Load Balancing and Grid Computing

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众说纷云基础篇

众说纷“云” :“云”是一组技术的集合相春雷云计算不是一种技术，它是一种概念，一种架构思想和服务理念，或者我们也可以说是一组技术的集合，是并行计算（Parallel Computing）、分布式计算（Distributed Computing）和网格计算（Grid Computing）的延展。

要问2010年中国IT行业的关键词是什么？相信大部分业界人士都会告诉你:“云计算”、“物联网”、“3G”等等，听云计算听的耳朵已经磨出厚厚的茧子了，但是依然相信很多人都还未能完全理解云计算的内涵到底是什么。

其实云计算并不是什么新的概念，早在20世纪60年代麦卡锡就提出了把计算能力作为一种像水和电一样的公用事业提供给用户。

云计算的第一个里程碑是，1999年，提出的通过一个网站向企业提供企业级应用的概念。

另一个重要进展是2002年亚马逊（Amazon）提供一组包括存储空间，计算能力甚至人力智能等资源服务的Web Service。

2005年亚马逊又提出了弹性计算云（Elastic Compute Cloud），也称亚马逊EC2的Web Service，允许小企业和私人租用亚马逊的云计算来运行他们自己的应用。

定义云计算云计算的定义讲了很多，不过为了全文完整性的考虑，不得不再做一次。

云计算的定义目前还没有形成固定标准，要不也不会很多人讨论微软云计算到底是不是“伪云”。

其实云计算的定义可以从狭义和广义两个方面讨论:从狭义云计算是指IT基础设施的交付和使用模式。

它通过网络以按需、易扩展的方式获得所需的资源（硬件、平台、软件），提供资源的网络被称为“云”。

“云”中的资源在使用者看来是可以无限扩展的，并且可以随时获取，按需使用，随时扩展，按使用付费。

这种特性经常被称为像水电一样使用IT基础设施。

广义云计算是指服务的交付和使用模式。

它指通过网络以按需、易扩展的方式获得所需的服务。

这种服务可以是IT和软件、互联网相关的，也可以是任意其他的服务。

云计算技术英语

云计算技术英语Title: Understanding Cloud Computing TechnologiesCloud computing has revolutionized the way businesses and individuals interact with technology. At its core, cloud computing is the delivery of computing resources and data storage over the internet. These resources are provided on-demand and can be scaled up or down as needed. Thisflexibility allows users to pay only for the services they use, rather than investing in expensive hardware and software that may not always be fully utilized.The foundation of cloud computing is built upon a myriadof technologies that work in harmony to provide seamless services. These technologies include virtualization, utility computing, service-oriented architecture, autonomic computing, and network-based computing, among others. Let's delve deeper into each of these key technologies.Virtualization is a cornerstone of cloud computing. It enables the creation of virtual machines (VMs) which are software-based emulations of physical servers. These VMs can run multiple operating systems and applications on a single physical server, maximizing resource utilization and reducing costs. Virtualization also allows for the rapid deployment and decommissioning of environments, providing agility and scalability to cloud services.Utility computing extends the concept of virtualization by treating computing resources like a metered service, similar to how utilities like electricity are billed based on consumption. This model allows cloud providers to offer flexible pricing plans that charge for the exact resources used, without requiring long-term contracts or minimum usage commitments.Service-Oriented Architecture (SOA) is a design pattern that structures an application as a set of interoperableservices. Each service performs a unique task and can be accessed independently through well-defined interfaces and protocols. In the cloud, SOA enables the creation of modular, scalable, and reusable services that can be quickly assembled into complex applications.Autonomic computing is a self-managing system that can automatically optimize its performance without human intervention. It uses advanced algorithms and feedback mechanisms to monitor and adjust resources in real-time. This technology is essential in the cloud where the demand for resources can fluctuate rapidly, and immediate responses are necessary to maintain optimal performance.Network-based computing focuses on the connectivity between devices and the efficiency of data transmission. Cloud providers invest heavily in high-speed networks to ensure low latency and high bandwidth for their services. The reliability and security of these networks are paramount toensure uninterrupted access to cloud resources and to protect sensitive data from breaches.In addition to these foundational technologies, cloud computing also relies on advanced security measures, such as encryption and multi-factor authentication, to safeguard data and applications. Disaster recovery strategies, includingdata backups and replication across multiple geographic locations, are also critical to ensure business continuity in the event of a failure or disaster.Cloud computing models are typically categorized intothree types: Infrastructure as a Service (IaaS), Platform asa Service (PaaS), and Software as a Service (SaaS). IaaS provides virtualized infrastructure resources such as servers, storage, and networking. PaaS offers a platform fordevelopers to build, test, and deploy applications, while abstracting the underlying infrastructure layers. SaaSdelivers complete software applications to end-users via theinternet, eliminating the need for local installations and maintenance.Choosing the right cloud service provider is crucial for businesses looking to leverage cloud computing. Providerslike Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer a range of services tailored to different needs and budgets. These platforms are designed to be highly scalable, reliable, and secure, with features such as automated scaling, load balancing, and comprehensive monitoring tools.Furthermore, cloud providers often offer specialized services for specific industries or use cases. For example, AWS offers Amazon S3 for object storage, Amazon EC2 for virtual servers, and Amazon RDS for managed databases. Microsoft Azure provides Azure Active Directory for identity management and Azure Machine Learning for building predictivemodels. GCP offers BigQuery for big data analytics and App Engine for scalable web application hosting.As cloud computing continues to evolve, new trends and innovations emerge. Edge computing, for instance, aims to bring computation closer to data sources by processing data at the edge of the network, reducing latency and bandwidth usage. Serverless computing, another rising trend, allows developers to focus solely on writing code without worrying about the underlying infrastructure, as the cloud provider dynamically manages the execution environment.In conclusion, cloud computing technologies have enabled a paradigm shift in how we approach IT resource management and consumption. By understanding the various technologies and models at play, businesses can make informed decisions about adopting cloud solutions that align with their strategic goals. As the landscape of cloud computing continues to mature, it will undoubtedly present newopportunities and challenges that must be navigated with a keen eye on technological advancements and market dynamics.。

计算机英语词汇.doc

RARP= reverse address resolution protocol 反向地址解析协议
TFTP=trival file transfer protocol简单文件传输协议
FTP＝file transfer protocol文件传输协议
SNMP＝simple network mangement protocol 简单网络管理协议
GARP=generic attribute registration protocol 通用属性注册协议
import-route 路由引入
traffic classification 流分类
VRRP=virtul router redundancy protocl 虚拟路由备份协议
port aggregation 端口捆绑
CEP
Connection end point连接端点
hdlc＝high-level data link control 高级数据链路控制
ppp ＝point to point protocol 点到点协议
stack 栈
connect-oriented 面向联接
mulitiplex 多路复用
buffering 缓存
source quench messages 源抑制报文
Campus network校园网
CNNIC中国互联网络信息中心
ChinaNET中国公用计算机互联网
CERNET中国教育科研网
CSTNET中国科学技术网
CHINAGBN国家公用经济信息能信网络
CCITT
Consultative committee international telegraph and telephone

网络名词大全

网络名词大全Internet（因特网）：该术语指最大的全球互联网，它联接上万个全球网络，并有一种将注意力集中在基于现实生活利用的研究和标准化上的"文化环境"。

许多领先适当的网络技术都来自于因特网环境。

因特网部分是从AＰRA网中演变而来的，它曾被称为DARPA Internet（ＤARＰA因特网。

不要与一般的术语internet 混淆起来。

Inverse ARP（逆向ARP）：逆向地址解析协议。

是在一个网络中建立动态路由的方法，允许一个存取服务器发现一个与虚拟电路相联的设备的网络地址。

IP（因特网协议）：是TCP／IP栈中的网络层协议，它提供一个无连接的互联网服务。

IP提供寻址的功能、服务类型的规范、分段存储和重新组合、以及安全的特性。

记录在RFC791文件中。

IP address（IP地址）：应用ICP/IP协议分配给主机的32位地址。

一个IP地址属于五种类型（A、B、C、D或E）之一，以四组用带小数点格式分割的八位字节来表示。

每个地址都包含一个网络号、一个可选的子网络号和一个主机号。

用网络号和子网络号共同进行路由选择，而主机号则用来在网络或者子网络中为一个单个的主机寻址。

利用子网掩码从IP地址中提取网络和子网络的信息。

IP地址又被称为因特网地址（Internet address）。

IP multicast（IP组播）：是一种路由选择技术，它允许IP业务量从一个信源向一个目的端传播，或者从许多信源向许多目的端传播。

不是将一个数据包传送给每个目的端，而是将一个数据包传送给一个由单独的IP目的端组地址进行识别的组播组。

IPX（互联网分组交换）：是NetWare网络层（第三层）协议，用于从服务器向工作站传输数据。

IPX与IP及XNS类似。

IS（中介系统）：是在一个OSI网络中的路由选择节点。

ISDN（综合业务数字网）：是由电话公司提供的通信协议，它允许电话网络传送数据、声音和其它的信源业务量。

软件行业常用英语短语

软件行业常用英语短语The software industry is a dynamic and rapidly evolving field that requires professionals to communicate effectively using a specialized vocabulary. Among the many English phrases commonly used in this industry, some stand out as particularly important for understanding and navigating the complex landscape of software development, project management, and technology-related business operations.One of the most ubiquitous phrases in the software industry is "user experience" or "UX." This term refers to the overall experience a user has when interacting with a software application or digital product. UX designers focus on creating intuitive, visually appealing, and seamless interfaces that cater to the needs and preferences of the target audience. Phrases like "user-friendly," "intuitive design," and "responsive layout" are all closely tied to the concept of UX.Another commonly used phrase is "agile methodology," which describes a flexible and iterative approach to software development. Agile teams prioritize adaptability, collaboration, and continuous improvement over rigid, linear processes. Key agile phrases include"scrum," "sprint," "daily standup," and "retrospective," all of which refer to specific practices and rituals within the agile framework.The term "MVP," or "minimum viable product," is also widely used in the software industry. An MVP is a stripped-down version of a product that contains the essential features necessary to gather user feedback and validate the product's core concept. Phrases like "pivot," "iterate," and "feature backlog" are often associated with the MVP development process.In the realm of software architecture, the phrase "scalability" is of paramount importance. Scalability refers to a system's ability to handle increasing amounts of work or users without compromising performance or stability. Phrases like "load balancing," "horizontal scaling," and "vertical scaling" are used to describe various strategies for ensuring scalability.The software industry also heavily relies on cloud computing technology, which has given rise to a host of related phrases. "Software as a Service" (SaaS), "Platform as a Service" (PaaS), and "Infrastructure as a Service" (IaaS) are all cloud-based service models that allow businesses to access and utilize computing resources on-demand. Phrases like "cloud migration," "serverless computing," and "containerization" are also common in this context.When it comes to software development, the term "version control" is essential. Version control systems, such as Git, allow teams to track changes, collaborate on code, and manage project histories effectively. Phrases like "commit," "merge," and "branch" are integral to the version control process.The software industry also heavily emphasizes the importance of data-driven decision-making. Phrases like "business intelligence," "data analytics," and "key performance indicators" (KPIs) are used to describe the process of collecting, analyzing, and leveraging data to inform strategic business decisions.In the realm of software testing, phrases like "unit testing," "integration testing," and "end-to-end testing" refer to different levels of testing that ensure the quality and reliability of software applications. The concept of "bug" (a software defect) and "debugging" (the process of identifying and fixing bugs) are also widely used.Finally, the software industry is heavily influenced by the need for strong cybersecurity measures. Phrases like "data encryption," "two-factor authentication," and "penetration testing" are used to describe the various techniques and technologies employed to protect digital assets and safeguard against cyber threats.In conclusion, the software industry is a dynamic and ever-evolving field that requires professionals to be fluent in a specialized vocabulary. The phrases discussed in this essay, such as "user experience," "agile methodology," "minimum viable product," "scalability," "cloud computing," "version control," "data-driven decision-making," "software testing," and "cybersecurity," are just a few examples of the many English terms that are essential for understanding and navigating the complex world of software development and technology-related business operations.。

云计算术语大全

云计算术语大全在云计算技术飞速发展的今天，了解和掌握相关的术语对于从业人员和普通用户都具有重要意义。

本文将为您带来云计算术语大全，帮助您更好地理解和应用云计算。

一、云计算的基本概念云计算（Cloud Computing）是指通过互联网将计算资源集中起来，按需付费使用，实现电脑存储和数据处理的方式，允许用户随时随地通过互联网访问和使用数据和应用程序。

1.云服务模型- 基础设施即服务（Infrastructure as a Service，IaaS）：提供虚拟化的计算、存储和网络资源，用户可以自行管理操作系统、应用程序和数据的运行环境。

- 平台即服务（Platform as a Service，PaaS）：在IaaS基础上，提供更高级别的开发环境，用户可以基于云平台进行应用程序的开发、测试和部署。

- 软件即服务（Software as a Service，SaaS）：以云服务的形式提供应用程序，用户无需关心底层基础设施和平台，只需通过云平台进行应用程序的访问和使用。

