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首页> 外文会议>1st EEF/Euro Summer School on Trends in Computer Science, 1st, Jul 3-7, 2000, Berg en Dal, The Netherlands >Distributed and Structured Analysis Approaches to Study Large and Complex Systems
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Distributed and Structured Analysis Approaches to Study Large and Complex Systems

机译：研究大型和复杂系统的分布式和结构化分析方法

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摘要

Both the logic and the stochastic analysis of discrete-state systems are hindered by the combinatorial growth of the state space underlying a high-level model. In this work, we consider two orthogonal approaches to cope with this "state-space explosion". Distributed algorithms that make use of the processors and memory overall available on a network of N workstations can manage models with state spaces approximately N times larger than what is possible on a single workstation, A second approach, constituting a fundamental paradigm shift, is instead based on decision diagrams and related implicit data structures that efficiently encode the state space or the transition rate matrix of a model, provided that it has some structure to guide its decomposition; with these implicit methods, enormous sets can be managed efficiently, but the numerical solution of the stochastic, model, if desired, is still a bottleneck, as it requires vectors of the size of the state space.
机译：离散状态系统的逻辑分析和随机分析都受到高级模型基础下状态空间组合增长的阻碍。在这项工作中，我们考虑了两种正交方法来应对这种“状态空间爆炸”。利用N个工作站网络上总体可用的处理器和内存的分布式算法，可以管理状态空间大约是单个工作站上可能的N倍的模型，而第二种方法是构成基本的范式转移有效地编码模型的状态空间或转换率矩阵的决策图和相关的隐式数据结构，只要它具有指导其分解的结构即可；使用这些隐式方法，可以有效地管理庞大的集合，但是如果需要，随机模型的数值解仍然是瓶颈，因为它需要状态空间大小的向量。

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