机载设备振动环境实测数据处理并行算法

为了提高机载设备振动环境实测数据处理效率,提出了一种并行化数据处理和振动环境谱编制方法。在SMP(symmetrical multi-processing)集群系统的多级体系结构下,以Welch(改进周期图法)算法为基础,进行了振动数据处理模型的并行化分析,对于并行化过程中存在的并行I/O、负载平衡等关键问题进行了讨论,提出了相应的解决方案。最终选择基于MPI/Open MP混合方式实现了算法。在搭建的集群环境下测试表明,12核参与运算的条件下,最高加速比可以达到7.4,有效提高了运算效率。     

用分块加权平均的不精确Newton法计算潮流问题

为研究电力系统中潮流方程的快速算法,将求解大型稀疏线性方程组的componentaveraging(CAV)方法应用于电力系统潮流方程的计算,提出了一种分块加权平均的不精确Newton法,给出了算法收敛性的证明。该方法的特点是易于组织并行计算,且算法灵活,无需对方程进行特殊处理,运算效率高,适应于解大型潮流方程。用IEEE662节点的电力系统对算法进行了串行实现,结果表明:该算法是可行的和快速的。     

并行层压缩树包分类算法

在层压缩树路由算法思想基础上提出了一种新的硬件包分类算法--并行层压缩树包分类算法.该算法是基于独立存储单元和多域并行处理并在FPGA内部实现的高速网络包分类算法,主要包括单通道并行搜索和多通道综合比较两大部分.仿真结果表明在40 MHz的搜索时钟频率下,该算法能够达到每秒2 M包头的处理速度,其空间性能明显优于其他算法,具有O（d）的时间复杂度（d为域的个数）和O（dN）的空间复杂度（N为规则数）.     

Asynchronous parallel evolutionary algorithms for constrained optimizations

Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the higher accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous parallel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm. National Laboratory for Parallel and Distributed Processing Foundation item: Supported by the Natonal Natural Science Foundation of China (No. 70071042, 50073043), the National 863 Hi-Tech Project of China (No. 863-306-ZT06-06-3) and the National Laboratory for Parallel and Distributed Processing. Biography: Kang Li-shan (1934-), male, Professor, research interests: parallel computing and evolutionary computation.     

结构动力分析显式积分并行算法与实现

在分布式并行计算机环境下开展有限元并行算法研究是计算力学领域的前沿课题之一。基于区域分裂法，提出了结构动力分析两种形式的显式积分法的并行算法及步骤；同时，在用Ｔｒａｎｓｐｕｔｅｒ组成的分布式ＭＩＭＤ并行计算机上，采用３Ｌ并行Ｆｏｒｔｒａｎ编写了计算程序，并将其移植到串并行混合有限元分析软件ＰＦＥＭ中；最后，通过对三维空间钢架结构的实际分析，不仅验证了算法和程序设计的正确性，而且结果表明算法具有较高的并行效率。当２个和３个ＣＰＵ工作时，并行效率分别为０．８和０．７。     

三维Poisson方程边值问题的块三对角可扩展并行算法

为探讨三维Poisson方程带Dirichlet边界条件边值问题的并行求解方法,本文使用块三对角可扩展并行算法对该系统进行求解,提出了反映差分格式内在并行性的概念——差分格式的并行度,利用此概念说了明差分格式自身内在并行性与并行算法性能的关系。此外,本文方法在上海大学“自强3000”计算机。七的数值实验表明,实验的结果与理论分析一致;在保证精度的前提下得到了线性加速比,其并行效率达到90％以上。     

无约束非线性规划的共轭梯度法研究综述

共轭梯度法是50多年来算法研究的热点课题,它最初是基于求解对称正定线性方程组提出的,随后推广到求解非线性无约束优化问题。现在,它已经成为数值最优化领域的一类重要方法,具有所需存储量小、局部和全局收敛性好的特性。综述了求解无约束非线性规划问题的共轭梯度法,总结了它近年来的研究状况,展望了未来的发展趋势。     

