A simple yet effective load balancing extension to the PVM software system

In this paper, a load balancing extension for the PVM software system is described. This extension includes slave processes, for obtaining host CPU load information, and a master process for gathering the load information. A programming interface is included to provide the PVM user with CPU load information and a load balancing process spawn. For tested applications, the load balancing extension exhibits considerable improvement in parallel runtime performance. Results presented include parallel runtime comparisons for basic matrix oriented algorithms, a parallel fractal image compression algorithm, and selected NAS parallel benchmarks.     

双Beta样条曲面在屏幕上自动调整和显示

介绍了双参数控制的双Ｂｅｔａ样条曲面的算法，投影变换，消隐处理，在屏幕上自动调整和显示的原理和实现方法，并给出应用实例。     

一种有效的动态任务分配方法

首先简单介绍了基于PVM的网络并行计算环境。接着重点讨论了任务分配方法和负载平衡问题。通过分析静态任务分配方法中存在的不足,提出了以计算能力的大小来动态分配任务的方法,避免了因个别计算机的低性能而影响了整个并行计算系统的加速比。实验表明这种策略实现简单,并且具有很好的并行效果。     

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。     

异步检查点容错PVM

以工作站簇为代表的计算环境是当前分布式系统和并行计算的研究重点之一,ＰＶＭ所提供的消息传递机制支持了高效的异构网络计算。但标准ＰＶＭ缺乏对系统容错的支持,这可以通过使用检查点的回滚恢复方式予以弥补。该文对如何在用户级实现ＰＶＭ全局容错,分析其设计思想和实现技术。主要思想是使用进行消息记录的异步检查点算法,并利用ＰＶＭ守护进程和全局调度进程进行控制,所有操作对应用程序都是透明的。利用该系统还可以进一步实现ＰＶＭ的透明进程迁移和负载均衡。     

Virtual data space – load balancing for irregular applications

Load balancing is a key issue in the development of parallel algorithms with irregular structures. Existing load balancing systems each support only one specific programming paradigm and thus are of limited use. The system VDS presented here allows concurrent use of various paradigms such as fork-join, weighted tasks, and static dags (directed acyclic graphs that are known in advance). The system provides visual performance evaluation tools to facilitate the efficient application of the system. VDS supports various communication interfaces including PVM and MPI. Thus, VDS-applications can be run on architectures ranging from workstation clusters to massively parallel systems.     

Jcluster: an efficient Java parallel environment on a large‐scale heterogeneous cluster

In this paper, we present Jcluster, an efficient Java parallel environment that provides some critical services, in particular automatic load balancing and high‐performance communication, for developing parallel applications in Java on a large‐scale heterogeneous cluster. In the Jcluster environment, we implement a task scheduler based on a transitive random stealing (TRS) algorithm. Performance evaluations show that the scheduler based on TRS can make any idle node obtain a task from another node with much fewer stealing times than random stealing (RS), which is a well‐known dynamic load‐balancing algorithm, on a large‐scale cluster. In the performance aspects of communication, with the method of asynchronously multithreaded transmission, we implement a high‐performance PVM‐like and MPI‐like message‐passing interface in pure Java. The evaluation of the communication performance is conducted among the Jcluster environment, LAM‐MPI and mpiJava on LAM‐MPI based on the Java Grande Forum's pingpong benchmark. Copyright © 2005 John Wiley & Sons, Ltd.     

A parallel multi-p method

A parallel implementation of the multi-p method is discussed, using the master/slave model and the Parallel Virtual Machine (PVM) message passing library. In a series of performance tests, significant speed-up was achieved in those typical cases for the p-version where there was sufficient computational granularity to justify use of the parallel method. These tests indicate that the algorithms devised for load distribution and load balancing are sufficiently robust. These tests also indicate that, even though communication overhead in a network environment is relatively high, there is significant potential for scaling the method to larger processor ensembles.     

系统负载与并行程序运行时间的关系

负载共享技术在并行处理中是至关重要的,通过对大量负载共享献的考究发现,以前的研究都是基于一定系统负载而进行平衡自救的设计,它们很少考虑到所选定的系统负载与程序运行时间之间的准确关系,为了确定系统负载对并行程序运行的影响,确定了影响并行程序运行的两个重要系统因素：ＣＰＵ负载和网络负载,为了不失一般性,也为了简化网络负载的测量,选用２ＮＡＳＰＶＭ并行Ｂｅｎｃｈｍａｒｋ作为实验测试对象;为了得到程序运     

PVM动态跟踪机制的改进和扩充

本文介绍了并行程序动态性能监测的一般概念和方法。在分析ＰＶＭ内部跟踪机制及其在动态跟踪方面缺陷的基础上,对其进行了相应的改进和扩充,并在一个基于ＰＶＭ的并行程序可视化性能分析系统ＶＥＮＵＳ中得到了实现。