Marker-Propagation Networks: A Quantitative Analysis

标记传播网络（Marker-propagation networks，MPN）是一种功能强大的计算模型，特别适用于人工智能和其他信息不确定和不完全指定的应用。 MPN指的是活动节点和标记链路的网络。 这种模式的力量和新奇源于这样一个事实，即处理仪器是可编程的数据模式的灵活长度称为标记。 节点处理由标记的到达激活。 通过网络的标记传播不是由目的地地址驱动的，相反，它们取决于节点中的局部条件、网络拓扑和每个标记中编程的信息。 这是MPN与其他并行处理模型之间的主要区别，也是MPN能够处理弱或不完全指定的应用程序的原因。 以前的工作在该地区的标记-传播处理假设更简单的模型。 本项目所研究的模型是异步运行的，并且具有高度的可编程性。 与在现有并行计算机上实现MPN相关的一些关键问题是可能的编程范例、并行源、负载平衡、设计权衡等。 在这个项目中采取的方法是定量的，这意味着该模型是在几个并行计算机上实现的，性能进行测量和结果进行比较。 MPN模型提供了可扩展性，并有可能成为未来专用于知识处理应用的超级计算机的首选架构。

Marker-propagation networks (MPNs) represent a powerful computational model especially suitable for artificial intelligence and other applications where information is often uncertain and incompletely specified. MPNs refer to networks of active nodes and labeled links. The power and novelty of this model derives from the fact that the processing instruments are programmable data patterns of flexible length called markers. Node processing is activated by the arrival of markers. Marker propagations through the network are not driven by destination addresses, instead, they depend on local conditions in the nodes, network topology and the information programmed in each marker. This is a major difference between MPNs and other parallel processing models, and it is the reason why MPNs are capable of handling weakly or incompletely specified applications. Previous work in the area of marker--propagation processing assumed much simpler models. The model studied in this project operates asynchronously, and is highly programmable. Some of the critical issues related to the implementation of MPNs on existing parallel computers are possible programming paradigms, sources of parallelism, load balancing, design tradeoffs, and others. The approach taken in this project is a quantitative one, meaning that the model is implemented on several parallel computers, performance is measured and the results compared. The MPN model offers scalability and has the potential of being a preferred architecture for future supercomputers dedicated to knowledge processing applications.