2.云部署模型- 公有云（Public Cloud）：基于云服务商提供的共享基础设施，面向公众用户提供服务，用户按需付费使用。

- 私有云（Private Cloud）：基于机构或企业自己建设和管理的云平台，只对内部人员提供服务，用于满足特定的安全和合规性需求。

- 混合云（Hybrid Cloud）：将公有云和私有云结合起来使用的部署模式，可以实现资源的灵活调配和扩展。

二、云计算相关术语1. 虚拟化（Virtualization）：将物理资源（计算、存储、网络）进行抽象，通过软件技术将其划分为多个虚拟资源，实现资源的共享和隔离。

2. 弹性扩展（Elastic Scalability）：根据实际需求，动态调整云计算资源的规模和容量，以适应业务的变化，提高资源利用率。

3. 自助服务（Self-Service）：云计算用户可以根据自己的需求，自主选择和配置计算、存储和网络资源，实现自助式的服务使用。

fset-421

FSET-421OverviewFSET-421 is a software framework designed to facilitate the development of efficient and scalable applications. It provides a set of libraries, tools, and guidelines that enable developers to create high-performance software solutions. This document outlines the features and benefits of FSET-421 and provides a brief guide on how to get started with using the framework.Features1.Scalability: FSET-421 is designed to handle large-scale applicationsand can efficiently scale to accommodate increasing workloads. It providesfeatures such as load balancing and distributed computing to ensure optimal performance in highly demanding environments.2.Performance Optimization: The framework includes variousoptimization techniques that improve application performance. It offers built-in profiling tools to identify performance bottlenecks and provides optimization guidelines to help developers write efficient code.3.Modularity: FSET-421 promotes a modular development approach,allowing developers to break down their applications into smaller, reusablecomponents. This modularity enhances code organization, promotes codesharing, and simplifies maintenance.4.Fault Tolerance: The framework includes robust error handlingmechanisms, ensuring that applications built using FSET-421 can gracefullyhandle exceptions and failures. It provides tools for error detection, reporting, and recovery, minimizing downtime and improving system reliability.5.Ease of Use: FSET-421 offers a user-friendly API and cleardocumentation, making it easy for developers to understand and use theframework. It provides extensive code examples and tutorials to facilitate the learning process.Benefits1.Accelerated Development: By leveraging the features and toolsprovided by FSET-421, developers can significantly speed up the development process. The framework simplifies complex tasks and provides ready-to-use components, allowing developers to focus on application logic rather than low-level implementation details.2.Improved Performance: FSET-421’s performance optimizationfeatures help maximize application efficiency. By following the framework’sguidelines and using its profiling tools, developers can identify and address performance bottlenecks, resulting in faster and more responsive applications.3.Increased Reliability: The fault tolerance mechanisms offered by FSET-421 ensure that applications can handle errors and failures gracefully. This enhances overall system reliability and minimizes the impact of potential issues on end-users.4.Enhanced Scalability: FSET-421’s scalability features make it well-suited for handling increasing workloads. With load balancing and distributed computing capabilities, the framework can efficiently scale to accommodate growing user demands without compromising performance.5.Simplified Maintenance: The modular development approach promoted by FSET-421 simplifies application maintenance. By breaking down applications into reusable components, developers can easily update and modify specific parts without affecting the entire system. This reduces development time and minimizes the risk of introducing new issues during maintenance.Getting StartedTo start using FSET-421, follow these steps:1.Installation: Download the FSET-421 package from the official website. Extract the contents and install any dependencies required by the framework.2.Project Setup: Create a new project directory for your application. Initialize a new project using the FSET-421 command-line tool and specify the desired configuration settings.3.Configure Environment: Ad just the framework’s environment configuration file according to your application’s needs. This file contains various settings related to logging, error handling, and performance optimization.4.Write Code: Begin writing your application code using the FSET-421 API. Refer to the official documentation and code examples to understand the framework’s features and best practices.5.Build and Test: Once the application code is ready, build your project and run comprehensive tests to ensure that everything is functioning as expected. Use the FSET-421 profiling tools to identify any performance issues that require optimization.6.Deployment: Deploy your application to the desired environment, following the specific guidelines provided by FSET-421. Make any necessaryconfiguration adjustments to ensure optimal performance in the targetenvironment.7.Maintenance and Updates: As your application evolves, make sureto regularly update your FSET-421 installation and follow any frameworkupdates that may be released. This ensures that your application remainscompatible with the latest features and improvements offered by theframework.ConclusionFSET-421 is an advanced software framework that provides a range of features to accelerate development, improve performance, and ensure the reliability and scalability of applications. By following the provided guidelines and leveraging the framework’s too ls, developers can create efficient, high-performance software solutions. Get started with FSET-421 today and experience the benefits it offers in your next project.。

信息技术专业术语大全

信息技术专业术语大全信息技术是当今社会中备受重视的领域，不仅在商业、科学、医疗等各个行业中得到广泛应用，而且也对我们的日常生活产生了深远影响。

在信息技术领域，有着许多专业术语，这些术语涵盖了各个方面的技术、概念和原理。

本文将会详细介绍信息技术领域中的2000个重要专业术语，希望能够为您提供一个全面而又系统的了解。

1. 人工智能（Artificial Intelligence, AI）: 一种复制人类智能行为的技术，包括机器学习、语音识别、图像识别等。

2. 云计算（Cloud Computing）: 一种通过互联网提供计算服务的模式，包括基础设施即服务（IaaS）、平台即服务（PaaS）和软件即服务（SaaS）。

3. 大数据（Big Data）: 指数据量大、类型多样的数据集合，需要特殊的处理技术和工具。

4. 虚拟现实（Virtual Reality, VR）: 一种通过计算机模拟的虚拟环境，用户可在其中进行互动体验。

5. 嵌入式系统（Embedded System）: 一种特定用途的计算机系统，通常嵌入在其他设备中以完成特定的功能。

6. 物联网（Internet of Things, IoT）: 通过互联网连接各种设备和物品，实现智能化和自动化控制。

7. 数据挖掘（Data Mining）: 从大量数据中发现模式、趋势和关联性的过程。

8. 信息安全（Information Security）: 保护信息系统免受未经授权的访问、使用、披露、破坏、修改、干扰或泄露的行为。

9. 前端开发（Front-end Development）: 开发用户界面和用户体验的技术，包括HTML、CSS、JavaScript等。

10. 后端开发（Back-end Development）: 开发应用程序后台和服务器端逻辑的技术，常涉及数据库和服务器端语言。

11. 数据库管理系统（Database Management System, DBMS）: 一种管理和组织数据的软件系统，可实现数据的存储、检索和管理。

服务器集群技术方案(2)

的计算机，利用高速通信网络组成一个单一的计算机系统，并以单一系统的模式加以管理。

其出发点是提供高可靠性、可扩充性和抗灾难性。

一个集群包含多台拥有共享数据存储空间的服务器，各服务器通过内部局域网相互通信。

当一台服务器发生故障时，它所运行的应用程序将由其它服务器自动接管。

在大多数模式下，集群中所有的计算机拥有一个共同的名称，集群内的任一系统上运行的服务都可被所有的网络客户使用。

采用集群系统通常是为了提高系统的稳定性和网络中心的数据处理能力及服务能力。

体系结构是否相同。

集群计算机按功能和结构可以分成以下几类:High-availability (HA) clustersLoad balancing clustersHigh-performance (HPC) clustersGrid computing普通是指当集群中有某个节点失效的情况下，其上的任务会自动转移到其他正常的节点上。

还指可以将集群中的某节点进行离线维护再上线，该过程并不影响整个集群的运行。

负载均衡集群运行时普通通过一个或者多个前端负载均衡器将工作负载分发到后端的一组服务器上，从而达到整个系统的高性能和高可用性。

这样的计算机集群有时也被称为服务器群 (Server Farm) 。

普通高可用性集群和负载均衡集群会使用类似的技术，或者同时具有高可用性与负载均衡的特点。

Linux 虚拟服务器(LVS)项目在Linux 操作系统上提供了最常用的负载均衡软件。

高性能计算集群采用将计算任务分配到集群的不同计算节点而提高计算能力，于是主要应用在科学计算领域。

比较流行的HPC 采用Linux 操作系统和其它一些免费软件来完成并行运算。

这一集群配置通常被称为Beowulf 集群。

这种集群通常运行特定的程序以发挥HPC cluster 的并行能力。

这种程序普通应用特定的运行库, 比如专为科学计算设计的MPI 库。

HPC 集群特殊适合于在计算中各计算节点之间发生大量数据通讯的计算作业，比如一个节点的中间结果或者影响到其它节点计算结果的情况。

基于Docker container技术实现高性能计算服务

基于Dockercontainer技术实现高性能计算服务
高性能计算机的衡量标准主要以计算速度(尤其是浮点运算速度)作为标准。

高性能计算机是信息领域的前沿高技术，在保障国家安全、推动国防科技进步、促进尖端武器发展方面具有直接推动作用，是衡量一个国家综合实力的重要标志之一。

在高性能计算(HPC)领域，空气质量预测、气象预测、地震监测、石油勘探、航天国防、科学研究等并行应用对计算能力的需求越来越高，同时金融、政府信息化、教育、企业、网络游戏等更广泛的领域对高性能计算的需求迅猛增长。

高性能计算机的主流体系结构收缩成了三种，即SM、CC-NUMA、Cluster。

在产品上，只有两类产品具有竞争力：一是高性能共享存储系统；二是工业标准机群，包括以IA 架构标准服务器为节点的PC机群和以RISC SMP标准服务器为节点的RISC机群。

集群计算机按功能和结构可以分成以下几类:高可用性集群High-availability (HA) clusters、负载均衡集群Load balancing clusters、高性能计算集群High-performance (HPC) clusters、网格计算Grid computing。

目前云平台以数据密集型、IO访问密集型为主，针对计算密集型未涉及，按照云计算和集群规模发展，参照docker container技术发展方向，容器云成为发展方向，商业化提供高性能计算服务（或称为超级计算）成为可能。

异构计算——分类、编程平台、应用

网络异构计算
nhc
• 分布式计算 • 集群计算 • 网格计算
网络异构计算
分布式计算（Distributed computing）
• 这个研究领域，主要研究分布式系统（Distributed system）如何进行计算。
• 分布式系统是一组电子计算机（computer），透过计算机网络相互连结与通讯后形成的系统。
分类
其中，shc又分为单机多计算方式和单机混合计算方式两大类。
• 前者在同一时刻允许以多种计算方式执行任务 • 后者在同一时刻只允许以一种计算方式执行任务，但在不同时刻计算可从一种方式自动切换到另一种方式，如simd(single instruction multiple data)和 mimd( multiple instruction multiple data)方式间的切换。
• 云计算是从集群技术发展而来，区别在于集群虽然把多台机器联了起来，但其某项具体任务执行的时候还是会被转发到某台服务器上，而云可以简单的认为是任务可以被分割成多个进程在多台服务器上并行计算。 • 云可以使用廉价的PC服务器，可以管理大数据量与大集群，关键技术在于能够对云内的基础设施进行动态按需分配与管理。
分类
nhc分为同类异型多机方式和异类混合多机方式两类。
• 同类异型多机方式中所使用的多机，它们的结构属同一类，即支持同一种并行性类型（如simd、mimd、向量等类型之一），但型号可能不同，因此性能可以各有差异。通常的now(Net of Workstations,工作站网络)或cow(Cluster of Workstations,工作站集群)为同类同型多机方式，因此可看成是同类异型多机方式中的特例。 • 异类混合多机方式中所使用的多机，它们的结构则属不同类型。

数据运载能力英语

数据运载能力英语Data Carrying CapacityData carrying capacity refers to the ability of a system or infrastructure to handle and transport large volumes of data efficiently and effectively. In today's digital age, where the demand for data transmission and storage is rapidly increasing, it is crucial for organizations to have a robust data carrying capacity to meet the needs of their users and customers. This article will explore the importance of data carrying capacity, its implications, and strategies to enhance it.The significance of data carrying capacity cannot be overstated. As the world becomes increasingly interconnected, data has become a valuable resource. From personal information to business data, the volume of data being generated and shared is growing exponentially. To ensure the smooth operation of digital services, data must be transported and stored securely and promptly. This is where data carrying capacity comes into play.A high data carrying capacity allows organizations to handle large data volumes without experiencing bottlenecks or disruptions. It ensures that data can flow seamlessly, enabling efficient communication, analysis, and decision-making. For example, in e-commerce, a robust data carrying capacity enables fast and reliable online transactions, inventory management, and personalized customer experiences.The implications of insufficient data carrying capacity can be severe. Without adequate capacity, data transmission can be slow, leading to delays, lags, and even system crashes. This can result in frustrated customers, missed opportunities, and financial losses. In addition, insufficient data carrying capacity may hinder the implementation of emerging technologies such as artificial intelligence, the Internet of Things, and big data analytics, which rely on large data sets and fast data processing.To enhance data carrying capacity, organizations can adopt several strategies. Firstly, investing in high-speed internet connectivity is crucial. This includes upgrading networkinfrastructure, implementing fiber optic cables, and leveraging 5G technology. Faster internet speeds ensure data can be transmitted quickly and efficiently, reducing latency and improving overall performance.Secondly, organizations can optimize data storage and management systems. Implementing cloud computing solutions and leveraging data centers can provide scalable storage options and reduce the burden on local servers. Cloud-based solutions also offer high availability and data redundancy, ensuring data can be accessed and recovered in the event of system failures or disasters.Furthermore, data compression and encryption techniques can be employed to reduce the size of data packets while maintaining security. This allows for more efficient data transmission and reduces bandwidth requirements. Similarly, data deduplication techniques can eliminate duplicate data, further optimizing data storage and transmission.Moreover, organizations can implement load balancing and traffic management techniques to distribute data across multiple servers and network paths. This prevents congestion and ensures that data can be processed and delivered in a timely manner. Additionally, implementing caching mechanisms can store frequently accessed data closer to the users, reducing latency and improving response times.In conclusion, data carrying capacity is essential in today's data-driven world. It enables organizations to handle and transport large volumes of data efficiently and effectively. The implications of insufficient data carrying capacity can be detrimental, leading to slow data transmission, system failures, and missed opportunities. To enhance data carrying capacity, organizations should invest in high-speed internet connectivity, optimize data storage and management systems, employ data compression and encryption techniques, implement load balancing and traffic management techniques, and leverage caching mechanisms. By doing so, organizations can ensure they have the necessary infrastructure and capabilities to meet the growing demand for data transmission and storage.。

