Performance Evaluation of a Parallel Simulation Environment
A VHDL-AMS Simulation Environment for an UWB

1368IEEETRANSACTIONSONCIRCUITSANDSYSTEMS—I:REGULARPAPERS,VOL.55,NO.5,JUNE2008AVHDL-AMSSimulationEnvironmentforanUWBImpulseRadioTransceiverMarioR.Casu,Member,IEEE,MarcoCrepaldi,andMariagraziaGraziano,Member,IEEE
Abstract—Ultra-wide-band(UWB)communicationbasedontheimpulseradioparadigmisbecomingincreasinglypopular.AccordingtotheIEEE802.15WPANLowRateAlternativePHYTaskGroup4a,UWBwillplayamajorroleinlocalizationapplica-tions,duetothehightimeresolutionofUWBsignalswhichallowaccurateindirectmeasurementsofdistancebetweentransceivers.KeyforthesuccessfulimplementationofUWBtransceiversisthelevelofintegrationthatwillbereached,forwhichasimu-lationenvironmentthathelpstakeappropriatedesigndecisionsiscrucial.Owingtothismotivation,inthispaperweproposeamultiresolutionUWBsimulationenvironmentbasedontheVHDL-AMShardwaredescriptionlanguage,alongwithapropermethodologywhichhelpstacklethecomplexityofdesigningamixed-signalUWBsystem-on-chip.WeappliedthemethodologyandusedthesimulationenvironmentforthespecificationanddesignofanUWBtransceiverbasedontheenergydetectionprin-ciple.Asaby-product,simulationresultsshowtheeffectivenessofUWBintheso-calledrangingapplication,thatistheaccurateevaluationofthedistancebetweenacoupleoftransceiversusingthetwo-way-rangingmethod.IndexTerms—Mixed-signalintegratedcircuits,ultra-wide-band(UWB)communications,VHDL-AMS.I.INTRODUCTIONACCORDINGtothedefinitionoftheFederalCommuni-cationsCommission(FCC)anultra-wide-band(UWB)signalischaracterizedbyabandwidthofminimum500MHzorbyafractionalbandwidthofatleast20%,regardlessofthetypeofmodulationorsystemoftransmission[1].In2002,FCCreleasedthespectrumbetween3.1and10.6GHzforunlicensedusewithUWBsignals,providedthatsevereaverageandpeakpowerconstraintsarerespected.ThebroadUWBdefinitionandthelargefreespectrumledtomanydifferentproposalsofmoreorlessconventionalmodulationstrategies[likewide-bandorthogonalfrequency-divisionmultiplexing(OFDM)andcode-divisionmultipleaccess(CDMA)],whichareenvisagedforapplicationslikewirelesspersonalareanetworks(WPANs),(see,forexample,theformerworkoftheIEEE802.15WPANHighRateAlternativePHYTaskGroup3a[2]andofthecurrentWiMediaAlliance[3]).However,UWBismorecommonlyreferredtoasa“baseband”or“impulse-based”communicationtechnology,becauseoneofthepossibilitytoexploitsuchalargebandwidthistodirectlysendfastrise-timeandshortdurationcarrier-freepulsestoawide-bandantenna.ThisiscertainlynotnewbecausetheoriginsrootbacktotheManuscriptreceivedJune30,2006;revisedDecember23,2006,andJune6,2007.ThispaperwasrecommendedbyAssociateEditorB.Zhao.TheauthorsarewiththeDepartmentofElectronics,PolitecnicodiTorino,I-10129Torino,Italy(e-mail:mario.casu@polito.it;marco.crepaldi@polito.it;mariagrazia.graziano@polito.it).DigitalObjectIdentifier10.1109/TCSI.2008.916402early60’sworkontime-domainelectromagnetics[4],buttherecentreleaseofthespectrumrenovatedtheinterestinthisfascinatingfieldofwirelesstransmission.ThispaperthusdealswiththesimulationofUWBcircuitsandsystemsunderthismoreacceptedsignificance,thatisofshorttimesupport,ontheorderofonenanosecond,basebandsignals.OneofthemostattractivefeaturesofUWBisthelocationingcapability,enabledbythepossibilityofisolatingthefirstechoofthesignalreceivedthroughamultipathchannel.Thelargebandwidthisthekeyforsuchaccuratetime-domainresolution,whichtranslatesintoanaccuratedistancemeasurement[5].UWBtransceiverswithlocationingcapabilitiesmayopenthewaytoanumberofapplicationswithintheWPANfield,likelogistics(packagetracking),security(localizingpersonsincontrolledareas),medicalapplications(monitoringofpa-tients),search-and-rescuefunctions(communicationswithfirefighters),controlofhomeappliances.AnIEEEstandardizationgroupiscurrentlyworkingtowardanalternativephysicallayerofthe802.15.4standardwiththeaimofenablinghigh-preci-sionlocalization(ontheorderof1m)[6].Adoptionofanewtechnologywilldependprimarilyonkeepingunitcostperdeviceandpowerconsumptionaslowaspossible.ThecompleteintegrationofUWBtransmitterandreceiverfunctionsinthesamesystem-on-chip(SoC),possiblyusingastandardCMOStechnology,isthencrucial.Ontheonehand,pulse-basedUWBsystemscanbesimplerthanclassicnarrowbandtransceivers,becausecontinuouswavecarriersarenotused,thenmakingtheSoCdesignsomewhateasier.Ontheotherhand,narrowbandconsolidatedtechniquesarenotsuitedtothiscase,asthedesignhastodealmostlywithtime-domainsignalrepresentationsratherthanfrequency-domain.Ontopofthat,themixed-signalnatureoftheSoC,duetothecoexistenceofdigital,analogandRFparts,makesthedesignprocessanontrivialtask.Wethusbelievethatthereisaneedforasimulationenvironmentwiththefollowingcharacteristics.•Ithastobeflexibleenoughtoallowbothrapidassessmentofsystem-levelchoicesandaccurateevaluationofcircuit-levelalternatives.•Theinteractionofthetwolevels(systemandcircuit)mustbeexplicitlybroughttolightinsuchawaythattheimpactofchangesatthelowerlevelarecapturedinthebehaviorofthehigherlevelsimulation.WecallMultiresolutionasimulatorwiththesefeatures.Inthedigitaldesigndomain,hardwaredescriptionlanguages(HDL)likeVHDLandVeriloghavebeenthekeytoenablethistypeofmultiresolutionsimulations,thuspavingthewaytothedesignofextremelycomplexintegratedcircuits.Theirextensiontotheanalogandmixed-signaldomain,viz.VHDL-AMSandVerilog-A,isrecognizedastheenablinginstrumentfortamingthecomplexityofSoCsthatfeatureanalog,digitalandRF
基于AMESim的轻型摩托车各混动构型仿真与对比分析

总754期第二十期2021年7月河南科技Henan Science and Technology基于AMESim的轻型摩托车各混动构型仿真与对比分析肖百卉董运达戚笑景王天麒王梓旭王达(吉林大学,吉林长春130022)摘要:为提高摩托车性能,节约开发费用,降低排放对环境的污染,本研究基于工程系统仿真高级建模环境(AMESim),分别对燃油、电动、串联混动以及并联混动等结构的轻型摩托车进行建模,同时以典型曲线作为所有测试的基本循环工况进行一维纵向动力学仿真,采用客观评价方法对动力性、经济性等指标进行对比和分析。
关键词:混合动力摩托车;建模;仿真;动力学分析;能耗计算中图分类号:U469.7文献标识码:A文章编号:1003-5168(2021)20-0016-05 Simulation and Comparative Analysis of Various HybridConfigurations of Light Motorcycles Based on AMESimXIAO Baihui DONG Yunda QI Xiaojing WANG Tianqi WANG Zixu WANG Da(Jilin University,Changchun Jilin130022)Abstract:In order to improve the performance of motorcycles,save development costs,and reduce environmental pollution caused by emissions,based on the Advanced Modeling Environment for performing Simulations of engineering systems(AMESim),this study carries out models of light motorcycles with fuel,electric,series hybrid and parallel hybrid structures,and uses typical curves as the basic cycle conditions for all tests to perform one-dimensional longitudinal dynamics simulation,and uses objective evaluation methods to compare and analyze indicators such as power and economy.Keywords:hybrid motorcycle;modeling;simulation;dynamic analysis;energy consumption calculation近年来,我国能源问题和环境问题逐渐凸显,摩托车作为燃油能源主要消耗者和空气环境的核心污染源之一,对其进行一场节能减排的产业技术革新刻不容缓。