A block varaint of the GMRES method for unsymmetric linear systems

Iterative methods that take advantage of efficient block operations and block communications are popular research topics in parallel computation. These methods are especially important on Massively Parallel Processors (MPP). This paper presents a block variant of the GMRES method for solving general unsymmetric linear systems. It is shown that the new algorithm with block sizes, denoted by BVGMRES (s.m), is theoretically equivalent to the GMRES (s·m) method. The numerical results show that this algorithm can be more efficient than the standard GMRES method on a cache besed single CPU computer with optimized BLAS kernels. Furthermore, the gain in efficiency is more significant on MPPs due to both efficient block operations and efficient block data communications. Our numerical results also show that in comparison to the standard GMRES method, the more PEs that are used on an MPP, the more efficient the BVGMRES(s,m) algorithm is.     

线性系统李雅普诺夫函数构造实现和并行算法

文中采用矩阵多分裂技术方法，论证了线性系统李雅普诺夫函数构造并行算法的收敛性，解决了计算机实现的技术问题，并行算法在计算机上进行数值实验的结果表明，此方法对求解大型问题是很有效的。     

A class of ebe time integration algorithms for transient finite element structural dynamical analysis

A new class of algorithms for transient finite element structural dynamical analysis which is amenable to an efficient implementation in parallel computers (especially Massively Parallel Computers) is proposed. The suitability of the method for parallel computation stems from the fact that, given an arbitrary partition of the finite element mesh, each element in the partition can be processed over a time step independently and simultaneously with the rest, and no global equation solving effort is involved. Although the proposed EBE time integration algorithms are shown to have the structure of an explicit scheme, they are unconditionally stable over a certain range of the algorithmic parameter.     

三对角方程组行处理法并行解法

利用行处理法和分治策略给出一个求解任意三对角方程组的并行迭代解法 ,证明了所给解法对任意相容性三对角方程组收敛 ,讨论了所给解法的迭代终止条件 ,进而讨论了其对应分布式MIMD并行迭代算法的设计法则 .按照并行解法 并行计算机 =并行算法的模式 ,使用给出的并行解法 ,可以给出一些求解三对角方程组的新的MIMD并行迭代算法 .     

模糊综合评价的两种新算法

基于模糊综合评价矩阵的不同类型,给出了模糊综合评价的两种新算法：对于数字型评判矩阵,采用指数加权算法;对于区间型评判矩阵,提出了误差分析算法,从而为现实生活中大量存在的综合评价问题提供了较完整的解决方法。     

Parallel genetic algorithm on PVM

In this paper we describe an implementation of some kinds of parallel genetic algorithms on the PVM. Parallel Virtual Machine, a portable parallel environment. We give details of a genetic algorithm running on many small subpopulations with an occasional identification and exchange of their useful information among subpopulations by means of message-passing functions of PVM. In this work, experiments were done to compare the parallel genetic algorithm and traditional sequential genetic algorithms.     

一种多波前并行处理的有限元算法

本文提出了一种求解大型有限元系统的新算法。该算法采用并行处理结构,首先将结构分成许多子区,然后利用多个波前在各个子区内并行地组集,消元,从而得到凝聚后的界面刚度阵和载荷阵。再串行组集和求解界面方程得界面位移,最后返回各个子区,并行求解内点应力和位移。从运算结果来看,该方法不但能有效地提高运算速度,减少计算时间,同时能有效地节省内存量,是一种求解大型结构有限元系统的有效途径。     

稠密线性方程组的多线程同步算法及其实现

构造线性方程组的若干多线程同步算法，给出它们在Delphi中的实现并用典型计算实例在多CPU计算机上进行测试。     

PDM:基于Hadoop的并行数据分析系统

提出了一款基于Hadoop的并行数据分析系统——PDM.该系统拥有大量以MapReduce为计算框架的并行数据分析算法,不仅包括传统的ETL、数据挖掘、数据统计和文本分析算法,还引入了基于图理论的SNA(社会网络分析)算法.详细阐述了并行多元线性回归算法和"多源最短路径"算法的原理和实现,其中,提出的"消息传递模型"能有效解决MapReduce难以处理邻接矩阵的问题;介绍了基于电信数据的典型应用,如采用并行k均值和决策树算法实现的"套餐推荐",利用并行PageRank算法实现的"营销关键点发现"等;最后通过性能测试,说明该系统适合高效地处理大规模数据.     

