局域网分布式并行计算环境的建立及应用

主要介绍了以PVM系统作为局域网并行计算平台,在工作站机群上,建立了局域网分布式并行计算环境,简单介绍了在此并行环境中开发的多层二维二相油藏数值模拟的并行计算软件,利用工作站机群开发并行油藏模拟软件是为了探索解决大规模油藏模拟的行之有效的方法。     

基于龙格库塔法的弹塑性有限元并行计算

基于MPI集群环境对弹塑性区域分解有限元并行计算进行研究。提出了基于三阶和四阶的龙格库塔（Runge-Kutta）方法对应力-应变关系进行积分的算法。积分过程中自动调整子步大小来控制积分过程中的误差。研制了采用最小残余平滑法的子结构预处理共轭梯度并行求解算法。算法在基于工作站机群的并行环境下实现。计算结果表明：该算法具有良好的并行加速比和效率,是一种有效的并行求解算法。     

基于有效并行求解策略的显式有限元分析并行算法

针对大规模结构非线性动力问题的有限元分析非常耗时,基于消息传递接口（MPI）机群环境,提出多种基于并行求解策略的显式有限元并行算法。基于显式消息传递的区域分解技术,采取重叠、非重叠区域分解技术及动态任务分配方法,通过将计算与通信重叠,优化处理器间的通信,对非重叠通信区域分解并行算法、重叠通信区域分解并行算法、群动态任务分配算法、动态任务分配算法及动态负载平衡算法进行研究。为在机群环境下实现非线性动力有限元分析,开发了基于有效并行求解策略的显式有限元并行算法。编写了基于消息传递编程模式的并行有限元程序,在工作站机群上实现了数值算例,分析了算法的性能,并与传统的Newmark算法进行了比较。算例表明：群动态任务分配算法的性能优于动态任务分配算法,低于区域分解算法的性能,动态负载平衡算法最优。对相同规模的问题提出的算法比Newmark算法快,优于Newmark算法。对结构非线性动力问题的有限元分析,所提出的并行算法是可行有效的。     

Linux环境下构架基于PVM的并行机群

详细地介绍了在Linux环境下如何构架基于PVM的工作站机群．给出了具体的步骤和基本配置过程。最后采用并行求和算法在4节点机群上采用Master／Slave编程模型进行实验测试。测试结果表明,该机群并行计算环境运行正常、稳定,数据规模越大．并行效率越高。当数据规模达到10^9数量级时,其并行效率达到100％。     

构架Linux环境下基于MPICH的工作站机群

本文详细地介绍了在Linux环境下如何构架基于MPICH的工作站机群,给出了具体的步骤和基本配置过程。最后采用并行求和算法、矩阵相乘并行算法和Multisets并行归并算法在该机群上进行实验测试。测试结果表明,该机群并行计算环境运行正常、稳定,该机群比在windows2000环境下的并行效率高1.12%。     

块带状线性方程组的分布式并行算法

本文首先根据分而治之的思想提出一种新的求解块三地角线性方程组的分布式并行算法,然后将该算法推广到块五对角线性方程组和块七地角线方程组的并行求解,并对算法进行了性能分析。 ＳＧＩ工作站机群和５８６微机群上试算表明,加速比呈线性增加。     

并行计算机的比较分析

现有高性的并行计算机大致分为并行向量处理机（PVP）、对称多处理机（SMP）、大规模并行处理机（MPP）、工作站机群（COW）、分布式共享存储处理机（DSM）。这5类计算机各有优缺点,就这5类计算机进行了介绍和比较。     

基于并行计算中的动态负载平衡

在很多应用中都出现负载平衡的问题,尤其是负载平衡在并行分布式计算系统中起到不同寻常的作用.以工作站机群为代表的网络计算环境是当前并行计算和分布式系统的研究重点之一,解决异构性问题和动态负载平衡是使用机群进行网络并行计算的关键.本文对并行计算中的动态负载平衡问题进行了分析并提出了一些解决办法.     

基于各向异性扩散方程的多层次并行图像去噪

针对利用各向异性扩散方程的去噪模型在求解中存在计算量大、耗时长、影响实时性等缺点,本文充分利用并行知识,提出了有效的解决方案。即基于各向异性扩散去噪模型,设计工作站机群平台,对噪声图像进行条状重叠的数据划分,以便实现算法节点内与节点间的两级并行策略:在机群结点内部采用共享内存结构,机群节点间采用分布内存结构,以二者的最优结合实现并行的层次结构化,从而得到一种高效的多层次并行图像去噪算法。实验结果表明,在基于混合模型的并行环境下,该算法能在一定程度上提高原算法的计算效率,不仅有效地缩短了运行时间,而且仍能获得与其相当的图像去噪质量。     

基于工作站机群并行求解有限元线性方程组

随着计算机高速网络技术的发展,工作站机群正在成为并行计算的主要平台.有限元线性方程组在土木工程结构分析中是最常见的问题.预处理共轭梯度法(PCGM)是求解线性方程组的迭代方法.对预处理共轭梯度法进行并行化并在两个不同的机群上实现,对存储方式进行详细分析,编程中采用了稀疏矩阵向量相乘的优化技术.数值结果表明,设计的并行算法具有良好的加速比和并行效率,说明并行计算能更快地求解大规模问题.     