安全管理器-过程解决方案安全管理器说明书

Process SolutionsSafety Manager combines Honeywell’s proven Quadruple Modular Redundancy (QMR®) 2oo4D technology with extensive process safety management expertise in integrating process safety data, applications, system diagnostics and critical control strategies.Honeywell’s IEC 61511 and IEC 61508 SIL 3 TÜV certified solution provides the optimal level of safety and process integration while still maintaining functional safety separation as mandated by those standards. Through Experion operational integration, all systems are unified into one operationally integrated architecture, providing a unique opportunity to improve safety, process availability and efficiency.Experion provides unprecedented connectivity through all levels of process and business operations to optimize work processes, improve routine maintenance efficiencies, enhance safety management and release personnel from manual processes.Benefits∙Safe and Secure– Safety Manager is designed to be securely integrated into customer systems and has passed very rigorous security testing as defined by ISA Security Compliance Institute (ISCI).Safety Manager was the first safety system to achieve Embedded Device Security Assurance (EDSA) certification. ISCI developed this certification within the framework of the ISA Industrial Automation and Control Systems security standards (ISA 99). Because of the built in protection mechanisms, the Experion Safety Manager is protected from cyber attacks and disruption of service. ∙High Availability Architecture–Honeywell’s field-proven QMR 2oo4D architecture provides the highest availability with a safe architecture. Applying QMR technology allows uninterrupted process operation in the event of any system degradation or on-process modification without jeopardizing the SIL 3 level. The optional Safety Manager A.R.T. (Advanced Redundancy Technique) provides additional benefits for locations where timely maintenance is not available.∙Easy and Intuitive Engineering and Modifications– Safety Builder, an intuitive and comprehensive configuration tool, provides plant-wide management of safety-critical databases and application programming for easy network design. TÜV-approved, menu-driven online modifications prevent errors while maintaining and optimizing the safety application.∙Defense-in-Depth– SafeNet and remote distributed Safety Manager provide the ability to design defense-in-depth safety strategies that maximize safety and security while minimizing risk and scope-of-loss concerns.∙Safety Networking - The networking capabilities of Safety Manager are unsurpassed. Up to 1024 redundant nodes can be included in one safety network, acting as one integrated safety solution. The SIL 4 certified SafeNet communication protocol guarantees fast and safe communication over any media and distance. The remote management capabilities support centralized management of all connected safety systems.Honeywell’s Safety Manager, part of the Experion® Process Knowledge System (PKS), enhances the safety, reliability and efficiency of critical processes. Experion® PKS – The Knowledge to Make it Possible.∙SafeNet Flexibility - SafeNet can run over any network, such as a dedicated separated safety network as well as the Honeywell Fault Tolerant Ethernet (FTE) network infrastructure. SafeNet is the only SIL 4 certifiedcommunication protocol available in process networks today.∙Self-Learning – Replacing any module, including the safety processor, is possible when the plant is in operation, and data and programs are automatically copied from the running processor. There is no manual loading required, which simplifies handling and avoids problems. The total system will continue to meet the stringent SIL 3 requirements.∙High Performance – Safety Manager has been optimized to manage large applications with over 1,000 I/O as well as high-speed applications with fast processing requirements of cycle times well below 100 milliseconds.∙Universal Safety I/O – Safety Manager Universal Safety I/O enables maximum architectural flexibility and lowest cost ofownership when safety is required at distributed locations. It has the unique feature that each channel can be configured individually to a different I/O type. Every Universal Safety I/O module has a capacity of 32 freely configurable channels, enabling savings on both installation and operational costs. By using soft-marshalling, the Universal Safety I/O module can be mounted close to the process unit, eliminating the need for marshalling panels, homerun cables and reducing oreliminating field auxiliary rooms. This approach is ideally suited to highly distributed applications such as oil and gas upstream applications, and reduces cost while increasing availability and efficiency. This reduces overall capital expenditure, as well as maintenance costs.∙Localized Safeguarding - With Universal Safety Logic Solver,the safety application can be distributed into the field close to the process unit while maintaining a transparent overview of the overall safety application. The unique feature of this Universal Safety IO module is the fact that besides being an IO module to Safety Manager, it can execute the safetyapplication locally. Safeguarding the process even in the event communications to the Safety Manager are interrupted.∙Standardized Solutions - Universal Channel Technology enables Universal Cabinet designs to be standardized,significantly reducing engineering cost and schedule when applied broadly across a project.∙Advanced Experion Integration – Supports Safety Manager integration in Experion, providing an integrated safety and control solution. It enables, for example, transmitter data sharing between the CEE (Control Execution Environment)controllers and Safety Manager, via direct peer to peer communication, to save installed and operational costs. Peer to peer communication further allows for alarm suppression,automatic bypassing and interlocks between shutdown and control functions as well as “soft landing” in case of process upset. It also provides easy operator access and full Console Station support. As p art of the “enter data only once”philosophy, the Experion-related properties are configured from the Safety Builder tool simplifying maintenance and reducing total cost of ownership.