Marker-Propagation Networks: A Quantitative Analysis

标记传播网络（mpn）是一种强大的计算模型，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是指由活动节点和标记链路组成的网络。该模型的强大和新颖源于这样一个事实，即处理工具是可编程的数据模式，具有灵活的长度，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址驱动的，相反，它们依赖于节点的本地条件、网络拓扑结构和每个标记中编程的信息。这是mpn和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。之前在标记-传播处理领域的工作假设了更简单的模型。本课题研究的模型是异步运行的，具有很高的可编程性。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、并行性的来源、负载平衡、设计权衡等。本项目采用的方法是定量的，这意味着该模型在几台并行计算机上实现，性能测量和结果比较。MPN模型提供了可扩展性，并且有潜力成为未来专用于知识处理应用程序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.