A Parallel Hybrid Web Document Clustering Algorithm and its Performance Study

Clustering web document is an important procedure in many web information retrieval systems. As the size of the Internet grows rapidly and the amount of information requests increases exponentially, the use of parallel computing techniques in large scale web document retrieval is unavoidable. We propose a parallel hybrid web document clustering algorithm, which combines the Principal Direction Divisive Partitioning (PDDP) algorithm with the K-means algorithm. Computational experiments were conducted to test the performance of the hybrid algorithm using three real life web document datasets, and the results were compared with that of the parallel PDDP algorithm and the parallel K-means algorithm. The experiments show that the quality of the clustering solutions obtained from the hybrid algorithm is better than that from the parallel PDDP or the parallel K-means. The parallel run time of the hybrid algorithm is similar to and sometimes less than that of the widely used K-means algorithm.     

面向异构架构的传递闭包并行算法

传统求图传递闭包的方法存在计算量大与计算时间长的问题。为加快处理大数据量的传递闭包算法的计算速度,结合算法密集计算和开放式计算语言（OpenCL）框架的特征,采用本地存储器优化的并行子矩阵乘和分块的矩阵乘并行计算,提出一种基于OpenCL的传递闭包并行算法。利用本地存储器优化的并行子矩阵乘算法来优化计算步骤,提高图形处理器（GPU）的存储器利用率,降低数据获取延迟。通过分块矩阵乘并行计算算法实现大数据量的矩阵乘,提高GPU计算核心的利用率。数据结果表明,与CPU串行算法、基于开放多处理的并行算法和基于统一设备计算架构的并行算法相比,传递闭包并行算法在OpenCL架构下NVIDIA GeForce GTX 1070计算平台上分别获得了593.14倍、208.62倍和1.05倍的加速比。     

嵌入式零树小波压缩和解压缩的并行化算法

嵌入式零树小波压缩算法是图像压缩技术中有效的压缩算法,但其压缩时间较长.对该算法进行了研究,并在多核机群系统下实现了该算法的并行算法,提高了算法的性能.实现了MPI和MPI+OpenMP两种并行算法,并将串行算法、MPI并行算法与MPI+OpenMP并行算法进行比较.结果显示,随着数据量的增多,MPI并行算法和MPI+OpenMP并行算法相对于串行算法的运行效率都有明显提高,其中MPI+OpenMP并行算法的效率更好.     

基于流水线的合成孔径雷达并行成像算法及实现

根据对距离多普勒（Range Doppler）成像算法的特点进行研究,提出了一种基于流水线的合成孔径雷达（SAR）并行成像算法。这种算法基于C/MPI编写并成功地在32节点的IBM PC集群实现。通过与已建立的通用的并行成像算法进行比较分析,得出基于流水线的并行算法是一种更适合SAR并行成像的一种算法,能够提供更高的并行效率。     

一种递归神经网络的快速并行算法

针对递归神经网络BP(Back Propagation)学习算法收敛慢的缺陷,提出一种新的递归 神经网络快速并行学习算法.首先,引入递推预报误差(RPE)学习算法,并且证明了其稳定性； 进一步地,为了克服RPE算法集中运算的不足,设计完整的并行结构算法.本算法将计算分配 到神经网络中的每个神经元,完全符合神经网络的并行结构特点,也利于硬件实现.仿真结果表 明,该算法比传统的递归BP学习算法具有更好的收敛性能.理论分析和仿真实验证明,该算法 与RPE集中运算算法相比可以大大节省计算时间.     

FMM算法的并行化方法

详细分析快速多极算法FMM(Fast Multipole Method)的基本原理,并对引力场的势函数的多极展开和泰勒局部展开进行了详细的推导.给出了串行FMM算法的伪码描述,并对其进行并行化分析、处理,对FMM算法进行了并行化研究.最后,在基于MPI的群集并行计算环境下进行大量的实验并采集实验数据,对算法进行并行化性能分析,得到较好的并行加速比和较高的并行效率.     

