Bandwidth optimal all-reduce algorithms for clusters of workstations

We consider an efficient realization of the all-reduce operation with large data sizes in cluster environments, under the assumption that the reduce operator is associative and commutative. We derive a tight lower bound of the amount of data that must be communicated in order to complete this operation and propose a ring-based algorithm that only requires tree connectivity to achieve bandwidth optimality. Unlike the widely used butterfly-like all-reduce algorithm that incurs network contention in SMP/multi-core clusters, the proposed algorithm can achieve contention-free communication in almost all contemporary clusters, including SMP/multi-core clusters and Ethernet switched clusters with multiple switches. We demonstrate that the proposed algorithm is more efficient than other algorithms on clusters with different nodal architectures and networking technologies when the data size is sufficiently large.     

基于自校正的供暖系统启动控制

供暖系统最佳启动控制的重点在于准确预测预热时间,获取供暖系统的准确预热时间,并以其控制供暖系统的启动,有助于整个供暖系统能耗最小化.针对具有间歇供暖系统供暖的建筑物,分析其内部温度在外界环境和热源影响下的变化过程和规律,从热力学角度建立了间歇供暖预热过程的数学模型.基于该模型,在间歇供暖的启动控制中引入了自校正控制,并进行了仿真和实验.结果表明,将自校正控制用于供暖系统的启动控制效果较好,能够节约能源.     

Efficient scheduling of MPI applications on networks of workstations

The availability of a large number of workstations connected through a network can represent an attractive option for high-performance computing for many applications. The message-passing interface (MPI) software environment is an effort from many organisations to define a de facto message-passing standard. In other words, the original specification was not designed as a comprehensive parallel programming environment and some researchers agree that the standard should be preserved as simple and clean as possible. Nevertheless, a software environment such as MPI should have somehow a scheduling mechanism for the effective submission of parallel applications on network of workstations. This paper presents an alternative lightweight approach called Selective-MPI (S-MPI), which was designed to enhance the efficiency of the scheduling of applications on an MPI implementation environment.     

基于群机系统的并行程序的最大加速比计算

加速比是并行程序的重要指标之一。在大多数并行系统中,在数据规 模确定的情况下,程序的加速比随节点工作站的增加而增加,但是大多数群机 系统的节点工作站是共享物理传输介质的,这使得许多并行程序的加速比在节 点机数目超过某一个值之后会随着节,点机的增加而减少。本文通过对群机系统 上并行程序执行时间的分析,论述了在数据规模确定的情况下,程序能够获得 的最大加速比和最短的计算时间,以及获得这个加速比和计算时间的节点机个 数。     

模糊自校正控制器在多温区温度控制系统中的应用

本文介绍了一种模糊自校正控制器，它能对一个系统中不同温区的温度进行精确控制。作者给出了该模糊控制系统的结构，着重讨论了补偿系数与调整因子的计算与确定方法。实验表明，该控制器具有良好的自适应性和动、静态特性。     

Self-tuning and stable adaptive control of a batch polymerization reactor

Two adaptive control techniques are evaluated by application to a realistic mathematical model of a suspension polyvinyl chloride (PVC) reactor. Both techniques, the self-tuning regulator and a globally stable adaptive control algorithm, prove to be very robust and give excellent control of the temperature or the rate of conversion in the PVC reactor by manipulating the heat removal rate from the reactor jacket. Satisfactory regulation and setpoint changes in the temperature and the conversion rate are obtained in each case even in the presence of measurement noise and highly nonlinear reactor dynamics. The performance of the two adaptive techniques is compared with the performance of a classical, PID controller. The adaptive controllers are shown to always outperform the PID controller.     