∙Built on QMR Technology – Safety Manager is based on the unique and field-proven QMR diagnostic-based technology with 2oo4D architecture. QMR enhances system flexibility,increases diagnostic messaging capabilities and improves system fault tolerance for critical applications. It enables the handling of multiple system faults within Experion Safety Manager, matching the needs of critical control applications.In addition, Safety Manager provides the basis for integrating SIL-rated field sensors and valve actuators, ensuring that safety functions are well established to protect complex and hazardous processes. It integrates SIL 1-3 safety transmitters (such as Honeywell ST3000 and STT250) or safety valve positioners for improved safety and field asset management.∙Optimized field maintenance - Without the need for extra infrastructure or engineering, HART devices are integrated within Honeywell’s Field Device Manager. This provides all required data for field asset management. To prevent inadvertent device changes, the safety manager prevents FDM from writing parameter changes unless the device safety lock has been disabled from Safety Builder.Compliance to Safety StandardsA major requirement for compliance with IEC 61511 and IEC 61508 is the availability of a change history of applications. With Safety Builder, change history is efficiently tracked with the Safety Audit Tracker through an automatically enabled audit trail. Difficult procedures or extensive loggings are not required. The Safety Audit Tracker, together with the automated embedded Application Verification mechanism, is all that is required.Safety Manager complies with the following international standards:∙For burner management: NFPA 85, 86, EN50156∙For emergency shutdown and other critical applications: IEC 61508, IEC61511, ISA S84.01, DIN V 19250, UL,FM, ATEX∙For fire and gas: EN54-2, NFPA 72, Lloyd’s Register and offshore installations ABSWith all SIL 3 safety hardware and software compliance tools, Safety Manager provides excellent protection for safety applications across multiple industries throughout the entire life of an installation. Safety Manager provides the basis for critical control and safety unification, reducing risks and installed costs, and improving safety while increasing uptime.Optimized Engineering EnvironmentSafety Builder software improves engineering and design efficiency. With simple drag and drop functionality, a complete and complex network can be designed within minutes without programming, saving valuable engineering and testing time. The complete network design is available on a one-page view without requiring additional documentation.An integrated editor facilitates fast and effective application design, allowing clear and distinct views of all logic with full compliance to IEC 61131 standards. Logic inputs, outputs and symbols are placed with drag and drop functionality from the toolbar and are easily configurable. Through the Safety Manager simulation mode any application can be loaded and tested on a minimum size system, a tool that facilitates easy application design and testing. The simulation mode also allows execution of online modifications and testing of all communication interfaces.In absence of a Safety Manager system the Honeywell’s UniSim® simulation environment for Safety Manager supports offline simulation as well. It can help in the early implementation phase of a project or as part of a plant-wide system simulation. It supports step by step simulation, freezing the application and building snapshots.Optimal Process AvailabilityApplying QMR technology to Safety Manager delivers unlimited runtime for single channel operation. This increases process availability, allowing uninterrupted process operation in the event of any system degradation. Without incurring any process downtime, the system can be kept up to date with the latest system software as well as application changes or additions through a four-step online system modification procedure The on-process modification to the application can be carried out remotely without physical presence to the system.I/O faults are detected and isolated on a per-channel basis and immediately reported to the appropriate level. This minimizes the time to repair and further increases system robustness.Integrated Operation and MaintenanceSafety Manager unifies critical safety process data with process control information, providing single-window access for operation and maintenance. When connected to the Honeywell FTE network through TÜV SIL 3 approved Universal Safety Interfaces, multiple Safety Managers can be unified into one safety system architecture.Safety Manager integration delivers fast, safe and reliable data exchange with Experion, enhancing operator and maintenance performance. In addition, Safety Manager extends the system proof test interval with inherent extensive system self-testing and diagnostic capability, reducing operational and maintenance costs. Integrated sequence of events (SOE) functionality for all process and safety-related activities supports analysis at a glance.Safeguards are built into Safety Manager to eliminate the possibility of systematic failures caused by errors made duringthe design, planning, construction, operation and decommissioning of the system. A systematic failure in thedesign of a common tool can result in an unsafe reaction of both the safety and control systems.Safety through SeparationSafety and control systems must be integrated to allow for smooth and safe plant operation, while still maintaining a safe separation where appropriate.∙Secure Separated Databases - Within H oneywell’s unique solution, separate databases store the safety and control strategies, and separate software modules are available for safety and control through dedicated tools such as Safety Builder and Control Builder. Maintaining separate tools with separate databases prevents unauthorized changes or corruptions, decreases safety risks and prevents common cause failures.∙Managed and Protected Database Environment - A unique, secure login scheme protects Safety Manager from off- and on-process changes. This login scheme uses a dedicated protection mechanism with several access levels for the engineering application, loading of the application in the controller and forcing points in Safety Manager. A user expiration mechanism downgrades the access level after auser-defined period of time elapses to protect the application from accidental or unauthorized changes when Safety Builder is unmanned over a specified period.