云服务器英语术语

云服务器英语术语以下搜集了一些在云服务器中会比较常遇到的一些术语，提供中英对照信息：1. 自由计算free computing2. 弹性可伸缩elastic and scalable3. 主机host / instance4. 硬盘hard disk/ volume5. 密钥key6. 公开密钥public key7. 映像image / mapping8. 负载均衡load balancing9. 对象存储object storage10. 弹性计算elastic computing11. 按秒计费charged by seconds12. 多重实时副本multiple real-time copy13. 安全隔离security isolation14. 异地副本long-distance copy15. 后端系统back-end system16. 前端系统front-end system17. 写时拷贝技术copy-on-write technique18. 控制台console19. 监控台dashboard20. 远程终端remote terminal21. 服务端口service port22. 模拟主机显示器simulation host display23. 路由器router24. 多路万兆光纤multiple 10000MB optical fiber25. 密码验证登录password authentication login26. 静态IP static IP27. 动态IP dynamic IP28. 混合云hybrid cloud29. SLA服务级别协议Service Level Agreement30. 分布式存储distributed storage31. 存储柜locker32. 云计算加速器cloud computing accelerator33. 美国国家标准技术研究所NIST（National Institute of Standards andTechnology ）34. 智能电网 smart gird35. 智慧城市 smart city36. 物联网 Internet of Things (IOT)37. 集成电路 Integrated Circuit38. 嵌套虚拟化 nested virtualization39. 内存 memory40. 千兆 Gigabyte41. 网卡 network card42. 单线程测试 single thread test43. 最大素数测试 largest prime test44. 单核CPU single-core CPU45. 双核CPU dual-core CPU46. 磁盘吞吐量 disk throughput47. 边界网关协议 BGP（Border Gateway Protocol）48. 语音控制 voice control49. 湿度 humidity50. 智能分析 intelligent analysis51. 面向服务的架构SOA（Service Oriented Architecture）52. 开源操作系统 Open Source Operating System53. 虚拟机 virtual machine54. 源代码 source code55. 文档 document56. 全媒体 omni-media57. API接口 API interface58. 快照 snapshot59. 工单系统 ticket system60. 堡垒机 fortress machine61. 单点登录 SSO single sign on62. 脚本管理 script management63. 拓扑管理 topology management64. 数据提取、转换和加载ETL（Extraction-Transformation-Loading ）65. 网络流量network traffic66. 域名绑定domain banding67. 文件外链external document linking68. 防篡改tamper-proofing69. 防抵赖non-repudiation70. 端到端end-to-end71. 全景透视panoramic perspective72. 多维度特征识别multidimensional characteristic identification73. 检索retrieval74. 存储矩阵storage matrix75. 示例代码sample code76. 可执行代码executable code77. 远程擦除remote wipe78. 底层固件bottom firmware79. 存储分级storage tiering80. 回写式高速缓存Write-back Cache81. 软件定义存储software defined storage82. 横向可扩展存储 transverse extensible storage83. 模块化数据中心Modular Data Center84. DNS域名系统Domain Name System85. 封顶capping86. 芯片chip87. 第三方软件开发商ISV （ Independent Software Vendor）88. 特征向量Feature Vector89. 远程异地备份remote backup90. 虚拟显示技术visual vision91. 虚拟现实Virtual Reality (VR)92. 数据记录器Data Recorder93. 业务连续性管理Business Continuity Management (BCM)94. 钢筋砼框架reinforced concrete frame95. 防爆墙blast wall96. 入侵检测探测器intrusion detector97. 弱电间low voltage room98. 门禁系统access control system99.网络接入商web portal provider100. 审计日志audit logs101.不间断电源 UPS（Uninterrupted Power Supply）102. 柴油发电机 diesel generator103. 地下储油罐 underground petrol tank104. 多节点集群 multi-node cluster105. 预案 emergency response plan106. 高速复制链路 high-speed copying link107. 容错级 fault tolerance108. 里程表 milestone109. 制冷密度 cooling density110. 千瓦 kilowatt111. 灭火方式 fire extinguishing method112. 防渗漏等级 anti-leakage level113. 机房均布荷载 computer room even load114. 全冗余 full redundancy115. 两路市电 two-way electricity116. 一路自卑应急 one-way self-prepared emergency power117. 9度烈度 9 degree seismic intensity118. 密文 ciphertext119. 专属机柜 exclusive rack120. 设备上下电支持upper and lower electricitysupport121. 网络布线 network cabling122. 实时热备份 real time thermal backup123. 桌面演练 desktop practice124. 模拟切换演练 simulated switch practice125. 园区占地面积 floor area of the park126. 规划建设面积 planning construction area127. 高速链路复制 high-speed copying link128. 7※24hours 7 multiply 24 hours129. 安全和访问控制 security and visiting control （物理环境）。

计算机网络英语词汇LMN

计算机网络英语词汇 L M N下面是店铺整理的计算机网络英语词汇 L M N，欢迎大家阅读! [L]LCD 液晶显示屏Light Cabel 光缆Leased line 专线LPT 打印终端LPT 打印终端接口LAN 局域网LU 6.2 LU 6.2协议Lotus Notes Lotus的Notes软件Logons and Logon Accounts 用户登录和登录帐号Login Scripts 登录原语Logical Units 逻辑单元Logical Links 逻辑链路LocalTalk LocalTalk网Local Procedure Calls 本地过程调用Local Loops 局部环路Local Groups 本地组Local Exchange Carrier 本地交换电信局Local Area Transport 局域传输协议Local Area NetWorks 局域网Local Access and Transport Area 本地访问和传输区域Load-Balancing Bridges 负载平衡桥接器，负载平衡网桥Link State Routing 链路状态路由选择Link Services Protocol，NetWare NetWare的链路服务协议Link Layer 链路层Link Access Procedure 链路访问规程Line Conditioning 线路调节Licensing Server API 许可证服务器 APILegacy Systems 保留系统Leased Line 租用线路Learning Bridges 自学习桥接器Leaf Objects 叶对象Layered Architecture 分层体系结构Large Internetwork Packet Exchange 大型网间分组交换Laptop Connections 膝上机联网LAN Workplace Products，Novell Novell的 LAN Workplace 产品，Novell的局域网 Workplace产品LAN Troubleshooting 局域网故障诊断LANtastic LANtastic局域网操作系统LAN Server 局域网服务器LAN Requester 局域网请求解释器LAN Manager，Microsoft Microsoft的局域网管理器，Microsoft的 LAN Manager[M]Mosaic 摩塞克浏览器MO 磁性光盘Mac OS Mac操作系统MO 磁光盘MCSE 微软认证系统工程师MUD 分配角色的游戏环境Mainbus 系统总线Mainboard 主板MAN 城域网Memory Stick Memory Stick 存储棒MSI MSI 微星科技Multistation Access Unit 多站访问部件Multipurpose Internet Mail Extension Internet多功能邮件传递扩展标准Multiprotocol Transport Network(MPTN)，IBM IBM的多协议传输网络Multiprotocol Router 多协议路由器Multiprotocol Networks 多协议网络Multiprocessor Systems 多处理器系统Multiprocessing 多处理器处理Multiplexing 多路复用技术Multimedia 多媒体Multidrop(Multipoint)Connection 多点连接MOTIS(Message Oriented Text Interchange System) MOTIS(面向消息的文本交换系统)Motif Motif 工具Modems 调制解调器Mobile Computing 移动计算Mirroring 镜像Middleware 中间件Microwave Communication 微波通信Micro-to-Mainframe Connectivity 微型计算机到大型计算机的互联性Microsoft At Work Architecture Microsoft At Work体系结构Microsegmentation 微分段Microkernel 微内核Microcom Networking Protocol(MNP) Microcom的联网协议MicroChannel Architecture(MCA)Bus 微通道体系结构(MCA)总线Metropolitan Area Networks 城域网Messaging Application Programming Interface 消息应用程序编程接口Messaging API，Inter-Application 应用程序间的消息传递 APIMessaging API，E-mail E-mail的消息传递 APIMessage Transfer Agent 消息传送代理Message Queuing Interface(MAI)，IBM IBM的消息排队接口[N]NOC 网络操作中心NAT 网址解析NOC 网络操作中心NAT 网址解析NDIS 网络驱动程序接口Network Architecture 网络体系结构NSR 渲染引擎NFS 网络文件系统NAT 网址转换NWLink IPX/SPX协议微软执行部分NetBIOS 网络基本输入/输出系统Network interface card 网卡NTFS(New Technology File System) NTFS(新技术文件系统) Novell Novell公司Node 节点，结点，网点Network Troubleshooting 网络故障诊断与维修Network Service Protocol，DEC DEC网络服务协议Networks 网络NetWork Management 网络管理Network Layer，OSI Model OSI模型的网络层Network Interface Card 网络接口卡Networking Blueprint 联网方案Network File System 网络文件系统Network Dynamic Data Exchange 网络动态数据交换Network Driver Standards 网络驱动程序标准Network Driver Interface Specification 网络驱动程序接口规范NetWork Control Program 网络控制程序Network Architecture 网络体系结构NetWare Volumes NetWare的(文件)卷宗NetWare Shell NetWare工作站外壳程序NetWare SFT Level Ⅲ NetWare的三级系统容错NetWare Products NetWare软件产品NetWare Loadable Module NetWare的可装入模块NetWare Link Service Protocol NetWare的链路服务协议NetWare Electronic Software Distribution NetWare的电子软件分发NetWare Disks，Partitions，and Volumes NetWare的磁盘、分区和卷宗NetWare Core Protocol NetWare的核心协议NetWare NetWare网络操作系统NetView，IBM IBM的NetView网络管理系统NetLS(Network License Server) NetLS(网络许可权服务器)。

IT专业英语词汇精选(L1)

