Performance evaluation of a multistring photovoltaic module with distributed DC-DC converters
《公共建筑节能(绿色建筑)工程施工质量验收规范》DBJ50-234-2016
（ 7 ） 本 规 范 第 16.2.10 条 依 据 国 家 标 准 《 太 阳 能 供 热 采 暖 工 程 技 术 规 范 》 GB50495-2009 第 5.3.5 条的规定。
（8）本规范第 3.4.4 条为绿色建筑工程涉及的建筑环境与资源综合利用子分部工程 验收方式的规定。
本规范由重庆市城乡建设委员会负责管理，由重庆市建设技术发展中心（重庆市建 筑节能中心）、重庆市绿色建筑技术促进中心负责具体技术内容解释。在本规范的实施 过程中，希望各单位注意收集资料，总结经验，并将需要修改、补充的意见和有关资料 交重庆市建设技术发展中心（重庆市渝中区牛角沱上清寺路 69 号 7 楼，邮编：400015， 电话：023-63601374，传真：023-63861277），以便今后修订时参考。
建设部备案号： J13144-2015
DB
重庆市工程建设标准 DBJ50-234-2016Leabharlann 公共建筑节能（绿色建筑）工程
施工质量验收规范
Code for acceptance of energy efficient public building(green building) construction
（3）本规范第 1.0.4、3.1.2、11.2.4、22.0.6、22.0.7 条内容分别依据国家标准《建 筑节能工程施工质量验收规范》GB50411-2007 第 1.0.5、3.1.2 条、11.2.3、15.0.5、15.0.5 条等强制性条文要求。
HPC Benchmarking and Performance Evaluation With Realistic Applications 1 INTRO GOALS OF HP
1 I NTRO : G OALS OF HPC B ENCHMARKING P ERFORMANCE E VAhmarking and performance evaluation, as viewed in this paper, is to assess the performance and understand characteristics of HPC platforms and their important applications. An obvious use of the gained results is the search for machines that are best for a given purpose. Equally important uses are the creation of yardsticks for research and the anticipation of needs of future HPC technology. The main thesis of this paper is that there is a dire need for basing such assessments on realistic computer applications. While the use of metrics that rely on measuring the behavior of simple program kernels has served us for many purposes in the past, it fails to answer questions that are of decisive importance today and in the future. These questions deal with the directions future HPC development should take, which have a direct impact on the competitiveness of our industry and nation. Benchmarking and Performance Evaluation allow us to answer two important areas of questions, to which we will refer as the Relevant Questions: 1) How do HPC platforms perform in solving todays important problems? We may learn that a biology application may take a full year to fold a certain protein on a present-day HPC platform. Beyond such absolute time metrics, we may ask how computer platforms perform relative to others for a certain application or application area. Furthermore, we may be interested in finding out how certain system components perform for these applications. For example, we may ask for absolute communication times, the fraction of IØtaken in the overall execution, or the percentage of peak cpu power exploited. 2) What are the characteristics of today’s computational applications and how do these characteristics change in future problems? We may want to know the memory required by a realistic weather forecast problem or how much communication will happen per quantity of computation. We may also ask how these properties change as a chemist increases the number of particles in a molecular dynamics simulation 100-fold. In this paper we focus on the first area of questions. Key to both tasks is the use of today’s realistic applications and real-
Rewrad and Performance session two
Break down the brief
A description and evaluation of at least three components of a total reward system in relation to performance management, 1 of which should be non financial What are the key words?
Skills needed to pass
Research skills • Go to the library • Look at relevant chapters of HR text books on the reading list. • Find Journal articles • Set your own parameters
Skills needed to pass
Time management and planning • Read gradually, don't do it all at the last minute • Do a little every week, build up your knowledge • Hand it in on time
Reward and Performance
Skill Development
The assessment
Imagine that you are working in an SME and have been asked by your manager to explain the fundamentals of a performance management system linked to reward. Write a report of no more than 2500 words (+10%), which summarises what you have found out. Your report could include the following • The purpose of performance management systems and the relationship performance management system must have to business objectives. • An evaluation of at least three components of performance management systems. • An evaluation of the relationship between motivation and performance management, referring to at least two motivational theories. • A description and evaluation of at least three components of a total reward system in relation to performance management, 1 of which should be non financial
performancetest工具结果解析 -回复
performancetest工具结果解析-回复Performancetest工具结果解析Performance testing is a crucial aspect of software development as it provides insights into the performance and scalability of an application. Performance testing involves using various tools and techniques to evaluate system response time, throughput, and stability under different workloads. One such tool widely used for performance testing is Performancetest. In this article, we will delve into the interpretation and analysis of Performancetest tool results.1. Introduction to Performancetest:Performancetest is a comprehensive performance testing tool developed by PassMark Software. It allows testers to assess the performance of their applications by simulating real-world scenarios and generating detailed performance reports. This tool supports a wide array of performance tests, including CPU, disk, memory, 2D and 3D graphics, networking, and more.2. Understanding Performancetest metrics:Performancetest provides a range of metrics that help gauge the performance of an application. Some of the important metricsinclude:2.1 CPU performance:This metric measures the performance of the CPU by performing complex calculations and generating a score. A higher score indicates better CPU performance.2.2 Disk performance:Disk performance evaluates the read and write speeds of a storage device. It determines how quickly data can be accessed from or written to the disk. The metric reports the transfer rate in MB/s, with higher values indicating faster disk performance.2.3 Memory performance:Memory performance tests the speed at which the system can read from and write to the RAM. It measures the memory's latency and throughput. A higher score indicates better memory performance.2.4 2D and 3D graphics performance:This metric assesses the graphical rendering capability of the system. It performs rendering operations and provides a score. A higher score suggests better graphics performance.2.5 Networking performance:Networking performance tests the speed at which data can be transferred over a network connection. It measures the throughput and latency of the network. Higher values indicate better networking performance.3. Interpreting Performancetest results:Performancetest generates detailed reports in tabular and graphical formats, making it easier to interpret and analyze the results. Here's a step-by-step guide to interpreting the results:3.1 Identify the performance metrics being measured:The first step is to identify the metrics being measured in the performance test. Look for the specific metrics mentioned in the results report, such as CPU performance, disk performance, memory performance, and so on.3.2 Analyze the scores or values:Next, analyze the scores or values associated with each metric. Compare the values with industry benchmarks or previous test results to gauge the performance of the application. Higher scoresor values generally indicate better performance.3.3 Look for any anomalies:Check for any significant deviations or anomalies in the results. Anomalies may indicate performance bottlenecks or issues that need further investigation. For example, a sudden drop in network performance could suggest a network configuration problem.3.4 Consider the workload:Consider the workload used during the test and how it relates to the application's real-world usage. If the workload simulated in the test does not align with the expected usage pattern, the results may not accurately reflect the application's performance.3.5 Identify performance limitations:Identify any performance limitations based on the results. This could include CPU bottlenecks, slow disk read/write speeds, memory constraints, graphics rendering issues, or network latency.4. Understanding the implications of results:Interpreting Performancetest results also involves understanding the implications of the findings. Here are a few key considerations:4.1 Scalability:Evaluate how the application performs under different workloads. Determine if the performance remains consistent or degrades as the workload increases. Scalability issues could indicate the need for optimization or infrastructure upgrades.4.2 Performance bottlenecks:Identify the factors causing performance bottlenecks and prioritize their resolution based on impact. This could involve optimizing code, improving database queries, or upgrading hardware resources.4.3 Dependency analysis:Analyze dependencies between different components of the system and identify any bottlenecks or performance issues. For example, slow disk performance may impact overall application performance.4.4 Root cause analysis:If performance issues are identified, conduct a root cause analysis to determine the underlying reasons. This could involve analyzinglogs, profiling the application, or using additional diagnostic tools.In conclusion, Performancetest is a powerful tool for evaluating the performance of software applications. Understanding and interpreting the results generated by this tool is crucial for identifying performance bottlenecks and optimizing the application. By following a systematic approach and considering various factors, testers can gain valuable insights from Performancetest results and enhance the overall performance of their applications.。
2024年度初中英语中考阅读理解课件
Weigh the evidence presented in the text to determine the truth or falsehood of a statement
10
Main ideas
2024/2/2
Identify the topic
03
If there are multiple choices, compare and contrast them
with the information in the text to find the best match
9
Inference and True/False Questions
2024/2/2
7
02
Common question types and problem solving techniques
Chapter
2024/2/2
8
Fact detail questions
2024/2/2
Identify key information
01
Look for specific details in the text that directly answers
Reading comprehension passages typically cover a range of topics, including but not limited to school life, daily activities, social issues, and science and technology
reasonable guess based on the information you have and move
A preliminary evaluation of the performance of multiple
ORIGINAL ARTICLEA preliminary evaluation of the performance of multipleionospheric models in low-and mid-latitude regions of China in 2010–2011Weihua Luo •Zhizhao Liu •Min LiReceived:23July 2012/Accepted:3June 2013ÓSpringer-Verlag Berlin Heidelberg 2013Abstract Ionospheric delay is a dominant error source in Global Navigation Satellite System (GNSS).Single-fre-quency GNSS applications require ionospheric correction of signal delay caused by the charged particles in the earth’s ionosphere.The Chinese Beidou system is devel-oping its own ionospheric model for single-frequency users.The number of single-frequency GNSS users and applications is expected to grow fast in the next years in China.Thus,developing an appropriate ionospheric model is crucially important for the Chinese Beidou system and worldwide single-frequency Beidou users.We study the performance of five globally accessible ionospheric models Global Ionospheric Map (GIM),International Reference Ionosphere (IRI),Parameterized Ionospheric Model (PIM),Klobuchar and NeQuick in low-and mid-latitude regions of China under mid-solar activity condition.Generally,all ionospheric models can reproduce the trend of diurnal ionosphere variations.It is found that all the models have better performances in mid-latitude than in low-latitude regions.When all the models are compared to the observed total electron content (TEC)data derived from GIM model,the IRI model (2012version)has the best agreement with GIM model and the NeQuick has the poorest agreement.The RMS errors of the IRI model using the GIM TEC as reference truth are about 3.0–10.0TECU in low-latitude regions and 3.0–8.0TECU in mid-latitude regions,as observed during a period of 1year with medium level of solar activity.When all the ionospheric models are inges-ted into single-frequency precise point positioning (PPP)to correct the ionospheric delays in GPS observations,the PIM model performs the best in both low and mid-latitudes in China.In mid-latitude,the daily single-frequency PPP accuracy using PIM model is *10cm in horizontal and *20cm in up direction.At low-latitude regions,the PPP error using PIM model is 10–20cm in north,30–40cm in east and *60cm in up component.The single-frequency PPP solutions indicate that NeQuick model has the lowest accuracy among all the models in both low-and mid-lati-tude