流场分析和设计的并行计算

网络连接机群是一种有效的并行计算工具,讨论了在此环境下流场分析和设计的并行计算问题。流场解中采用了Euler方程作为主控方程,并用有限体积方法和时间隐式方法进行求解。在MPI／PVM环境下用分区方法作了二维翼型和三维机翼绕流的并行计算。数值算例表明流场计算的正确性和并行计算的有效性,并讨论了影响加速比和并行效率的各种因素。用耦合流场解和并行遗传优化算法做了二维翼型和三维机翼的单目标／双目标数值优化。算例表明：使用的适应函数优于传统线性组合法构成的,遗传算法计算三维优化问题时必须并行化。     

Improvement and Parallelism of k-Means Clustering Algorithm

The k-means clustering algorithm is one of the most commonly used algorithms for clustering analysis. The traditional k-means algorithm is, however, inefficient while working on large numbers of data sets and improving the algorithm efficiency remains a problem. This paper focuses on the efficiency issues of cluster algorithms. A refined initial cluster centers method is designed to reduce the number of iterative procedures in the algorithm. A parallel k-means algorithm is also studied for the problem of the operation limitation of a single processor machine when given huge data sets. The analytical results demonstrate that these improvements can greatly enhance the efficiency of the k-means algorithm, i.e., allow the grouping of a large number of data sets more accurately and more quickly. The analysis has theoretical and practical importance for work on the improvement and parallelism of cluster algorithms.     

循环三对角Toeplitz线性方程组的分组降阶算法

 运用并行算法中分而治之的思想,给出了一种求解循环三对角Toeplitz线性方程组的分组降阶串行算法。与求解同类问题的传统算法相比,分组降阶算法的优点在于它不仅大幅度减少了内存占用量,而且还大幅度减少了算术运算量。分组降阶算法可以通过3个步骤来实现。第一步是分组降阶,其基本思路是将一个n=μm阶的方程组按行分成μ组,每组m个方程;n维解向量也对应地分成μ组。第二步是构造参数方程组,也就是依据三对角系数矩阵的特点,给出各组解之间的关系式,把不属于该组的解分量看作参数。第三步是求解参数方程组和原方程组,在这一步中,首先求解参数方程组,然后再代入相应分组的关系式便可求出所有的解分量。对于三对角Toeplitz线性方程组,同样能减少内存占用量,从而在计算机性能不变的情况下,提高求解问题的规模,但与求解三对角Toeplitz线性方程组的传统算法相比运算量有所增加。数值实验结果表明,对于特定规模的方程组来说,总存在一个最佳的分组个数使得计算时间最少;随着方程组阶数的提高,最佳分组的个数也增大。     

Parallel Frequent Pattern Discovery： Challenges and Methodology

Parallel frequent pattern discovery algorithms exploit parallel and distributed computing resources to relieve the sequential bottlenecks of current frequent pattern mining （FPM） algorithms. Thus, parallel FPM algorithms achieve better scalability and performance, so they are attracting much attention in the data mining research community. This paper presents a comprehensive survey of the state-of-the-art parallel and distributed frequent pattern mining algorithms with more emphasis on pattern discovery from complex data （e.g., sequences and graphs） on various platforms. A review of typical parallel FPM algorithms uncovers the major challenges, methodologies, and research problems in the field of parallel frequent pattern discovery, such as work-load balancing, finding good data layouts, and data decomposition. This survey also indicates a dramatic shift of the research interest in the field from the simple parallel frequent itemset mining on traditional parallel and distributed platforms to parallel pattern mining of more complex data on emerging architectures, such as multi-core systems and the increasingly mature grid infrastructure.