IT专业英语词汇精选(L1)L Labelling 标记L Lambert 朗伯（亮度单位）L Language 语言L Length 长度L Line 线路L Load 负载L Local 局部L Lumen 流明（光通亮单位）L Luminance 发光度.l Lex的源码文件格式〖后缀〗.l Lisp的源码文件格式〖后缀〗.l WATCOM wlink的链接器指令文件格式〖后缀〗L&A Lundeen & Associates 伦丁联合公司（美国，出品企业内部网设备）L2 Cache Level 2 Cache 二级高速缓存（通常由SRAM组成）L2F Level 2 Forwarding 二级转发，第二层转发（思科公司开发的网络协议）L2TF Layer Two Tunneling Protocol 第二层隧道协议（IETF的）L6 Laboratories Low Level Linked List Language 实验室低级连接表语言（美国贝尔电话公司研制）L8R latter 后者〖网语〗LA L. Alderman L. 奥尔德曼（美国南加州大学教授，1994年首次推出分子计算机）LA Laboratory Automation 实验室自动化la Lao People's Republic 老挝人民共和国（域名）LA Launch Antenna 发射天线LA Layered Architecture 层体系结构LA Library Association of the United Kingdom 英国图书馆协会LA Line Adapter 线路适配器LA Line Amplifier 线路放大器LA Linear Amplifier 线性放大器LA Link Acknowledgement 链路确认LA Link Address 链接地址LA Link Allotter 链路分配器LA Load Address 装入地址LA Local Address 本地地址，局部地址LA Local Area 本地区，区域LA Logical Address 逻辑地址LA Logout Analysis 退网程序分析，事件记录分析LA Lucas Arts 卢卡斯艺术公司（美国，1982年成立，世界著名娱乐光盘制造商，二十世纪十大电脑游戏公司之一）LAA LASER Attenuator Assembly 激光衰减器组件LAA Library Association of Australia 澳大利亚图书馆协会LAA Link Attention Acknowledgement 链路警示信号确认LAB Local Area Broadcast 局部区域广播LAB Logic Array Block 逻辑阵列块.lab NCSS和SOLO的数据文件格式〖后缀〗.lab Q+E for MS Excel的邮件标签文件格式〖后缀〗LABS Low –Altitude Bombing System 低空轰炸系统LAC Launcher Assignment Console 发射台作业控制台LAC Line Address Counter 行地址计数器LAC Load Accumulator 装入程序累加器LAC Low Complement of Address 地址少量补充LACE Library Advisory Council for England 英国图书馆咨询委员会LACE Local Automatic Circuit Exchange 当地自动线路交换机LACN Local Area Communication Network 局域通信网络LACN Local Area Computer Network 局域计算机网络LACR Low Altitude Coverage Radar 低空探测雷达LAD Language Acquisition Device 语言捕获装置LAD Load Address 装入地址LAD Logical Analysis Device 逻辑分析设备LAD Low Accuracy Data 低精度数据LADD Local Area Data Distribution 本地区数据分配LADS Local Area Data Set 本地区数据设置LADS Local Area Distributed System 本地区分布式系统LADT Local Area Data Transport 本地区数据传送LAE Least Absolute Error 最小绝对误差LAEC Los Angeles Electronic Club 洛杉矶电子俱乐部LAMA Local Automatic Message Accounting 当地报文自动计数LAMC Language and Mode Converter 语言与方式转换器LAMP Library Addition and Maintenance Point 程序库补充维护点LAMP Logic Analyzer for Maintenance Planning 用于维护计划的逻辑分析程序LAN Local Area Network 局域网，本地网，局网LANACS LAN Asynchronous Connection Server 局域网异步连接服务器LAND LAN Directory 局域网目录LANDP LAN Distributed Platform 局域网分布平台（IBM的）LANE Local Area Network Emulation 局域网仿真Lanet (Limitless ATM Network) 无限的异步传输模式网络LANI LAN Interconnection 局域网互连LAN-IC LAN Interconnect Service 局域网互连业务LANNET Large Artificial Nerve NETwork 大型人工神经网络LANS Local Area Network Server 局域网服务器LANSH Local Area Network Switch Hub 局域网开关集线器LAP Learning Activity Package 学习活动程序包LAP Line Access Point 线路接入点LAP Link Access Procedure 链路访问规程，链路接入规程LAP Link Access Protocol 链路存取协议LAP A (Byte Oriented Link Access Procedure) 面向字节的链路访问规程LAP B (Bit Oriented Link Access Procedure) 面向位的链路访问规程LAPB Link Access Procedure Balanced 平衡式链路访问规程，均衡式链路接入协议LAPD Link Access Procedure for D channel D通道链路访问规程，数字信道链路接入规程〖ISDN〗LAPF Link Access Procedure for Frame –mode 针对帧方式的链路访问规程LAPM Link Access Procedure for Modems 用于调制解调器的链路访问规程LAMP Link Access Protocol for Modems 用于调制解调器的链路访问协议LAPS Light Addressable Potentionmetric Sensor 光寻址电位分析传感器LAR Last Address Register 最后地址寄存器LAR Least Absolute Residual 最小绝对剩余，最小绝对残差LAR Local Acquisition Radar 局部捕获雷达，局部探测雷达LAR Local Address Register 当地地址寄存器LARAM Line Addressable RAM 行定址随机存取存储器LARCT LAst Radio ConTact 最后一次无线电联系LARPS Local And Remote Printing Station 本地和远程打印站LARR Large Area Record Reader 大面积纪录阅读器LARS Laser Angular Rate Sensor 激光角速率检测器LARS Launch Area Recovery System 发射区回收系统LARS Local Area Radio System 局域无线电系统LAS Launch Auxiliary System 发射辅助系统LAS Learning Apprentice System 新手学习系统LAS Light Activated Switch 光敏开关LAS Local Address Space 当地地址空间LAS Logical compare Accumulator with Storage 累加器与存储器的逻辑比较LAS Loop Actuating Signal 环路启动信号LASAM Laser Semi –Active Missile 激光半主动式制导导弹LASCR Light Activated Silicon Controlled Rectifier 光敏可控硅整流器LASD Local Access Statistical Demultiplexer 本地接入统计式解双工器，局部接入统计反复用器LASER Light Amplification by Stimulated Emission of Radiation 激光，激光器，受激辐射式光放大器LASL Los Alamos Scientific Laboratory 洛斯阿拉莫斯科学实验室LASS Light –Activated Silicon Switch 由光触发的硅开关LAT Local Area Transport 局域传送，本地区传送LATA Local Access Transmit Area 本地访问传送区域LATOS Language for Temporal Ordering Specification 用于暂时定制规范的语言LAU LAN Access Unit 局域网接入单元LAU Language Assignation Unique 单赋值语言LAU Line Adapter Unit 线路适配器单元LAURA Linear Automatic Reliability Analysis 线性自动可靠性分析LAWN Local Area Wireless Network 局域无线网LAX LAN Access eXchange 局域网存取交换机.lay APPLAUSE的字表页面布局文件格式〖后缀〗LB Laminar Bus 分层总线lb Lebanon 黎巴嫩（域名）LB Line Buffer 线路缓冲器LB Line Block 线路阻塞LB Line Busy 占线LB Linear Burst 线性突发LB Link Block 链路阻塞LB List Box 列表框〖编程〗LB Load Balance 加载平衡LB Local Battery 局部电池LB Local Bus 本地总线，局部总线LB Lower Byte 低位字节LBA Least Busy Alternative 最不忙选择对象LBA Local Battery Apparatus 本地电池设备LBA Logical Block Address(ing) 逻辑块寻址〖硬盘〗LBC Left Bounded Context 左限界上下文LBC Line Balance Converter 线路平衡转换器LBC Line Bus Controller 线路总线控制器LBC Local Bus Controller 本地总线控制器LBD Logic Block Diagram 逻辑方框图.lbg dBase IV的卷标发生器数据文件格式〖后缀〗LBID Local Buffer IDentifier 局部缓冲区标识符LBIR Laser Beam Image Recorder 激光束图像记录器LBIR Laser Beam Image Reproducer 激光束图像重现器.lbl dBase IV、Clipper 5、dBFast和FOXBASE的标签文件格式〖后缀〗LBM Local Buffer Memory 局部缓冲区（器）存储器.lbm DeluxePaint位图图形文件格式〖后缀〗.lbm XLib线性位图图形文件格式〖后缀〗.lbo dBase IV的编译标记文件格式〖后缀〗LBR Laser Beam Recording 激光束纪录LBR Low Bit Rate 低位率格式（Xing公司专用）.lbr 由LU (lue220.arc)生成的压缩存档文件格式〖后缀〗LBRV Low –Bit –Rate V oice 低比特率语音（编码技术）LBS Load Balance System 加载平衡系统LBS Load Balancing and Scheduling 加载的平衡和调度LBT Line Busy Tone 线路忙音LBT Listen –Before –Talk 先听后说LBT LoopBack Test 回送测试.lbt FoxPro的标签备忘录文件格式〖后缀〗LBX Local Bus Accelerator 局域总线加速器.lbx FoxPro的卷标文件格式〖后缀〗LC Leased Channel 租用信道LC Letter of Credit 信用证LC Level Control 电平控制LC Library of Congress 国会图书馆（美国）LC Line Concentrator 线路集中器，集线器LC Line Connector 线路连接器，接线器LC Link Control(ler) 链路控制（器）LC Liquid Crystal 液晶LC Local Call 室内电话LC Local Control 本地控制LC Logic Channel 逻辑通道LC Logic Corporation 逻辑组合LC Loop Check 环路检查LC Low Complexity 低复杂性〖MPEG〗LC Lower Case 小写体LC Lower Control 下限控制LC Luminance Channel 亮度通道lc St. Lucia 圣卢西亚（域名）LCA Line Control Adapter 线路控制适配器LCA Lotus Communications Architecture 莲花公司的通信体系结构LCB Line Control Block 线路控制块LCB Link Control Block 连接控制块LCB Logic Control Block 逻辑控制块LCC Laboratory Control Computer 实验室控制计算机LCC Language for Conversational Computing 会话式计算语言LCC Launch Control Center 发射控制中心LCC Leaderless Chip Carrier 无管脚芯片载体，无引线芯片载体LCC Library of Congress Classification 国会图书馆分类法（美国）LCC Life Cycle Costs 寿命周期成本LCC Line Concentration Controller 线路集中控制器LCC Link Controller Connector 链路控制器连接口LCC Local Communication Console 本地通信控制台LCC Logical Channel Control Module 逻辑通道控制语言LCC Lost Call Cleared 丢失的呼叫被清除LCCC Leaderless Ceramic Chip Carrier 无引线陶瓷芯片载体LCCC Library of Congress Computer Catalog 国会图书馆计算机编目（美国）LCCCN Library of Congress Catalog Card Number 国会图书馆目录卡片编号LCCM LAN Control Client Manager 局域网控制客户机管理器LCCP LANtastic Customer Control Panel LANtastic顾客控制面板LCCS Large Capacity Core Storage 大容量磁芯存储体LCD Legacy Corporation Database 公司祖传数据库，企业商贸数据库LCD Light Coupled Device 光耦合器件LCD Liquid Crystal Display 液晶显示（器）LCD Local Clock Distribution 本地时钟分配LCD Loss of Cell Delineation 单元描绘丢失LCD Lost Call Delayed 丢失的呼叫延时LCDM Logarithmic Companded Delta Modulation 对数压扩后的增量调制LCDTL Load Compensation Diode Transistor Logic 负载补偿二极管晶体管逻辑（电路）LCF Logical Channel Fill 逻辑通道填充LCF Low Cost Fiber 低成本光纤.lcf Norton Guides compiler的链接器控制文件格式〖后缀〗LCFS Last Come First Served 后来先服务LCGD Liquid Crystal Graphic Display 液晶图形显示LCGI Local Common Gateway Interface 本地通用网关接口LCGN Logical Channel Group Number 逻辑通道组编号LCGS Laboratories Command Guidance System 实验室指挥领导系统LCH LatCH 锁存器LCH Logical CHannel 逻辑通道LCH Logical CHannel queue 逻辑通道队列LCH Lost Call Held 丢失的呼叫挂起LCID Local Character set IDentifier 局部字符集标识符LCK Library Construction Kit 库构筑工具包.lck Paradox的文件锁格式〖后缀〗LCL Library Control Language 程序库控制语言LCL Linkage Control Language 连接控制语言LCL Logical Connection Layer 逻辑连接层LCL Longitudinal Conversion Loss 纵向转换损失LCL Lower Control Limit 控制下限.lcl FTP Software PC/TCP的数据文件格式〖后缀〗LCLV Liquid Crystal Light Valve 液晶光阀LCM LANdesk Client Manager LANdesk的客户机管理器LCM LANdesk Configuration Manager LANdesk的配置管理器LCM Large Capacity Memory 大容量存储器LCM Large Core Memory 大容量磁心存储器LCM Line Control Memory 线路控制存储器LCM Line Control Module 线路控制模块LCM Link Control Module 链路控制模块LCM Liquid Crystal Module 液晶模块LCM Load Control Module 加载控制模块LCM Local Customer Manager 当地客户管理器LCM Logical Channel Map 逻辑通道映射LCMD Logarithmic Companded Delta Modulation 对数压扩增量调制LCMP Loosely Coupled MultiProcessor 松散配合多处理器LCN Local Communications Network 本地通信网LCN Local Computer Network 本地计算机网络LCN Logical Channel Number 逻辑通道编号LCN Logically Connected Node 逻辑连接节点LCN Loosely –Coupled Network 松散耦合网络.lcn WordPerfect的经文文件格式〖后缀〗LCOS Line Class Of Service 线路业务种类LCP Language Conversion Program 语言转换程序LCP Line Control Processor 线路控制处理器LCP Link Configuration Protocol 链路配置协议LCP Link Control Procedure 链路控制规程LCP Link Control Program 链路控制程序LCP Link Control Protocol 链路控制协议LCP Loading Control Program 装入控制程序LCP Logical Construction of Program 程序逻辑结构LCPBX Large Computerized Private Branch eXchange 大型计算机化私用分支交换机LCR Inductance –Capacitance –Resistance 电感、电容、电阻LCR Least Cost Routing 最低成本路由选择LCR Line Call Rate 线路呼叫率LCRIS Loop Cable Record Inventory System 环路电缆纪录式存货清点系统LCS Landing Control System 着陆控制系统LCS Large Capacity(Core) Storage 大容量（磁芯）存储体LCS Laser Communications System 激光通信系统LCS Learning Classifier System 学习分类器系统LCS Library Computer System 图书馆计算机系统LCS Line Conditioning Signal 线路调节信号LCS Local Communication Server 本地通信服务器LCS Low order Connection Supervision 低位连接监督LCS Loudness Contour Selector 响度范围选择器.lcs ACT!历史文件的数据文件格式〖后缀〗LCSU Local Concentrator Switching Unit 本地集线器切换装置LCT Line and Circuit Tester 线路电路检测器LCT Line Control Table 线路控制表LCT Local Civil Time 当地民用时间LCT Logic Channel Terminal 逻辑通道终端LCTL Longitudinal Conversion Transfer Loss 纵向转换传送损失LCU Level Conversion Unit 电平转换器LCU Line Control Unit 线路控制器，行控制器LCU Local Control Unit 本地控制器LCU Loop Control Unit 环路控制器LCW Line Control Word 线路控制字，行控制字.lcw Lucid 3-D的电子表格文件格式〖后缀〗LD Laser Diode 激光二极管〖DVD〗LD Laser Disk 激光唱盘LD Light Director 灯光调度LD Limiter –Discriminator 限幅器－鉴别器LD Link Disconnect 链路断开LD Linear Decision 线性判定LD Logic Driver 逻辑驱动器LD Long Distance 长途电话局，远程LD Loop Disconnect 环路断开.ld Telix的长途码文件格式〖后缀〗.ld1 dBase的覆盖图文件格式〖后缀〗LDA LoaD A direct 直接送入累加器A〖指令〗LDA Local Data Administrator 本地数据管理员LDA Local Data Area 本地数据区LDA Local Display Adapter 本地显示器适配器LDA Locate Drum Address 查找磁鼓地址LDA Logical Device Address 逻辑设备地址LDAP Light(weight) Directory Access Protocol 简单目录访问协议，轻权目录访问协议，轻量级地址薄访问协议LDAPD Lightweight Directory Access Protocol Daemon 轻型目录访问协议后台驻留程序〖因特网〗LDB Large Data Base 大型数据库LDB Local Data Base 本地数据库LDB Logical Data Base 逻辑数据库.ldb 微软Access的数据文件格式〖后缀〗LDBS Large Data Base System 大型数据库系统LDBS Local Data Base System 本地数据库系统LDBU Legato Data Backup Utility 连续演奏（唱）数据备份工具LDC Latitude Data Computer （本文来自第一范文网，转载请保留此标记。

citrix服务器作用是什么

citrix服务器作用是什么?1、Citrix基于移动工作环境的解决方案，可以帮助用户在任何时间、任何地点、利用任何设备、通过任何网络连接——从无线、有线到Web——随时访问所需的应用程序，从而可以安全、自由地实现实时业务处理。

