书面汉语的全切分分词算法模型

本文首先讨论了全切分研究的问题，然后从一般性出发提出了全切分的求解公式、切分树、全切分DAG(无环有向)图和全切分的抽象算法模型，在此基础上特别指出了全切分普遍存在的重复切分问题．进而，本文针对串行全切分分词算法和剪技的方法进行了研究，给出了串行全切分分词方法的算法模型．     

贪婪算法之拓扑排序的实现

拓扑排序是图的应用领域中一种重要运算,可以根据拓扑序列串行地安排活动。本文给出了拓扑排序的贪婪算法．讨论了算法中用到的数据结构．本文采用邻接袁和栈以C＋＋语言进行仿真．给出了仿真结果。     

柔性作业调度的串并行模型对比与求解

作业处理中的柔性使得作业调度更为灵活,作业中操作的执行顺序满足拓扑排序是作业调度的前提。是否允许没有优先关系的操作在不同的机器上同时执行是区分串行和并行调度的条件。文中以共生进化算法求解一个复杂的作业调度模型为例,给出了算法实现串行调度和并行调度的具体区别,并给出了串行和并行调度的结果。结果表明,并行相对于串行对算法效率的提高与柔性大小相关,与作业的规模成反比。     

基于模糊细胞神经网络的彩色图像形态学重构

利用彩色图像的ＲＧＢ空间分解,在模糊细胞神经网络上实现了彩色图像数学形态学的基本算子,并讨论了该实现相对于常规串行计算机算法的优越性和局阴,进一步地利用按分量的灰度重构,实现了彩色重构算法,最后讨论了该重构算法在抑帛镐频噪声中的应用,给出的仿真结果对对于推广模糊细胞神经网络在彩色图像实时处理和硅眼等模拟逻辑中的应用有着重要的意义。     

SLI算法及并行预编译系统设计

本文探讨并行预编译系统的设计,并着重讨论串行程序中的并行成分识别算法。我们先给出几种在循环体内进行数据相关检测的定理和方法,然后介绍SLI算法——一种实用而精确的数据相关检测方法。     

嵌入式协处理器中除法和平方根计算的整合设计

在浮点处理元中串行实现除法和平方根计算虽然速度慢,但设计简单规则,占用资源少,有利于嵌入式的应用。结合嵌入式协处理器LSC87的研制,给出了串行实现除法和平方根计算的基4SRT算法,介绍了确定SRT选择常数过程中不确定区域的验证方法;给出了除法与平方根计算可共用的基4SRT查询表设计;同时讨论了迭代冗余结果向非冗余二进制的转换。本协处理器设计量大限度 地利用了通用数据路径来完成SRT算法的实现,节约了设计资源,并缩短了迭代时间。     

求解线性丢番图方程（组）的串,并行算法

本文给出了求解线性丢番图方程(组)的串行算法和在CREW-SM-SIMD并行计算模型上的并行算法,并对上述算法进行了分析。     

伽罗瓦连接不动点的并行算法

为了提高伽罗瓦连接所有不动点的计算速度和效率,在计算伽罗瓦连接不动点的串行算法(CbO)基础上,通过处理所有不动点的不相交子集方法,将串行算法并行化,启动P个处理器同时并行运行,使每个处理器都并行地计算它的所有不动点,证明了此算法的正确性,并分析了它的渐近式复杂性。实验给出了算法在各种数据集上的效率及可扩展性,表明PCbO并行算法效率优于其串行算法。     

改进的JLBNM算法及其并行化

JLBNM图像恢复算法能得到较高的图像恢复质量(PSNR),但其中跳跃步长和环顾步长的确定是影响算法好坏的关键因素.文中给出了图像有无细节的定性化描述,先对图像本身的信息进行处理,然后根据图像内容的相关性来确定步长,在算法操作上更简单了.分析了串行的JLBNM算法的可并行化问题,并讨论了基于SMP的JLBNM并行化过程中的数据划分问题和处理器之间的通信问题,最后给出了相应的并行JLBNM算法和实验结果.     

改进的JLBNM算法及其并行化

JLBNM图像恢复算法能得到较高的图像恢复质量（PSNR）．但其中跳跃步长和环顾步长的确定是影响算法好坏的关键因素。文中给出了图像有无细节的定性化描述．先对图像本身的信息进行处理，然后根据图像内容的相关性来确定步长，在算法操作上更简单了。分析了串行的JLBNM算法的可并行化问题，并讨论了基于SMP的JLBNM并行化过程中的数据划分问题和处理器之间的通信问题，最后给出了相应的并行JLBNM算法和实验结果。     

PARALLEL ALGORITHMS TO COMPUTE THE EIGENVALUES AND EIGENVECTORS OFSYMMETRIC TOEPLITZ MATRICES∗

Abstract

In this paper we present two parallel versions of bisection method to compute the spectrum of symmetric Toeplitz matrices. Both parallel algorithms have been implemented and analysed on a virtual shared memory multiprocessor using a portable message-passing environment. The algorithms very efficiently parallelize the sequential method, and the application of a dynamic strategy to distribute the computations produces better results than the use of a static method. We also improve the performance of the original sequential algorithm by applying Newton's method for the final approximation of the eigenvalues. However, the bad results of the sequential algorithm produce low speedups when we compare the parallel methods with the best available sequential algorithm.     