∙Dedicated Software and Hardware - Using dedicated and specifically developed hardware and software in accordance with the IEC61508 safety standard reduces the risk of a common cause failure. Using dedicated hardware and software for both safety and control protects the safety system from any defects in control-related operations. In addition, the safety and control strategies are developed by different groups using dedicated methods.Conversely, using the same hardware or software for both safety and control increases the possibility of systematic controller failures, including those that result from design errors. A clear separation reduces the effort for testing and designing safety systems.∙Secure Environment - It is crucial that critical control and SIS are protected from intentional or accidental cyber threats. In general, functional security in combination with functional safety is critical to assessing the overall integrity of a SIS. Safety Manager architecture is secure by design and is certified to the Embedded Device Security Assurance program as defined by the ISA Security Compliance Institute. Adherence to this standard provides assurance of safety, security and robustness, meeting stringent industry best practices and performance benchmarks.In addition, Safety Manager is protected from outside threats by an embedded certified hardware firewall. This firewall isolates the safety application during runtime execution from external devices so they can never jeopardize the safety or availability of the application. With this firewall and the use of a SIL 4 certified proprietary protocol between safety managers, the data integrity between control and safety is protected and guaranteed.∙Safety Inside - Using dedicated firmware for safety and control ensures that safety is embedded into the system—no additional programming is needed to establish the required safety level. Strategies with a common platform for safety and control require that safety be built into the application. This customized safety level is a manual process and requires fundamental knowledge of the safety system to establish safety functions without jeopardizing the integrity of the application.Honeywell’s integrated control and safety solution is driven by the separation principle—hardware and software diversification, integrated operator interface, integrated data processing and analysis, and integrated alarm management.For More InformationTo l earn more about Honeywell’s Safety manager, visit our website or contact your Honeywell account manager. Honeywell Process Solutions Honeywell1250 West Sam Houston Parkway South Houston, TX 77042Honeywell House, Arlington Business Park Bracknell, Berkshire, England RG12 1EB UK Shanghai City Centre, 100 Junyi Road Shanghai, China 20051 The operational integration provided with Experion and Safety Manager allows plant personnel to have a seamless interface to the process while maintaining safe separation. This allows for a wide range of applications to be monitored plant-wide from any operator console. A complete overview of all information needed from the operator’s point of view is available through Experion Station or Experion Console Station. This communication architecture, supplied by Honeywell, delivers a scalable solution, from small control and safety networks to huge plant architectures with over 100,000 monitored I/O points. Interoperability of Safety Manager with the SafeNet protocol extends the functionality of one Safety Manager and allows for plant-wide implementation, binding the separate functionalities into one safety application with different protection layers.Engineering ExcellenceHoneywell’s Global Safety Discipline program enables consistent project execution excellence across Honeywell engineering locations. TÜV certified procedures and resources guarantee a global and transparent safety project execution by using certified standard builds, including templates, guidelines solution libraries, checklists, methodologies and tools.Safety Manager HMIWeb Solution Pack shapes and faceplates provide all projects with a highly flexible and functional library, enabling maximum advantage of the principles of safe and effective operations as described by the Abnormal Situation Management (ASM) Consortium.Honeywell Safety ServicesHoneywell’s offerings go beyond supplying hardware and software, establishing a unique safety knowledge community located in our expertise centers around the world in North America, Europe, South Africa, Asia and Australia.Over 200 certified safety engineers employed in these centers offer a wide range of consulting, project and lifecycle support services, including:∙Safety system audits∙Process hazard and risk assessment ∙SIL classification∙IEC61508 and IEC61511 CFSE training ∙Safety requirement specification development ∙FEED studies with customers to jointly develop their requirements∙IEC61508, IEC61511 and ISA S84 compliant solutions development∙Safety Instrumented Systems implementation ∙Live, hot cutover implementation and execution of revamp projects∙Installation and commissioning ∙SIL verification ∙SIL validation ∙Periodic proof-testing ∙System maintenance∙Solution Enhancement Support Program (SESP)∙Parts managementPN-12-25-ENG March 2013© 2012 Honeywell International Inc.Experion®, QMR® and UniSim® are registered trademarks of Honeywell International Inc.。
微软R服务器:解决规模化问题说明书

February 4th, 2016 -Welcome!Global Community Millions of users Can be Scaled to Big Data, Big AnalyticsScalabilityR from Microsoft brings•Free and open source R distribution•Enhanced and distributed by Revolution Analytics SQL Server R Services•Built in Advanced Analytics and Stand Alone Server Capability •Leverages the Benefits of SQL 2016 Enterprise Edition Microsoft R Server•Microsoft R Server for Redhat Linux•Microsoft R Server for SUSE Linux•Microsoft R Server for T eradata DB•Microsoft R Server for Hadoop on Redhat•Enhanced Open Source R distribution•Based on the latest Open Source R (3.2.2)•Built, tested and distributed by Microsoft•Enhanced by Intel MKL Library to speed up linear algebra functions •Compatible with all R-related software•CRAN packages, RStudio, third-party R integrations, …•Revolutions Open-Source R packages•Reproducible R Toolkit –Checkpoint , miniCRAN•ParallelR–parallelise execution via ‘foreach’ loop•Rhadoop–rhdfs, rhbase, ravro, rmr2, plyrmr•AzureML–read/write data to AzureML, publish R code as ML API•MRAN website •Enhanced documentation and learning resources•Discover 6500 free add-on R packages•Open source (GPLv2 license) -100% free to download, use and shareCRAN R compared to Microsoft R Open•Matrix calculation –up to 27x faster•Matrix functions –up to 16x faster•Programation–0x faster •More efficient and multi-threaded math computation.