2、Citrix基于应用部署的解决方案，可以在几分钟之内在整个机构、乃至全球范围内，实现应用系统的在线运转，而不是像过去那样需要几个月的时间。

解决方案能够单点控制、集中管理、易于扩展，并通过任何方式进行连接，获取全功能特征的应用程序，节省了资金投入并提高了生产效率。

Citrix3、Citrix 基于远程办公室连接的解决方案，已被无数事实证明所证明，这是一种优秀的远程接入技术，它能为你的新办公室或新并购的异地机构提供各种无障碍连接。

这项工作以前可能需要几周时间才能完成，而现在只需要几分钟。

因此，这一方案将帮助用户提高工作效率，减少运行成本，而且保证全局系统更加安全可靠。

Citrix MetaFrame和Web应用程序Citrix Systems MetaFrame技术为IT规划人员提供了面对这些挑战的方法，它所实施的网络架构不仅可以支持Web应用程序，并且支持当前几乎所有部署在台式机的应用程序。

Citrix MetaFrame将现有的Web应用程序和架构部署到用户，同时满足IT规划人员所面临的四个首要挑战：? 带宽/网络? 性能? 硬件成本? 集中化管理带宽/网络。

MetaFrame XP?使IT管理员将浏览器作为发布应用程序分发到用户。

在该架构下，客户端系统只收到ICA数据流，包括鼠标的移动，键盘击键和屏幕的变化。

从而降低了网络利用率，同时使得浏览器与Web及应用服务器可以共存于高速骨干网。

因此，应用程序的性能将不会受到因处理大量请求而变得不可预见的网络可用性的影响。

由于Web应用程序的突发性，将ICA与基于浏览器的应用程序进行对比的最佳方法不是平均带宽利用率。

这是因为，每时每刻都存在一个会话，当一个ICA会话被激活时，就会在网络上来回传送一定的网络流量。

Parallel and Distributed Computing and Systems

Proceedings of the IASTED International ConferenceParallel and Distributed Computing and SystemsNovember3-6,1999,MIT,Boston,USAParallel Reﬁnement of Unstructured MeshesJos´e G.Casta˜n os and John E.SavageDepartment of Computer ScienceBrown UniversityE-mail:jgc,jes@AbstractIn this paper we describe a parallel-reﬁnement al-gorithm for unstructuredﬁnite element meshes based on the longest-edge bisection of triangles and tetrahedrons. This algorithm is implemented in P ARED,a system that supports the parallel adaptive solution of PDEs.We dis-cuss the design of such an algorithm for distributed mem-ory machines including the problem of propagating reﬁne-ment across processor boundaries to obtain meshes that are conforming and non-degenerate.We also demonstrate that the meshes obtained by this algorithm are equivalent to the ones obtained using the serial longest-edge reﬁne-ment method.Weﬁnally report on the performance of this reﬁnement algorithm on a network of workstations.Keywords:mesh reﬁnement,unstructured meshes,ﬁnite element methods,adaptation.1.IntroductionTheﬁnite element method(FEM)is a powerful and successful technique for the numerical solution of partial differential equations.When applied to problems that ex-hibit highly localized or moving physical phenomena,such as occurs on the study of turbulence inﬂuidﬂows,it is de-sirable to compute their solutions adaptively.In such cases, adaptive computation has the potential to signiﬁcantly im-prove the quality of the numerical simulations by focusing the available computational resources on regions of high relative error.Unfortunately,the complexity of algorithms and soft-ware for mesh adaptation in a parallel or distributed en-vironment is signiﬁcantly greater than that it is for non-adaptive computations.Because a portion of the given mesh and its corresponding equations and unknowns is as-signed to each processor,the reﬁnement(coarsening)of a mesh element might cause the reﬁnement(coarsening)of adjacent elements some of which might be in neighboring processors.To maintain approximately the same number of elements and vertices on every processor a mesh must be dynamically repartitioned after it is reﬁned and portions of the mesh migrated between processors to balance the work.In this paper we discuss a method for the paral-lel reﬁnement of two-and three-dimensional unstructured meshes.Our reﬁnement method is based on Rivara’s serial bisection algorithm[1,2,3]in which a triangle or tetrahe-dron is bisected by its longest edge.Alternative efforts to parallelize this algorithm for two-dimensional meshes by Jones and Plassman[4]use randomized heuristics to reﬁne adjacent elements located in different processors.The parallel mesh reﬁnement algorithm discussed in this paper has been implemented as part of P ARED[5,6,7], an object oriented system for the parallel adaptive solu-tion of partial differential equations that we have devel-oped.P ARED provides a variety of solvers,handles selec-tive mesh reﬁnement and coarsening,mesh repartitioning for load balancing,and interprocessor mesh migration.2.Adaptive Mesh ReﬁnementIn theﬁnite element method a given domain is di-vided into a set of non-overlapping elements such as tri-angles or quadrilaterals in2D and tetrahedrons or hexahe-drons in3D.The set of elements and its as-sociated vertices form a mesh.With theaddition of boundary conditions,a set of linear equations is then constructed and solved.In this paper we concentrate on the reﬁnement of conforming unstructured meshes com-posed of triangles or tetrahedrons.On unstructured meshes, a vertex can have a varying number of elements adjacent to it.Unstructured meshes are well suited to modeling do-mains that have complex geometry.A mesh is said to be conforming if the triangles and tetrahedrons intersect only at their shared vertices,edges or faces.The FEM can also be applied to non-conforming meshes,but conformality is a property that greatly simpliﬁes the method.It is also as-sumed to be a requirement in this paper.The rate of convergence and quality of the solutions provided by the FEM depends heavily on the number,size and shape of the mesh elements.The condition number(a)(b)(c)Figure1:The reﬁnement of the mesh in using a nested reﬁnement algorithm creates a forest of trees as shown in and.The dotted lines identify the leaf triangles.of the matrices used in the FEM and the approximation error are related to the minimum and maximum angle of all the elements in the mesh[8].In three dimensions,the solid angle of all tetrahedrons and their ratio of the radius of the circumsphere to the inscribed sphere(which implies a bounded minimum angle)are usually used as measures of the quality of the mesh[9,10].A mesh is non-degenerate if its interior angles are never too small or too large.For a given shape,the approximation error increases with ele-ment size(),which is usually measured by the length of the longest edge of an element.The goal of adaptive computation is to optimize the computational resources used in the simulation.This goal can be achieved by reﬁning a mesh to increase its resolution on regions of high relative error in static problems or by re-ﬁning and coarsening the mesh to follow physical anoma-lies in transient problems[11].The adaptation of the mesh can be performed by changing the order of the polynomi-als used in the approximation(-reﬁnement),by modifying the structure of the mesh(-reﬁnement),or a combination of both(-reﬁnement).Although it is possible to replace an old mesh with a new one with smaller elements,most -reﬁnement algorithms divide each element in a selected set of elements from the current mesh into two or more nested subelements.In P ARED,when an element is reﬁned,it does not get destroyed.Instead,the reﬁned element inserts itself into a tree,where the root of each tree is an element in the initial mesh and the leaves of the trees are the unreﬁned elements as illustrated in Figure1.Therefore,the reﬁned mesh forms a forest of reﬁnement trees.These trees are used in many of our algorithms.Error estimates are used to determine regions where adaptation is necessary.These estimates are obtained from previously computed solutions of the system of equations. After adaptation imbalances may result in the work as-signed to processors in a parallel or distributed environ-ment.Efﬁcient use of resources may require that elements and vertices be reassigned to processors at runtime.There-fore,any such system for the parallel adaptive solution of PDEs must integrate subsystems for solving equations,adapting a mesh,ﬁnding a good assignment of work to processors,migrating portions of a mesh according to anew assignment,and handling interprocessor communica-tion efﬁciently.3.P ARED:An OverviewP ARED is a system of the kind described in the lastparagraph.It provides a number of standard iterativesolvers such as Conjugate Gradient and GMRES and pre-conditioned versions thereof.It also provides both-and -reﬁnement of meshes,algorithms for adaptation,graph repartitioning using standard techniques[12]and our ownParallel Nested Repartitioning(PNR)[7,13],and work mi-gration.P ARED runs on distributed memory parallel comput-ers such as the IBM SP-2and networks of workstations.These machines consist of coarse-grained nodes connectedthrough a high to moderate latency network.Each nodecannot directly address a memory location in another node. In P ARED nodes exchange messages using MPI(Message Passing Interface)[14,15,16].Because each message has a high startup cost,efﬁcient message passing algorithms must minimize the number of messages delivered.Thus, it is better to send a few large messages rather than many small ones.This is a very important constraint and has a signiﬁcant impact on the design of message passing algo-rithms.P ARED can be run interactively(so that the user canvisualize the changes in the mesh that results from meshadaptation,partitioning and migration)or without directintervention from the user.The user controls the systemthrough a GUI in a distinguished node called the coordina-tor,.This node collects information from all the other processors(such as its elements and vertices).This tool uses OpenGL[17]to permit the user to view3D meshes from different angles.Through the coordinator,the user can also give instructions to all processors such as specify-ing when and how to adapt the mesh or which strategy to use when repartitioning the mesh.In our computation,we assume that an initial coarse mesh is given and that it is loaded into the coordinator.The initial mesh can then be partitioned using one of a num-ber of serial graph partitioning algorithms and distributed between the processors.P ARED then starts the simulation. Based on some adaptation criterion[18],P ARED adapts the mesh using the algorithms explained in Section5.Af-ter the adaptation phase,P ARED determines if a workload imbalance exists due to increases and decreases in the num-ber of mesh elements on individual processors.If so,it invokes a procedure to decide how to repartition mesh el-ements between processors;and then moves the elements and vertices.We have found that PNR gives partitions with a quality comparable to those provided by standard meth-ods such as Recursive Spectral Bisection[19]but which(b)(a)Figure2:Mesh representation in a distributed memory ma-chine using remote references.handles much larger problems than can be handled by stan-dard methods.3.1.Object-Oriented Mesh RepresentationsIn P ARED every element of the mesh is assigned to a unique processor.V ertices are shared between two or more processors if they lie on a boundary between parti-tions.Each of these processors has a copy of the shared vertices and vertices refer to each other using remote ref-erences,a concept used in object-oriented programming. This is illustrated in Figure2on which the remote refer-ences(marked with dashed arrows)are used to maintain the consistency of multiple copies of the same vertex in differ-ent processors.Remote references are functionally similar to standard C pointers but they address objects in a different address space.A processor can use remote references to invoke meth-ods on objects located in a different processor.In this case, the method invocations and arguments destined to remote processors are marshalled into messages that contain the memory addresses of the remote objects.In the destina-tion processors these addresses are converted to pointers to objects of the corresponding type through which the meth-ods are invoked.Because the different nodes are inher-ently trusted and MPI guarantees reliable communication, P ARED does not incur the overhead traditionally associated with distributed object systems.Another idea commonly found in object oriented pro-gramming and which is used in P ARED is that of smart pointers.An object can be destroyed when there are no more references to it.In P ARED vertices are shared be-tween several elements and each vertex counts the number of elements referring to it.When an element is created, the reference count of its vertices is incremented.Simi-larly,when the element is destroyed,the reference count of its vertices is decremented.When the reference count of a vertex reaches zero,the vertex is no longer attached to any element located in the processor and can be destroyed.If a vertex is shared,then some other processor might have a re-mote reference to it.In that case,before a copy of a shared vertex is destroyed,it informs the copies in other processors to delete their references to itself.This procedure insures that the shared vertex can then be safely destroyed without leaving dangerous dangling pointers referring to it in other processors.Smart pointers and remote references provide a simple replication mechanism that is tightly integrated with our mesh data structures.In adaptive computation,the struc-ture of the mesh evolves during the computation.During the adaptation phase,elements and vertices are created and destroyed.They may also be assigned to a different pro-cessor to rebalance the work.As explained above,remote references and smart pointers greatly simplify the task of creating dynamic meshes.4.Adaptation Using the Longest Edge Bisec-tion AlgorithmMany-reﬁnement techniques[20,21,22]have been proposed to serially reﬁne triangular and tetrahedral meshes.One widely used method is the longest-edge bisec-tion algorithm proposed by Rivara[1,2].This is a recursive procedure(see Figure3)that in two dimensions splits each triangle from a selected set of triangles by adding an edge between the midpoint of its longest side to the opposite vertex.In the case that makes a neighboring triangle,,non-conforming,then is reﬁned using the same algorithm.This may cause the reﬁnement to prop-agate throughout the mesh.Nevertheless,this procedure is guaranteed to terminate because the edges it bisects in-crease in length.Building on the work of Rosenberg and Stenger[23]on bisection of triangles,Rivara[1,2]shows that this reﬁnement procedure provably produces two di-mensional meshes in which the smallest angle of the re-ﬁned mesh is no less than half of the smallest angle of the original mesh.The longest-edge bisection algorithm can be general-ized to three dimensions[3]where a tetrahedron is bisected into two