基于PVM的动态可伸缩并行调试器设计

ＰＶＭ（ｐａｒａｌｌｅｌ ｖｉｒｔｕａｌ ｍａｃｈｉｎｅ）正在被越来越广泛地使用，但ＰＶＭ应用程序的并行调试还是一个有待解决的难题。文中介绍了自行设计的基于ＰＶＭ的动态可伸缩并行调试器ｄｄｂｇ，主要内容包括并行调试界面、总体设计和一些关键的实现技术。ｄｄｂｇ与其它已有的消息传递并行调试器相比，最大特点是其动态可伸缩结构，它能根据应用的需要自动分布调试服务。     

A fast convergent iterative boundary element method on PVM cluster

This paper reports a fast convergent boundary element method on a Parallel Virtual Machine (PVM) (Geist et al., PVM: Parallel Virtual Machine, A Users' Guide and Tutorial for Networked Parallel Computing. MIT Press, Cambridge, 1994) cluster using the SIMD computing model (Single Instructions Multiple Data). The method uses the strategy of subdividing the domain into a number of smaller subdomains in order to reduce the size of the system matrix and to achieve overall speedup. Unlike traditional subregioning methods, where equations from all subregions are assembled into a single linear algebraic system, the present scheme is iterative and each subdomain is handled by a separate PVM node in parallel. The iterative nature of the overall solution procedure arises due to the introduction of the artificial boundaries. However, the system equations for each subdomain is now smaller and solved by direct Gaussian elimination within each iteration. Furthermore, the boundary conditions at the artificial interfaces are estimated from the result of the previous iteration by a reapplication of the boundary integral equation for internal points. This method provides a consistent mechanism for the specification of boundary conditions on artificial interfaces, both initially and during the iterative process. The method is fast convergent in comparison with other methods in the literature. The achievements of this method are therefore: (a) simplicity and consistency of methodology and implementation; (b) more flexible choice of type of boundary conditions at the artificial interfaces; (c) fast convergence; and (d) the potential to solve large problems on very affordable PVM clusters. The present parallel method is suitable where (a) one has a distributed computing environment; (b) the problem is big enough to benefit from the speedup achieved by coarse-grained parallelisation; and (c) the subregioning is such that communication overhead is only a small percentage of total computation time.     

PVM与网络并行计算

松散耦合的异构型并行处理系统近年来又有较大的发展，这种只能支持粗粒度并行的计算环境，由于通讯硬件的更新和软件环境的开发，其性能已经能够与某些ＭＰＰ机相媲美，因而形成并行处理领域一个强有力的分支－网络计算，本文围绕ＰＶＭ，讨论网络计算的软件环境，概述其特色和发展，并与其它基于消息传递的软件环境相比较，最后预测ＰＶＭ未来可能面临的问题和发展方向。     

介于消息传递界面和并行应用之间的通信库系统功能分析

介于消息传递界面和并行应用之间的通信库系统对开发高性能的可靠、易维护的大型并行应用提供了支持。文章以BLACS和OilCL为例,从进程抽象命名,基于源的消息选择,非阻塞通信和集体通信等方面分析了介于消息传递系统和并行应用之间的通信库系统功能。     

A study on optimal task decomposition of networked parallel computing using PVM

A numerical study is performed to investigate the effect of task decomposition on networked parallel processes using Parallel Virtual Machine (PVM), In our study, a PVM program distributed over a network of workstations is used in solving a finite difference version of a one dimensional heat equation, where natural choice of PVM programming structure would be the master-slave paradigm with the aim of finding an optimal configuration resulting in least computing time including communication overhead among machines. Given a set of PVM tasks comprised of one master and five slave programs, it is found that there exists a pseudo optimal number of machines, which does not necessarily coincide with the number of tasks, that yields the best performance when the network is under a light usage. Increasing the number of machines beyond this optimal one does not improve computing performance since increase in communication overhead among the excess number of machines offsets the decrease in CPU time obtained by distributing the PVM tasks among these machines. However, when the network traffic is heavy, the results exhibit a more random characteristic that is explained by the random nature of data transfer time.     

基于PVM的并行调试环境

并行软件开发环境的研究已日益成为并行计算和并行处理的重点。本文简介了可移植的消息传递环境ＰＶＭ，讨论了以此为目标的图形监视环境ＸＰＶＭ，阐明ＸＰＶＭ环境与实际需要的ＰＶＭ并行调试环境之间的差距，并在此基础上探讨了并行调试环境开发中的技术难点及其设计要求。     

基于PVM的并行Lisp机制与实现

本文提出了一种基于分布存储结构和消息传递平台的并行Ｌｉｓｐ语言，给出了Ｌｉｓｐ语言的并行机制，举例说明了并行Ｌｉｓｐ程序的设计方法，并用一个实际系统ＰＡＲＬｉｓｐ阐明了在ＰＶＭ上的主要实现方法，给出了性能测试结果。     

一种实用的动态负载平衡方法及实现

并行计算为提高效率,需要根据节点的负载情况动态分配任务,以达到平衡各处理节点负载的目的,最终提高系统总体效率.以并行计算平台PVM为背景,提出了将大任务分解成子任务,并根据调度系数概念实现动态平衡负载的策略.以公式形式提出了一种具体的任务分配方案及其与计算能力和负载能力三者之间的关系,并结合并行快速排序,验证了任务分配方法的正确性和可行性,实现了高效的并行计算.     

一种基于PVM的二重网格并行Gauss-Seidel迭代算法

为了克服分组Gauss-Seidel迭代算法迭代次数较多的缺陷,提出了一种基于PVM平台下的二重网格并行Gauss-Seidel迭代算法,并编程进行了数值试验,试验结果表明较分组Gauss-Seidel迭代算法优越。     

基于并行模糊神经网络的车牌识别研究

车辆牌照的自动识别是目标自动识别的一种重要形式，针对车牌识别的后期技术，即牌照识别技术做了研究并提出了一种新的车牌识别方法，该网络由BP神经网络识别模块和模糊控制器构成，为了便于硬件实现，各模块相互独立，最后利用PVM网络在虚拟并行平台上实现了该识别网络，实验结果表明，和标准BP网络相比，该算法具有更加良好的性能，满足实时识别车牌的要求。