•Benefits math intensive processing.•No benefit to program logic and data transform9CRAN, MRO, MRS Comparison Datasize In-memory In-memory In-Memory or Disk Based Speed of AnalysisSingle threaded Multi-threaded Multi-threaded, parallel processing 1:N servers Support Community Community Community + CommercialAnalytic Breadth & Depth 7500+ innovative analytic packages 7500+ innovative analytic packages 7500+innovative packages + commercial parallel high-speed functionsLicense Open Source Open Source Commercial license.Supported release with indemnity MicrosoftR OpenMicrosoft R ServerMicrosoft R Server is a broadly deployable enterprise-class analytics platform based on R that is supported, scalable and secure. Supporting a variety of big data statistics, predictive modeling and machine learning capabilities, R Server supports the full range of analytics –exploration, analysis, visualization and modelingHigh-performance open source R plus:•Data source connectivity to big-data objects•Big-data advanced analytics•Multi-platform environment support•In-Hadoop and in-Teradata predictive modeling •Development and production environment support •IDE for data scientist developers•Secure, Scalable R DeploymentDeployRR Open R ServerDevelopRR Open Microsoft R ServerDeployRDevelopR ConnectR•High-speed & direct connectorsAvailable for:•High-performance XDF •SAS, SPSS, delimited & fixed format text data files •Hadoop HDFS (text & XDF)•Teradata Database & Aster •EDWs and ADWs•ODBCScaleR•Ready-to-Use high-performance big data big analytics•Fully-parallelized analytics•Data prep & data distillation•Descriptive statistics & statistical tests•Range of predictive functions•User tools for distributing customized R algorithms across nodes•Wide data sets supported –thousands of variables DistributedR•Distributed computing framework •Delivers cross-platform portabilityR+CRAN•Open source R interpreter•R 3.1.2•Freely-available huge range of R algorithms•Algorithms callable by RevoR •Embeddable in R scripts•100% Compatible with existing R scripts, functions and packagesMRO•Performance enhanced R interpreter•Based on open source R•Adds high-performancemath library to speed uplinear algebra functionsScaleR –Parallel + “Big Data”Stream data in to RAM in blocks. “Big Data” can be any data size. We handle Megabytes to Gigabytes to Terabytes… Our ScaleR algorithms workinside multiple cores / nodesin parallel at high speedInterim results are collectedand combined analytically toproduce the output on theentire data setXDF file format is optimised to work with the ScaleR library and significantly speeds up iterative algorithm processing.Scale R –Parallelized Algorithms & Functions ▪Data import –Delimited, Fixed, SAS, SPSS,OBDC ▪Variable creation & transformation▪Recode variables▪Factor variables▪Missing value handling▪Sort, Merge, Split▪Aggregate by category (means, sums)▪Min / Max, Mean, Median (approx.)▪Quantiles (approx.)▪Standard Deviation▪Variance▪Correlation▪Covariance▪Sum of Squares (cross product matrix for set variables)▪Pairwise Cross tabs▪Risk Ratio & Odds Ratio▪Cross-Tabulation of Data (standard tables & long form)▪Marginal Summaries of Cross Tabulations ▪Chi Square Test ▪Kendall Rank Correlation ▪Fisher’s Exact Test ▪Student’s t -Test ▪Subsample (observations & variables)▪Random Sampling Data Preparation Statistical Tests Sampling Descriptive Statistics▪Sum of Squares (cross product matrix for set variables)▪Multiple Linear Regression ▪Generalized Linear Models (GLM) exponential family distributions: binomial, Gaussian, inverse Gaussian, Poisson, Tweedie. Standard link functions: cauchit, identity, log, logit, probit. User defined distributions & link functions.▪Covariance & Correlation Matrices ▪Logistic Regression ▪Classification & Regression Trees ▪Predictions/scoring for models▪Residuals for all models Predictive Models ▪K-Means ▪Decision Trees ▪Decision Forests ▪Gradient Boosted Decision Trees ▪Naïve Bayes Cluster Analysis Classification Simulation Variable Selection ▪Stepwise Regression ▪Simulation (e.g. Monte Carlo)▪Parallel Random Number Generation Combination ▪rxDataStep ▪rxExec ▪PEMA-R API Custom AlgorithmsScaleR -Performance comparisonMicrosoft R Server has no data size limits in relation to size of available RAM. When open source R operates on data sets that exceed RAM it will fail. In contrast Microsoft R Server scales linearly well beyond RAM limits and parallel algorithms are much faster.▪US flight data for 20 years▪Linear Regression on Arrival Delay▪Run on 4 core laptop, 16GB RAM and 500GB SSDExample of In-Database Acceleration •5+ hours to 40 seconds:rowsmin u t e s R on a server pulling data via SQL R on a server Invoking RRE ScaleR Inside the EDWDistributedR ScaleR ConnectR DevelopR Code Portability Across Platforms In the Cloud Workstations &ServersLinux Windows EDWTeradata HadoopHortonworks Cloudera MapR + HD Insights + Hadoop Spark + Azure ML Azure Marketplace + SQL Server v16 Microsoft R Server Coming SoonDistributedR -Remote ExecutionAlgorithm MasterBig DataPredictive AlgorithmAnalyzeBlocks InParallelLoad BlockAt A TimeDistribute Work,Compile ResultsThe Results:•Even Faster Computation•Larger Data Set Capacity•Fewer Security Concerns•No Data Movement, No Copies Work“Pack and Ship” Requeststo Remote EnvironmentsResultsMicrosoft R Server functions• A compute context defines remote connection •Microsoft R functions prefixed with rx •Current compute context determines processing location### SETUP HADOOP ENVIRONMENT VARIABLES ###myHadoopCCC <-RxHadoopMR()### HADOOP COMPUTE CONTEXT ###rxSetComputeContext(myHadoopCC)### CREATE HDFS, DIRECTORY AND FILE OBJECTS ###hdfsFS <-RxHdfsFileSystem()AirlineDataSet <-RxXdfData(“AirlineDemoSmall/AirlineDemoSmall.xdf”), fileSystem = hdfsFS )### ANALYTICAL PROCESSING ###### Statistical Summary of the datarxSummary(~ArrDelay+DayOfWeek, data= AirlineDataSet, reportProgress=1)### CrossTab the datarxCrossTabs(ArrDelay ~ DayOfWeek, data= AirlineDataSet, means=T)### Linear Model and plothdfsXdfArrLateLinMod <-rxLinMod(ArrDelay ~ DayOfWeek + 0 , data = AirlineDataSet) plot(hdfsXdfArrLateLinMod$coefficients)### SETUP LOCAL ENVIRONMENT VARIABLES ###myLocalCC <-“localpar ”### LOCAL COMPUTE CONTEXT ###rxSetComputeContext(myLocalCC)### CREATE LINUX, DIRECTORY AND FILE OBJECTS ###linuxFS <-RxNativeFileSystem() )AirlineDataSet <-RxXdfData (“AirlineDemoSmall/AirlineDemoSmall.xdf ”, fileSystem = linuxFS )Local Parallel processing –Linux or WindowsIn –HadoopScaleR models can be deployed from a server or edge node to run in Hadoop without any functional R model re-coding for map-reduceComputecontext