A fast hybrid time-synchronous/event approach to parallel discrete event simulation of queuing networks
A fast hybrid time-synchronous/event approach to parallel discrete event simulation of queuing networks
复制标题
排队网络并行离散事件仿真的快速混合时间同步/事件方法
DOI：
10.1109/wsc.2008.4736142
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
P. Fishwick
中科院分区：
文献类型：
--
作者：
Hyungwook Park;P. Fishwick
计算架构的趋势是向多核中央处理单元（cpu）和图形处理单元（gpu）发展。一个价格合理且高度并行化的GPU是面向流处理的单指令多数据（SIMD）架构的实际示例。虽然GPU架构和语言很容易用于固有的基于时间同步的仿真模型，但不太清楚是否或如何将它们用于具有异步行为的队列模型仿真。我们推导了一个两步的过程，允许在排队网络上进行SIMD风格的模拟，首先在集群上执行SIMD计算，然后用GPU实验进行这项研究。两步过程同步模拟近似时间事件，然后根据误差分析趋势对输出统计中的误差进行补偿，从而减小输出统计中的误差。我们提出的研究结果表明，虽然输出是近似的，但人们可以迅速获得相当准确的汇总统计数据。
The trend in computing architectures has been toward multi-core central processing units (CPUs) and graphics processing units (GPUs). An affordable and highly parallelizable GPU is practical example of Single Instruction, Multiple Data (SIMD) architectures oriented toward stream processing. While the GPU architectures and languages are fairly easily employed for inherently time-synchronous based simulation models, it is less clear if or how one might employ them for queuing model simulation, which has an asynchronous behavior. We have derived a two-step process that allows SIMD-style simulation on queuing networks, by initially performing SIMD computation over a cluster and following this research with a GPU experiment. The two-step process simulates approximate time events synchronously and then reduces the error in output statistics by compensating for it based on error analysis trends. We present our findings to show that, while the outputs are approximate, one may obtain reasonably accurate summary statistics quickly.