A graphical development and debugging environment for parallel programs

To provide high-level graphical support for PVM (Parallel Virtual Machine) based program development, a complex programming environment (GRADE) is being developed. GRADE currently provides tools to construct, execute, debug, monitor and visualize message-passing parallel programs. It offers a high-level graphical programming abstraction mechanism to construct parallel applications by introducing a new graphical language called GRAPNEL. GRADE also provides the programmer with the same graphical user interface during the program design and debugging stages. A distributed debugging engine (DDBG) assists the user in debugging GRAPNEL programs on distributed memory computer architectures. Tape/PVM and PROVE support the performance monitoring and visualization of parallel programs developed in the GRADE environment.     

Multi-Grid, Multi-User Workflows in the P-GRADE Grid Portal

Computational Grids connect resources and users in a complex way in order to deliver nontrivial qualities of services. According to the current trend various communities build their own Grids and due to the lack of generally accepted standards these Grids are usually not interoperable. As a result, large scale sharing of resources is prevented by the isolation of Grid systems. Similarly, people are isolated, because the collaborative work of Grid users is not supported by current environments. Each user accesses Grids as an individual person without having the possibility of organizing teams that could overcome the difficulties of application development and execution more easily. The paper describes a new workflow-oriented portal concept that solves both problems. It enables the interoperability of various Grids during the execution of workflow applications, and supports users to develop and run their Grid workflows in a collaborative way. The paper also introduces a classification model that can be used to identify workflow-oriented Grid portals based on two general features: Ability to access multiple Grids, and support for collaborative problem solving. Using the approach the different potential portal types are introduced, their unique features are discussed and the portals and Problem Solving Environments (PSE) of our days are classified. The P-GRADE Portal as a Globus-based implementation for the classification model is also presented. The work described in this paper is supported by the Hungarian Grid project (IHM 4671/1/2003), by the Hungarian OTKA project (No. T042459) and a collaboration project with the University of Reading.     

Campus Grids Meet Applications: Modeling, Metascheduling and Integration

Air Quality Forecasting (AQF) is a new discipline that attempts to reliably predict atmospheric pollution. An AQF application has complex workflows and in order to produce timely and reliable forecast results, each execution requires access to diverse and distributed computational and storage resources. Deploying AQF on Grids is one option to satisfy such needs, but requires the related Grid middleware to support automated workflow scheduling and execution on Grid resources. In this paper, we analyze the challenges in deploying an AQF application in a campus Grid environment and present our current efforts to develop a general solution for Grid-enabling scientific workflow applications in the GRACCE project. In GRACCE, an application’s workflow is described using GAMDL, a powerful dataflow language for describing application logic. The GRACCE metascheduling architecture provides the functionalities required for co-allocating Grid resources for workflow tasks, scheduling the workflows and monitoring their execution. By providing an integrated framework for modeling and metascheduling scientific workflow applications on Grid resources, we make it easy to build a customized environment with end-to-end support for application Grid deployment, from the management of an application and its dataset, to the automatic execution and analysis of its results.The work has been performed as part of the University of Houston’s Sun Microsystems Center of Excellence in Geosciences [38].     

JPI:基于纯Java语言的异构并行处理支持平台

针对使用Ｐａｒａｌｌｅｌ Ｖｉｒｕａｌ Ｍａｃｈｉｎｅl（ＰＶＭ）和ＭｅｓｓａｇｅＰａｓｓｉｎｇ Ｉｎｔｅｒｆａｃｅ（ＭＰＩ）软件包的解决方案,该软件包用纯Ｊａｖａ语言实现了类似于ＰＶＭ和ＭＰＩ所提供的任务调度、通信和全局归约操作等方面的功能,基于ＪＰＩ的并行程序的运行和性能测试表明,ＪＰＩ不仅解决了并行程序在异构环境中的无缝移植问题,并且能够为包括网络密集型在内的并行程度提供有效的开发、运行支持。     

Optimizing Grid-Based Workflow Execution

Large-scale applications can be expressed as a set of tasks with data dependencies between them, also known as application workflows. Due to the scale and data processing requirements of these applications, they require Grid computing and storage resources. So far, the focus has been on developing easy to use interfaces for composing these workflows and finding an optimal mapping of tasks in the workflow to the Grid resources in order to minimize the completion time of the application. After this mapping is done, a workflow execution engine is required to run the workflow over the mapped resources. In this paper, we show that the performance of the workflow execution engine in executing the workflow can also be a critical factor in determining the workflow completion time. Using Condor as the workflow execution engine, we examine the various factors that affect the completion time of a fine granularity astronomy workflow. We show that changing the system parameters that influence these factors and restructuring the workflow can drastically reduce the completion time of this class of workflows. We also examine the effect on the optimizations developed for the astronomy application on a coarser granularity biology application. We were able to reduce the completion time of the Montage and the Tomography application workflows by 90% and 50%, respectively.     