regions of China.This study suggests that the PIM model may be considered for single-frequency GNSS users in China to achieve a good positioning accuracy in both low-and mid-latitude regions.Keywords Ionospheric model ÁPrecise point positioning (PPP)ÁIonospheric model evaluation ÁLow-and mid-latitude regions of ChinaIntroductionThe range error resulting from the ionospheric delay can be up to 100m in magnitude and can significantly affect the accuracy of the positioning,navigation,velocity and tim-ing.The ionospheric delay of dual-frequency Global Navigation Satellite System (GNSS)users can be precisely corrected by measurements from dual-frequency signals.W.Luo ÁZ.Liu (&)ÁM.Li (&)Department of Land Surveying and Geo-Informatics (LSGI),The Hong Kong Polytechnic University,Hung Hom,Kowloon,Hong Kong,China e-mail:lszzliu@.hk M.Lie-mail:lim@W.LuoCollege of Electronics and Information Engineering,South-Central University for Nationalities,Wuhan,China M.LiGNSS Research Center,Wuhan University,Wuhan,ChinaGPS SolutDOI 10.1007/s10291-013-0330-zTo thefirst-order approximation,the ionospheric range error can be estimated as d=40.3TEC/f2,where f is the signal frequency and TEC represents total electron con-tents.TEC is the integral of the electron density along a ray path from GNSS receiver to satellite.There are still numerous single-frequency GNSS users who need correc-tion information to mitigate their ionospheric range errors.To correct ionospheric delay for Global Positioning System(GPS)users,ideally a simple and accurate iono-spheric model can be used.The simplest one to use might be the well-known broadcast model or Klobuchar model (Klobuchar1987).With eight broadcast parameters,the ionospheric correction can be calculated in real time but the accuracy is limited to about50–60%(Bidaine and Warnant2011).China is progressively developing its own Beidou system,and similarly,one such ionospheric model is needed(Shi et al.2012).Similar to the GPS,the Beidou Ionospheric Model(BIM)uses eight parameters to broad-cast ionospheric correction information(Wu et al.2012). Basically,two approaches are available to correct the ionospheric delay errors for single-frequency GNSS users. One approach is to use the existing ionospheric models to compute ionospheric correction.The ionospheric models are usually established on the basis of some physical principles as well as some historical ionospheric data.The other approach is to use real-time ionospheric TEC observation data derived from GNSS networks to construct a numerical model and then compute ionospheric correc-tion for single-frequency users.In thefirst approach for ionosphere correction,several ionospheric models exist and can be used for GNSS ion-ospheric correction.Generally,these models can be clas-sified into three main categories(Feltens et al.2011):(1) physical models based on ionospheric physics and chem-istry,such as SAMI2(Huba et al.2000);(2)semi-physical models that simplify the physical models by reducing the input parameters,such as Parameterized Ionospheric Model(PIM)(Daniell et al.1995);(3)empirical or semi-empirical models based on observations,such as Interna-tional Reference Ionosphere(IRI)model(Bilitza2001; Bilitza and Reinisch2008)and the NeQuick model(Rad-icella and Leitinger2001).For the purpose of correcting ionospheric range delay for satellite navigation systems,a lot of research has evaluated different ionospheric models.For example,Fa-rah(2008)compared the TEC derived from Klobuchar model and NeQuick model with TEC from the Interna-tional GNSS Service(IGS)Global Ionospheric Mapping (GIM)products in different regions under different solar activities.Zhang et al.(2010)compared the latitudinal distribution of TEC derived from the DORIS with the NeQuick model.Klobuchar model and NeQuick model were also compared by Radicella et al.(2008).Nafisi and Beranvand(2005)compared the Klobuchar TEC with the IGS TEC for a special region.Differences exist between the TEC calculated from ionospheric models and the observed ones.The performances of different ionospheric models vary with geographic regions.For example,the NeQuick model which is used for the Galileo satellite navigation system is shown to be more accurate in Wuhan than in Beijing (Wang et al.2007).In North America and Europe,the NeQuick model has shown a remarkably better perfor-mance than the IRI and Klobuchar models to reproduce the characteristics of slant TEC(Coisson et al.2004).Goodwin and Breed(2001)compared the GPS-derived TEC obser-vations in Australia with TEC derived from IRI and PIM models and found that the discrepancy between the observations and the models was about3–7TECU.Zhang et al.(2006a)compared the results using the IRI90and Klobuchar models in China and Europe and indicated that IRI90may be more accurate than Klobuchar.Zhang et al. (2006b)compared ionosonde-derived TEC with the IGS-derived satellite-based TEC as well as IRI2001-modeled TEC at a low-latitude station in Hainan Province,China. They indicated that IRI data can reproduce the semi-annual variation of TEC.The differences between IRI TEC and IGS TEC can be as large as20TECU.Bi et al.(2012) evaluated the accuracy of the Klobuchar model,the Ne-Quick model and GIM from Center for Orbit Determina-tion in Europe for mid-latitude regions and indicated that GIM may be used as a reference.Wu et al.(2012)com-pared the Beidou Ionospheric Model with the GPS Klob-uchar model and found that the Beidou Ionospheric Model outperforms the Klobuchar model by7.8–35.3%in the single-frequency single-point positioning in the Northern Hemisphere including Asia,Europe and North America.However,in the past years,only a few ionospheric models have been evaluated simultaneously(Orus et al. 2002;Coisson et al.2004).A careful study of the perfor-mances of different ionospheric models in China is par-ticularly necessary because of the expected large number of single-frequency Beidou/GNSS users.For the Chinese Beidou satellite navigation system,the Beidou Ionospheric Model(BIM)that is similar to the GPS broadcast model has been specifically designed.The performance of the BIM in China region was evaluated in Wu et al.(2012). But more work needs to be done;for instance,only1day of data from two stations in China was analyzed to evaluate the BIM.It is inadequate to give a statistically meaningful conclusion without data analysis using more stations and longer period of observations.For the China region,some regional nowcasting and forecasting ionospheric models have been developed for the purpose of radiowave and space physics research in China.A Chinese Reference Ionosphere(CRI)wasGPS Solutdeveloped based on the International Reference Ionosphere by incorporating the ionospheric data collected in China region(Liu et al.1994).Liu et al.(2005)introduced an iteration method to forecast region f0F2based on the long-term observations.In order to forecast f0F2,more than10h of data ahead of the prediction epoch should be acquired. Furthermore,the forecasting f0F2can be used for TEC forecasting(Liu et al.2008).Wan et al.(2007)developed an ionospheric TEC nowcasting system over the China region based on GPS data from four GPS stations in China. The real-time TEC map is published on the Internet (/TEC.asp).Some of the above model studies such as the one by Liu et al.(1994)were not for TEC calculation.The other work such as Wan et al. (2007)focused on TEC calculation.However,no numeri-cal TEC data or TEC model was available for public access.Thus,we did not include these Chinese ionospheric models in the study.Unfortunately,the TEC data from another model for the China region,the Beidou Ionospheric Model,were not available at present for this study.Thus,it is not included as well.We present a comparative evaluation of TEC quality from Klobuchar,IRI,PIM and NeQuick models with respect to the TEC derived from GIM data produced by Jet Propulsion Laboratory(JPL).These models are chosen for comparison with the JPL GIM model because(1)these models are openly accessible by single-frequency GNSS users,and they basically belong to thefirst approach of ionosphere modeling as discussed above and(2)JPL uses real-time GPS TEC data to update the GIM model.The GIM model largely represents the second approach of ion-ospheric modeling as discussed above,though it also uses Bent and IRI model as background model when GPS TEC data are not available(Mannucci et al.1998).In order to evaluate the performance of all these models in the China region for single-frequency users,the absolute accuracies of these models are further evaluated in a single-frequency GPS positioning program.This is achieved using the model-derived TEC data to correct the ionospheric range delay in single-frequency GPS data.We study the TECs at different geographic regions over China for a1-year period from October2010to September2011.Through comprehensive evaluation of multiple ionospheric models,this study will help identify a suitable ionospheric model to correct the ionospheric error for single-frequency GNSS users in the China region under mid-solar activity condition. Modeling overviewIn this section,thefive ionospheric models are introduced and briefly described,including their input and output parameters and their applications.Klobuchar modelKlobuchar model(Klobuchar1987)is a relatively simple ionospheric model built on a simple cosine function,with a maximum delay at14:00local time and a constant offset of 5ns during nighttime.It is the model used by the GPS single-frequency users.The model period and amplitude are described by a third-degree polynomial in local time and geomagnetic latitude.The required input parameters include location of the station,elevation and azimuth of GPS satellite and eight Klobuchar coefficients.The eight coefficients are time-varying and broadcast in the GPS navigation message.The Klobuchar model assumes an ideal smooth behavior of the ionosphere;thus,important dailyfluctuations cannot be accounted for.The accuracy of the model is limited to 50–60%of the total effect during quiet space weather conditions(Bidaine and Warnant2011;Filjar et al.2009). Under some special circumstances,such as severe iono-spheric activity at low elevations,the model performs poorly.However,this model can be implemented easily and the computation is fast due to its simplicity.IRI modelThe International Reference Ionosphere(IRI)is a widely used empirical model in the ionosphere community(Bilitza 2001).It is an internationally recommended standard for the specification of plasma parameters in earth’s iono-sphere(Bilitza and Reinisch2008).It is developed based on many available and reliable data sources for the iono-spheric plasma,such as a worldwide ionosonde network, radar data and satellite and rocket measurements.Over the years,this model has been steadily improved and several versions have been released(Bilitza and Reinisch2008). The most recent version of IRI model is IRI-2012,and it is used in this study(Bilitza et al.2011).For a given location(latitude,longitude),local time(or universal time)and date,the IRI provides the electron den-sity,electron temperature,ion temperature and ion compo-sition in the height interval from50to2,000km and the TEC. The IRI model can reproduce TEC variations reasonably well with respect to observational TEC(Wilkinson et al.2001). The IRI provides several options to calculate the electron density and TEC,such as the f0F2/hmF2model and the topside ionosphere.For this research,the f0F2/hmF2model is from International Union of Radio Science(URSI)model,the D-region is described by IRI-95and the topside ionosphere is represented by NeQuick model(refer to the next section).NeQuick modelNeQuick is a monthly median3-D empirical ionospheric model that uses a combination of multiple Epstein layers toGPS Solutproduce an analytic function for the ionospheric electron density profile and TEC up to a height of20,000km (Hochegger et al.2000).The NeQuick model is divided into two parts:(1)the bottom side,up to the F2-layer peak consisting of a sum offive semi-Epstein layers with modeled thickness parameters and(2)the topside described by means of the sixth semi-Epstein layer with a height-dependent thickness parameter that is empirically deter-mined.The input parameters include location(latitude, longitude),time(year,month,day,UT),and monthly smoothed sunspot number(R12)or daily solarflux.The NeQuick model can depict day-to-day variations in the range delay corrections when it takes the daily average sunspot number into account.In European regions,Ne-Quick can provide better ionospheric delay corrections than the Klobuchar and IRI models(Farah2008).In the Galileo system,the ionosphere correction accuracy can reach about70%using the NeQuick model(Bidaine and Warnant2011).NeQuick is used as the official ionosphere correction model incorporated in Galileo receivers(Bida-ine and Warnant2011),and the model coefficients