tetrahedrons by inserting a triangle between the midpoint of its longest edge and the two vertices not in-cluded in this edge.The reﬁnement propagates to neigh-boring tetrahedrons in a similar way.This procedure is also guaranteed to terminate,but unlike the two dimensional case,there is no known bound on the size of the small-est angle.Nevertheless,experiments conducted by Rivara [3]suggest that this method does not produce degenerate meshes.In two dimensions there are several variations on the algorithm.For example a triangle can initially be bisected by the longest edge,but then its children are bisected by the non-conforming edge,even if it is that is not their longest edge[1].In three dimensions,the bisection is always per-formed by the longest edge so that matching faces in neigh-boring tetrahedrons are always bisected by the same com-mon edge.Bisect()let,and be vertices of the trianglelet be the longest side of and let be the midpoint ofbisect by the edge,generating two new triangles andwhile is a non-conforming vertex doﬁnd the non-conforming triangle adjacent to the edgeBisect()end whileFigure3:Longest edge(Rivara)bisection algorithm for triangular meshes.Because in P ARED reﬁned elements are not destroyed in the reﬁnement tree,the mesh can be coarsened by replac-ing all the children of an element by their parent.If a parent element is selected for coarsening,it is important that all the elements that are adjacent to the longest edge of are also selected for coarsening.If neighbors are located in different processors then only a simple message exchange is necessary.This algorithm generates conforming meshes: a vertex is removed only if all the elements that contain that vertex are all coarsened.It does not propagate like the re-ﬁnement algorithm and it is much simpler to implement in parallel.For this reason,in the rest of the paper we will focus on the reﬁnement of meshes.5.Parallel Longest-Edge ReﬁnementThe longest-edge bisection algorithm and many other mesh reﬁnement algorithms that propagate the reﬁnement to guarantee conformality of the mesh are not local.The reﬁnement of one particular triangle or tetrahedron can propagate through the mesh and potentially cause changes in regions far removed from.If neighboring elements are located in different processors,it is necessary to prop-agate this reﬁnement across processor boundaries to main-tain the conformality of the mesh.In our parallel longest edge bisection algorithm each processor iterates between a serial phase,in which there is no communication,and a parallel phase,in which each processor sends and receives messages from other proces-sors.In the serial phase,processor selects a setof its elements for reﬁnement and reﬁnes them using the serial longest edge bisection algorithms outlined earlier. The reﬁnement often creates shared vertices in the bound-ary between adjacent processors.To minimize the number of messages exchanged between and,delays the propagation of reﬁnement to until has reﬁned all the elements in.The serial phase terminates when has no more elements to reﬁne.A processor informs an adjacent processor that some of its elements need to be reﬁned by sending a mes-sage from to containing the non-conforming edges and the vertices to be inserted at their midpoint.Each edge is identiﬁed by its endpoints and and its remote ref-erences(see Figure4).If and are sharedvertices,(a)(c)(b)Figure4:In the parallel longest edge bisection algo-rithm some elements(shaded)are initially selected for re-ﬁnement.If the reﬁnement creates a new(black)ver-tex on a processor boundary,the reﬁnement propagates to neighbors.Finally the references are updated accord-ingly.then has a remote reference to copies of and lo-cated in processor.These references are included in the message,so that can identify the non-conforming edge and insert the new vertex.A similar strategy can be used when the edge is reﬁned several times during the re-ﬁnement phase,but in this case,the vertex is not located at the midpoint of.Different processors can be in different phases during the reﬁnement.For example,at any given time a processor can be reﬁning some of its elements(serial phase)while neighboring processors have reﬁned all their elements and are waiting for propagation messages(parallel phase)from adjacent processors.waits until it has no elements to reﬁne before receiving a message from.For every non-conforming edge included in a message to,creates its shared copy of the midpoint(unless it already exists) and inserts the new non-conforming elements adjacent to into a new set of elements to be reﬁned.The copy of in must also have a remote reference to the copy of in.For this reason,when propagates the reﬁne-ment to it also includes in the message a reference to its copies of shared vertices.These steps are illustrated in Figure4.then enters the serial phase again,where the elements in are reﬁned.(c)(b)(a)Figure5:Both processors select(shaded)mesh el-ements for reﬁnement.The reﬁnement propagates to a neighboring processor resulting in more elements be-ing reﬁned.5.1.The Challenge of Reﬁning in ParallelThe description of the parallel reﬁnement algorithm is not complete because reﬁnement propagation across pro-cessor boundaries can create two synchronization prob-lems.Theﬁrst problem,adaptation collision,occurs when two(or more)processors decide to reﬁne adjacent elements (one in each processor)during the serial phase,creating two(or more)vertex copies over a shared edge,one in each processor.It is important that all copies refer to the same logical vertex because in a numerical simulation each ver-tex must include the contribution of all the elements around it(see Figure5).The second problem that arises,termination detection, is the determination that a reﬁnement phase is complete. The serial reﬁnement algorithm terminates when the pro-cessor has no more elements to reﬁne.In the parallel ver-sion termination is a global decision that cannot be deter-mined by an individual processor and requires a collabora-tive effort of all the processors involved in the reﬁnement. Although a processor may have adapted all of its mesh elements in,it cannot determine whether this condition holds for all other processors.For example,at any given time,no processor might have any more elements to re-ﬁne.Nevertheless,the reﬁnement cannot terminate because there might be some propagation messages in transit.The algorithm for detecting the termination of parallel reﬁnement is based on Dijkstra’s general distributed termi-nation algorithm[24,25].A global termination condition is reached when no element is selected for reﬁnement.Hence if is the set of all elements in the mesh currently marked for reﬁnement,then the algorithmﬁnishes when.The termination detection procedure uses message ac-knowledgments.For every propagation message that receives,it maintains the identity of its source()and to which processors it propagated reﬁnements.Each prop-agation message is acknowledged.acknowledges to after it has reﬁned all the non-conforming elements created by’s message and has also received acknowledgments from all the processors to which it propagated reﬁnements.A processor can be in two states:an inactive state is one in which has no elements to reﬁne(it cannot send new propagation messages to other processors)but can re-ceive messages.If receives a propagation message from a neighboring processor,it moves from an inactive state to an active state,selects the elements for reﬁnement as spec-iﬁed in the message and proceeds to reﬁne them.Let be the set of elements in needing reﬁnement.A processor becomes inactive when:has received an acknowledgment for every propa-gation message it has sent.has acknowledged every propagation message it has received..Using this deﬁnition,a processor might have no more elements to reﬁne()but it might still be in an active state waiting for acknowledgments from adjacent processors.When a processor becomes inactive,sends an acknowledgment to the processors whose propagation message caused to move from an inactive state to an active state.We assume that the reﬁnement is started by the coordi-nator processor,.At this stage,is in the active state while all the processors are in the inactive state.ini-tiates the reﬁnement by sending the appropriate messages to other processors.This message also speciﬁes the adapta-tion criterion to use to select the elements for reﬁnement in.When a processor receives a message from,it changes to an active state,selects some elements for reﬁne-ment either explicitly or by using the speciﬁed adaptation criterion,and then reﬁnes them using the serial bisection algorithm,keeping track of the vertices created over shared edges as described earlier.When itﬁnishes reﬁning its ele-ments,sends a message to each processor on whose shared edges created a shared vertex.then listens for messages.Only when has reﬁned all the elements speciﬁed by and is not waiting for any acknowledgment message from other processors does it sends an acknowledgment to .Global termination is detected when the coordinator becomes inactive.When receives an acknowledgment from every processor this implies that no processor is re-ﬁning an element and that no processor is waiting for an acknowledgment.Hence it is safe to terminate the reﬁne-ment.then broadcasts this fact to all the other proces-sors.6.Properties of Meshes Reﬁned in ParallelOur parallel reﬁnement algorithm is guaranteed to ter-minate.In every serial phase the longest edge bisectionLet be a set of elements to be reﬁnedwhile there is an element dobisect by its longest edgeinsert any non-conforming element intoend whileFigure6:General longest-edge bisection(GLB)algorithm.algorithm is used.In this algorithm the reﬁnement prop-agates towards progressively longer edges and will even-tually reach the longest edge in each processor.Between processors the reﬁnement also propagates towards longer edges.Global termination is detected by using the global termination detection procedure described in the previous section.The resulting mesh is conforming.Every time a new vertex is created over a shared edge,the reﬁnement propagates to adjacent processors.Because every element is always bisected by its longest edge,for triangular meshes the results by Rosenberg and Stenger on the size of the min-imum angle of two-dimensional meshes also hold.It is not immediately obvious if the resulting meshes obtained by the serial and parallel longest edge bisection al-gorithms are the same or if different partitions of the mesh generate the same reﬁned mesh.As we mentioned earlier, messages can arrive from different sources in different or-ders and elements may be selected for reﬁnement in differ-ent sequences.We now show that the meshes that result from reﬁning a set of elements from a given mesh using the serial and parallel algorithms described in Sections4and5,re-spectively,are the same.In this proof we use the general longest-edge bisection(GLB)algorithm outlined in Figure 6where the order in which elements are reﬁned is not spec-iﬁed.In a parallel environment,this order depends on the partition of the mesh between processors.After showing that the resulting reﬁned mesh is independent of the order in which the elements are reﬁned using the serial GLB al-gorithm,we show that every possible distribution of ele-ments between processors and every order of parallel re-ﬁnement yields the same mesh as would be produced by the serial algorithm.Theorem6.1The mesh that results from the reﬁnement of a selected set of elements of a given mesh using the GLB algorithm is independent of the order in which the elements are reﬁned.Proof:An element is reﬁned using the GLBalgorithm if it is in the initial set or reﬁnementpropagates to it.An element is reﬁnedif one of its neighbors creates a non-conformingvertex at the midpoint of one of its edges.Thereﬁnement of by its longest edge divides theelement into two nested subelements andcalled the children of.These children are inturn reﬁned by their longest edge if one of their edges is non-conforming.The reﬁnement proce-dure creates a forest of trees of nested elements where the root of each tree is an element in theinitial mesh and the leaves are unreﬁned ele-ments.For every element,let be the reﬁnement tree of nested elements rooted atwhen the reﬁnement procedure terminates. Using the GLB procedure elements can be se-lected for reﬁnement in different orders,creating possible different reﬁnement histories.To show that this cannot happen we assume the converse, namely,that two reﬁnement histories and generate different reﬁned meshes,and establish a contradiction.Thus,assume that there is an ele-ment such that the reﬁnement trees and,associated with the reﬁnement histories and of respectively,are different.Be-cause the root of and is the same in both reﬁnement histories,there is a place where both treesﬁrst differ.That is,starting at the root,there is an element that is common to both trees but for some reason,its children are different.Be-cause is always bisected by the longest edge, the children of are different only when is reﬁned in one reﬁnement history and it is not re-ﬁned in the other.In other words,in only one of the histories does have children.Because is reﬁned in only one reﬁnement his-tory,then,the initial set of elements to reﬁne.This implies that must have been reﬁned because one of its edges became non-conforming during one of the reﬁnement histo-ries.Let be the set of elements that are present in both reﬁnement histories,but are re-ﬁned in and not in.We deﬁne in a similar way.For each reﬁnement history,every time an ele-ment is reﬁned,it is assigned an increasing num-ber.Select an element from either or that has the lowest number.Assume that we choose from so that is reﬁned in but not in.In,is reﬁned because a neigh-boring element created a non-conforming ver-tex at the midpoint of their shared edge.There-fore is reﬁned in but not in because otherwise it would cause to be reﬁned in both sequences.This implies that is also in and has a lower reﬁnement number than con-。

