一类基于Euler—Richardson外插的并行算法

构造了一类基于Ｅｕｌｅｒ－Ｒｉｃｈａｒｄｓｏｎ局部外插的并行算法，设计了使各处理机计算量分配更加平衡的方案，分析了方法的精度，稳定性，计算复杂性以及加速比和效率。数值试验结果表明方法是有效的，文中所构造的算法可用于大系统的数字仿真和科学计算。     

一个基于Semijoin的并行join算法SBABJ

本文在并行ｊｏｉｎ法ＡＢＪ＾＋的基础上提出一个基于Ｓｅｍｉｊｏｉｎ的改进算法ＳＢＡＢＪ。我们在多台Ｓｕｎ工作站是实现了该算法，并对ＡＢＪ＾＋和ＳＢＡＢＪ＾＋进行了能测试。算法分析和实验结果表明了并行ｊｏｉｎ算法ＳＢＡＢＪ＾＋优于ＡＢＪ＾＋算法。     

Euler－Bernoulli梁的反馈镇定

讨论端点带负荷的Ｂｅｒｎｏｕｌｌｉ梁的横向振动的控制问题．分析了系统的谱结构，提出一种非线性边界反馈来镇定梁的横向振动，并证明了相应的闭环系统的渐近稳定性，最后给出了一些数值模拟结果．     

基于串行FastLSA的并行算法

提出了一种基于串行Fast LSA算法的两个序列比对的并行算法。主要是对海量级的序列比对,目的是减少串行Fast LSA算法的时间和空间的复杂度。实验结果表明该算法完全可以并行化,而且空间复杂度降到线性空间。     

Systolic矩阵乘并行算法的改进及其在HPF中的应用

本文在分析Ｓｙｓｔｏｌｉｃ算法原理及其不足的基础上，提出了一种改进的算法，并讨论了该算法在ＨＰＦ并行库中的应用。在国产并行机上，将该算法与优化数字扩展数学库（ＤＸＭＬ）的调用相结合进行实际测试，取得了较好的性能。     

并行数据库中JOIN运算的并行算法

JOIN运算的并行算法一直是并行数据库领域中研究的热点问题,先后出现了一系列并行JOIN算法或改进算法,但它们都存在着通信效率较低、负载调度频繁等问题。本文针对这些问题,在分析比较前人工作的基础上对湖＋算法与ABJ＋算法加以改进,得到了效率更高的并行JOIN算法ABJ＋＋。     

求解一类边界问题的最小曲面的数值并行算法

本文给出了具有最小面积约束的一类边界问题的数值求法，同时实现了该算法的并行化，在算例中，介绍了利用Jacobi迭代求解曲顶柱体顶面面积最小值的一种并行算法，并阐述了解决这一问题的实际意义，算例结果表明，该并行算法的并行效率令人满意。     

一类Toeplitz三对角方程组的一种分布式并行算法

文中提出一类Toeplitz三对角方程组的一种分布式并行算法。该算法以系数矩阵的分解为基础,充分利用了系数矩阵结构的特殊性,算法因并行化而引入的冗余计算量非常少,算法的通信机制简单,通信量仅与处理 机台数p有关,与方程组规模n无关,算法具有很高的并行效率,理论分析和数值试验表明,其加速比Sp(n)→p(n→ ∞）,此为线性加速比的理想情况。文中给出了算法在分布存储多计算机系统上的数值试验结果。     

三维复杂表面形状的分数维分析和内插

分维理论近年来已有效地用于图像处理与分析以及计算机图形学领域.本文综述介绍分维理论的基本概念和几种有效的分维特征,这些特征是描述复杂图像(图形)的有用工具,并对抽取图像分维数D及其分布函数F(t)的算法和利用分维Brown函数进行图像(图形)内插的算法进行了归纳讨论.     

Parallel computers for region-level image processing

It is well known that parallel computers can be used very effectively for image processing at the pixel level, by assigning a processor to each pixel or block of pixels, and passing information as necessary between processors whose blocks are adjacent. This paper discusses the use of parallel computers for processing images at the region level, assigning a processor to each region and passing information between processors whose regions are related. The basic difference between the pixel and region levels is that the regions (e.g. obtained by segmenting the given image) and relationships differ from image to image, and even for a given image, they do not remain fixed during processing. Thus, one cannot use the standard type of cellular parallelism, in which the set of processors and interprocessor connections remain fixed, for processing at the region level. Reconfigurable cellular computers, in which the set of processors that each processor can communicate with can change during a computation, are more appropriate. A class of such computers is described, and general examples are given illustrating how such a computer could initially configure itself to represent a given decomposition of an image into regions, and dynamically reconfigure itself, in parallel, as regions merge or split.     

一种适用于大规模变量的并行遗传算法研究

当前MapReduce并行编程模型得到了广泛的应用.相对于传统的基于PVM或者MPI的并行编程方式,它在执行时间和处理问题规模等方面有明显优势.针对并行遗传算法的特点,提出基于MapReduce实现一种典型的并行遗传算法——粗粒度并行算法的方法,用以解决大规模变量问题.实验平台采用Hadoop,硬件条件为普通的服务器集群.在多目标优化问题测试中,当问题规模达到一定、处理变量数超过10E+7时,并行算法效率比串行提高数倍,并且能突破内存瓶颈.根据MapReduce自身特点调整其参数,改变并行程度,分析其对并行执行时间的影响.     

