Lock-free simulation algorithm to enhance the performance of sequential and parallel DEVS simulators in shared-memory architectures

• Published in: Journal of parallel and distributed computing Vol. 203; p. 105105
• Main Authors: Cárdenas, Román, Arroba, Patricia, Risco-Martín, José L.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2025
• Subjects: Discrete-event simulation; Lock-free parallelism; Parallel simulation; Discrete-event simulation; Lock-free parallelism; Parallel simulation
• ISSN: 0743-7315
• DOI: 10.1016/j.jpdc.2025.105105

This paper presents a new algorithm for the Discrete EVent System Specification (DEVS) formalism that improves the performance of simulating complex systems by reducing the number of iterations through the model components in each simulation step. It also minimizes unnecessary visits to model components by propagating simulation routines only when necessary. Additionally, we provide two parallel versions of this new simulation algorithm that use work-stealing scheduling and avoid locking mechanisms without compromising the validity of the execution in shared-memory architectures. We implemented the proposed algorithms in the xDEVS simulator and evaluated their performance using the DEVStone synthetic benchmark. The results show that the proposed algorithms outperform state-of-the-art alternatives. For computationally intensive models, parallel implementations achieve high parallelism efficiency. Furthermore, they are more resilient to model complexity than the sequential algorithm, showing better performance for complex models even without computational overhead in state transition functions. •A new simulation algorithm for DEVS models that significantly reduces execution overhead in each simulation step.•Two parallel versions of this algorithm, without locking mechanisms to reduce thread contention and enhance simulation speed.•Work-stealing scheduling improves load balance in dynamically multi-threaded programs without flattening the DEVS model.•Parallel implementations achieve efficiencies above 90% in complex, computation-intensive models with minimal overhead.•The proposed algorithms outperform the fastest state-of-the-art simulators in computationally intensive scenarios.