Parallel and sequential transformations on digital images

In this paper, the relation between parallel and sequential algorithms is discussed. We regard algorithms as definitions of transformations and investigated the relation between the sets of transformations defined by parallel and sequential algorithms. Three problems are treated mainly. The problems and the results for the problems may be summarized as follows. (1) Characterization of transformations which are both parallel and sequential—A necessary and sufficient condition for a transformation to be both parallel and sequential has been established. (2) Equivalence problems—The equivalence problem for two algorithms, one of which is parallel, is decidable, hence, the equivalence problem for two sequential algorithms is undecidable, i.e. an algorithm for deciding whether or not two given algorithms, one of which is parallel, define the same transformation has been presented. However, we have shown there is no algorithm for deciding whether or not two given sequential algorithms define the same transformation. (3) Translation problems—An algorithm for translating a parallel (sequential) algorithm into an equivalent sequential (parallel) algorithm has been presented.     

序列模式数据挖掘算法的并行化研究

序列模式在许多领域都有着重要的应用,大量的数据和模式需要高效的、可扩展的并行算法.针对目前序列模式挖掘算法存在的普遍问题,在对串行序列模式数据挖掘算法研究的基础上,本文提出了一种并行的序列模式数据挖掘算法.通过理论分析与实验验证可知:该并行数据挖掘算法,在海量数据的情形下,能很好地提高数据挖掘的效率.     

Variation in efficiency of parallel algorithms

This paper presents the investigation of the variation in efficiency of a parallel algorithm as a function of the number of processors. A parallel algortithm was developed which predicted the behavior of a typical linear engineering system by solving a set of linear equilibrium equations. Experimental results of the algorithm were obtained by simulating the parallel process on a uniprocessor. The results showed the parallel method to have certain advantages over certain sequential methods, thus showing promise for this parallel algorithm in being competitive against some sequential algorithms.     

基于BIDE的多核并行闭合序列模式挖掘

基于经典的BIDE算法,提出一种多核并行闭合序列模式挖掘算法——MT_BIDE。该算法在频繁序列扩展判断前进行剪枝,在扩展过程中动态调整频繁序列及其伪投影数据集,平衡不同线程间挖掘闭合序列模式的计算量差异。实验结果表明,该算法具有较高的运行效率和加速比。     

Maisie: a language for the design of efficient discrete-eventsimulations

Maisie is a C-based discrete-event simulation language that was designed to cleanly separate a simulation model from the underlying algorithm (sequential or parallel) used for the execution of the model. With few modifications, a Maisie program may be executed by using a sequential simulation algorithm, a parallel conservative algorithm or a parallel optimistic algorithm. The language constructs allow the run-time system to implement optimizations that reduce recomputation and state saving overheads for optimistic simulations and synchronization overheads for conservative implementations. This paper presents the Maisie simulation language, describes a set of optimizations, and illustrates the use of the language in the design of efficient parallel simulations     

三维前处理有限差分网格剖分的并行化技术

针对前处理中网格剖分模块计算量大、处理速度慢等问题,对网格剖分串行算法进行并行化处理,利用多线程并行机制加以实现。同时对该并行算法进行测试。实验结果表明,该算法相对于串行算法在处理速度上具有绝对优势,且其加速度和并行效率均会随网格精度的提高以及基本实体个数的增多而提高。     

Optimal Sequential And Parallel Algorithms To Compute A Steiner Tree On Permutation Graphs

This paper presents an optimal sequential and an optimal parallel algorithm to compute a minimum cardinality Steiner set and a Steiner tree. The sequential algorithm takes O ( n ) time and parallel algorithm takes O (log n ) time and O ( n /log n ) processors on an EREW PRAM model.     

Efficient strategies for parallel mining class association rules

Mining class association rules (CARs) is an essential, but time-intensive task in Associative Classification (AC). A number of algorithms have been proposed to speed up the mining process. However, sequential algorithms are not efficient for mining CARs in large datasets while existing parallel algorithms require communication and collaboration among computing nodes which introduces the high cost of synchronization. This paper addresses these drawbacks by proposing three efficient approaches for mining CARs in large datasets relying on parallel computing. To date, this is the first study which tries to implement an algorithm for parallel mining CARs on a computer with the multi-core processor architecture. The proposed parallel algorithm is theoretically proven to be faster than existing parallel algorithms. The experimental results also show that our proposed parallel algorithm outperforms a recent sequential algorithm in mining time.     

PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation

This paper deals with the study of the cooperation between parallel processing and evolutionary computation to obtain efficient procedures for solving multiobjective optimisation problems. We propose a new algorithm called PSFGA (parallel single front genetic algorithm), an elitist evolutionary algorithm for multiobjective problems with a clearing procedure that uses a grid in the objective space for diversity maintaining purposes. Thus, PSFGA is a parallel genetic algorithm with a structured population in the form of a set of islands. The performance analysis of PSFGA has been carried out in a cluster system and experimental results show that our parallel algorithm provides adequate results in both, the quality of the solutions found and the time to obtain them. It has been shown that its sequential version also outperforms other previously proposed sequential procedures for multiobjective optimisation in the cases studied.