R script –sets where the model will runFunctionalmodel R script –does not need to change to run in HadoopDistributedR -In-Hadoop•Uses Hadoop nodes for Rcomputations•Eliminate data movementlatency on very large data•Remove data duplication•Faster model development•No MapReduce R coding•Develop better modelsusing all the data= Microsoft R ServerMethod 1: Local (Linux) parallel processing using all cores on one node, copying data from HDFS to store in local Linux file-system.Compute ContextHadoopCompute Context HadoopCompute Context Local ParallelLinux (Local) File-SystemHDFSCsv, XdfProcessing Data1 Edge node1:n data nodes 1:n disks1:(n x number of nodes) disksCsv, Xdf Linux FS Read / writeMethod 1(“Beside” or “Edge”)Copy to Local FileMethod 2:Local (Linux) parallel processing using all cores on one node, streaming data from / to HDFSCompute ContextHadoop Compute Context Hadoop Compute Context Local ParallelCompute Context HadoopLinux (Local) File-SystemHDFSCsv, Xdf1:n nodes 1:n disks1:(n x number of nodes) disks1 Edge nodeMethod 3Method 3:Hadoop (Map-Reduce) parallel processing using all cores on n nodes, using HDFS data on each nodeCompute ContextHadoop Compute Context Hadoop Compute Context Local ParallelCompute Context HadoopLinux (Local) File-SystemHDFSCsv, XdfProcessing Data1:n nodes 1:n disks1:(n x number of nodes) disksCsv, XdfHDFSRead / write(“inside”)R script sent to data nodes1 Edge nodeR model script sent to Master Node: 1.Starts a master process 2.Distribute work3.Master tasks for each node4.Master initiates distributed work1.Hadoop schedules mapper for each split2.Algorithm computes intermediate result3.Reducer combines intermediate results5.Master process evaluates completion6.Iterates as required by the algorithm7.Returns consolidated answer to scriptDistributedR -What processing mode to use? Analytic data set size and processing complexity (e.g. simple summary statistics vs iterative algorithm) guide the use of Method 1 and 2 (Edge Node / Server Linux local processing) vs Method 3 (in-Hadoop processing)Low Medium High Small Data< 10GBMedium Data< 50GBBigger Data> 50GB Edge Node LinuxprocessingIn-HadoopprocessingLocal Linuxfile-systemHadoopfile-system LegendProcessingComplexity Data Size。
企业绩效管理【外文翻译】

外文文献翻译译文一、外文原文Corporate Performance ManagementAbstractTwo of the most important duties of a chief executive officer are (1)to formulate strategy and (2) to manage his company's performance。
In this article we examine the second of these tasks and discuss how corporate performance should be modeled and managed. We begin by considering the environment in which a company operates, which includes, besides outside stakeholders, the industry it belongs and the market it supplies,and then proceed to explain how the functioning of a company can be understood by an examination of its business, operational and performance management models. Next we describe the structure recommended by the authors for a corporate planning, control and evaluation system, the most important part of a corporate performance management system. The core component of the planning system is the corporate performance evaluation model,the structure of which is mapped into the planning system’s database, simulation models and budgeting too ls’ structures,and also used to shape information contained in the system’s products, besides being the nucleus of the language used by the system's agents to talk about corporate performance. The ontology of planning, the guiding principles of corporate planning and the history of "MADE”,the corporate performance management system discussed in this article,are reviewed next, before we proceed to discuss in detail the structural components of the corporate planning and control system introduced before. We conclude the article by listing the main steps which should be followed when implementing a performance planning, control and evaluation system for a company.1.IntroductionTwo of the most important corporate tasks for which a chief executive officer is primarily responsible are (1)to formulate strategy and (2)to manage thecompany’s performance. In this article we examine the second of these tasks and discuss how corporate performance should be modeled and managed。
面向平行应急管理高效能仿真方法

AEROSPACE SHANGHAI第36卷2019年第4期19面向平行应急管理高效能仿真方法杨凯,张连怡,梅铮,张啥(北京仿真中心航天系统仿真重点实验室,北京100854)摘要:基于人工社会的应急管理建模与仿真系统具有大规模、领域知识动态、多样异质、复杂时空演化等特点,给“预测-应对”型应急管理仿真带来了极大的挑战。
针对面向平行应急管理仿真詢特点,分析应急管理对仿真平台功能要求,对基于知识的人工社会初始场景构建、基于半监督机器学习的应急管理仿真建模、基于自组织增量学习的仿真模型在线评估,以及仿真云中的并行作业调度方法等方面进行了研究。
在公共安全风险防控领域,对大规模社会媒体网络用户群体行为进行仿真,并对群体异常行为进行检测。
通过社会媒体网络仿真应用实例,验证了高效能仿真平台的先进仿真手段能有效提升平行应急管理仿真能力。
关键词:平行应急管理;高效能仿真;人工社会计算;公共安全风险防控;异常行为检测中图分类号:TP391.9文献标志码:A DOI:10.19328/ki.l006-1630.2019.04.003 Research on High Efficiency Simulation Method for Parallel Emergency ManagementYANG Kai,ZHANG Lianyi,MEI Zheng,ZHANG Han(Science and Technology on Special System Simulation Laboratory,Beijing Simulation Center,Beijing100854,China)Abstract:The emergency management modeling and simulation system based on artificial social computational experiments has the characteristics of large-scale,domain knowledge dynamics,heterogeneity,complex space-time evolution,and so on,which brings great challenges to the"predictive-response"emergency management simulation.For the above challenges,we analyze the requirements o£the parallel emergency management simulation platform, and discuss the high-performance simulation methods including the knowledge-based initial artificial social scene construction,the emergency management simulation modeling based on semi-supervised machine learning,the online evaluation of simulation model based on self-organized incremental learning,and the parallel job scheduling of the simulation cloud.The application of the abnormal behavior detection in the social media network can be used to prove that the advanced simulation methods of high-performance simulation platform can improve the parallel emergency management simulation capability.Keywords:parallel emergency management;high-performance simulation;artificial social computing;public safety risk prevention and control;abnormal behavior detectiono引盲随着建模与仿真技术在多学科中的不断应用,仿真平台越来越数字化、网络化、智能化、集成化、虚拟化、协同化。
ad hoc(1)