一种基于MPP的并行归并算法

文中提出并分析了并行归并算法ＰＭＦＳ;基于曙光－１０００大规模并行计算机系统,给出了ＰＭＦＳ算法应用实例的实验结果,并将ＰＭＦＳ算法推广得到的并行归并排序算法与ＰＳＲＳ算法进行了比较。     

Parallel exact sampling and evaluation of Gaussian Markov random fields

Markov chain Monte Carlo algorithms are computationally expensive for large models. Especially, the so-called one-block Metropolis-Hastings (M-H) algorithm demands large computational resources, and parallel computing seems appealing. A parallel one-block M-H algorithm for latent Gaussian Markov random field (GMRF) models is introduced. Important parts of this algorithm are parallel exact sampling and evaluation of GMRFs. Parallelisation is achieved with parallel algorithms from linear algebra for sparse symmetric positive definite matrices. The parallel GMRF sampler is tested for GMRFs on lattices and irregular graphs, and gives both good speed-up and good scalability. The parallel one-block M-H algorithm is used to make inference for a geostatistical GMRF model with a latent spatial field of 31,500 variables.     

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.     

A parallel mixed time integration algorithm for nonlinear dynamic analysis

This paper presents a parallel mixed time integration algorithm formulated by synthesising the implicit and explicit time integration techniques. The proposed algorithm is an extension of the mixed time integration algorithms [Comput. Meth. Appl. Mech. Engng 17/18 (1979) 259; Int. J. Numer. Meth. Engng 12 (1978) 1575] being successfully employed for solving media-structure interaction problems. The parallel algorithm for nonlinear dynamic response of structures employing mixed time integration technique has been devised within the broad framework of domain decomposition. Concurrency is introduced into this algorithm, by integrating interface nodes with explicit time integration technique and later solving the local submeshes with implicit algorithm. A flexible parallel data structure has been devised to implement the parallel mixed time integration algorithm. Parallel finite element code has been developed using portable Message Passing Interface software development environment. Numerical studies have been conducted on PARAM-10000 (Indian parallel supercomputer) to test the accuracy and also the performance of the proposed algorithm. Numerical studies indicate that the proposed algorithm is highly adaptive for parallel processing.     

集群环境下的并行聚类算法

探讨在集群环境下，如何设计并行聚类算法。作为一种低成本、通用并行系统，集群系统的通信能力，相对于节点的计算能力，是一个瓶颈。所以在集群环境下，设计并行聚类算法时，应采用数据并行的思想。从理论上，对采用数据并行思想后，影响聚类算法的加速比和聚类质量的因素进行了分析，然后通过一个验证算法PCIT(Parallel clustering algorithm based on Index Tree)证实了理论分析的正确性。研究结果可以为以后设计更好的数据并行聚类算法提供理论依据。     

基于MPI并行计算的信号稀疏分解

在研究信号稀疏分解理论及其最常用的匹配追踪算法的基础上,针对MP算法存在的计算量过大的问题,提出一种基于并行计算系统实现信号稀疏分解的方法。该方法利用8台微机,采用MPI消息传递机制,以100 M高速以太网作为互联网络,构建了一套Beowulf 并行计算系统,在此系统上通过编制并行程序来实现MP算法。实际测试表明这种方法具有很高的并行计算效率,分解时间从单机75 min左右下降到8机并行11 min左右,大大提高了信号稀疏分解的速度。     

基于流水光总线阵列上Valiant并行归并排序的实现

基于流水总线的可重构线性阵列系统（LARPBS）是一种建立在光总线上的并行计算模型，许多研究工作者已经在该模型上设计出了一些高效的并行算法。文章提出了一种基于LARPBS模型上Vnliant并行归并的实现算法，利用该法对长度为N的序列进行排序，最坏情况下可以使用N个处理器在O（logNloglogN)时间完成。     

GF(p)上椭圆曲线密码的并行基点选取算法研究

提出一种GF(p)上椭圆曲线密码系统的并行基点选取算法,该算法由并行随机点产生算法和并行基点判断算法两个子算法组成,给出了算法性能的理论分析和实验结果.结果表明:各并行处理器单元具有较好的负载均衡特性;当执行并行基点判断算法,其标量乘的点加计算时间是点倍数计算时间的三倍时,算法的并行效率可达90%.因此该算法可用于椭圆曲线密码(Elliptic Curve Cryptography,ECC)中基点的快速选取,从而提高ECC的加/解密速度.     

