Out of Order and Causally Correct: Ready Event Discovery through Data-Dependence Analysis

Data-dependence analysis can identify causally-unordered events in a pending event set. The execution of these events is independent from all other scheduled events, making them ready for execution. These events can be executed out of order or in parallel. This approach may find and utilize more parallelism than spatialdecomposition parallelization methods, which are limited by the number of subdomains and by synchronization methods. This work provides formal definitions that use data-dependence analysis to find causally-unordered events and uses these definitions to measure parallelism in several discrete-event simulation models. A variant of the event-graph formalism is proposed, which assists with identifying ready events, by more clearly visualizing data dependencies between event types. Data dependencies between two event types may be direct or indirect, where the latter case considers the scheduling of intermediate events. Data dependencies and scheduling dependencies in a discrete-event simulation model are used to define time-interval limits that support the identification of events that are ready for execution. Experimental results from serial simulation testing demonstrate the availability of numerous events that are ready for execution, depending on model characteristics. The mean size of the ready-event set varies from 1.5 to 110 for the tested models, depending on the model type, the size of the model, and delay distribution parameters. These findings support future work to develop a parallel capability to dynamically identify and execute ready events in a multi-threaded environment.

Recommended citation: Jensen E, Leathrum J, Lynch CJ, Smith K, and Gore R (2025). "Out of Order and Causally Correct: Ready Event Discovery through Data-Dependence Analysis." 2025 ACM/IEEE/SCS 39th Workshop on Principles of Advanced and Distributed Simulation.
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