are broadcast as part of the Galileo navigation message.The model has also been adopted as an ITU standard(ITU-R 2007).PIM modelThe Parameterized Ionospheric Model(PIM)(Daniell et al. 1995)is a semi-analytic global ionospheric and plasma-spheric model based on the parameterized output of an empirical plasmaspheric model and several regional theo-retical ionosphere models.The ionospheric part combines the results of several theoretical ionosphere models,cov-ering the E and F layers for all latitudes,longitudes and local times.The inputs of PIM include the solarflares and geomagnetic index in addition to the information of loca-tion and time.PIM outputs include electron density profiles in the altitudinal range from90km to25,000km,ion composition and also TEC for all levels of solar and geo-magnetic activities.The computation of PIM is fast while the physical features of the ionosphere are retained.GIM modelThe global ionosphere TEC data are routinely computed by data analysis centers at IGS,including Jet Propulsion Labo-ratory(JPL)that uses more than200worldwide ground-based GPS receivers(Mannucci et al.1998).The TEC data are published at a2-h time interval as Global Ionosphere Maps (GIMs)(ftp:///pub/gps/products/ionex). The vertical total electron content(VTEC)is calculated in a solar-geomagnetic reference frame using bi-cubic splines on a spherical grid.The GIM covers±87.5°latitude and±180°longitude ranges and has a spatial resolution of2.5°and5°in latitude and longitude,respectively.The ionospheric TEC values can be interpolated using the surrounding GIM grids.GIM TEC map can predict more than90%of iono-spheric TEC.Total accuracy of GIM data was evaluated around3.7–3.9TECU(Sekido et al.2003).Generally,GIM can provide accurate TEC values and it can be taken as a powerful tool for monitoring global ionosphere in near real time(Ho et al.1997).In this research,the GIM TEC data will be used as reference to which the TEC from other models are compared.TEC model comparison resultIn this section,TEC derived from the theoretical NeQuick, Klobuchar,PIM and IRI models are compared with the observational model GIM over a1-year period(October 2010to September2011)in the low and mid-latitude in China region.For comparison purpose,the monthly mean TEC is calculated:TEC Model;j¼1mX mi¼1TEC Model;ijwhere TEC Model;ij is the TEC at the j th epoch of i th day for a given ionospheric model(including the GIM model);m denotes the number of days in a month;and the TEC Model;j is the monthly mean TEC for the j th epoch for a given ionospheric model.Since the TECs from the models are calculated at an interval of30s,the number of epochs in 24h is2,880.The30-s interval is selected because the TEC data are used to correct30-s GPS observations in order to evaluate the accuracy of each ionospheric model for single-frequency GPS positioning.Furthermore,the root mean square(RMS)for each model using the GIM as reference is calculated as below.RMS¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1nX nj¼1TEC Model;jÀTEC GIM;jÂÃ2v uu twhere TEC GIM;j is the monthly mean TEC from the GIM model for the j th epoch.Since there are2,880epochs in 24-h period,the n has a value of2,880in the above equation.Test site selectionThe Chinese mainland spans from the equatorial region to mid-latitudes.To evaluate the performances of different ionospheric models in different regions,eight stations at various latitudes are chosen as test sites,as shown in Tables1and2.In the TEC computation using differentGPS Solutmodels,the upper integral height is set as20,000km (except IRI model as2,000km).Low-latitude geographic regionTo demonstrate the performance of each ionospheric model in low-latitude region,the monthly mean TEC calculated over1-month period(here July)using thefive ionospheric models at two low-latitude stations(HKTU and WUHN) are shown in Fig.1.It can be seen that the TEC derived from multiple ionospheric models have a shape very similar to that of GIM TEC.The maximum TEC value occurs around08 UT(LT=UT?8h),and the minimum one is around20 UT.It can be seen that the IRI model has a better agreement with the GIM data than other models.At the HKTU station,IRI overestimates the TEC during4–10 UT but underestimates TEC during12–22UT,by a few TECU with respect to the GIM model.The other models consistently underestimate the TEC by more than10 TECU with respect to the GIM model.At HKTU station, the disagreement between Klobuchar and GIM model can be as large as20TECU.The underestimation of the models with respect to GIM is not purely due to modeling error.One reason for such an underestimation might be attributed to the difference in modeling heights.The GPS-based GIM model directly measures TEC from ground up to the GPS satellite height*20,200km,while the mod-eling heights of most models are significantly lower.In Thompson et al.(2009),it is estimated that vertical TEC of the plasmasphere contribution from1,400to20,200km is3TECU.Mannucci et al.(1998)also estimated that the electron content above1,300km is1–3TECU.The good agreement between IRI and GIM models may be explained by the fact that the GIM model uses the cli-matological models such as IRI and Bent models where there are TEC data gaps like the China region(Mannucci et al.1998).However,this agreement indicates the good consistency only between the two models.The absolute accuracies of these ionospheric models need further evaluation through the PPP computation discussed in the following section.Figure2shows the RMS of the monthly mean TEC calculated from four ionospheric models from October 2010to September2011at four low-latitude stations.The figure shows that the RMS of NeQuick model is apparently much larger than those of other models.From October to April at HKTU and KUNM stations(low latitudes in low-latitude region),the RMS errors of the NeQuick model are much larger than other models and can be as large as60 TECU.At LHAZ and WUHN stations(high latitudes in low-latitude region),the NeQuick model has larger RMS errors but the error size is much smaller,approximately20 TECU.Generally,the Klobuchar and IRI model have better performance than other models.At stations HKTU and KUNM,the RMS for Klobuchar model is smaller than IRI model during November2010and January2011with the values\5.5TECU while the RMS for IRI model is smaller than Klobuchar model in most months of the rest of the year.At the other two stations LHAZ and WUHN(near the EIA crest),the RMS of Klobuchar model is smaller than IRI model from October2010to March2011,with the values\5TECU.In other months,the IRI model generally has a better performance than the Klobuchar model.Table1The location of four low-latitude stationsStation name Latitude(°)Longitude(°)Height(m)t.(°)HKTU22.3028114.179534.60510.9285 KUNM25.0295102.79721,986.26613.7175 LHAZ29.657391.10403,624.60918.8285 WUHN30.5317114.357325.86819.1589Table2The location of four mid-latitude stationsStation name Latitude(°)Longitude(°)Height(m)t.(°)XIAN34.3687109.2215463.91822.9702 BJFS39.6087115.892587.46828.3223 CHAN43.7907125.4442273.35232.7320 HRBN45.7026126.6204198.32434.6790GPS SolutMid-latitude geographic regionFigure3displays the performance of monthly mean TEC of multiple ionospheric models in July2011at two mid-latitude stations XIAN and HRBN.Generally,the modeled TEC has similar variation shapes as that of GIM TEC,but the differences between the modeled TEC and the observed TEC(from GIM)are more remarkable at mid-latitude regions than those in low-latitude regions.Consistent with low-latitude regions,the IRI model overall has the best agreement with the GIM model.It can be seen that the PIM and NeQuick have the largest disagreement with the GIM model,consistently underestimating the TEC with respect to the GIM model.Figure4shows the RMS of various models at four mid-latitude stations.The IRI and Klobuchar models clearly have smaller RMS than NeQuick and PIM models.For the Klobuchar model,the maximum RMS occurs in March. For the IRI model,the maximum RMS error occurs in May.The performance of IRI model is consistent with that in low-latitude region as shown in Fig.2.Generally,the NeQuick model has the largest RMS value among all themodels.This is also consistent with its performance in low-latitude region shown in Fig.2.Comparing Figs.4and2,it can be found that the RMS values of all the models in mid-latitude region are much smaller than those in low-latitude region.Taking the Ne-Quick model for example,the largest RMS of NeQuick in low-latitude region can be as large as70TECU(Nov2010 at HKTU station).However,the largest NeQuick RMS error in mid-latitude region is only about20TECU(Sep-tember2011at HRBN station).The other three models also have smaller RMS values in mid-latitude region than in low-latitude region.In other words,all the four models have better performances in mid-latitude region than low-latitude region.This might be explained as the ionosphere in low-latitude region is more active and disturbed than in mid-latitude region.Validation by single-frequency precise point positioning (PPP)In the analysis presented above,the observed TEC data from GIM model are used as a reference to which the other four models are compared.The GIM model is chosen as reference in this study because this model largely depends on the TEC observations from global GPS data.The GIMGPS Solutmodel has much frequent(normally2h)update than other models.However,the model assessment shown above is actually a relative one with respect to the performance of GIM model.In order to validate the performance of all the ionospheric models in an absolute sense,thefive models, including the GIM model,are further evaluated in the position domain using a single-frequency precise point positioning(PPP)approach.In this approach,the single-frequency data are processed in a PPP mode,although all of the8tested sites are Chinese Continuously Operating Reference Stations(CORS)and generate dual-frequency observations.The purpose of using single-frequency data only is to apply the ionospheric models to correct the ionospheric delays,instead of using dual-frequency observations that would eliminate the ionospheric effects. Since the ionospheric delay is the dominant error in single-frequency PPP,the accuracy of which can be used as an indicator of the absolute accuracy of ionospheric models.The daily GPS data from the8test stations are processed in static mode at an interval of30s for the period October 2010to September2011.The Positioning And Navigation Data Analyst(PANDA)software package(Liu and Ge 2003)developed at Wuhan University is used to compute the single-frequency PPP solutions.PANDA can deliver precise point positioning solutions of a few millimeters when dual-frequency static GPS data are used(Ge et al. 2008).The single-frequency PPP errors corresponding to the GIM,IRI,PIM,Klobuchar and NeQuick ionospheric models are illustrated in Fig.5.The results from two sta-tions are included:one mid-latitude station BJFS and one low-latitude station KUNM.In the comparison,the true coordinates are obtained by computing a weekly dual-fre-quency PPP solution.As said earlier,the daily PPP solu-tions from the PANDA can achieve a high accuracy of a few millimeters(Liu and Ge2003).It can be seen from Figs.5and6that the single-frequency PPP solutions using ionospheric models have positioning errors of a few deci-meters in horizontal and even meters in height.The dual-frequency PPP solutions are thus sufficiently accurate being used as a reference to evaluate the single-frequency ones.It can be clearly seen that the single-frequency PPP solutions corresponding to NeQuick model have the worst accuracy.This is consistent with the NeQuick perfor-mances shown in Figs.2and4.Particularly at the low-latitude station KUNM,the PPP solution errors using NeQuick model may reach up to8meters.In addition,the accuracy of PPP solutions using NeQuick model is not stable over time.Significant variations over seasons are exhibited,particularly at low-latitude station KUNM. Generally,the NeQuick model has a better precision during May to September,which is consistent with Figs.2and4. Compared to other models,Fig.5shows that the PIM can provide the best correction for the single-frequency GPS data in China.The RMS of the daily positioning errors between single-frequency PPP and the truth coordinates for all the8sta-tions is shown in Fig.6.The x axis shows the station names,arranged in the descending order of latitude.The RMS values in the east,north and up directions for all the ionospheric models clearly show a strong correlation with latitude.At higher latitudes,the positioning accuracies get higher.This is consistent with the results shown in Figs.2 and4.This can be explained since the ionosphere inGPS Solut。
Performance Evaluation for Decentralized Operations
$150
$1,555 2,100 650 $3,655 $3,655 $800 $800
This is shown on the Vice-President’s budget production report (Slide 16).