大数据常见术语解释（全文）

大数据常见术语解释（全文）大数据常见术语解释（全文）胡经国大数据（B ig Data），是指无法在可承受的时间范围内用常规软件工具进行捕捉、管理和处理的数据集合，是需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能力的海量、高增长率和多样化的信息资产。

大数据的出现产生了许多新术语，这些术语往往比较难以理解。

为此，我们根据有关大数据文献编写了本文，供大家认识大数据参考。

1、聚合（Aggregation）聚合是指搜索、合并、显示数据的过程。

2、算法（Algorithms）算法是指可以完成某种数据分析的数学公式。

3、分析法（Analytics）分析法用于发现数据的内在涵义。

4、异常检测（Anomaly Detection）异常检测用于在数据集中搜索与预期模式或行为不匹配的数据项。

除了“Anomalies”以外，用来表示“异常”的英文单词还有以下几个：outliers，exceptions，surprises，contaminants。

它们通常可提供关键的可执行信息。

5、匿名化（Anonymization）匿名化使数据匿名，即移除所有与个人隐私相关的数据。

6、应用（Application）在这里，应用是指实现某种特定功能的计算机软件。

7、人工智能（Artificial Intelligence）人工智能是指研发智能机器和智能软件；这些智能设备能够感知周围的环境，并根据要求作出相应的反应，甚至能自我学习。

8、行为分析法（Behavioural Analytics）行为分析法是指根据用户的行为如“怎么做”，“为什么这么做”以及“做了什么”来得出结论，而不是仅仅针对人物和时间的一门分析学科。

它着眼于数据中的人性化模式。

9、大数据科学家（Big Data Scientist）大数据科学家是指能够设计大数据算法使得大数据变得有用的人。

10、大数据创业公司（Big Data Startup）大数据创业公司是指研发最新大数据技术的新兴公司。

网格计算中基于二阶段的Min-Min调度算法

网格计算中基于二阶段的Min-Min调度算法程红霞;杨臻;谭新莲【摘要】针对复杂网格计算环境中传统Min-Min调度算法不能得到一个负载均衡调度的问题,提出一种实现负载均衡的改进Min-Min调度算法.算法的实现包括两个阶段,在第一阶段,执行传统的Min-Min算法,确定出具有最小执行时间的任务和计算它的资源;在第二阶段,选择具有负载较重的资源,将负载重新分配到负载较轻的资源上,有效利用第一阶段的空闲资源.实验结果表明,该调度算法相比传统的Min-Min算法,能够减少完工时间,提高资源利用率.%Aiming at the issue that the traditional Min-Min scheduling algorithm can not get a load balancing scheduling in the complex grid computing environment,an improved Min-Min scheduling algorithm to achieve load balancing was proposed.The implementation for the algorithm consisted of two phases.In the first phase,through the implementation of the traditional MinMin algorithm,the tasks with minimum execution time and the resources used to calculate them were determined.In the second phase,the resources with heavy load were chosen and they were reassigned to the resources with light load so as to effectively utilize the idle resources in first phase.Experimental results show that the proposed scheduling algorithm,compared with the traditional Min-Min algorithm,can not only reduce the completion time,but also improve the resource utilization.【期刊名称】《计算机工程与设计》【年(卷),期】2017(038)012【总页数】5页(P3334-3338)【关键词】网格计算;二阶段调度;负载均衡;最小-最小算法;完工时间;资源利用率【作者】程红霞;杨臻;谭新莲【作者单位】郑州师范学院信息科学与技术学院,河南郑州 450044;郑州师范学院信息科学与技术学院,河南郑州 450044;郑州大学信息工程学院,河南郑州450002【正文语种】中文【中图分类】TP311目前，网格计算已经成为传统超级计算环境中开发并行应用的一个事实上的选择；最复杂的科学、工程和商业问题需要大量的资源来完成，网格计算能有效利用资源的空闲时间，网格计算被认为是解决这些问题的最佳方案[1]；网格计算是把地理上分布的资源和各种应用相结合，提交其作业(任务)的用户无需知道用于完成他们作业的资源的位置。