Performance Evaluation of Mobility Models for Wireless Ad hoc Networks Amrita A. Agashe Dr. S.K. Bodhe Electronics Dept. JJMCOE, Jaysingpur Electronics Dept.BVCOE, Pune Maharashtra, India Maharashtra, IndiaAbstractWith current advances in technology, wireless networks are increasing in popularity. These networks allow users the freedom to travel from one location to another without interruption of their computing services. Ad hoc networks, a subset of wireless networks, allow the formation of a wireless network without the need for access point. All participating users in an ad hoc network agree to accept and forward messages, to and from each other. With this flexibility, wireless networks have the ability to form anywhere, at any time, as long as two or more wireless users are willing to communicate. Mobile nodes within an ad hoc network move from one location to another; however, finding ways to model these movements is not obvious. In order to evaluate an ad hoc network performance it is necessary to develop and use mobility models that accurately represent movements of the mobile nodes. In this paper we present performance evaluation of various entity mobility models in terms of the traveling patterns of mobile node.Key words -Entity mobility models, Mobile Ad Hoc network, Travelling pattern1. IntroductionThe ad hoc mobility models are the continuous time stochastic process, which characterizes the movement of nodes in two-dimensional spaces. According to the movement pattern of each type, each node movement consists of sequence of random length interval, during which a mobile node (MN) moves in constant speed and constant direction. The speed and direction of each node varies according to various mobility models. In the network environment like ad hoc network synthetic mobility models are used because they attempt to realistically represent the behavior of mobile node. In the performance evaluation of handoff algorithm for mobile ad-hoc network, the handoff algorithm should be tested under realistic conditions and realistic movements of the mobile user. A mobility model should attempt to mimic the movements of real mobile node, also the changes in speed and direction of node must occur in reasonable time slots. The various synthetic entity mobility models used for Ad-hoc network are as follows.1) Random Walk Mobility Model: A simple mobility model based on random direction and speeds.2) Random Waypoint Mobility Model: A model that includes pause times between changes in destination and speed.3) Random Direction Mobility Model: A model that forces mobile node to travel to the edge of simulation area before changing direction and speed.4) A Boundless Simulation Area Mobility Model: A Model that converts 2D rectangular simulation area into a torus-shaped simulation area.5) Gauss Markov Mobility Model: A model that uses one tuning parameter to vary the degree of randomness in mobility pattern.6) Uniform Mobility Model: Uniform mobility model collects good features of Random walk mobility model, Random waypoint mobility model and Random direction mobility model.7) Urban Mobility Model: A model that simulates the urban environment. It is basically an enhancement to the common Manhattan mobility model [4].2. Synthetic Entity Mobility Models1. Random Walk Mobility ModelSince many entities in nature move in extremely unpredictable ways, the Random Walk Mobility Model was developed to mimic this erratic movement [1]. In this mobility model, a mobile node moves from its current location to a new location without taking pause and by randomly choosing a direction and speed inFirst International Conference on Emerging Trends in Engineering and Technologywhich to travel. Each node is assigned an initial location ),(00y x and a destination is ),(11y x . The new speed is chosen uniformly from predefined ranges ),(10v v independently of all previous destinations and speed and direction in the range )2,0(π. When mobile node, reaches the simulation boundary, it bounces off the simulation border with an angle, determined by the incoming direction then continues along this new path. Figure 1 shows traveling pattern of MN using Random Walk Mobility Model with speed of mobile node uniformly chosen between 0 and 3 m/s.Figure 1. Traveling Pattern of MN using RandomWalk Mobility Model2. Random Waypoint Mobility ModelThe Random waypoint mobility model includes pause times between changes in direction and/or speed. A mobile node begins by staying in one location for a certain period of time (i.e. the pause time).Figure 2. Traveling Pattern of MN using RandomWaypoint Mobility ModelOnce this time expires, the mobile node chooses arandom destination in the simulation area and a speedthat is uniformly distributed between ).,(Maxspeed Minspeed Upon arrival, the mobile nodepauses for a specified time period before starting the process again. In our simulation the speed of mobilenode is uniformly chosen between 0 and 3 m/s. Figure 2 shows an example traveling pattern of an MN using the Random Waypoint Mobility Model. It has been noted that the movement pattern of a MN using the Random Waypoint Mobility Model is similar to the Random Walk Mobility Model if pause time is zero and ),(10v v = ).,(Maxspeed Minspeed3. Random Direction Mobility ModelIn the Random Waypoint mobility model, there occurs clustering of nodes in one part of simulation area. To overcome this Random Direction Mobility Model is developed. In this mobility Model, mobile node chooses a random direction and travels to the border of the simulation area. In that direction once the simulation boundary is reached, the mobile node pauses for a specified time, chooses another angular direction, and continues the process i.e. node hits the domain boundary and gets reflected back, just like billiards like reflection. Figure 3 shows the traveling pattern of Random Direction Mobility Model.Figure 3. Traveling Pattern of MN using Random Direction Mobility Model4. Boundless Simulation Area Mobility ModelIn the boundless simulation area mobilitymodel, a relationship between the previous direction of travel and velocity of a mobile node with its current direction of travel and velocity exists. A velocity vector ν= (ν,θ) is used to describe a mobile node’svelocity v as well as its direction θ. The mobile node’s position is represented as (x, y ). It has been observed that boundless simulation area mobility model converts a 2D rectangular simulation area into a torus-shaped simulation area, allowing mobile nodes to travel unobstructed. Both the velocity vector and position areupdated at every ∆t time steps according to the following formulas.)4.....(....................);........(sin *)()()()3.....(....................);........(cos *)()()()2.(........................................,.........)()()1....