Cost Centers
Budget Performance Report Vice-President, Production For the Month Ended October 31, 2006 Over Budget Actual Budget
Responsibility Centers
Cost Centers Managers are held accountable for controlling costs.
Profit Centers Managers are held accountable for costs and making decisions that impact revenues favorably.
Responsibility Centers
Investment Centers Managers are held accountable for costs and revenues and are also held accountable for the efficient use of assets.
Objectives
1. List and explain the advantages and After of studying this disadvantages decentralized chapter, you should operations. be able to: accounting 2. Prepare a responsibility report for a cost center. 3. Prepare responsibility accounting reports for a profit center. 4. Compute and interpret the rate of return on investment, the residual income, and the balanced scorecard for an investment center.
Performance Evaluation of a Multi-Zone Application in Different OpenMP Approaches
Performance Evaluation of a Multi-Zone Application in Different OpenMP Approaches∗Haoqiang JinNAS Division,NASA Ames Research Center,Moffett Field,CA94035-1000hjin@Barbara Chapman,Lei HuangDepartment of Computer Science,University of Houston,Houston,TX77004{chapman,leihuang}@NAS Technical Report NAS-07-009,October2007AbstractWe describe a performance study of a multi-zone application benchmark implemented in several OpenMP approaches that exploit multi-level parallelism and deal with unbalancedworkload.The multi-zone application was derived from the well-known NAS Parallel Bench-marks(NPB)suite that involvesflow solvers on collections of loosely coupled discretizationmeshes.Parallel versions of this application have been developed using the Subteam conceptand Workqueuing model as extensions to the current OpenMP.We examine the performanceimpact of these extensions to OpenMP and compare with hybrid and nested OpenMP ap-proaches on a large shared memory parallel system.1IntroductionSince its introduction in1997,OpenMP has become the de facto standard for shared memory parallel programming.The notable advantages of the model are its global view of memory space that simplifies programming development and its incremental approach toward parallelization. However,it is a big challenge to scale OpenMP codes to tens or hundreds of processors.One of the difficulties is a result of limited parallelism that can be exploited on a single level of loop nest.Although the current standard[8]allows one to use nested OpenMP parallel regions, the performance is not very satisfactory.One of the known issues with nested OpenMP is its lack of support for thread team reuse at the nesting level,which affects the overall application performance and will be more profound on multi-core,multi-chip architectures.There is no guarantee that the same OS threads will be used at each invocation of parallel regions although many OS and compilers have provided support for thread affinity at a single level.To remedy this deficiency,the NANOS compiler team[1]has introduced the GROUPS clause to the outer parallel region to specify a thread group composition prior to the start of nested parallel regions, and Zhang[13]proposed extensions for thread mapping and grouping.Chapman and collaborators[5]proposed the Subteam concept to improve work distribution by introducing subteams of threads within a single level of thread team,as an alternative for nested OpenMP.Conceptually,a subteam is similar to a process subgroup in the MPI context. The user has control over how threads are subdivided in order to suit application needs.The subteam proposal introduced an onthreads clause to a work-sharing directive so that the work, including the implicit barrier at the end of the construct,will be performed among the subset of threads within the team.One of the prominent extensions to the current OpenMP is the Workqueuing(or Taskq) modelfirst introduced by Shah et al.[9]and implemented in the Intel C++compiler[10]. It was designed to work with recursive algorithms and cases where work units can only be determined dynamically.Because of its dynamic nature,Taskq can also be used effectively in an unbalanced workload environment.The Taskq model will be included in the coming OpenMP 3.0release[4].Although thefinal tasking directive in OpenMP3.0will not be the same as the original Intel Taskq proposal,it should still be quite intuitive to understand what potential the more dynamic approach can offer to applications.In this study,we will compare different OpenMP approaches for the parallelization of a multi-zone application benchmark and report performance results from a large shared memory machine.In Section2,we briefly discuss the application under consideration.The different implementations of our benchmark code are described in Section3and the machine description and performance results are presented in Section4.We conclude our study in Section5where we also elaborate on future work.2Multi-Zone Application BenchmarkThe multi-zone application benchmarks were developed[6,11]as an extension to the origi-nal NAS Parallel Benchmarks(NPBs)[2].These benchmarks involve solving the application benchmarks BT,SP,and LU on collections of loosely coupled discretization meshes(or zones). The solutions on the meshes are updated independently,but after each time step they ex-change boundary value information.This strategy,which is common among many production structured-meshflow solver codes,provides relatively easy to exploit coarse-grain parallelism between zones.Since the individual application benchmark also allowsfine-grain parallelism within each zone,this NPB extension,named NPB Multi-Zone(NPB-MZ),is a good candidate for testing hybrid and multi-level parallelization tools and strategies.NPB-MZ contains three application benchmarks:BT-MZ,SP-MZ,and LU-MZ,with prob-lem sizes defined from Class S to Class F.The difference between classes comes from how the number of zones and the size of each zone are defined in each benchmark.We focus our study on the BT-MZ benchmark because it was designed to have uneven-sized zones,which allows us to test various load balancing strategies.For example,the Class B problem has64zones with sizes ranging from3K to60K mesh points.Previously,the hybrid MPI+OpenMP[6]and nested OpenMP[1]programming models have been used to exploit parallelism in NPB-MZ beyond a single level.These approaches will be briefly described in the next section.3Benchmark ImplementationsIn this section,we describefive approaches of using OpenMP and its extension to implement the BT-MZ benchmark.Three of the approaches exploit multi-level parallelism and the other two are concerned with balancing workload dynamically.3.1Hybrid MPI+OpenMPThe hybrid MPI+OpenMP implementation exploits two levels of parallelism in the multi-zone benchmark in which OpenMP is applied forfine grained intra-zone parallelization and MPI is used for coarse grained inter-zone parallelization.Load balancing in BT-MZ is based on a bin-packing algorithm with an additional adjustment from OpenMP threads[6].In this strategy,multiple zones are clustered into zone groups among which the computational workload is evenly distributed.Each zone group is then unqiuely assigned to each MPI process for parallel execution.The procedure involves sorting zones by size in descending order and bin-packing into zone groups.Exchanging boundary data within each time step requires MPI many-to-many communication.The hybrid version is fully described in Ref.[6]and is part of the standard NPB distribution.We will use the hybrid version as the baseline for comparison with other OpenMP implementations.3.2Nested OpenMPThe nested OpenMP implementation is based on the two-level approach of the hybrid version except that OpenMP is used for both levels of parallelism.The inner level parallelization for loop parallelism within each zone is essentially the same as that of the hybrid version.The only addition is the“num_threads”clause to each inner parallel region to specify the number of threads.Thefirst(outer)level OpenMP exploits coarse-grained parallelism between zones.A code sketch of the iteration loop using nested OpenMP is illustrated in Fig.1.The outer level parallelization is adopted from the MPI approach:workloads from zones are explicitly distributed among the outer-level threads.The difference is that OpenMP now works on the shared data space as opposed to private data in the MPI version.The load balancing is done statically through the same bin-packing algorithm where zones arefirst sorted by size,then assigned to the least loaded thread one by one.The routine“map_zones”returns the number and list of zones(num_proc_zones and proc_zone_id)assigned to a given thread(myid)as well as the number of threads(nthreads)for the inner parallel regions.This information is then passed to the“num_threads”clause in the solver routines.The MPI communication calls inside“exch_qbc”for boundary data exchange are replaced with direct memory copy and proper barrier synchronization.In order to reduce the fork-and-join overhead associated with the inner-level parallel regions, a variant was also created:a single parallel construct is applied to the time step loop block and all inner-parallel regions are replaced with orphaned“omp do”constructs.This version,namely version2,will be discussed together with thefirst version in the results section.!$omp parallel private(myid,...) myid=omp_get_thread_num()call mapproc zonethreads(nthreads)do k=2,nz-1solve for u in the current zone end do !$omp parallel private(myid,...) myid=omp_get_thread_num()call mapthread!$omp parallel private(zone,...) do step=1,nitercall exch_qbc(u,...)!$omp do schedule(runtime)do iz=1,num_zoneszone=zone_sort_id(iz)call adi(u(zone),...)end doend do!