Document Type

Conference Paper

Publication Date

2025

DOI

10.1145/3726301.3728416

Publication Title

SIGSIM-PADS '25: 39th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, 2025

Pages

88-89

Conference Name

SIGSIM-PADS '25: 39th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, June 23-26, 2025, Santa Fe, USA

Abstract

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 spatial-decomposition 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 about 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.

Rights

© 2025 Copyright held by the owner/authors.

This work is licensed under a Creative Commons Attribution-Sharealike 4.0 International (CC BY-SA 4.0) License.

Original Publication Citation

Jensen, E. J., Leathrum, J., Lynch, C., Smith, K., & Gore, R. (2025). Out of order and causally correct: Ready-event discovery through data-dependence analysis. In Ernest Page & Adeline Uhrmacher (Eds.), SIGSIM-PADS '25: 39th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (pp. 88-89). Association for Computing Machinery. https://doi.org/10.1145/3726301.3728416

ORCID

0000-0002-7938-2704 (Jensen), 0009-0009-5620-2860 (Leathrum), 0000-0002-4830-7488 (Lynch), 0000-0002-5026-4501 (Smith), 0000-0003-4065-6146 (Gore)

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