ACM Transactions on Programming Languages and Systems (TOPLAS)
A bridging model for parallel computation
Communications of the ACM
Performance bounds on parallel self-initiating discrete-event simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
An analysis of rollback-based simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
An evaluation of the Chandy-Misra-Bryant algorithm for digital logic simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on parallel and distributed systems performance
Parallelism analyzers for parallel discrete event simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the seventh annual ACM symposium on Parallel algorithms and architectures
An important factor for optimistic protocol on distributed systems: granularity
WSC '95 Proceedings of the 27th conference on Winter simulation
Analysis of bounded time warp and comparison with YAWNS
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Performance Analysis of Synchronized Iterative Algorithms on Multiprocessor Systems
IEEE Transactions on Parallel and Distributed Systems
A capacity planning tool for batch parallel processing systems
MS '08 Proceedings of the 19th IASTED International Conference on Modelling and Simulation
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In this paper we propose a model to predict the performance of synchronous discrete event simulation. The model considers parameters including the number of active objects per cycle, event execution granularity and communication cost. We derive a single formula that predicts the performance of synchronous simulation.We have benchmarked several VHDL circuits on SGI Origin 2000. The benchmark results show that the prediction model explains more than 90% of parallel simulation execution time. We also measure the effect of computation granularity over performance. The benchmark results show that although higher granularity can have better speedup because of dominance of computation over communication, the computational granularity cannot overshadow the inherent synchronization cost. This model can be used to predict the speed-up expected for synchronous simulation, and to decide whether it is worthwhile to use synchronous simulation before actually implementing it.