$omp end parallel #pragma omp parallel private(zone) for(step=1;step<=niter;step++){ exch_qbc(u,...);#pragma intel omp taskqfor(iz=0;iz<num_zones;iz++){zone=zone_sort_id[iz];#pragma intel omp task\captureprivate(zone)adi(&u[zone],...);}}Figure2:Code segment using OpenMP runtime scheduling(left)and Intel taskq directives (right).approach described in previous sections for handling load balance.The trade-offis potentially higher overhead associated with dynamic scheduling and less thread-data affinity as would be achieved in a static approach.To examine the potential performance trade-off,we developed an OpenMP version that solely focuses on the coarse-grained parallelization of different zones of the multi-zone benchmark.As illustrated in Fig.2left panel,this version is much simpler and compact.The“omp do”directive is applied to the loop nest over multiple zones.There is no explicit coding for load balancing,which is achieved through the OpenMP dynamic runtime schedule.The use of the“schedule(runtime)”clause allows us to compare different OpenMP loop schedules.A“zone_sort_id”array is used to store zone ids in different sorting schemes.3.5Workqueuing ModelWe developed a Taskq version of the BT-MZ benchmark based on the Intel workqueuing model. Because Intel implemented Taskq only in its C++compiler for C/C++applications and there is no other vendor compiler available at this point for testing the concept,we had tofirst convert the Fortran implementation of BT-MZ to the C counterpart.To minimize the performance impact from such a conversion,we did the following:•Fortran multi-dimensional arrays are converted to linearized C arrays,such as u(m,i,j,k)->u[m+5*(i+nxmax*(j+ny*k))],•The restrict qualifier is added to pointer variables in subroutine argument to enable compiler to perform optimization without pre-assumed pointer aliasing,for examplevoid add(double*restrict u,double*restrict rhs,..).Once we had the C version,the Taskq implementation of the BT-MZ benchmark(Fig.2right panel)is straightforward.Each work unit for a task is defined by the solver on an individual zone.The“intel omp taskq”directive is added to the loop nest over zones.Inside the zone loop nest,the“intel omp task”directive is used to generate tasks for each loop iteration and the zone value is preserved for each task by the“captureprivate”clause.The implicit synchronization at the end of the taskq construct guarantees the completion of all tasks beforegoing to the next iteration.Again,to test the performance impact of workload ordering,we use “zone_sort_id”to store the sorted zone ids.4Performance ResultsIn this section,we present performance results obtained on a large parallel system.We willfirst give a brief description of the system and programming support.4.1Testing EnvironmentOur performance studies were conducted on an SGI Altix3700BX2system that is one of the20 nodes in the Columbia supercomputer installed at NASA Ames Research Center[3].The Altix BX2node has512Intel Itanium2processors,each clocked at1.6GHz and containing9MB on-chip L3data cache.Approximately1TB of global shared-access memory is provided through the SGI scalable non-uniform memory accessflexible(NUMAflex)architecture.The underlying NUMAlink4interconnect provides6.4GB/s bandwidth.A single Linux operating system runs on the Altix system,providing an ideal environment for shared-memory programming such as OpenMP.The system is equipped with SGI message-passing toolkit(MPT1.12)that supports MPI programming.We used the Intel Fortran,C/C++9.1compilers for IA64that support OpenMP 2.5as well as the Taskq model.All of our experiments were run under the PBSpro batch system in a shared environment.In order to reduce variation in timing and improve performance,the “dplace”placement tool was used to bind processes/threads to physical processors.For testing the OpenMP Subteam concept as described in Section3.3,we employed the OpenUH research compiler[7]that was extended to support the“onthreads”clause.This is essentially a source-to-source translation process and the generated code is then compiled with a native compiler.A small runtime library was developed to support basic subteam functions, such as loop iteration scheduling and synchronization for subteam threads.4.2Multi-level ParallelismIn order to compare different multi-level parallel versions of the BT-MZ benchmark,wefirst examine the performance impact from varying the number of zone groups on a given number of CPUs.The left panel of Fig.3plots benchmark timing in seconds as a function of the number of groups at32CPUs for the Class B problem size.The notation“N g×N t”denotes the number of zone groups(N g)formed for thefirst level parallelism and the number of threads(N t)for the second level parallelism within each group.In the hybrid MPI+OpenMP version,N g is the same as the number of MPI processes and N t is the number of OpenMP threads per MPI process.N g in the nested OpenMP versions is the number of outer-level threads,and in the subteam version is the number of subteams.Overall the subteam version is very close in performance to the MPI+OpenMP hybrid ver-sion.This indicates that the data layout of the subteam version is very similar to that of the hybrid version,even though Subteam uses shared data arrays and MPI uses private data arrays. At single level parallelization,either N×1or1×N,the performance of three approaches is very0204060T i m e (s e c )32×116×28×44×82×161×32Number of CPUs (N g ×N t )2481632641282565121248163264128256Number of CPUsFigure 3:Timing comparison of nested OpenMP,Subteam,and MPI+OpenMP versions of BT-MZ for the Class B problem,on the left for a given number of CPUs and on the right as a function of CPU counts.The results are from the SGI Altix.close.Between the two ends,the nested-OpenMP v1performs consistently 30-80%worse than the other two versions.By reducing the number of inner-level parallel regions in the second version (v2),the performance of nested OpenMP improved substantially,although it still lags behind.The large overhead associated with the inner parallel regions is likely due to the inability of the OpenMP runtime library to reuse threads efficiently at the second level.Even though the dplace tool binds the first-level threads properly,it has no control over the second-level threads.This result is consistent with the previous observation by Ayguade et al.[1]The best performance is achieved by maximizing the number of zone groups as long as the workload can be balanced.For Class B,the optimal number of zone groups is 16.Beyond 16CPUs,multi-level parallelism is needed for additional performance gain.Both subteam and hybrid versions follow this analysis,but the nested OpenMP tends to prefer a larger number of threads at the outer level,especially when the total number of CPUs increases.The scaling results of BT-MZ from the best combinations of zone-groups and threads are summarized in right panel of Fig.3.Both the subteam and hybrid versions scale well up to the measured CPU counts.Up to 16CPUs,when only the outer-level parallelism is employed,the nested OpenMP versions performs similarly to the other two versions.Beyond 16CPUs,nested OpenMP suffers from large overhead associated with the second-level parallelism and becomes much worse at larger CPU counts.To understand better why the nested OpenMP codes suffer from performance degradation in the multi-level mode,we collected additional performance information from hardware coun-ters available on the Altix and the results from the 8×4runs are compared with the hybrid MPI+OpenMP runs in Fig.4.The nested OpenMP v1has the highest stalled cycles and L3cache misses,which is an indication of thread-data mismatch.Stalled cycle is usually a result of waiting on resources,in particular memory.Although the nested OpenMP v2reduced stalled cycles,but it has large L3cache misses.Three pure OpenMP codes have somewhat higher TLB misses;but on the Altix,a TLB miss has less impact on the overall performance.Other counters,such as L1and L2cache misses,have similar values for all four codes and are not included in the graph.0.00.51.01.52.02.5V a l u e R e l a t i v e t o H y b r i d s t a l l e d c y c l e s L 3 h i t r a t e L 3 m i s s r a t i o T L B m i s s m i s p r e d i c t e d b r a n c h e s c y c l e s w i t h n o i n s t r s w a l l c l o c k t i m eFigure 4:Comparison of hardware performancecounter results obtained on the Altix for the 8×4runs of the four BT-MZ versions.5101520G f l o p /s Figure 5:Performance comparison of dif-ferent schedule kinds for BT-MZ Class B,16threads.Numbers in the graph indi-cate chunk sizes.The last bar is for a static schedule without chunk size.4.3Unbalanced WorkloadTo test the effectiveness of OpenMP runtime schedule kinds and more dynamic approaches on unbalanced workload,we focus on the single-level OpenMP versions of BT-MZ as described in Sections 3.4and 3.5,which exploit parallelism among unbalanced zones.No nested parallelism is considered here.4.3.1Impact of Schedule KindThe results from 16-thread runs using different runtime schedule kinds and chunk sizes are shown in Fig.5.The “dynamic,1”schedule produces the best result for the given problem.As the chunk size increases,the performance decreases.The “guided ”schedule is only slightly worse.The “static ”schedule without chunk size (the last bar in the graph)shows its limita-tion in dealing with unbalanced workload and is as much as 50%worse than the “dynamic,1”schedule.The “static,1”(or cyclic)schedule improves the performance but not sufficiently.4.3.2Workload Ordering on PerformanceAs noted in the benchmark description,the zone workload in BT-MZ was designed to be uneven.Class B contains 64zones whose sizes,shown in Fig.6on the left,range from 3K to 60K mesh points.The right graph in Fig.6shows the performance impact of three different orderings of zones in size on the “dynamic,1”schedule:natural (original)order,descending order,and ascending order.For comparison,the graph also includes results from a single-level OpenMP version that uses the static bin-packing algorithm for load balancing.This version is essentially the same as the nested OpenMP v1described in Section 3.2without the nested parallelism.We observe that by sorting zones into descending order,the performance can improve by as much as 45%(18to 26Gflop/s on 16threads).This result supports the observation reported by Van Zee et al.[12]in their FLAME code using the workqueuing model.020K 40K 60K D a t a S i z e 0102030405060Zone Number248163264G f l o p /s 1248163264Number of ThreadsFigure 6:Performance impact of different workload orderings on the “dynamic ”schedule.Re-sults from the static bin-packing approach are included for comparison.The impact of different workload orderings on the “guided ”schedule (not shown in the graph)is very similar to that on the “dynamic ”schedule.It is worth noting that the dynamic approach for unbalanced workload is only slightly worse (∼15%beyond 16threads)than the static bin-packing approach.However,the programming effort in the former case is considerably less.4.3.3Workqueuing ModelBefore going into the workqueuing (or taskq)model,we first examine the performance change as a result of converting the code from Fortran to C.Due to pointer aliasing,a C code can suffer from the constraint in compiler optimization for pointers.In order to reduce or even eliminate pointer aliasing,one can either use the “restrict ”modifier or rely on compiler flags.The Intel compiler provides the option “-fno-alias ”for this purpose.Table 1summarizes the results of the OpenMP C version of BT-MZ using different compiler aliasing options and compares with the Fortran version.The no-alias option produces more than twice as much improvement in performance as the default aliasing bining with the “restrict ”modifier,the C code performs very close to the Fortran counterpart.This combined option was used in collecting the C results below.Table 1:Comparison of results from different aliasing options for the Class B,BT-MZ on 16threads.CaseGflop/s 49.86“restrict ”keyword16.175C 23.49-fno-alias Combination 25.718Fortran26.124Figure 7compares the Intel Taskq version of BT-MZ with the single-level OpenMP versions (both C and Fortran)using dynamic scheduling for load balancing.It is encouraging to note that the Taskq version has similar performance to the single-level OpenMP C version using the “dynamic,1”schedule up to 32threads.Only at 64threads the dynamic-schedule version out-performs the Taskq version by about 20%.As illustrated by the two panels in the figure,sorting workload into descending order improves overall performance for Taskq as paring to the Fortran version,the performance of Taskq gets worse at larger thread counts,primarily due to the difference between Fortran and C.248163264G