Exploiting Task-Based Parallelism for Parallel Discrete Event Simulation

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 cluster...

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Vydané v:Proceedings - Euromicro Workshop on Parallel and Distributed Processing s. 562 - 566
Hlavní autori: Wang, Yizhuo, Gao, Zhiwei, Ji, Weixing, Zhang, Han, Qing, Duzheng
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.03.2018
Predmet:
Computational modeling
Dynamic scheduling
Kernel
Load management
Message systems
multi-core cluster
parallel discrete event simulation
Parallel processing
Task analysis
task-based parallel programming
work-stealing
ISSN:2377-5750
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Popis
Shrnutí: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.
ISSN:2377-5750
DOI:10.1109/PDP2018.2018.00095