Adaptive methods for irregular parallel discrete event simulation workloads

Parallel Discrete Event Simulations (PDES) running at large scales involve the coordination of billions of very ne grain events distributed across a large number of processes. At such large scales optimistic synchronization protocols, such as TimeWarp, allow for a high degree of parallelism between processes, but with the additional complexity of managing event rollback and cancellation. This can become especially problematic in models that exhibit imbalance resulting in low event eciency, which increases the total amount of work required to run a simulation to completion. Managing this complexity becomes key to achieving a high degree of performance across a wide range of models. In this paper, we address this issue by analyzing the relationship between synchronization cost and event eciency. We rst look at how these two characteristics are coupled via the computation of Global Virtual Time (GVT). We then introduce dynamic load balancing, and show how, when combined with low overhead GVT computation, we can achieve higher e-ciency with less synchronization cost. In doing so, we achieve up to 2 better performance on a variety of benchmarks and models of practical importance.