ACM Transactions on Programming Languages and Systems (TOPLAS)
Parallel discrete event simulation
Communications of the ACM - Special issue on simulation
Global Virtual Time and distributed synchronization
PADS '95 Proceedings of the ninth workshop on Parallel and distributed simulation
Incremental state saving in SPEEDES using C++
WSC '93 Proceedings of the 25th conference on Winter simulation
Simulating Lyme disease using parallel discrete event simulation
WSC '96 Proceedings of the 28th conference on Winter simulation
Simulating Lyme disease using parallel discrete event simulation
WSC '96 Proceedings of the 28th conference on Winter simulation
Distributed simulation of spatially explicit ecological models
Proceedings of the eleventh workshop on Parallel and distributed simulation
Dynamic load balancing in parallel discrete event simulation for spatially explicit problems
PADS '98 Proceedings of the twelfth workshop on Parallel and distributed simulation
Proceedings of the 32nd conference on Winter simulation
Simulating spatially explicit problems on high performance architectures
Journal of Parallel and Distributed Computing - Parallel and Distributed Discrete Event Simulation--An Emerging Technology
A Component Model for Discrete Event Simulation
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
Accelerating Spatially Explicit Simulations of Spread of Lyme Disease
ANSS '05 Proceedings of the 38th annual Symposium on Simulation
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Lyme Disease affects many people in the northeastern United States. One of the most important mech anisms that sustains the epidemic is the interaction between white-footed mice (Peromyscus leuco pus) and deer ticks (Ixodes scapularis). When mice move around in their territory they carry diseased ticks to new locations. Our system simulates the different developmental stages of the tick through its active spring/summer period. The system uses the optimistic protocol for Parallel Discrete Event Simulation. In this paper, we present the model of the spread of the disease. We describe how we parallelize the problem, and we sketch a new global virtual time algorithm used in our system. We present performance benefits resulting from a parallel platform.