numerical solutions of a hyperbolic conservation law (Sod's shock tube problem) and the wave equation.