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An Adaptive Persistence and Work-stealing Combined Algorithm for Load Balancing on Parallel Discrete Event Simulation

机译:并行离散事件仿真中负载均衡的自适应持久性与工作窃取相结合的算法

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Load imbalance has always been a crucial challenge in Parallel Discrete Event Simulation (PDES). In the past few years, we have witnessed an increased interest in using multithreading PDES on multi/many-core platforms. In multithreading PDES, migrating logical processes and coordinating threads are more convenient and cause lower overhead, which provides a better circumstance for load balancing. However, current algorithms, including the persistence-based scheme and work-stealing-based scheme, have their drawbacks. On one hand, persistence-based load balancers, which use the historical data to predict the future, will inevitably make some error. On the other hand, the work-stealing scheme ignores the application-related characteristic, which may limit the potential performance improvement. In this article, we propose an adaptive persistence and work-stealing combined dynamic load balancing algorithm (APWS). The algorithm detects load imbalance, adaptively rebalances the distribution of logical processes, and uses a greedy lock-free work-stealing scheme to eliminate bias at runtime. We assess the performance of the APWS algorithm by a series of experiments. Results demonstrate that our APWS algorithm achieves better performance in different scenarios.
机译:负载不平衡一直是并行离散事件模拟(PDES)中的关键挑战。在过去的几年中,我们见证了在多核/多核平台上使用多线程PDES的兴趣日益浓厚。在多线程PDES中,迁移逻辑进程和协调线程更为方便,并导致较低的开销,这为负载平衡提供了更好的环境。但是,当前的算法,包括基于持久性的方案和基于工作窃取的方案,都有其缺点。一方面,使用历史数据来预测未来的基于持久性的负载均衡器不可避免地会产生一些错误。另一方面,工作窃取方案忽略了与应用程序相关的特性,这可能会限制潜在的性能改进。在本文中,我们提出了一种自适应的持久性和工作窃取相结合的动态负载平衡算法(APWS)。该算法检测负载不平衡,自适应地重新平衡逻辑进程的分布,并使用贪婪的无锁工作窃取方案来消除运行时的偏差。我们通过一系列实验评估了APWS算法的性能。结果表明,我们的APWS算法在不同情况下均能实现更好的性能。

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