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Managing Pending Events in Sequential and Parallel Simulations Using Three-tier Heap and Two-tier Ladder Queue

机译:使用三层堆和两层梯子队列管理顺序和并行模拟中的待处理事件

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Performance of sequential and parallel Discrete Event Simulations (DES) is strongly influenced by the data structure used for managing and processing pending events. Accordingly, we propose and evaluate the effectiveness of our multi-tiered (two-and three-tier) data structures and our Two-tier Ladder Queue, for both sequential and optimistic parallel simulations on distributed memory platforms. Our experiments compare the performance of our data structures against a performance-tuned version of the Ladder Queue, which has been shown to outperform many other data structures for DES. The core simulation-based empirical assessments are in C++ and are based on 2,500 configurations of well-established PHOLD and PCS benchmarks. In addition, we use an Avian Influenza Epidemic Model (AIM) for experimental analyses. We have conducted experiments on two computing clusters with different hardware to ensure our results are reproducible. Moreover, to fully establish the robustness of our analysis and data structures, we have also implemented pertinent queues in Java and verified consistent, reproducible performance characteristics. Collectively, our analyses show that our three-tier heap and two-tier ladder queue outperform the Ladder Queue by 60x in some simulations, particularly those with higher concurrency per Logical Process (LP), in both sequential and Time Warp synchronized parallel simulations.
机译:顺序和并行离散事件模拟(DES)的性能在很大程度上受到用于管理和处理未决事件的数据结构的影响。因此,我们提出并评估了多层(两层和三层)数据结构和两层梯形队列的有效性,以用于分布式存储平台上的顺序和乐观并行仿真。我们的实验将我们的数据结构的性能与经过性能调整的Ladder Queue版本进行了比较,该版本已被证明优于DES的许多其他数据结构。基于核心仿真的经验评估是C ++语言,是基于2500种已建立的PHOLD和PCS基准的配置。此外,我们使用禽流感流行模型(AIM)进行实验分析。我们在两个使用不同硬件的计算群集上进行了实验,以确保我们的结果可重复。此外,为了完全建立分析和数据结构的健壮性,我们还用Java实现了相关的队列,并验证了一致的,可再现的性能特征。总的来说,我们的分析表明,在顺序仿真和时间扭曲同步并行仿真中,我们的三层堆和两层阶梯队列在某些模拟中的性能比梯形队列高60倍,尤其是每个逻辑进程(LP)并发性更高的那些。

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