ParaGA:一个并行遗传算法的C++类库

介绍了一基于ＰＶＭ并行环境的并行遗传算法的Ｃ＋＋类库ＰａｒａＧＡ的设计和实现,ＰａｒａＧＡ以使用方便和灵活为主要目标,提供了透明的并行机制,使不具有并行程序经验的用户可以方便地编写并行遗传算法的程序,高级用户也可通过类库提供的若干方法来获得的优化的可行性能,类库采用粗粒度模型,支持并行遗传算法的３种迁移模式及ＳＰＭＤ和Ｍａｓｔｅｒ／Ｓｌａｖｅ两种编程模式,ＰａｒａＧＡ也提供了实现负载平衡分与及利用     

An adaptive task-level fault-tolerant approach to Grid

A strong failure recovery mechanism handling diverse failures in heterogeneous and dynamic Grid is so important to ensure the complete execution of long-running applications. Although there have been various efforts made to address this issue, existing solutions either focus on employing only one single fault-tolerant technique without considering the diversity of failures, or propose some frameworks which cannot deal with various kinds of failures adaptively in Grid. In this paper, an adaptive task-level, fault-tolerant approach to Grid is proposed. This approach aims at handling quite a complete set of failures arising in Grid environment by integrating basic fault-tolerant approaches. Moreover, this paper puts forward that resource consumption (not received enough attention) is also an important evaluation metric for any fault-tolerant approach. The corresponding evaluation models based on mean execution time and resource consumption are constructed to evaluate any fault-tolerant approach. Based on the models, we also demonstrate the effectiveness of our approach and illustrate the performance gains achieved via simulations. The experiments based on a real Grid have been made and the results show that our approach can achieve better performance and consume less resource.     

Dependable Grid Workflow Scheduling Based on Resource Availability

Due to the highly dynamic feature, dependable workflow scheduling is critical in the Grid environment. Various scheduling algorithms have been proposed, but seldom consider the resource reliability. Current Grid systems mainly exploit fault tolerance mechanism to guarantee the dependable workflow execution, which, however, wastes system resources. The paper proposes a dependable Grid workflow scheduling system (called DGWS). It introduces a Markov Chain-based resource availability prediction model. Based on the model, a reliability cost driven workflow scheduling algorithm is presented. The performance evaluation results, including the simulation on both parametric randomly generated DAGs and two real scientific workflow applications, demonstrate that compared to present workflow scheduling algorithms, DGWS improves the success ratio of tasks and diminishes the makespan of workflow, so improves the dependability of workflow execution in the dynamic Grid environments.     

PVM环境下实时检错机制的研究与实现

PVM（Parallel Virtual Machine）作为一种主流的集群并行计算环境，能够应付主机和网络故障并且提供轮询机制和故障通知原语来建立容错应用。由于PVM原有的故障检测机制不能满足实时性要求，因此提出并实现了两种改进PVM检错机制实时性能的方法，使得对主机故障事件的发现时间缩短了一万倍以上。     

基于服务质量的网格工作流系统研究

网格工作流可以定义成为完成特定目标而在异构和分布的资源上以一定次序执行的网格应用服务的集合.在网格工作流中引入服务质量,为网格服务的调度提供了依据,使得工作流的执行更加满足用户需求.通过使用服务质量可以对网格工作流进行选择和执行,从而更加符合用户的期望.提出了一种典型的基于服务质量的网格工作流管理体系结构及相关的服务质量参数体系,讨论了基于服务质量的工作流调度算法,并给出一个基于快速遗传算法的应用实例.     

Performance measurement,visualization and modeling of parallel and distributed programs using the AIMS toolkit

Writing large-scale parallel and distributed scientific applications that make optimum use of the multiprocessor is a challenging problem. Typically, computational resources are underused due to performance failures in the application being executed. Performance-tuning tools are essential for exposing these performance failures and for suggesting ways to improve program performance. In this paper, we first address fundamental issues in building useful performance-tuning tools and then describe our experience with the AIMS toolkit for tuning parallel and distributed programs on a variety of platforms. AIMS supports source-code instrumentation, run-time monitoring, graphical execution profiles, performance indices and automated modeling techniques as ways to expose performance problems of programs. Using several examples representing a broad range of scientific applications, we illustrate AIMS' effectiveness in exposing performance problems in parallel and distributed programs.     