医学影像非刚性配准的并行加速及优化

三次B样条函数拟合小形变需要大量控制点,且非刚性配准的迭代算法和归一化互信息计算量巨大,使得非刚性配准缓慢.为了提高配准速度,提出基于B样条函数的二级并行算法,其中对归一化互信息使用数据并行算法;对梯度下降流使用任务并行算法,并将数据并行算法嵌入到任务并行算法中.为减少计算量,提出图像多层次局部熵提取自由形变场活动控制点的算法,使活动控制点仅分布于待配准的目标之上,并使用B样条系数的快速算法进一步减少计算量;对由于控制点分布优化造成的各线程块并行计算量不平衡的问题,使用类似于Greedy算法的计算平衡算法使各线程块的计算量均衡.实验结果表明,使用B样条系数快速算法可以减少约50％的B样条系数计算量;与串行算法相比,使用二级并行算法以及控制点分布优化算法可以达到60～80倍的加速效果;比现有的数据并行配准算法可提速约6倍.     

Extended Kalman Filter Training of Neural Networks on a SIMD Parallel Machine

The extended Kalman filter (EKF) algorithm has been shown to be advan- tageous for neural network trainings. However, unlike the backpropagation (BP), many matrix operations are needed for the EKF algorithm and therefore greatly increase the computational complexity. This paper presents a method to do the EKF training on a SIMD parallel machine. We use a multistream decoupled extended Kalman filter (DEKF) training algorithm which can provide efficient use of the parallel resource and more improved trained network weights. From the overall design consideration of the DEKF algorithm and the consideration of maximum usage of the parallel resource, the multistream DEKF training is realized on a MasPar SIMD parallel machine. The performance of the parallel DEKF training algorithm is studied. Comparisons are performed to investigate pattern and batch-form trainings for both EKF and BP training algorithms.     

一种加群Z上离散对数问题的DNA计算算法

加群Zp+上离散对数问题在公钥密码系统分析中具有非常广泛的应用。研究一种加群Zp+上离散对数问题的DNA计算算法。算法主要由解空间生成器、并行乘法器、并行加法器、解转换器及解搜索器组成。其中解空间生成器借鉴传统计算机中3表算法的思想,将解空间的生成分为3个部分来完成,极大减少了非法解的搜索空间。本算法的生物操作时间复杂度为O(k2),需要O(1)个试管数、O(2k)条DNA链,最长DNA链长为O(k2)(其中k为加群上离散对数问题群阶p的二进制编码位数)。最后,通过DNA计算通用的试验方法对算法进行了仿真,验证了算法的可行性和有效性。     

基于GSPN和人工免疫算法的并行测试任务调度研究

针对ATS中并行测试任务调度复杂、难以优化的问题,提出了一种广义随机Petri网和人工免疫算法相结合的任务调度优化算法.首先对并行测试系统建立广义随机Petri网(GSPN)模型,然后将激发的变迁序列集作为并行测试任务调度路径;将免疫克隆选择算法(ICSA)应用到并行测试系统任务调度问题中,并提出一种自适应克隆选择算子,搜索最优任务调度路径,得到以测试时间最短为目标的最优任务调度方案.用某型雷达接收机并行测试系统对该算法进行仿真验证,结果表明,与改进的混合遗传算法(IHGA)相比,该算法能够便捷地得到任务调度最优序列,且测试效率更高.     

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.     

关于并行随机数发生器的若干算法

１．引 言 随机数发生器是 Ｍｏｎｔｅ Ｃａｒｌｏ模拟方法的最基本工具．并行计算机的出现和发展,为并行 Ｍｏｎｔｅ Ｃａｒｌｏ方法的研究提供了有力的工具．Ｍｏｎｔｅ Ｃａｒｌｏ方法本身具有很强的并行性,但是,要把在串行计算机上使用Ｍｏｎｔｅ Ｃａｒｌｏ方法程序移植到并行计算机上运行,首先就需要解决随机数发生器的并行化问题．针对各类不同的并行计算机结构,可以构成不同的并行随机数发生器的算法． 乘同余发生器是应用最广泛的随机数发生器之一,无论是在串行计算机上,还是在并行计算机上都具有极大的影响和深人的应用．…