f l o p /s 1248163264Number of Threads1248163264Number of ThreadsFigure 7:Performance comparison of the Taskq version with the single-level OpenMP versions (in both C and Fortran)using the “dynamic,1”schedule.5ConclusionWe have presented performance evaluation of four different OpenMP approaches in dealing with multi-level parallelism and unbalanced workload,and compared with a hybrid MPI+OpenMP method.The nested OpenMP approach suffered from performance degradation as a result of large overhead and lack of thread reuse when invoking the inner level parallelism on the SGI Altix.By minimizing the number of inner level parallel regions we improved nested OpenMP performance dramatically.Another potential way to reduce overhead associated with nested parallel regions is by proper support of thread affinity with thread reuse as proposed by others [1,13].The approach based on the Subteam extension to OpenMP overcame some of the limitations with nested OpenMP and showed promise in achieving performance close to that of the hybrid MPI+OpenMP method.Our study also points out the importance of extending the Subteam proposal to include API for subteam creation and management.It is very encouraging that the more dynamic approach provided by the workqueuing model showed great potential in dealing with unbalanced workload.This model can benefit from using a weight factor in scheduling tasks.For future work,we would like to conduct our experiments on more platforms,in particularto study the support of nested parallelism from different compilers and runtime systems.A natural extension is to investigate the performance characteristics of nested parallelism under the workqueuing model.It is also important to extend our experience from a single benchmark application to more realistic applications.AcknowledgmentsThe authors would like to acknowledge fruitful discussions with Johnny Chang,Robert Hood, and support from the staffat NAS division for many experiments conducted on the Columbia supercomputer.References[1]E.Ayguade,M.Gonzalez,X.Martorell,and G.Jost,“Employing Nested OpenMP for theParallelization of Multi-Zone Computational Fluid Dynamics Applications,”J.of Parallel and Distributed Computing,special issue,ed.B.Monien,Vol.66,No.5,2006,p686. [2]D.Bailey,J.Barton,sinksi,and H.Simon,“The NAS Parallel Benchmarks,”NASTechnical Report RNR-91-002,NASA Ames Research Center,1991.[3]R.Biswas,M.J.Djomehri,R.Hood,H.Jin,C.Kiris,and S.Saini,“An Application-BasedPerformance Characterization of the Columbia Supercluster,”in Proc.of SC05,Seattle, WA,November12-18,2005.[4]M.Bull,“OpenMP3.0Overview,”presented at the OpenMP BoF at the SC06conference,2006./futures/.[5]B.Chapman,L.Huang,H.Jin,G.Jost,and B.de Supinski,“Toward Enhancing OpenMP’sWork-Sharing Directives,”in the Euro-Par06Conference,Dresden,Germany,2006;LNCS 4128,2006,pp.645-654.[6]H.Jin and R.F.Van der Wijngaart,“Performance Characteristics of the Multi-Zone NASParallel Benchmarks,”J.of Parallel and Distributed Computing,special issue,ed.B.Monien,Vol.66,No.5,2006,p674.[7]OpenUH Research Compiler,/openuh.[8]The OpenMP Standard,/.[9]S.Shah,G.Haab,P.Petersen,and J.Throop,“Flexible Control Structure for Parallelismin OpenMP,”in the European Workshop on OpenMP(EWOMP99),1999.[10]E.Su,X.Tian,M.Girkar,G.Haab,S.Shah,and P.Petersen,“Compiler Support of theWorkqueuing Execution Model for Intel SMP Architectures,”in the European Workshop on OpenMP(EWOMP02),2002.[11]R.F.Van der Wijngaart and H.Jin,“The NAS Parallel Benchmarks,Multi-ZoneVersions,”NAS Technical Report NAS-03-010,NASA Ames Research Center,2003./Software/NPB/.[12]F.Van Zee,P.Bientinesi,T.M.Low,R.Van de Geijn,“Scalable Parallelization of FLAMECode via the Workquenuing Model,”submitted to ACM Trans.on Math.Software,2006.[13]G.Zhang,“Extending the OpenMP Standard for Thread Mapping and Grouping,”in theInternational Workshop on OpenMP(IWOMP06),Reims,France,2006.。
A COMPARATIVE PERFORMANCE EVALUATION OF THE AURAMS AND CMAQ AIR QUALITY MODELLING SYSTEMS
A COMPARATIVE PERFORMANCE EVALUATION OF THE AURAMS AND CMAQAIR QUALITY MODELLING SYSTEMSSteven C. Smyth*, Weimin Jiang, Helmut Roth, and Fuquan YangICPET, National Research Council of Canada, Ottawa, Ontario, Canada Véronique S. Bouchet, Michael D. Moran, Hugo Landry, and Paul A. MakarEnvironment Canada, Toronto, Ontario, Canada and Montréal, Québec, Canada1. INTRODUCTIONA comparative performance evaluation has been conducted for the AURAMS (A Unified Regional Air-quality Modelling System) and CMAQ air-quality (AQ) modelling systems, developed by Environment Canada (EC) and the U.S. Environmental Protection Agency (EPA), respectively. This study was unique in that many aspects of the AURAMS and CMAQ simulations were purposefully “aligned” in order to reduce some of the common sources of differences between the models.The two AQ models were run for a 696-hour period starting at 0100 UTC 1 July 2002 and ending at 0000 UTC 30 July 2002 over a continental North American domain. Each model used 42-km horizontal grid spacing and the model domains cover approximately the same land area. Emission files were generated from the same Canadian, U.S., and Mexican raw emissions inventories and the same biogenic emissions model (BEISv3.09). The raw emissions were processed using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system, developed by the Carolina Environmental Program (CEP). Meteorological fields generated by the EC GEM (Global Environmental Multiscale) meteorological model were used as input to both the emissions processor and the two chemical transport models (CTMs).Both AQ models were run in their “native” states to provide a baseline comparison of the two sets of modelling results. Despite the best-possible alignment of the two models, there still exist many differences between the “native” states of the models. Some key differences related to the model setup and operation include differences in “native” map projections, vertical coordinate systems, gas phase chemical mechanisms, aerosol representations, meteorological pre-processors, and emissions processing methods. *Corresponding author: Steven C. Smyth, Institute for Chemical Process and Environmental Technology, National Research Council of Canada, 1200 Montreal Road, Bldg M-2, Ottawa, ON, Canada K1A 0R6; e-mail: steve.smyth@nrc-cnrc.gc.ca In addition, there are also numerous differences in detailed scientific processes embedded in the models. All of these differences contribute to each models’ behaviour and performance.Our methodology, however, precludes or greatly reduces the impacts of the other components of the system aside from the CTMs themselves, and gives us more confidence that the observed differences result from the AQ models, as opposed to the meteorological and/or emissions inputs to those models.2. MODELLING SYSTEMS DESCRIPTION 2.1. The AURAMS Modelling SystemAURAMS is an AQ modelling system with size- and composition-resolved particulate matter (PM) representation. The AURAMS modelling system consists of three major components: an emissions processor; a meteorological driver; and a CTM (Gong et al., 2006). The AURAMS system also contains pre-processors that calculate biogenic emissions using the Biogenic Emissions Inventory System v3.09 (BEISv3.09) and transform emissions and meteorological data into a form useable by the CTM.Emissions are processed using the SMOKE processor/model (CEP, 2005) and converted from I/O API format to Recherche en PrévisionNumérique (RPN) standard format (Chartier, 1995) for use in AURAMS.Predicted meteorological fields from the GEM model (Côté et al., 1998a,b) stored at every AURAMS advection time step (15 min) are used to drive the AURAMS CTM after transformation by the AURAMS meteorological pre-processor (Cousineau, 2003).AURAMS was designed to be a "one atmosphere” or “unified" model in order to address a variety of interconnected tropospheric air pollution problems ranging from ground-level ozone (O3) to PM to acid rain. As such, AURAMS treats gas-phase species and PM formation and evolution with time, as well as their interactions through gaseous, aqueous, and heterogeneous reactions and physical processes.Nine chemical components, are considered to contribute to PM composition: sulphate (SU), nitrate (NI), ammonium (AM), black carbon (EC), primary organic aerosol (PC), secondary organic aerosol (OC), crustal material (CM), sea salt (SE), and particle-bound water (WA). AURAMS represents the PM size distribution using 12 size bins, ranging from 0.01 to 40.96 µm in diameter, with the PM chemical components assumed to be internally mixed in each size bin.The gas phase chemistry is modelled using a modified version of the ADOM-II (Acid Deposition and Oxidant Model) chemical mechanism (Stockwell and Lurmann, 1989; Lurmann et al., 1986). Although AURAMS includes sea-salt emissions, sea-salt chemistry is not considered. Organic aerosol chemistry is modelled using the approach of Odum et al. (1996), and Jiang (2003). Inorganic aerosol calculations are performed using multiple chemical domain activity coefficient iterations (Makar et al, 2003). Aqueous-phase processes in AURAMS include size-resolved aerosol activation, aqueous-phase chemistry and wet deposition (Gong et al., 2006).For this comparative modelling study, AURAMS v1.3.1b was run with 12-hour restarts for the complete 696-hour simulation period.2.2. The CMAQ Modelling SystemSimilar to the AURAMS modelling system, the CMAQ modelling system (Byun and Ching, 1999) consists of three major components: an emissions processing system; a meteorological driver; and a CTM. The CMAQ modelling system also contains three other pre-processors for initial conditions, boundary conditions, and photolysis rates.As with AURAMS, emissions files are generated by the SMOKE processor/model. In contrast to AURAMS, the emissions files are used directly by CMAQ without file format conversion.GEM was used as the meteorological driver in CMAQ in order to align with the AURAMS modelling system. The GEM meteorological fields are processed by the GEM Meteorology Chemistry Interface Program (GEM-MCIP) (Yin, 2004), developed in-house at the National Research Council of Canada (NRC), to transform the GEM fields into a form useable by SMOKE and CMAQ.In CMAQ, particle size distributions are represented as a superposition of three log-normal sub-distributions or modes: Aitken or i-mode; accumulation or j-mode; and coarse or c-mode (Binkowski and Roselle, 2003). The PM concentrations generated by CMAQ cover the complete log-normal distributions and are not directly comparable with size-resolved measurement data. In this study, the PMx postprocessor, developed in-house at NRC (Jiang and Yin, 2001; Jiang et al., 2006b), is used to calculate particle size distribution parameters and PM concentrations within required particle size ranges. Calculations are done on the basis of classical aerodynamic diameter (Jiang et al.,2006a) to facilitate comparison with field measurement data (Jiang et al., 2006b).CMAQ considers 12 species to contribute to PM composition: sulphate (ASO4), nitrate (ANO3), ammonium (ANH4), primary anthropogenic organics (AORGPA), secondary anthropogenic organics (AORGA), secondary biogenic organics (AORGB), elemental carbon (AEC), other fine PM mass (A25), aerosol water (AH2O), soil derived dust (ASOIL), other coarse mass (ACORS), and sea-salt (ACL and ANA).CMAQ v4.6 was run for the 696-hour simulation period using 24-hour restarts, the SAPRC-99 chemical mechanism (Carter,2000a,b), and the AERO4 aerosol module.A more detailed description of the AURAMS and CMAQ AQ modelling systems, including CTM science components and 3-D grid definition, is available in Smyth et al. (2007).2.3. Comparison of the AURAMS andCMAQ GridsFig. 