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An adaptive approach for parallel discrete event simulation thread pool prediction

• JiMing Su
• Yiping Yao
• Feng Zhu

(2024). ECMS 2024, 38th Proceedings
Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2024
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-84-5
ISBN: 978-3-937436-83-8 (CD) Communications of the ECMS Volume 38, Issue 1, June 2024, Cracow, Poland June 4th – June 7th, 2024

https://doi.org/10.7148/2024-0352

Jiming su, Yiping yao, Feng zhu (2024). An adaptive approach for parallel discrete event simulation thread pool prediction, ECMS 2024, Proceedings Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation. doi:10.7148/2024-0352

The number of threads in the thread pool is a critical factor that significantly influences the efficiency of parallel execution in Parallel Discrete Event Simulation (PDES). However, current methodologies, including such as static configuration, iterative search, solution based on system state information, and machine learning primarily cater to the domain of parallel computing. These approaches fail to consider PDES-specific characteristics like logical clock synchronization and the interaction among various simulation parameters, thereby posing challenges in accurately predicting the optimal number of threads required for achieving peak performance in PDES. In response to this, this paper proposes an adaptive multivariate power coefficient probability prediction method that considers the interrelated parameters in PDES which collectively influence runtime behavior. The method models various factors affecting PDES efficiency as multivariate power coefficients, incorporating a probability model and bias term to capture the variability of the simulation system. It constructs a nonlinear correlation model between the number of threads and simulation speedup, and derives the number of threads by calculating the optimal speedup of PDES. Experimental results demonstrate that this method achieves an average relative error of 6.4% in predicting the optimal number of threads, with performance improvement achieved by utilizing these predicted threads reaching 93.28% compared to ideal performance improvement relative to serial execution.