ACM Transactions on Programming Languages and Systems (TOPLAS)
Rollback sometimes works...if filtered
WSC '89 Proceedings of the 21st conference on Winter simulation
Limitation of optimism in the time warp operating system
WSC '89 Proceedings of the 21st conference on Winter simulation
Virtual time II: storage management in conservative and optimistic systems
PODC '90 Proceedings of the ninth annual ACM symposium on Principles of distributed computing
Optimal memory management for time warp parallel simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on parallel and distributed systems performance
PADS '93 Proceedings of the seventh workshop on Parallel and distributed simulation
The MIMDIX environment for parallel simulation
Journal of Parallel and Distributed Computing - Special issue on parallel and discrete event simulation
GTW: a time warp system for shared memory multiprocessors
WSC '94 Proceedings of the 26th conference on Winter simulation
Adaptive memory management and optimism control in time warp
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Logical process size in parallel simulations
WSC '96 Proceedings of the 28th conference on Winter simulation
Combining optimism limiting schemes in time warp based parallel simulations
Proceedings of the 30th conference on Winter simulation
Efficient Execution of Time Warp Programs on Heterogeneous, NOW Platforms
IEEE Transactions on Parallel and Distributed Systems
Distributed Simulation of Large-Scale PCS Networks
MASCOTS '94 Proceedings of the Second International Workshop on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems
Interference resilient PDES on multi-core systems: towards proportional slowdown
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
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In standard optimistic parallel event simulation, no restriction exists on the maximum lag in simulation time between the fastest and slowest logical processes (LPs). Over-optimistic applications exhibit a large lag, which encourages rollback and may degrade performance. We investigate an approach for controlling over-optimism that classifies LPs as FAST, MEDIUM, or SLOW and migrates FAST and/or SLOW processes. FAST LPs are aggregated, forcing them to compete for CPU cycles. SLOW LPs are dispersed, to limit their competition for CPU cycles. The approach was implemented on distributed Georgia Tech Time Warp(GTW)(Das et al. 1994) and experiments performed using the synthetic application P-Hold(Fujimoto 1990). For over-optimistic test cases, our approach was found to perform 1.25 to 2.75 times better than the standard approach in terms of useful work and to exhibit execution times shorter than or equal to the standard computation.