离散事件仿真和事件视界,第2部分:事件列表管理

J. Steinman
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引用次数: 28

摘要

事件视界是一个非常重要的概念,适用于并行和顺序离散事件模拟。通过利用事件视界,并行模拟可以使用可变时间宽度的自适应“呼吸”时间周期,以无风险的方式乐观地处理事件(即,不需要反消息)。此外,利用事件视界可以显著减少事件列表管理的开销,这在几乎所有离散事件模拟中都很常见。本文是PADS94以前报告的工作的继续。在该报告中,使用hold模型在平衡条件下推导了事件视界的完整数学公式。因此,使用各种形式的beta密度函数来验证分析模型的预测结果。第二份报告描述了如何将事件视界的概念也应用于事件列表管理。通过利用事件视界,提高了几种优先级队列数据结构的性能,包括:链表、各种二叉树和堆。为了完整起见,提供了对这些修改过的数据结构以及其他相关背景信息的比较详细的描述。本文提供了每种优先级队列数据结构的性能结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discrete-Event Simulation and the Event Horizon Part 2: Event List Management
The event horizon is a very important concept that applies to both parallel and sequential discrete-event simulations. By exploiting the event horizon, parallel simulations can processes events optimistically in a risk-free manner (i.e., without requiring antimessages) using adaptable "breathing" time cycles with variable time widths. Additionally, exploiting the event horizon can significantly reduce the overhead of event list management that is common to virtually all discrete-event simulations. This paper is a continuation of work previously reported at PADS94. In that report, a complete mathematical formulation of the event horizon was derived under equilibrium conditions using the hold model. Various forms of the beta density function were consequently used to verify the predicted results of the analytic model. This second report describes how the concept of the event horizon can also be applied to event list management. By exploiting the event horizon, the performance of several priority queue data structures are improved including: linked lists, various binary trees, and heaps. A somewhat detailed description of these modified data structures along with other relevant background information is provided for completeness. Performance results for each of these priority queue data structure is provided.
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