Dynamic Load Balancing of Unbalanced Computations Using Message Passing

Dynamic Load Balancing of Unbalanced Computations Using Message Passing James Dinan1,Stephen Olivier2,Gerald Sabin1,Jan Prins2,P.Sadayappan1,and Chau-Wen Tseng31Dept.of Comp.Sci.and Engineering2Dept.of Computer ScienceThe Ohio State University Univ.of North Carolina at Chapel HillColumbus,OH43221Chapel Hill,NC27599{dinan,sabin,saday}@{olivier,prins}@3Dept.of Computer ScienceUniv.of Maryland at College ParkCollege Park,MD20742tseng@AbstractThis paper examines MPI’s ability to support continu-ous,dynamic load balancing for unbalanced parallel ap-plications.We use an unbalanced tree search benchmark (UTS)to compare two approaches,1)work sharing using a centralized work queue,and2)work stealing using ex-plicit polling to handle steal requests.Experiments indicate that in addition to a parameter deﬁning the granularity of load balancing,message-passing paradigms require addi-tional parameters such as polling intervals to manage run-time ing these additional parameters,we ob-served an improvement of up to2X in parallel performance. Overall we found that while work sharing may achieve bet-ter peak performance on certain workloads,work steal-ing achieves comparable if not better performance across a wider range of chunk sizes and workloads.1IntroductionThe goal of the Unbalanced Tree Search(UTS)parallel benchmark is to characterize the performance that a par-ticular combination of computer system and parallel pro-gramming model can attain when solving an unbalanced problem that requires dynamic load balancing[16].This is accomplished by measuring the performance of executing parallel tree search on a richly parameterized,unbalanced workload.In particular,the workloads explored by UTS 1-4244-0910-1/07/$20.00c 2007IEEE.exemplify computations where static partitioning schemes cannot yield good parallelism due to unpredictability in the problem space.Shared memory and Partitioned Global Address Space (PGAS)programming models(e.g.OpenMP,UPC,CAF,or Titanium)provide a natural environment for implementing dynamic load balancing schemes through support for shared global state and one-sided operations on remote memory. On shared memory machines,the ability to ofﬂoad coher-ence protocols,synchronization operations,and caching of shared data into the hardware gives these systems a great advantage over distributed memory systems where support-ing such global operations often results in high latency and runtime overhead.For this reason,the best performance on distributed memory systems can often only be achieved through direct management of communication operations using explicit message passing[3].Parallel programming models based on two-sided mes-sage passing pose signiﬁcant challenges to the implemen-tation of parallel applications that exhibit asynchronous communication patterns.Under these models,establish-ing support for computation-wide services such as dynamic load balancing and termination detection often introduces complexity through the management of additional non-blocking communication operations and the injection of ex-plicit progress directives into an application’s critical path. The need to explicitly manage this complexity for high per-formance exposes the programmer to additional opportuni-ties for race conditions,starvation,and deadlock.In this paper,we present an implementation of the UTS benchmark using the Message Passing Interface(MPI).Weexplore approaches to mitigating the complexity of support-ing dynamic load balancing under MPI and investigate tech-niques for explicitly managing load balancing activity to in-crease performance.Our discussion begins with a presen-tation of the Unbalanced Tree Search problem followed by a discussion of the implementation of both centralized and distributed load balancing schemes using MPI.We then ex-plore techniques forﬁne tuning load balancing activity to enhance performance.Finally,we evaluate these schemes on a blade cluster and characterize the performance and scalability attainable in the presence of dynamic load bal-ancing for such systems.2Background2.1Unbalanced Tree Search BenchmarkThe unbalanced tree search(UTS)problem is to count the number of nodes in an implicitly constructed tree that is parameterized in shape,depth,size,and imbalance.Implicit construction means that each node contains all information necessary to construct its children.Thus,starting from the root,the tree can be traversed in parallel in any order as long as each parent is visited before its children.The imbalance of a tree is a measure of the variation in the size of its sub-trees.Highly unbalanced trees pose signiﬁcant challenges for parallel traversal because the work required for differ-ent subtrees may vary greatly.Consequently an effective and efﬁcient dynamic load balancing strategy is required to achieve good performance.The overall shape of the tree is determined by the tree type.A major difference between tree types is the proba-bility distribution(binomial or geometric)used to generate children for each node.A node in a binomial tree has m children with probability q and has no children with proba-bility1−q,where m and q are parameters of the class of binomial trees.When qm<1,this process generates aﬁ-nite tree with expected size11−qm .Since all nodes followthe same distribution,the trees generated are self-similar and the distribution of tree sizes and depths follow a power law[14].The variation of subtree sizes increases dramati-cally as qm approaches1.This is the source of the tree’s imbalance.The nodes in a geometric tree have a branching factor that follows a geometric distribution with an expected value that depends on the depth of the node.In order to simplify our discussion,we focus here on geometric trees having aﬁxed branching factor,b.Another tree parameter is the value r of the root node.Multiple instances of a tree type can be generated by varying this parameter,hence providing a check on the validity of an implementation.A more com-plete description of tree generation is presented elsewhere [16].2.2MPIThe Message Passing Interface(MPI)is an industry standard message passing middleware created by the MPI Forum[11].MPI deﬁnes a parallel programming model in which communication is accomplished through explicit two-sided messages.Under this model,data must be explic-itly transmitted between processors using Send()and Recv() primitives.2.3Dynamic Load BalancingThe technique of achieving parallelism by redistributing the workload as a computation progresses is referred to as dynamic load balancing.In this work,we examine two dif-ferent dynamic load balancing schemes:work sharing and work stealing.These two schemes were chosen because they represent nearly opposite points in the design space for dynamic load balancing algorithms.In particular,work stealing is an inherently distributed algorithm which is well suited for clusters whereas work sharing is inherently cen-tralized and is best suited for shared memory systems.Hierarchical schemes have also been proposed that of-fer scalable dynamic load balancing for distributed memory and wide-area systems.These schemes offer greater scal-ability and tolerance for high latency links.However,they are often constructed using work sharing or work stealing algorithms(e.g.Hierarchical Stealing[17]).2.3.1Work SharingUnder the work sharing approach,processors balance the workload using a globally shared work queue.In UTS, this queue contains unexplored tree nodes.Work in the shared queue is grouped into units of transferable work called chunks and the chunk size,c,parameter deﬁnes the number of tree nodes contained within a chunk.In order to perform depth-ﬁrst search,each processor also maintains a depth-ﬁrst stack containing the local col-lection of unexplored nodes,or fringe,of the tree search. When a processor has exhausted the work on its local stack, it gets another chunk of unexplored nodes from the shared queue.If no work is immediately available in the shared queue,the processor waits either for more work to become available or for all other processors to reach a consensus that the computation has ended.When a processor does have local work,it expands its local fringe and pushes the generated children onto its stack.If this stack grows to be larger than two chunks,the processor sends a chunk of its local work to the shared queue,allowing the surplus work to be performed by other processors that have become idle.MPI-2has been introduced to provide one-sided put/get semantics, however in the context of this work we speciﬁcally target the popular two-sided model of MPI-1.2.3.2Work StealingWhile work sharing uses global cooperation to facilitate load balancing,work stealing takes a distributed approach. Under work stealing,idle processors search among the other processors in the computation in order toﬁnd surplus work. In contrast to work sharing,this strategy places the burden ofﬁnding and moving tasks to idle processors on the idle processors themselves,minimizing the overhead to proces-sors that are making progress.For this reason,work steal-ing is considered to be stable because no messages are sent when all processors are working[2].In comparison,work sharing is unstable because it requires load balancing mes-sages to be sent even when all processors have work.3Algorithms3.1Work Sharing in MPIBecause MPI provides the programmer with a dis-tributed memory view of the computation,the most natural way to implement work sharing under MPI is with a work manager.This work manager’s job is to maintain the shared work queue,to service work releases and work requests, and to detect termination.Because the parallel performance of our work sharing implementation depends greatly on the speed with which the manager is able to service requests, the manager does not participate in the computation.In order to efﬁciently service requests from all proces-sors in the computation,the work manager posts two non-blocking MPI Irecv()descriptors for each worker in the computation:one for work releases and one for work re-quests.Work releases are distinguished from work requests by the message’s tag.When a worker generates surplus work,it releases a chunk of work to the manager.When a worker requests a chunk of work from the queue manager it sends a work request message to the manager and blocks waiting for a response.If the manager is not able to imme-diately service the processor’s request for work,it adds the processor to the idle queue and services the request once more work becomes available.If the manager detects that all processors have become idle and no work is available in the queue,it concludes that the computation has terminated and it sends all processors a termination message.3.1.1Out of Order Message Receipt in MPIThe MPI speciﬁcation guarantees that between any two threads,the program order of blocking operations is ob-served[11].However,in the presence of send buffering and non-blocking receive operations,this guarantee may mis-lead the incautious programmer into relying on an ordering that can be violated.We encountered this exact problem in our initial implementation which created a very hard-to-ﬁnd race condition:occasionally the queue manager would lose a chunk of work,resulting in premature termination.The cause of the race condition was that the manager was receiving messages out of order.A worker would release a chunk of work to the queue manager using a blocking send operation and quickly exhaust its local work,sending out a blocking work request to the queue manager.Both send operations would be buffered at the sender,immediately re-turning in the sender’s context.The larger work release message would then be transmitted by the MPI runtime sys-tem using rendezvous protocol whereas the smaller work request message would be transmitted using eager protocol. Because of this,they would arrive at the work manager out of order and when the manager polled its receive descrip-tors it would see the work request before seeing the work release.If all other processors were in the idle queue at the time the last work request message was received,the queue manager would detect termination early,never having seen the incoming release message.Rather than solve this problem by using unbuffered sends,we implemented a simple but effective timestamp-ing scheme.Under this scheme,each worker keeps track of the number of chunks it has released to the shared queue and transmits this count along with each work request.The queue manager also maintains a count of the number of chunks it has received from each worker.When the man-ager attempts to detect termination it compares these counts and if they don’t match,the manager knows that there are still outstanding messages in-ﬂight and it continues to poll its receive descriptors.3.2Work Stealing in MPIIn general,stealing is a one-sided operation.However, due to MPI’s two-sided communication model,processors that have exhausted their local work are unable to directly steal chunks of computation from other processors.Instead, idle processors must rely on cooperation from busy proces-sors in order to obtain work.In order to facilitate this model for work stealing we created an explicit polling progress engine.A working processor must periodically invoke the progress engine in order to observe and service any incom-ing steal requests.The frequency with which a processor enters the progress engine has a signiﬁcant impact on per-formance and has been parameterized as the polling inter-val,i.If a processor has received a steal request at the time it calls into the progress engine,it checks to see if it has sur-plus work and attempts to satisfy the request.If enough work is available,a chunk of work is sent back to the thief (requesting)processor.Otherwise,the victim responds with a“no work”message and the thief moves on to its nextpotential victim.Under this approach,processors with no work constantly search for work to become available until termination is detected.However,because each processor posts only a single receive descriptor for steal requests,the total number of steal requests serviced per polling interval is stable and is bounded by the number of processors in the computation.3.2.1Distributed Termination DetectionOur work stealing implementation uses a modiﬁed ver-sion of Dijkstra’s well-known termination detection algo-rithm[8].In this algorithm,a colored token is circulated around the processors in a ring in order to reach a consen-sus.In our implementation the token can be any of three colors:white,black,or red.Processor0owns the token and begins its circulation.Each processor passes the token along to its right as it becomes idle,coloring it white if it has no work and has not given work to a processor to its left or black if it has work or has given work to a processor to its left.In order to address the same out-of-order mes-sage receipt race condition encountered in the work sharing implementation,the token carries with it two counters:one counting the total number of chunks sent and another count-ing the total number of chunks received.Whenever processor0receives the token it checks whether a consensus for termination has been reached.If the token is white and both counters are equal then termi-nation has been reached and processor0circulates a red token to inform the other processors.Otherwise,processor 0colors the token white,resets the counters with its local counts and recirculates the token.3.2.2Finalizing MPI with Outstanding Messages During the termination phase of the computation,all pro-cessors continue searching for work until they receive the red token.To avoid deadlock,steal requests and their cor-responding steal listeners must be non-blocking.Because of this,any processor can have both outstanding Send()and Recv()operations when it receives the red token.Many MPI implementations(e.g.MPICH,Cray MPI, LAM,etc...)will allow the user to simply discard these outstanding messages on termination via the collective MPI Finalize().However,the MPI speciﬁcation states that a call to MPI Finalize()should not complete in the presence of any such messages.Some MPI implementa-tions,notably SGI’s implementation,do honor these se-mantics.Under these runtime systems,any program that calls MPI Finalize()withoutﬁrst cleaning up its outstand-ing messages will hang.MPI does provide a way to cancel outstanding messages by calling MPI Cancel().However this function is not com-pletely supported on all platforms.Notably,MPICH does not support canceling send operations so any code that re-lies on MPI Cancel()will have limited portability.In addi-tion to this,the speciﬁcation states that for any non-blocking operation either MPI Cancel()can succeed or MPI Test() but not both.Therefore trying to cancel a message that has succeeded will result in a runtime error.However simply calling MPI Test()once before calling MPI Cancel()will introduce a race condition.Thus,it would seem that the MPI speciﬁcation does not provide any safe mechanism for terminating a computation in the presence of outstanding messages!Our solution to this problem was to introduce another stage to our termination detection algorithm that acts as a message fence.In this new stage we color the token pink before coloring it red.When the pink token is circulated all processors cease to introduce new steal requests and update the token’s message counters with counts of the number of steal messages sent and the number received.The pink to-ken then circulates until all control messages have been ac-counted for(usually1or2circulations in practice).This is detected by processor0by comparing the token’s coun-ters to ensure that they are equal.Once they are,processor 0colors the token red informing all nodes that communi-cation has reached a consistent state and it is now safe to terminate.3.3Managing Load Balancing OverheadWe deﬁne the overhead of a dynamic load balancing scheme to be the amount of time that working processors must spend on operations to support dynamic load balanc-ing.In the following sections,we describe polling-based solutions that allow us to reduce the overhead for each dy-namic load balancing scheme byﬁne tuning the frequency of load balancing operations to better match particular sys-tems and workloads.3.3.1Work StealingOverhead in our work stealing implementation is naturally isolated to the polling-based progress engine.Working pro-cessors must periodically invoke the progress engine to ser-vice any incoming steal requests.The frequency with which these calls are made is parameterized as the polling interval. If calls are not made frequently enough then steal requests may go unnoticed and the load may become imbalanced. However,if calls are made too frequently then performance will be lost due to the overhead of excess polling. Algorithm1Work stealing polling interval1:if Nodes Processed%Polling Interval=0then2:Progress Engine()3:end ifIn the case of work stealing,we have experimentally ob-served that the optimal polling interval does not vary with the chunk size or the workload.Instead,the optimal polling interval is aﬁxed property of the combination of hardware and runtime systems.3.3.2Work SharingOverhead in the work sharing scheme is incurred when working processors must release a chunk of their work to the work manager.These communication operations are not initiated by a request for work,instead they must occur pe-riodically in order to ensure the load remains balanced.For this reason,work sharing is unstable.In order toﬁne tune the performance of our work sharing implementation,we have introduced the release interval,i, parameter.The release interval deﬁnes how frequently a re-lease operation is permitted.Thus,in order for a working processor to release work to the work manager,the proces-sor must now have enough work as well as sufﬁcient elapsed time since its last release.Algorithm2Work sharing release interval1:if Have Surplus Work()andNodes Processed%Polling Interval=0then2:Release Work()3:end ifThe polling optimal interval for our work stealing scheme is a system property that does not vary with respect to chunk size and workload.However,under work sharing, the optimal release interval does vary with respect to these parameters.This is because each of these parameters con-trols different aspects of the load balancing overhead.Un-der work stealing the frequency with which working proces-sors must perform load balancing(i.e.overhead)operations depends only on the frequency with which steal requests are generated.The frequency with which these requests are generated is inﬂuenced only by the workload and the load balance achieved using the chosen chunk size.Therefore, the polling interval does not directly affect the total volume of load balancing operations.Instead,the polling interval attempts to achieve better performance by trading latency in servicing load balancing requests for reduced overhead of checking for the these requests.In contrast to this,the work sharing release interval at-tempts to directly inhibit the frequency with which working processors perform load balancing operations by allowing no more than one release per period.Thus,the overhead of our work sharing scheme is not only related to how fre-quently a processor generates surplus work,but also to how often it is permitted to release such work.4Experimental Evaluation4.1Experimental FrameworkOur experiments were conducted on the Dell blade clus-ter at UNC.This system is conﬁgured with3.6GHz P4 Xeon nodes,each with4GB of memory;the interconnection network is Inﬁniband;and the inﬁniband-optimized MV A-PICH MPI environment[15]was used to run our experi-ments.Our experimental data was collected for two unbalanced trees,each with approximately4million nodes.T1corre-sponds to a geometric tree with a depth limit of10and a ﬁxed branching factor of4.T3corresponds to a binomial tree with2000initial children,a branching factor of8and a branching probability of0.124875.A signiﬁcant differ-ence between T1and T3is that T3maintains a much larger fringe during the search,allowing it to be balanced using a larger chunk size.4.2Impact of Polling Interval on StealingFigure1shows the performance of our work stealing implementation over a range of polling intervals,for a32-processor execution.From thisﬁgure,we can see that intro-ducing the polling interval parameter allows us to improve performance by40%-50%on these workloads.However, polling intervals that are too large can result in performance loss by increasing the steal response latency disproportion-ately to the polling overhead.We can also see that the optimal polling interval for the stealing progress engine is roughly independent of both the chunk size and the workload.Because of this,on a given system the polling interval can beﬁxed and only the chunk size must be tuned to achieve optimal performance for a given workload.Based on the data collected here,we have chosen i=8as the polling interval for our test system.4.3Impact of Release Interval on SharingFigure2shows the performance of our work sharing im-plementation over a range of release intervals,also for a 32-processor execution.From these two graphs,we can see that tuning the release interval allows us to achieve over2X performance improvement on T1,but very little improve-ment on T3.This is because the performance achievable on T3is most dependent on the choice chunk size.From thisﬁgure,we also observe that the optimal re-lease interval and chunk size both vary with respect to a given workload and that the optimal chunksize also varies with respect to the release interval.While the best perfor-mance for T3is achieved with the release interval i=32 and chunk size c=50,T1’s best performance is achievedFigure1.Impact of polling interval on MPI work stealing on Dell Blade cluster using32processors Figure2.Impact of release interval on MPI work sharing on Dell Blade cluster using32processorsfor i=256,c=5.However,from the data collected we can see that i=128is a reasonable compromise for both workloads and in order to draw a comparison between our two load balancing schemes weﬁx i=128for our system.4.4Performance ComparisonFigures3and4show the performance in millions of nodes per second for the work sharing and work stealing implementations on trees T1and T3.We can immediately see that the overhead of maintaining a shared work queue is a signiﬁcant impediment to performance in the work shar-ing implementation and that it leads to poor scaling and sig-niﬁcant performance loss with more than32processors.In contrast to this,work stealing is more stable with respect to chunk size and is able to scale up to64processors.Byﬁxing the release and polling intervals,we are able to focus on the relationship between chunk size,workload, and performance.This means that under both dynamic load balancing schemes and for a given workload,the frequency of load balancing is inversely proportional to the chunk size. This is because any work in excess of two chunks is consid-ered available for load balancing.Thus,very small chunk sizes lower the cutoff between local and surplus work,cre-ating more opportunities for load balancing to occur.Like-wise,very large chunk sizes increase the cutoff between lo-cal and shareable/stealable work,reducing the number of chances for performing load balancing.Because of this, performance is lost for small chunk sizes due to high load balancing overhead and performance is lost for very large chunk sizes as the inability to perform load balancing leads to poor work distribution.This trend is especially apparent under work sharing where smaller chunk sizes increase the frequency of release operations,quickly overwhelming the work manager with load balancing requests.In comparison,under work steal-ing load balancing operations only occur in response to a processor exhausting its local work.Thus,work stealing is better able to facilitate theﬁne-grained load balancing re-quired by T1while work sharing struggles as communica-tion with the work manager becomes a bottleneck.For workloads such as T3which can tolerate more coarse-grained load balancing,work sharing is able to achieve performance rivaling that of work stealing even though one of its processors does no work.This is because processors spend much less time idle as the queue manager is able to satisfy work requests more quickly than can be achieved under work stealing.However,this performance isFigure3.Performance of work sharing vs.chunk size(i=128) Figure4.Performance of work stealing vs.chunk size(i=8)only available over a small range of chunk sizes due to the delicate balance between contention to communicate with the work manager and achieving an even work distribution.On tree T3we can also see that the work sharing imple-mentation is very sensitive to the message latency.This is visible at chunk size50in Figure3where the larger chunk size has caused the MPI runtime to switch from eager to rendezvous protocol for work transfers.On this tree,we can also see that even though it is better suited for work sharing,we are unable to achieve scalability past32proces-sors as the work manager’s latency grows proportionately in the number of processors.4.5Load Balancing VisualizationFigure5shows Paraver[10]traces for16threads run-ning UTS on tree T1on the Dell Blade cluster.The dark blue segments of the trace represent time when a thread is working and the white segments represent time when a thread is searching for work.Under work stealing,A yel-low line connects two threads together via a steal operation. Steal lines have been omitted from the work sharing trace in order to improve readability.Under work sharing,all load balancing operations happen with respect to the manager (processor16)who performs no work.Therefore,any pro-cessor’s transition from the idle state(white)to the working state(blue)must be the result of a work transfer from the manager to the idle node.Inﬁgure5(a),we can see that under work stealing ex-ecution is divided into three stages:initialization,steady-state,and termination.Steal activity is high as the fringe expands and collapses in the initialization and termination stages.However,for over60%of the runtime the workload remains well distributed,leading to relatively low steal ac-tivity.Figure5(b)shows a similar trace for work sharing on tree T1.From this trace,we can see that a great deal of time has been wasted on idle processors.This is because as each processor releases work to the manager,it releases its nodes closest to the root.Because the geometric tree is depth limited,this causes each processor to frequently give away the nodes that lead to the good load balance achieved under work stealing.5Related WorkMany different schemes have been proposed to dynam-ically balance the load in parallel tree traversals.A thor-ough analysis of load balancing strategies for parallel depth-。