1. The AURAMS polar stereographic domain and CMAQ Lambert conformal conic domain, displayed on the Lambert projection map.The AURAMS modelling system uses a polar stereographic (PS) map projection as the basis for defining the horizontal grid. For this study, the Environment Canada “cont42” PS domain was adopted, consisting of 150 columns and 106 rows, with 42-km grid resolution.The CMAQ modelling system uses a Lambert conformal conic (LCC) map projection in its “native” state. For this study, the LCC domain wasdefined to “match” the AURAMS PS domain. The developed LCC grid consists of 139 columns and 99 rows with 42-km grid resolution and covers approximately the same geographical area as the PS grid as shown in Fig. 1.3. COMPARISON OF METEOROLOGICAL AND EMISSIONS INPUTSEvaluation of the processed meteorological fields input into each AQ modelling systemshowed minor differences in surface temperature, pressure, and specific humidity with normalized mean errors (NME) ranging from 0.25% to 3.8% when calculated over all over-lapping surface grid cells and the entire simulation period. These differences result from differences in themeteorological pre-processors and differences inmap projections, which affect grid alignment.Fig. 2. Comparison of AURAMS and CMAQanthropogenic and biogenic emissions totalled over the simulation period and respective domains.Fig. 2 shows the comparison AURAMS and CMAQ anthropogenic and biogenic emissions totalled over each respective domain, all vertical levels, and the entire simulation period. The total emissions are similar for all criteria contaminants. More detailed analysis showed that totalanthropogenic emissions were virtually identical for the two modelling systems, while there were slight differences in biogenic emissions of NO and VOCs. Since only minor variations in input temperature and pressure were present andidentical biogenic emission factors were used, it is likely that the differences in biogenic emissions result from differences in solar radiation, BELD3 land-use data, and/or the implementation of the BEISv3.09 model in the modelling systems.Analysis of temporal and spatial emissionpatterns (not shown) suggested that differences in input emissions can be caused by differences in speciation profiles, differences in processing major-point sources, and the differences in “true”grid cell size. The PS domain uses 15 900 42-km grid cells to cover approximately the same land area as the LCC domain which uses 13 761 42-km grid cells, meaning that the average PS grid cells are slightly smaller in “true” size than average LCC grid cells. Because of this, an average LCC grid cell contains more emission sources than a corresponding average PS grid cell.4. COMPARISON OF O 3 PERFORMANCEPerformance statistics for AURAMS- and CMAQ-modelled ground-level ozone (O 3) in comparison with measurement data aresummarized in Table 1. Hourly measurement data was taken from the National Air Pollution Surveillance (NAPS) and Air Quality Service(AQS) networks, administered by EC and the U.S. EPA, respectively.Both models over-predicted O 3 concentrations. AURAMS had a lower bias than CMAQ with a normalized mean bias (NMB) of 17.9% versus 44.5% for CMAQ. In terms of error, the models’ performance was more similar, with AURAMS performing slightly better with a NME of 45.6% versus 52.8% for CMAQ. In terms of correlation, the AURAMS r 2 of 0.39 was slightly lower than the CMAQ value of 0.44.Table 1. AURAMS and CMAQ O 3 and daily peak O 3 statistics for 3-30 July 2002.O 3 (ppb) daily peak O 3 (ppb)performance statisticsAURAMS CMAQ AURAMS CMAQno. sites 1245 1247 1245 1247 n 774 946 776 154 20 599 20 635 meas. mean 35.64 35.62 60.77 60.72 mod. mean 42.03 51.46 66.66 70.16 MB 6.38 15.85 5.89 9.44 NMB (%) 17.91 44.50 9.68 15.56 ME 16.25 18.82 16.38 14.29 NME (%) 45.59 52.83 26.96 23.54 % withinfactor of 2 71.3 70.3 94.5 96.2r 2 0.393 0.438 0.350 0.505CMAQ’s larger O 3 errors were largely due to its inability to correctly predict night-time lows, as shown in Fig. 3, which shows the time-series’ of observed as well as AURAMS-, and CMAQ-modelled O 3 concentrations averaged over all O 3 measurement sites.Performance statistics for daily peak O 3 is also shown in Table 1. Both models over-predicted the daily peaks with AURAMS having a slightly better NMB of 9.7%, whereas CMAQ had a NMB of 15.6%. In contrast, CMAQ had a slightly lower NME and a higher r 2. Both models did well inpredicting the time of the daily peaks, withAURAMS daily peak concentrations within 3-hours of the measured daily peak 65.2% of the time, while CMAQ was slightly better with a value of68.8%.Fig. 3. Site-averaged observed as well as AURAMS-,and CMAQ-modelled O 3 concentrations.5. COMPARISON OF TOTAL PM 2.5PERFORMANCE Hourly measurement data was taken from theNAPS and AQS networks to evaluate theperformance of total PM 2.5. Statistics arepresented in Table 2. Both models under-predicted total PM 2.5, although AURAMS’ under-prediction was much less than CMAQ with a NMBof -15.4% versus -64.5% for CMAQ. However,just as with O 3, the models’ errors were moresimilar, with NMEs of 67.1% and 70.6% forAURAMS and CMAQ, respectively. The similarityin error shows that there was cancellation ofpositive and negative biases in the AURAMSresults, which contributed to the better NMB.Table 2. AURAMS and CMAQ total PM 2.5 and daily peak total PM 2.5 statistics for 3-30 July 2002. PM 2.5 (µg m -3) daily peak PM 2.5 (µg m -3) performance statisticsAURAMS CMAQ AURAMS CMAQ no. sites 350 354 350 354 n 211 141 213 707 7444 7538 meas. mean 14.43 14.40 28.26 28.14mod. mean 12.21 5.11 21.65 9.12MB -2.23 -9.29 -6.60 -19.02NMB (%) -15.42 -64.52 -23.38 -67.59 ME 9.69 10.16 16.32 19.47NME (%) 67.12 70.59 57.74 69.18 % withinfactor of 2 49.8 29.5 59.5 25.8r 20.074 0.151 0.040 0.086 Table 2 also shows the performance statistics for daily peak PM 2.5. AURAMS performed better with a NMB of -23.4% and NME of 57.7% while CMAQ had a NMB of -67.6% and a NME of 69.2%. CMAQ did slightly better in predicting the time of the daily peaks with 34.2% of modelled PM 2.5 daily peaks within 3-hours of the measured daily peak, while AURAMS had a value of 28.7%. Station representativeness likely influenced the under-prediction of PM 2.5 concentrations for both models as most measurement sites were located in urban areas. In addition, the relatively large 42-km grid cells likely caused a “dilution” of PM 2.5, especially when comparing the modelledconcentrations to the high localised concentrations expected around urban centres.6. COMPARISON OF SPECIATED PM 2.5 PERFORMANCESpeciated PM 2.5 performance was evaluated using 24-hr averaged measurement data from theNAPS network and the U.S. EPA administered Speciation Trends Network (STN).As shown in Table 3, AURAMS bias wasmuch better than CMAQ for PM 2.5 sulphate (SO4),ammonium (NH4), and total organic aerosols(TOA), and somewhat worse for nitrate (NO3) and elemental carbon (EC). For NO3, AURAMS over-predicted by approximately 121% while CMAQ under-predicted by approximately 71%. Both models significantly under-predicted TOA concentrations. However, the AURAMS modelled station-mean was over 4 times larger than the CMAQ value, showing better overall TOA performance. This difference was likely due to differences in secondary organic aerosol (SOA) schemes. In addition, the under-prediction in both models was partly due to forest fire emissions not being included in the emissions processing. The inclusion of forest fire emissions would likelyimprove performance for total PM and TOA. In terms of error, the AURAMS and CMAQresults were very similar for PM 2.5 SO4, NH4, and EC, while AURAMS’ NME for NO3 was much larger than CMAQ’s. For TOA, AURAMS had abetter NME of 65.6% versus 91.2% for CMAQ. In terms of correlation, r 2 values for the two models were generally close but CMAQ’s were higher.It is interesting to note that the poor correlation coefficients for both models for total PM 2.5 (Table2) may be largely due to their inability in accurately predicting the organic component (Table 3). Alarge portion of the measured aerosol mass is TOA, while the correlation coefficient for this component is poor for both models.The differences in AURAMS and CMAQ PM 2.5 species are likely due to a number of reasons, including differences in the speciation of PM emissions, aerosol chemistry, wet/dry removal mechanisms, and chemical boundary conditions.Table 3. AURAMS and CMAQ performance statistics for PM 2.5 sulphate (SO4), nitrate (NO3), ammonium (NH4), elemental carbon (EC), and total organic aerosols (TOA) for 3-30 July 2002.PM 2.5Species model no.sitesn meas. mean(µg m -3)mod.mean (µg m -3)MB (µg m -3) NMB (%) ME (µg m -3) NME (%) % withinfactor of 2r 2 AURAMS 5.31 0.29 5.72 3.03 60.34 57.8 0.367SO4 CMAQ 221 1229 5.032.44 -2.59 -51.51 2.83 56.26 47.2 0.524 AURAMS 2.00 1.09 121.2 1.58 174.7 30.0 0.397NO3 CMAQ 204 1112 0.9020.263 -0.64 -70.87 0.71 79.00 14.8 0.437 AURAMS 1.64 0.009 0.58 0.885 54.20 60.9 0.428NH4 CMAQ 221 1230 1.630.821 -0.811 -49.68 0.929 56.90 51.2 0.458 AURAMS 0.284 -0.240 -45.83 0.302 57.55 44.7 0.166EC CMAQ 205 1180 0.5240.338 -0.187 -35.59 0.306 58.42 45.7 0.204 AURAMS 3.92 -6.64 -62.85 6.93 65.64 33.1 0.0005TOACMAQ 205 1180 10.560.928 -9.64 -91.22 9.64 91.25 2.0 0.00667. COMPARISON OF PM COMPOSITIONFig. 