Synchronization Techniques in Parallel Discrete Event Simulation

Lindén, J. 2018. Synchronization Techniques in Parallel Discrete Event Simulation. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1634. 57 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-0241-6. Discrete event simulation is an important tool for evaluating system models in many fields of science and engineering. To improve the performance of large-scale discrete event simulations, several techniques to parallelize discrete event simulation have been developed. In parallel discrete event simulation, the work of a single discrete event simulation is distributed over multiple processing elements. A key challenge in parallel discrete event simulation is to ensure that causally dependent events are processed in the correct order, so that the same simulation trajectory is produced as in a sequential simulation. To preserve chronology between events processed in parallel, different synchronization protocols have been devised, each carrying a cost in performance. This thesis presents techniques for reducing synchronization costs in two approaches to parallel discrete event simulation, viz., optimistic space-parallel and share-everything parallel discrete event simulation. Firstly, we develop a concurrent priority queue, to be used as, e.g., a central event queue in the share-everything approach to parallel discrete event simulation. The priority queue is based on skiplists. It improves over previous queues by incurring fewer global synchronization operations, thereby inducing less contention and improving scalability. Secondly, we study how to improve the performance of optimistic parallel discrete event simulation by disseminating accurate estimates of timestamps of future events. We present techniques for obtaining the estimates in two different methods for simulation of spatial stochastic models. The estimates allow processing elements to determine when to pause simulation with high precision, thereby reducing the amount of performed useless work. Finally, we observe that in the applications that we have studied, the phenomena of interest are often non-homogeneous and migrate over time. Due to this, the work distribution tends to become unbalanced among the processing elements. A solution is to rebalance the work dynamically. We propose a fine-grained local dynamic load balancing algorithm for parallel discrete event simulation. The load balancing algorithm reduces the number of events arriving out-of-order, thereby reducing the amount of time spent on corrective actions.