A generalized histogram clustering scheme for multidimensional image data

A clustering procedure called HICAP (HIstogram Cluster Analysis Procedure) was developed to perform an unsupervised classification of multidimensional image data. The clustering approach used in HICAP is based upon an algorithm described by Narendra and Goldberg to classify four-dimensional Landsat Multispectral Scanner data. HICAP incorporates two major modifications to the scheme by Narendra and Goldberg. The first modification is that HICAP is generalized to process up to 32-bit data with an arbitrary number of dimensions. The second modification is that HICAP uses more efficient algorithms to implement the clustering approach described by Narendra and Goldberg.(¹) This means that the HICAP classification requires less computation, although it is otherwise identical to the original classification. The computational savings afforded by HICAP increases with the number of dimensions in the data.     

基于Web的远程集群监控系统的设计与实现

集群系统的商品化部件构成特点在具有高性价比优点的同时,也带来了可用性和可管理性差的缺点,因此集群系统的监控就变得特别重要。该文结合国家高性能计算中心(西安)的Linux集群系统给出了一种基于Web的集群监控系统的体系结构框架以及实现策略,详细介绍了数据采集、信息收集和存储以及状态的可视化各个模块的具体实现,基于WEB的实现策略使该系统具有平台无关性和监控远程性的优点。     

Auto-CFD-NOW: A pre-compiler for effectively parallelizing CFD applications on networks of workstations

Computational Fluid Dynamics (CFD) applications are highly demanding for parallel computing. Many such applications have been shifted from expensive MPP boxes to cost-effective Networks of Workstations (NOW). Auto-CFD-NOW is a pre-compiler that transforms Fortran CFD sequential programs to efficient message-passing parallel programs running on NOW. Our work makes the following three unique contributions. First, this pre-compiler is highly automatic, requiring a minimum number of user directives for parallelization. Second, we have applied a dependency analysis technique for the CFD applications, called analysis after partitioning. We propose a mirror-image decomposition technique to parallelize self-dependent field loops that are hard to parallelize by existing methods. Finally, traditional optimizations of communication focus on eliminating redundant synchronizations. We have developed an optimization scheme which combines all the non-redundant synchronizations in CFD programs to further reduce the communication overhead. The Auto-CFD-NOW has been implemented on networks of workstations and has been successfully used for automatically parallelizing structured CFD application programs. Our experiments show its effectiveness and scalability for parallelizing large CFD applications. This work is supported in part by the China National Aerospace Science Foundation, and by the U.S. National Science Foundation under grants CCR-9812187, CCR-0098055, CCF-0325760, CCF 0514078, and CNS 0549006.     

VP4:基于机群的pvm并行程序性能可视化工具

本文介绍了一个通用的pvm并行程序性能可视化软件工具VP~4。针对工作站机群的特点,它采用多层次性能数据采集方法和基于事件的采取策略,这样可以在尽量减少“侵入影响”的前提下,采集并汇总全部性能数据。VP~4对汇总的性能数据进行处理后,利用图形与动画生成各种易于使用的可视化性能视图。通过实验表明,本软件工具可以有效的帮助用户发现性能瓶颈,辅助用户开发高性能的并行程序。     

