U.S.-France (INRIA) Cooperative Research: Parallel Numeri- cal Methods for Transient Solutions of Markov Chains

这个为期三年的奖项支持美国-法国（INRIA）合作社 研究马尔可夫链中的并行计算方法。 北卡罗来纳州州立大学的威廉·斯图尔特和伯纳德 法国雷恩INRIA的菲利普是美国和法国的 协调员。 该项目涉及更多的美国和法国 并行计算、计算机性能 计算、并行算法和数值方法。 的 研究人员建议研究新的方法， 大型稀疏马尔可夫链的平衡解， 电脑 他们提出了新的技术来计算特殊的 随机矩阵和小矩阵的情况下，并检查一个 使用矢量处理器的各种计算机体系结构，共享 存储器多处理器、分布式存储器多处理器和 大规模并行架构。 该项目将受益于 互补的专门知识和分享基本设备。 的 美国和法国的调查人员为这次合作带来了专业知识 在计算机科学和应用数学中。 的基本 并行处理架构的设备是可用的 在参与的美国机构和INRIA，法国国家 信息科学与自动化研究所。 INRIA 是一所世界级的计算机和信息科学研究所， 领导着一个欧洲研究组织联盟， 领域的 马尔可夫链是描述真实的 系统和排队网络。 这项研究将非常 在电信、计算机性能和 制造系统建模 这一领域的进展将使 深远的影响，因为马尔可夫链是用于其他模型 科学和工程领域。

This three-year award supports U.S.-France (INRIA) cooperative research in parallel computational methods in Markov Chains. William Stewart of North Carolina State University and Bernard Phillipe of INRIA in Rennes, France are the U.S. and French coordinators. The project involves additional U.S. and French researchers in parallel computing, computer performance evaluation, parallel algorithms and numerical methods. The investigators propose to examine new methods for finding equilibrium solutions of large, sparse Markov Chains using parallel computers. They propose new techniques for computing special cases of stochastic matrices and small matrices and to examine a variety of computer architectures using vector processors, shared memory multiprocessors, distributed memory multiprocessors and massively parallel architectures. The project will benefit from complementary expertise and sharing of essential equipment. The U.S. and French investigators bring to this collaboration expertise in computer science and applied mathematics. The essential equipment for doing parallel processing architectures are available at participating U.S. institutions and at INRIA, the French national research institute for information science and automation. INRIA is a world class institute in computer and information science and leads a European consortium of research organizations in these fields. Markov Chains are useful mathematical devices for describing real systems and queuing networks. The proposed research will be very important in telecommunications, computer performance and manufacturing systems modeling. Advances in this area will have far-reaching effects since Markov Chains are models used in other areas of science and engineering.