(..........].........),0,)(min[max()(max t t v t y t t y t t v t x t t x t t t t v v t v t t v θθθθ+=∆++=∆+∆+=∆+∆+=∆+Here νmax is the maximum velocity defined in the simulation, ∆ν is the change in velocity which is uniformly distributed between )*,*(max max t A t A ∆∆−, A max is the maximum acceleration of given mobile node, ∆θ is the change in direction which is uniformly distributed between (-α*∆t,α*∆t), and α is the maximum angular change in the direction, a mobile node is traveling. In the boundless simulation area mobility model, mobile node, that reach one side of the simulation area, continue traveling and reappear on the opposite side of the simulation area[2]. Figure 4 illustrates an example path of a mobile node using theBoundless simulation area mobility model.Figure 4. Traveling Pattern of MN using Boundless Simulation Area MobilityModel5. Gauss-Markov Mobility ModelThe Gauss-Markov Mobility Model was designed to adapt to different levels of randomness via one tuning parameter [2]. The value of speed and direction at the n th instant is calculated based upon the value of speed and direction at the (n-1)thinstance and a random variable using the following equations s n =)5......(....................)1()1(121−−−+−+n x n s s s ααα)6...(....................)1()1(121−−−+−+=n x n n d d d d αααwhere s n and n d are the new speed and direction of the mobile node at time interval n ; where 01≤≤αis the tuning parameter used to vary the randomness, At time interval n , a mobile node’s position is given by the equations.)7........(........................................cos 111−−−+=n n n n d s x x)8........(........................................sin 111−−−+=n n n n d s y y Where (n n y x ,) and (11,−−n n y x ) are the x and y co-ordinates of the mobile nodes position at the n th and (n-1) st time interval and 75.0=α. As shown in the figure 5 the Gauss-Markov Mobility Model can eliminate the sudden stops and sharp turns encountered in the Random walk mobility model by allowing past velocities and past directions to influence future velocities and directions respectively.Figure 5 Traveling Pattern of MN using Gauss-Markov Mobility Model6. Uniform Mobility ModelUniform mobility model collects good features of Random walk mobility model, Random waypoint mobility model and Random direction mobility model. [5]. For each node the initial position is chosen in two simple steps: Choose an initial path and then choose a point on the path. The probability density of any chosen path is proportional to the length of path. A convenient way to do this is by rejection sampling. For this following steps are adopted [3].(1) Generate two points ),(11y x and ),(22y x uniformly on the unit square.(2) Compute 2])()[(21212212y y x x r −+−= .(3) Generate a random value 1U uniformly on )1,0(. (4) If r U <1 , then accept ),(11y x and ),(22y x .Otherwise go to step 1.(5) Generate a random value 2U uniformly on )1,0(. (6)The initial location of node is )9.......(..........).........)1(,)1((22122212y U y U x U x U −+−+Figure 6 shows traveling pattern of node using Uniform mobility model.Figure 6 Traveling Pattern of MN using UniformMobility Model7.Urban Mobility ModelIn this mobility model Manhattan city – like environment is present. Horizontal and vertical streets are parallel and equidistant from each other. The intersection points of the streets form a set of crossing points. Figure 7 shows the traveling Pattern of MN using Urban Mobility Model.Figure 7 Traveling Pattern of MN using UrbanMobility ModelThis model is able to mimic a real city, where it is desirable not to have a strict delimiting perimeter. In this way users who reach a bound can freely pass through it. For the algorithm there are five different states [6]: no movement, horizontal step backward, horizontal step forward, vertical step forward, vertical step backward. In this model two pauses in row are not permitted.3 ConclusionMobile ad hoc networks performance can heavily depend on mobility model.Random walk and Random waypoint mobility models create random movement and speeds independent of previous locations and speeds of mobile nodes. Gauss Markov can eliminate the sudden stops and sharp turns encountered in Random walk and Random waypoint model. Random direction mobility model creates unrealistic scenario since; it is unlike for people to spread themselves evenly throughout an area and pause only at the edge of this area. . In the boundless simulation area mobility model movement of nodes is internal surface of the 3D torus shaped solid. Urban mobility model aims to represent a limited section of a city. In the uniform mobility model only initial location, speed and pause time are sampled from stationary distribution and all subsequent node destinations, speeds and pause times are sampled from the uniform distribution. This can be treated as a kind of random waypoint mobility model. This mobility model eliminates initial discrepancy which is present in Random waypoint mobility model.References[1] Tracy Camp et.al. “ A Survey of mobility models for mobile ad hoc network research” , Wireless Communication & Mobile Computing :Special issue on mobile Ad hoc Networking: Research ,Trends and Applications, Vol. 2, no. 5, pp 483-502, 2002[2]Vanessa Ann Davies, “Evaluating mobility models within an mobile ad hoc network”: Master of science Thesis, Colorodo School of Mines,2000.[3]William Navidi and Tracy Camp, “Stationary distributions for the Random waypoint mobility model”, IEEE transactions on mobile computing, Vol. 3, No. 1, January-March 2004.[4] Seema Bandyopadhyay, et. Al.“Stochastic properties of mobility models in mobile ad hoc networks”, IEEE Transactions on mobile computing, Vol. 6 , No. 11, November 2007[5] S. K. Bodhe, Amrita A. Agashe and Anil A. Agashe, “Uniform mobility model for ad hoc networks”,Advances in Computer Science and Engineering: Reports and Monographs Vol. 2, Imperial College Press, London. Copyright 2007.。
ANSYS CFX 高性能计算配置指南

CFX并行任务提交
GUI提交: windows上:
CFX并行任务提交
CFX并行任务提交
GUI提交: Unix/Linux上:
直接进入…/ansys_inc/v120/cfx/bin下,运行./cfx5solve,界面同windows
CFX并行任务提交
命令行提交: Windows上:cfx5solve –def Benchmark.def -start-method "MPICH2 Local Parallel" part 4(单机并行) Unix/Linux上:cfx5solve -def Benchmark.def -start-method "MPICH2 Distributed Parallel" –par-dist cn01*4,cn02*4
MPI软件安装配置
Unix/Linux下: 对于以下平台,安装ansys的同时就装好了HP-MPI UNIX: hpux, hpux-ia64, osf Linux: linux, linux-amd64, linux-ia64
SGI下,各台机器上: /usr/lib/array/array.conf 中添加: array me machine localhost 例如:array default host1 host2 host3
END # SIMULATION CONTROL
MPI软件安装配置
Windows下(每个节点): 安装
<install_dir>\v121\CFX\bin\cfx5parallel -install-hpmpi-service <install_dir>\v121\CFX\bin\cfx5parallel -install-mpich2-service