Tracking clusters in evolving data streams over sliding windows

Mining data streams poses great challenges due to the limited memory availability and real-time query response requirement. Clustering an evolving data stream is especially interesting because it captures not only the changing distribution of clusters but also the evolving behaviors of individual clusters. In this paper, we present a novel method for tracking the evolution of clusters over sliding windows. In our SWClustering algorithm, we combine the exponential histogram with the temporal cluster features, propose a novel data structure, the Exponential Histogram of Cluster Features (EHCF). The exponential histogram is used to handle the in-cluster evolution, and the temporal cluster features represent the change of the cluster distribution. Our approach has several advantages over existing methods: (1) the quality of the clusters is improved because the EHCF captures the distribution of recent records precisely; (2) compared with previous methods, the mechanism employed to adaptively maintain the in-cluster synopsis can track the cluster evolution better, while consuming much less memory; (3) the EHCF provides a flexible framework for analyzing the cluster evolution and tracking a specific cluster efficiently without interfering with other clusters, thus reducing the consumption of computing resources for data stream clustering. Both the theoretical analysis and extensive experiments show the effectiveness and efficiency of the proposed method. Aoying Zhou is currently a Professor in Computer Science at Fudan University, Shanghai, P.R. China. He won his Bachelor and Master degrees in Computer Science from Sichuan University in Chengdu, Sichuan, P.R. China in 1985 and 1988, respectively, and Ph.D. degree from Fudan University in 1993. He served as the member or chair of program committee for many international conferences such as WWW, SIGMOD, VLDB, EDBT, ICDCS, ER, DASFAA, PAKDD, WAIM, and etc. His papers have been published in ACM SIGMOD, VLDB, ICDE, and several other international journals. His research interests include Data mining and knowledge discovery, XML data management, Web mining and searching, data stream analysis and processing, peer-to-peer computing. Feng Cao is currently an R&D engineer in IBM China Research Laboratories. He received a B.E. degree from Xi'an Jiao Tong University, Xi'an, P.R. China, in 2000 and an M.E. degree from Huazhong University of Science and Technology, Wuhan, P.R. China, in 2003. From October 2004 to March 2005, he worked in Fudan-NUS Competency Center for Peer-to-Peer Computing, Singapore. In 2006, he received his Ph.D. degree from Fudan University, Shanghai, P.R. China. His current research interests include data mining and data stream. Weining Qian is currently an Assistant Professor in computer science at Fudan University, Shanghai, P.R. China. He received his M.S. and Ph.D. degree in computer science from Fudan University in 2001 and 2004, respectively. He is supported by Shanghai Rising-Star Program under Grant No. 04QMX1404 and National Natural Science Foundation of China (NSFC) under Grant No. 60673134. He served as the program committee member of several international conferences, including DASFAA 2006, 2007 and 2008, APWeb/WAIM 2007, INFOSCALE 2007, and ECDM 2007. His papers have been published in ICDE, SIAM DM, and CIKM. His research interests include data stream query processing and mining, and large-scale distributed computing for database applications. Cheqing Jin is currently an Assistant Professor in Computer Science at East China University of Science and Technology. He received his Bachelor and Master degrees in Computer Science from Zhejiang University in Hangzhou, P.R. China in 1999 and 2002, respectively, and the Ph.D. degree from Fudan University, Shanghai, P.R. China. He worked as a Research Assistant at E-business Technology Institute, the Hong Kong University from December 2003 to May 2004. His current research interests include data mining and data stream.     

焦炉集气管压力的自校正控制

结合炼焦生产工艺,分析焦炉集气管压力的运行特点,建立了该被控对象的受控自回归(CAR)数学模型;结合自校正动态调整参数的优势和常规PID的优点,设计适合该模型的PID自校正控制器,并选取合适的递推参数估计算法;借助S7-300PLC的硬件高可靠、运算高速性和Step7软件的编程高效灵活性,在实际工程中实现了对集气管压力的过程控制。控制结果表明运行压力稳定,控制方法鲁棒性好,具有推广价值。     

一种实用的自校正PID控制器设计与仿真研究

针对多功能除湿机的温度控制问题,设计了一种极点配置自校正PID控制器,给出了系统的CARMA模型,引入带遗忘因子的最小二乘实时参数估计算法和带数字滤波器的增量式PID控制器算法,同时给出了极点配置自校正PID的整定方法过程,建立PID参数与系统参数及控制性能指标之间的关系式,并进行了MATLAB仿真.仿真结果表明,自校正PID控制系统能够实时估计被控对象的参数,实时整定控制器参数,自适应被控过程的变化,具有较强的实时参数估计和自校正能力,该PID控制器在多功能除湿机的温度控制中得到成功应用.     