基于残余平滑预处理共轭梯度算法的有限元并行计算

针对弹塑性问题的有限元分析非常耗时,基于消息传递接口(MPI)集群环境,提出了残余平滑的子结构预处理共轭梯度并行算法。采取区域分解,将子结构通过界面条件处理为独立的有限元模型。整体分析时,每个处理器仅存储与其相关的子结构信息并生成局部刚度矩阵。采用对角存储方式和最小残余平滑法,设计出了结合残余平滑(MR)的并行子结构预处理共轭梯度(PCG)算法。并行算法中对负载平衡进行了探讨,对处理器间的通信进行了优化。利用子步法对弹塑性应力应变进行积分,根据预定的容许值自动调整每个子步的大小来控制积分过程的误差。在工作站集群上实现了数值算例,分析了算法的性能,计算性能与传统的PCG算法进行了比较。算例显示:所提算法具有良好的加速比和效率,优于传统的PCG算法,对弹塑性问题的有限元分析,是一种有效的并行求解算法。     

Explicit nonlinear dynamic finite element analysis on homogeneous/heterogeneous parallel computing environment

This paper presents parallel computational strategies to implement explicit nonlinear finite element analysis code onto distributed memory parallel computers for solving large-scale problems in structural dynamics. Implementation details on both homogeneous and heterogeneous parallel processing environments are considered in detail in this paper. Implementation of an explicit nonlinear finite element dynamic analysis code on homogeneous systems is discussed first and this is later moved onto heterogeneous systems. Domain decomposition with explicit message passing is preferred for parallel implementation. The message passing implementation in the parallel algorithm is based on MPI (Message Passing Interface) libraries. Implementation aspects of overlapped, non-overlapped domain decomposition techniques, Dynamic Task Allocation (DTA) and clustering techniques for DTA and their relative merits are presented. The interprocessor communications are optimised by overlapping with computations to improve the performance of the domain decomposition based explicit dynamic analysis finite element code.The issues related to implementation of finite element code for nonlinear dynamic analysis on heterogeneous parallel computing environment are later presented. A new dynamic load-balancing algorithm is developed for this purpose and it is integrated with the domain decomposition based parallel explicit finite element code to test our algorithms on a coarse grain heterogeneous cluster of workstations. Numerical experiments have been carried out on PARAM-10000, an Indian parallel computer and also on cluster of Unix workstations.     

油藏数值模拟有限元并行计算方法研究

针对目前油藏数值模拟普遍采用的有限差分法计算精度低的问题,提出了兼顾计算精度、计算速度问题的有限元油藏数值模拟方法,即在建立了油藏数值模拟数学模型的基础上通过有限元数值分析方法建立有限元数值模型,但有限元在油藏数值模拟时存在单机计算困难、计算时间长的问题,为此提出了利用区域分解技术的油藏数值模拟并行计算方法,最后将该方法通过实例进行检验,取得了良好的加速比和并行效率。     

Parallel Computing on an Ethernet Cluster of Workstations: Opportunities and Constraints

Parallel computing on clusters of workstations is receiving much attention from the research community. Unfortunately, many aspects of parallel computing over this parallel computing engine is not very well understood. Some of these issues include the workstation architectures, the network protocols, the communication-to-computation ratio, the load balancing strategies, and the data partitioning schemes. The aim of this paper is to assess the strengths and limitations of a cluster of workstations by capturing the effects of the above issues. This has been achieved by evaluating the performance of this computing environment in the execution of a parallel ray tracing application through analytical modeling and extensive experimentation. We were successful in illustrating the effect of major factors on the performance and scalability of a cluster of workstations connected by an Ethernet network. Moreover, our analytical model was accurate enough to agree closely with the experimental results. Thus, we feel that such an investigation would be helpful in understanding the strengths and weaknesses of an Ethernet cluster of workstation in the execution of parallel applications.     

Adaptive data parallel computing on workstation clusters

Many important parallel applications are data parallel, and may be efficiently implemented on a workstation cluster by allocating each workstation a contiguous partition of the data domain. Implementation on non-dedicated clusters, however, is complicated by the possibility of changes in workstation availability. For example, a personal workstation may be reclaimed by its primary user for interactive use. In such situations, a node must be removed from the collection of workstations forming the “virtual parallel machine” allocated to the application, and data redistributed accordingly. Conversely, workstations may become available to join the virtual parallel machine.This paper identifies fundamental characteristics of efficient policies for data redistribution following addition/removal of workstations from the cluster. The following conclusions are obtained based on mathematical analysis and simulations: (a) allocating data to a new node from the center of the data domain substantially reduces data migration costs compared to allocation from the edge; (b) addition in groups is beneficial compared to repeated single additions; and (c) even a large number of incremental adjustments of the data domain partitions, owing to successive additions/removals of nodes, do not appear to substantially degrade partition quality compared to that obtained by partitioning from scratch. We believe that these observations can be fruitfully incorporated in the design of workstation cluster support systems for data parallel computing.     

一种同构机群系统中的处理机分配算法

机群系统的分布式计算环境为并行处理技术带来了新的研究与应用问题，正成为并行计算的热点问题．如何合理、有效地将并行任务划分到机群系统的结点上，将直接影响系统的执行性能．本文分析影响系统执行效率的执行开销因素，同时提出一个启发式的处理机分配算法.     

用于并行计算的PC机群

随着计算机技术的高速发展，使用机群进行并行计算也越来越流行，尤其是利用工作站机群进行并行计算已经十分普遍。但使用ＰＣ机群进行并行计算的系统还很少，这种ＰＣ机群由一组ＰＣ机（４８６，５８６）通过网络互连组成。本文介绍现有的几个ＰＣ机群和我们研制的一个ＰＣ机群计算环境     

并行蒙特卡罗方法的应用

该文采用蒙特卡罗方法对欧式期权定价问题进行模拟,并用可移植消息传递标准MPI在分布式存储结构的机群系统上设计并实现了并行算法。该算法有效的解决了金融计算中巨大计算量的问题,在很大程度上提高了计算效率,缩短了计算时间,获得了很好的性能。