Adaptive service scheduling for workflow applications in Service-Oriented Grid

When the workflow application is executed in Service-Oriented Grid (SOG), performance issues such as service scheduling should be considered, to achieve high and stable performance in execution. However, most of the prior works on workflow management neither study the performance issues nor provide evaluation methodologies on the performance of Grid Services. Therefore, it is infeasible to apply for the service scheduling problem in SOG. In this paper, we propose and model evaluation metrics for the Grid Service performance. The metrics are extracted based on common properties of Grid Services and are used to quantify and evaluate the performance of an individual Grid Service. With these metrics, we develop a service scheduling scheme with a list scheduling heuristic, to choose proper and optimal Grid Services for tasks in workflow applications. It ensures high performance in the execution of the workflow applications. In addition, we propose a low-overhead rescheduling method, referred to as Adaptive List Scheduling for Service (ALSS), to adapt to the dynamic nature of a grid environment. ALSS provides stable performance for workflow applications, even in abnormal circumstances. Finally, we design an experimental environment with actual traces and perform simulations to quantify the benefits of our approach. Throughout the experiments, we demonstrate that ALSS outperforms conventional scheduling methods. Our scheme produces a scheduling performance that is superior to AHEFT by 50.2%, SLACK by 50.8%, HEFT by 68.3%, MaxMin by 72.0%, MinMin by 71.0%, and Myopic by 69.8%.     

基于Win32平台上的PVM并行程序设计

着重介绍了在PVM平台上进行并行程序设计的方法，包括如何构造基于Win32平台上的PVM运行环境，进行任务和数据划分，并提出了一个Master／Slave结构的并行程序设计模式；最后给出一个并行计算在物探处理应用的例子。来对并行程序的设计方法进行概括性的说明。     

Visualization and debugging in a heterogeneous environment

The authors' experiences with visualization and debugging of parallel virtual machine (PVM) applications and two of the tools they have devised to facilitate these tasks are described. One of the tools is a graphical monitoring package called Xab that can visually display PVM activities inside an application running across a network. The other is a graphical programming environment called Hence, which helps the user write, compile, execute, and trace heterogeneous distributed programs. The authors discuss their early work, the present research, and the future directions of these experimental projects     

基于对象的可靠群组通信机制研究

近年来,网格计算技术日益成为用来解决数据和计算密集型应用的可行方案,网格运行平台本身和在网格环境中的并行应用都需要大量的点对多点的群组通信.提出一种灵活、可容错的群组通信机制.该机制基于远程方法调用(RMI),可为分布式并行应用提供高效、可容错的群组通信.通信方法可以在本地对象、远程对象,或一组对象中激活.这种通信采用异步方式,通信发起者可以选择全等待或必要性等待两种机制来获取通信结果.从而最大程度地保证通信的可靠性或高效性.     

网格环境下费用约束的科学工作流可靠调度算法

网格基础设施是目前科学工作流应用规划、部署和执行的主要支撑环境.然而由于网格资源的自治、动态及异构性,如何在保障用户QoS约束下有效调度科学工作流是一个研究热点.针对费用约束下的科学工作流调度问题,为了提高其执行的可靠性,本文使用随机服务模型描述资源节点的动态服务能力并考虑本地任务负载对资源执行性能的影响,给出一种资源可靠性的评估方法,在此基础上提出一种费用约束下的科学工作流可靠调度算法RSASW.仿真实验结果表明RSASW算法相对于GAIN3,GreedyTime-CD及PFAS算法,对工作流的执行具有很好的可靠性保障.     

一种改进PVM检错机制实时性能的方法

PVM的设计目标是连接一个异构的计算机和网络的集合，给用户提供一个并行计算平台来运行他们的MPP或集群计算应用。运行在集群上的应用经常碰到诸如网络故障、主机故障和进程故障等各种异常故障，可靠性是这类平台面临的主要问题。PVM能够应付主机和网络故障并且提供轮询机制和故障通知原语来建立容错应用。由于在设计PVM时对在异构环境下软件的适用性投入了太多的考虑，PVM原有的故障检测机制不能满足实时性的要求。为了解决这个问题，我们提出并实现了一种改进PVM检错机制实时性能的方法，使得对主机故障事件的发现时间缩短了五千多倍。     

Performance metrics and ontologies for Grid workflows

Many Grid workflow middleware services require knowledge about the performance behavior of Grid applications/services in order to effectively select, compose, and execute workflows in dynamic and complex Grid systems. To provide performance information for building such knowledge, Grid workflow performance tools have to select, measure, and analyze various performance metrics of workflows. However, there is a lack of a comprehensive study of performance metrics which can be used to evaluate the performance of a workflow executed in the Grid. Moreover, given the complexity of both Grid systems and workflows, semantics of essential performance-related concepts and relationships, and associated performance data in Grid workflows should be well described. In this paper, we analyze performance metrics that performance monitoring and analysis tools should provide during the evaluation of the performance of Grid workflows. Performance metrics are associated with multiple levels of abstraction. We introduce an ontology for describing performance data of Grid workflows and illustrate how the ontology can be utilized for monitoring and analyzing the performance of Grid workflows.