Parallel strategies for the local biological sequence alignment in a cluster of workstations

Recently, many organisms have had their DNA entirely sequenced. This reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. Sequence comparison is a basic operation in DNA sequencing projects, and most sequence comparison methods currently in use are based on heuristics, which are faster but offer no guarantees of producing the best alignments possible. In order to alleviate this problem, Smith–Waterman proposed an algorithm. This algorithm obtains the best local alignments but at the expense of very high computing power and huge memory requirements. In this article, we present and evaluate our experiments involving three strategies to run the Smith–Waterman algorithm in a cluster of workstations using a Distributed Shared Memory System. Our results on an eight-machine cluster presented very good speed-up and indicate that impressive improvements can be achieved depending on the strategy used. In addition, we present a number of theoretical remarks concerning how to reduce the amount of memory used.     

Evolving clusters in gene-expression data

Clustering is a useful exploratory tool for gene-expression data. Although successful applications of clustering techniques have been reported in the literature, there is no method of choice in the gene-expression analysis community. Moreover, there are only a few works that deal with the problem of automatically estimating the number of clusters in bioinformatics datasets. Most clustering methods require the number k of clusters to be either specified in advance or selected a posteriori from a set of clustering solutions over a range of k. In both cases, the user has to select the number of clusters. This paper proposes improvements to a clustering genetic algorithm that is capable of automatically discovering an optimal number of clusters and its corresponding optimal partition based upon numeric criteria. The proposed improvements are mainly designed to enhance the efficiency of the original clustering genetic algorithm, resulting in two new clustering genetic algorithms and an evolutionary algorithm for clustering (EAC). The original clustering genetic algorithm and its modified versions are evaluated in several runs using six gene-expression datasets in which the right clusters are known a priori. The results illustrate that all the proposed algorithms perform well in gene-expression data, although statistical comparisons in terms of the computational efficiency of each algorithm point out that EAC outperforms the others. Statistical evidence also shows that EAC is able to outperform a traditional method based on multiple runs of k-means over a range of k.     

面向大数据的内存数据管理研究现状与展望

对面向大数据的内存数据管理技术的相关研究进行综述。梳理大数据环境下数据管理技术发展的脉络和格局的变化;分析新环境下的内存数据管理技术面临的发展机遇与研究挑战;介绍相关的前沿研究,其中包括分布式编程模型、混合存储体系结构、内存数据管理等;给出技术和管理上的发展展望。     

深圳区域高速公路数据管理中心分析与设计

为保证广东省联网收费的顺利实施和保护深圳区域高速公路业主的合法利益,介绍了广东省联网收费系统框图以及深圳高速数据管理中心的基本功能。在数据管理中心的硬件中,系统主机和存储设备是核心部件,核心部件采用了基于SAN（存储区域网）结构的解决方案,并根据系统功能确定了数据管理中心核心硬件的参数,该方案得到了国内相关专家和高速公路设计单位的认可并满足深圳高速未来5年的发展需要。     

自校正DMC-PID过热汽温控制系统仿真

为了克服在过热汽温控制中对象参数变化对系统控制带来的困难,提出了自校正DMC-PID控制方法.保留了通常过热汽温控制采用的串级控制结构,能够快速有效地克服干扰.同时通过参数辨识得到非参数模型进行动态矩阵控制(DMC),即自校正DMC,无需从广义对象的阶跃模型的上下界中选取一条标称的阶跃响应曲线去实现DMC控制,实际应用起来更简单.仿真结果表明,控制方法有良好的控制效果,改善了模型失配时的鲁棒性,在过热汽温控制中是十分有效的.     

高校校园管理信息系统数据共享的设计

在各类管理信息系统的开发过程中,有效实现各部门信息分系统数据共享是成功开发此类系统的关键环节.在分析高校校园管理信息系统的基础上,讨论了高校管理信息系统中信息共享模型,提出了实现信息共享的3种方案,即相同实体对象公共存储、基于对象规范化的共享方式、基于系统互操作框架（EMIF）的信息共享机制,分析了各自的特点和适用性.