@inproceedings{accc8194e2fa4f47af53c291e8862489,
title = "Exploiting Task-Based Parallelism for Parallel Discrete Event Simulation",
abstract = "Today large-scale simulation applications are becoming common in research and industry. A significant fraction of them run on multi-core clusters. Current parallel simulation kernels use multi-process and multi-thread to exploit inter-node parallelism and intra-node parallelism on multi-core clusters. We exploit task-base parallelism in parallel discrete event simulation (PDES) kernels, which is more fine-grained than thread-level and process-level parallelism. In our system, every simulation event is wrapped to a task. Work-stealing task scheduling scheme is applied to achieve dynamic load balancing among the multi-cores, and a graph partitioning approach is applied in partitioning simulation entities among the cluster nodes. Experimental results show that our PDES kernel outperforms existing PDES kernels by fully exploiting task parallelism.",
keywords = "multi-core cluster, parallel discrete event simulation, task-based parallel programming, work-stealing",
author = "Yizhuo Wang and Zhiwei Gao and Weixing Ji and Han Zhang and Duzheng Qing",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018 ; Conference date: 21-03-2018 Through 23-03-2018",
year = "2018",
month = jun,
day = "6",
doi = "10.1109/PDP2018.2018.00095",
language = "English",
series = "Proceedings - 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "562--566",
editor = "Igor Kotenko and Ivan Merelli and Pietro Lio",
booktitle = "Proceedings - 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018",
address = "United States",
}