基于HRR划分的并行RDB~n树Join算法

文章首先介绍了PDBMS采用的Hash-Round-Robin(HRR)数据划分方法以及基于该划分方法的并行RDBn树,最后着重、详细地给出了基于该树的并行Join算法,分析了该算法的效率。     

DEM生成算法并行化研究

数字高程模型DEM（Digital Dlevation Model），是一种表示三维空间连续起伏状态的数学模型，如今在各行业应用十分广泛。针对DEM生成过程中计算复杂、数据量大的特点，在分析几种常用的DEM生成算法的基础上，以线性内插算法为样本，对DEM生成算法的并行化处理问题进行了深入研究。研究中，分别从数据并行和算法并行的角度，对DEM生成算法并行化进行了分析，并在网络分布式机群下进行了数据处理实验，取得了较好的并行处理效果。最后，进一步根据实验结果，讨论了责任发解方法的并行效率，提出了DEM生成算法并行化的有效途径。     

Two-Dimensional Parallel Thinning Algorithms Based on Critical Kernels

Critical kernels constitute a general framework in the category of abstract complexes for the study of parallel thinning in any dimension. The most fundamental result in this framework is that, if a subset Y of X contains the critical kernel of X, then Y is guaranteed to have “the same topology as X”. Here, we focus on 2D structures in spaces of two and three dimensions. We introduce the notion of crucial pixel, which permits to link this work with the framework of digital topology. We prove simple local characterizations, which allow us to express thinning algorithms by way of sets of masks. We propose several new parallel algorithms, which are both fast and simple to implement, that yield symmetrical or non-symmetrical skeletons of 2D objects in 2D or 3D grids. We prove some properties of these skeletons, related to topology preservation, to minimality, and to the inclusion of the topological axis. The latter may be seen as a generalization of the medial axis. We also show how to use critical kernels in order to provide simple proofs of the topological soundness of existing thinning schemes. Finally, we clarify the link between critical kernels, minimal non-simple sets, and P-simple points.

基于矢量模型的语音线性预测研究

研究语音参数线性预测的并行处理问题。通过把语音源序列的相邻样本分组能够构成一个均方差平稳的语音向量自回归序列,在Hilbert空间中运用正交投影原理导出具有高度并行处理能力的一预测编码策略,由此可推出参数线性预测的并行处理自适应算法。同传统格型算法相比,这种算法的计算复杂度及存贮量有明显改善。最后通过仿真运算检测了算法的性能。     

Efficient External Memory Algorithms by Simulating Coarse-Grained Parallel Algorithms

Abstract. External memory (EM) algorithms are designed for large-scale computational problems in which the size of the internal memory of the computer is only a small fraction of the problem size. Typical EM algorithms are specially crafted for the EM situation. In the past, several attempts have been made to relate the large body of work on parallel algorithms to EM, but with limited success. The combination of EM computing, on multiple disks, with multiprocessor parallelism has been posted as a challenge by the ACM Working Group on Storage I/ O for Large-Scale Computing. In this paper we provide a simulation technique which produces efficient parallel EM algorithms from efficient BSP-like parallel algorithms. The techniques obtained can accommodate one or multiple processors on the EM target machine, each with one or more disks, and they also adapt to the disk blocking factor of the target machine. When applied to existing BSP-like algorithms, our simulation technique produces improved parallel EM algorithms for a large number of problems.     

基于OpenMP的两个地学基础空间分析算法的并行实现及优化

研究了两个基础空间分析算法(大量线段求交、点面叠加)的实现方法,并基于单机多核环境,利用()pcnMP实现了并行算法。通过分析并行算法,得到了无法取得线性加速比的原因,即负载不均衡,内存管理采用全局方法,仍然是“串行”的。基于此,通过对数据进行有效的排序并利用()pcnMP的动态调度方式进行调度;改进了现有的并发内存分配技术,并将其用于并行算法的内存管理。利用以上方法对并行算法进行了优化,测试表明,优化后的算法具有较为理想的近线性加速比,单机四核环境下,每个核心的计算效率不低于80%。     

并行数据库上的并行CMD－Join算法

并行数据库在多处理机之间的分布方法(简称数据分布方法)对并行数据操作算法的性能影响很大.如果在设计并行数据操作算法时充分利用数据分布方法的特点,可以得到十分有效的并行算法.本文研究如何充分利用数据分布方法的特点,设计并行数据操作算法的问题,提出了基于CMD多维数据分布方法的并行CMD－Join算法.理论分析和实验结果表明,并行CMD－Join算法的效率高于其它并行Join算法.     

Memetic Algorithms for Parallel Code Optimization

Discovering the optimum number of processors and the distribution of data on distributed memory parallel computers for a given algorithm is a demanding task. A memetic algorithm (MA) is proposed here to find the best number of processors and the best data distribution method to be used for each stage of a parallel program. Steady state memetic algorithm is compared with transgenerational memetic algorithm using different crossover operators and hill-climbing methods. A self-adaptive MA is also implemented, based on a multimeme strategy. All the experiments are carried out on computationally intensive, communication intensive, and mixed problem instances. The MA performs successfully for the illustrative problem instances.