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INVESTIGATION OF NUMERICAL PERFORMANCE OF PARTITIONING AND PARALLEL PROCESSING OF MARKOV CHAIN (PPMC)FOR COMPLEX DESIGN PROBLEMS

机译:复杂设计问题马尔可夫链(PPMC)分区和并行处理的数值性能研究

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Divide-and-conquer strategies have been utilized to perform evaluation calculations of complex network systems, such as reliability analysis of a Markov chain. This paper focuses on partitioning of Markov chain for a multi-modular redundant system and the fast calculation using parallel processing. The complexity of Markov chain is first reduced by eliminating the connections with low transition probabilities associated with a threshold parameter. The transition probability matrix is then reordered and partitioned such that a worse-case reliability is evaluated through the calculations in only the diagonal sub-matrices of the transition probability matrix. Since the calculations of the sub-matrices are independent to each other, the numerical efficiency can be greatly improved using parallel computing. The numerical results showed the selection of threshold parameter is a key factor to numerical efficiency. In this paper, the sensitivity of the numerical performance of Partitioning and Parallel-processing of Markov Chain (PPMC) to the threshold parameter has been investigated and discussed.
机译:分而治之策略已用于执行复杂网络系统的评估计算,例如马尔可夫链的可靠性分析。本文着重研究马尔可夫链在多模块冗余系统中的划分以及使用并行处理的快速计算。首先,通过消除具有与阈值参数关联的低转移概率的连接,降低了马尔可夫链的复杂性。然后,对转移概率矩阵进行重新排序和分区,以便仅通过在转移概率矩阵的对角子矩阵中进行计算来评估最坏情况的可靠性。由于子矩阵的计算彼此独立,因此使用并行计算可以大大提高数值效率。数值结果表明,阈值参数的选择是影响数值效率的关键因素。本文研究并讨论了马尔可夫链分割和并行处理数值性能对阈值参数的敏感性。

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