4. Average PM 2.5 composition over all grid cells and last 648 hours of simulation period for (a) AURAMS and (b) CMAQ.Fig. 5. Average PM 2.5 composition over land grid cells only and last 648 hours of simulation period for (a) AURAMS and (b) CMAQ.Fig. 4 shows the average PM 2.5 composition of AURAMS and CMAQ averaged over all grid cells of their respective domains. The results show very different PM compositions between themodels with over 50% of PM 2.5 mass coming from sea-salt aerosols in AURAMS, whereas only 5.2% of CMAQ’s PM 2.5 mass is from sea-salt aerosols.As shown in Fig. 5, when the average PM 2.5 composition is calculated by averaging over land grid cells only, the contribution of sea-salt aerosols to the total mass decreases to 15.2% for AURAMS and 1.8% for CMAQ, showing that sea-saltaerosols contribute a much larger amount to the total PM mass in AURAMS than they do in CMAQ, even if only land grid cells are considered.8. SUMMARY AND CONCLUSIONSIn general, the two modelling systems showed similar levels of error for O 3, total PM 2.5, and most PM 2.5 species. However, in terms of bias, AURAMS performed better for all investigated species, except for PM 2.5 nitrate. This enhanced bias performance was somewhat due to the cancellation of positive and negative biases as indicated by the similar levels of error.This study reflects the best effort up to now to closely align many operational aspects of the two modelling systems. However, due to the complexity of the model structures and thenumerous interconnected science processes, it is difficult to assess the contributions of individual science processes to the differences in model performance. Improved modularity at the process level would make such scientific process assessment more feasible.9. NOTEResults from the brief abstract submitted before and the extended abstract here differ substantially due to incorrect land-use fieldsgenerated for the previous AURAMS run. A new AURAMS run was conducted with the correct land-use fields supplied by Environment Canada (EC), and the results are reported in this extended abstract. Investigation is underway to determine the source of the processing error.10. ACKNOWLEDGEMENTSThe authors acknowledge Radenko Pavlovic and Sylvain Ménard of EC for their efforts in transferring data and code to NRC and for their support in helping to understand the various AURAMS related material. Also, we thankWanmin Gong of EC for her comments and help in identifying the problem in land-use data.Several organizations provided information and/or funding support to this project and their contributions are greatly appreciated. The Pollution Data Division of EC supplied the Canadian raw emissions inventories used in this modelling study. The U.S. EPA provided U.S. raw emissions data and the Carolina Environmental Program distributed CMAQ, and SMOKE. Colorado State University developed the VIEWS database and the Meteorological Service of Canada operates the NAtChem database, both of which were used to obtain measurement data. Funding was provided by EC and by the Program of Energy Research and Development (PERD). 11. REFERENCESBinkowski, F.S., and S.J. Roselle, 2003: Models-3 community multiscale air quality (CMAQ) modelaerosol component 1. Model description. J.Geophy. Res., 108, No. D6, 4183.Byun, D.W., and J.K.S. Ching, 1999: Science algorithms of the EPA Models-3 CommunityMultiscale Air Quality (CMAQ) modelling system.U.S. EPA Report EPA/600/R-99/030.Carolina Environmental Program, 2005: SMOKE User Manual Version 2.2. [Available online at/cep/empd/products/smoke/version2.2/manual.pdf]Carter, W.P.L., 2000a: Documentation of the SAPRC-99 chemical mechanism for VOCreactivity assessment. Final Report to California Air Resources Board Contract 92-329 andContract 95-308.[/~carter/reactdat.htm.] Carter, W.P.L., 2000b: Implementation of the SAPRC-99 chemical mechanism into theModels-3 framework. Report to the UnitedStates Environmental Protection Agency.[/~carter/reactdat.htm.] Chartier, Y., 1995. An Introduction to RPN Standard Files. Section informatique, Recherche enPrévision Numérique, Atmospheric EnvironmentService, Environment Canada, 41 pp.Côté, J., J.-G. Desmarais, S. Gravel, A. Méthot , A.Patoine, M. Roch and A. Staniforth, 1998a: Theoperational CMC/MRB Global EnvironmentalMultiscale (GEM) model. Part 1: Designconsiderations and formulation. Mon. Wea. Rev., 126, 1373-1395.Côté, J., J.-G. Desmarais, S. Gravel, A. Méthot, A.Patoine, M. Roch, and A. Staniforth, 1998b: Theoperational CMC-MRB Global EnvironmentMultiscale (GEM) model. Part II: Results. Mon.Wea. Rev., 126, 1397-1418.Cousineau, S., 2003: All you need to know about the AURAMS Meteorological Post Processor(AMPP) version 2.1 – Purpose, installation,description. Air Quality Modelling ApplicationsGroup Operations Branch, CanadianMeteorological Centre, Montréal, Québec. Gong, W., A.P. Dastoor, V.S. Bouchet, S. Gong, P.A.Makar, M.D. Moran, B. Pabla, S. Ménard, L-P.Crevier, S. Cousineau, and S. Venkatesh, 2006: Cloud processing of gases and aerosols in aregional air quality model (AURAMS). Atmos.Res., 82, 248-275.Jiang, W., and D. Yin, 2001: Development and application of the PM x software package forconverting CMAQ modal particulate matterresults into size-resolved quantities. Institute for Chemical Process and EnvironmentalTechnology Tech. Rep. PET-1497-01S, 44 pp. Jiang, W., 2003: Instantaneous secondary organic aerosol yields and their comparison withoverall aerosol yields for aromatic andbiogenic hydrocarbons. Atmos. Environ.37,5439-5444.Jiang, W., É. Giroux, H. Roth, and D. Yin, 2006a: Evaluation of CMAQ PM results using size-resolved field measurement data: the particlediameter issue and its impact on modelperformance assessment. Air Pollution Modeling and Its Applications XVII, 571-579.Jiang, W., S. Smyth, É. Giroux, H. Roth, and D. Yin, 2006b: Differences between CMAQ fine modeparticle and PM2.5 concentrations and theirimpact on model performance in the LowerFraser Valley. Atmos. Environ.40, 4973-4985. Lurmann, F.W., A.C. Lloyd, and R. Atkinson, 1986: A chemical mechanism for use in long-rangetransport/acid deposition computer modeling. J.Geophys. Res., 91, 10905-10936.Makar, P.A., V.S. Bouchet, A. Nenes, 2003 : Inorganic chemistry calculations using HETV – a vectorized solver for the SO42—NO3—NH4+ system based on the ISORROPIA algorithms. Atmos.Environ.37, 2279-2294.Odum, J.R., T. Hoffman, F. Bowman, D. Collins, R.C.Flagan and J.H. Seinfeld, 1996: Gas/particlepartitioning and secondary organic aerosolyields, Environ. Sci. Technol.,30, 2580-2585. Smyth, S.C., W. Jiang, H. Roth, and F. Yang, 2007:A comparative performance evaluation of theAURAMS and CMAQ air quality modellingsystems – REVISED. Report Number PET-1572-07S, ICPET, NRC, Ottawa, ON. Stockwell, W.R., and F.W. Lurmann, 1989:Intercomparison of the ADOM and RADM Gas-Phase Chemical Mechanisms. Electrical PowerResearch Institute Topical Report, EPRI, 3412Hillview Avenue, Palo Alto, Ca., 254 pp.Yin, D., 2004: A Description of the Extension for Using Canadian GEM data in MCIP and a BriefUser’s Guide for GEM-MCIP. Institute forChemical Processing and EnvironmentalTechnology, 44 pp.。
performance evaluation理工英语4
performance evaluation理工英语4 Title: Performance Evaluation in EngineeringIntroduction:Performance evaluation plays a crucial role in various fields, including engineering. It allows organizations to assess the efficiency and effectiveness of their employees, processes, and systems. This article aims to delve into the concept of performance evaluation in the context of engineering, highlighting its importance and providing a comprehensive understanding of the subject.I. Importance of Performance Evaluation in Engineering:1.1 Ensuring Quality Output:- Performance evaluation enables organizations to identify and address any shortcomings in the engineering processes, ensuring high-quality outputs.1.2 Enhancing Efficiency:- By evaluating individual and team performance, organizations can identify areas for improvement, leading to increased efficiency in engineering tasks.1.3 Promoting Innovation:- Performance evaluation encourages engineers to think creatively and find innovative solutions to problems, fostering a culture of continuous improvement within the organization.II. Key Metrics for Performance Evaluation in Engineering:2.1 Technical Skills:- Assessing an engineer's technical skills, including their proficiency in relevant software, tools, and technologies, is crucial for evaluating their performance.2.2 Problem-Solving Abilities:- Evaluating an engineer's ability to analyze problems, identify potential solutions, and implement effective strategies is essential for performance assessment.2.3 Communication Skills:- Effective communication is vital in engineering, and evaluating an engineer's ability to communicate ideas, collaborate with team members, and present information accurately is important.2.4 Time Management:- Assessing an engineer's ability to manage time efficiently, meet deadlines, and prioritize tasks helps in evaluating their overall performance.2.5 Adaptability and Learning:- Performance evaluation should consider an engineer's adaptability to changing technologies and their willingness to learn and upgrade their skills.III. Methods of Performance Evaluation in Engineering:3.1 Self-Assessment:- Engineers can evaluate their own performance by reflecting on their achievements, identifying areas for improvement, and setting goals for professional development.3.2 Peer Evaluation:- Colleagues within the engineering team can provide valuable insights into an engineer's performance, offering a different perspective and identifying strengths and weaknesses.3.3 Supervisory Evaluation:- Supervisors can assess an engineer's performance based on their observations, feedback from clients or stakeholders, and the achievement of predetermined goals.3.4 360-Degree Feedback:- This evaluation method involves input from multiple sources, including supervisors, peers, subordinates, and clients, providing a comprehensive view of an engineer's performance.3.5 Key Performance Indicators (KPIs):- Organizations can establish specific KPIs for engineering tasks, such as project completion time, error rates, or customer satisfaction, to measure and evaluate performance objectively.IV. Challenges and Solutions in Performance Evaluation in Engineering:4.1 Subjectivity:- Performance evaluation in engineering can be subjective due to varying opinions and biases. Implementing clear evaluation criteria and providing proper training to evaluators can help mitigate this challenge.4.2 Quantifying Technical Skills:- Assessing technical skills can be challenging, but utilizing standardized tests, certifications, and practical assessments can provide a more objective evaluation.4.3 Balancing Individual and Team Performance:- Evaluating individual performance while considering the collaborative nature of engineering work requires a balanced approach. Incorporating team-based evaluations and recognizing collective achievements can address this challenge.Conclusion:In conclusion, performance evaluation in engineering is essential for organizations to ensure quality output, enhance efficiency, and promote innovation. By focusing on key metrics, utilizing appropriate evaluation methods, and addressing challenges,organizations can effectively evaluate the performance of engineers and drive continuous improvement in the field of engineering.。
商务英语阅读(下册)(第二版) Unit 2 绩效考核共74页
Outline expectations for improvement. When you address areas where improvement is needed, outline your expectations for improvement and how you intend to help the employee meet them. For example, if an employee is speaking harshly with other employees and does not seem tolerant with patients, give the employee some examples of his or her behavior and offer some suggestions to resolve the problem, such as role-playing sessions or a communication skills/customer-service workshop or seminar. Define the boundaries by letting the employee know what is acceptable and what will not be tolerated, and then establish a plan for monitoring performance and re-evaluating the employee.
英语作文阅卷仲裁 阈值
英语作文阅卷仲裁阈值English answer.Thresholds for adjudication of English essay marking.The assessment of student writing is a complex and challenging task. In order to ensure fairness and consistency in marking, it is essential to establish clear and objective thresholds for adjudication. These thresholds should be based on a sound understanding of the assessment criteria and the expected level of student performance.The following thresholds are proposed for the adjudication of English essay marking:Threshold 1: Fail (0-49%)。
The essay does not meet the basic requirements of the task.There is a lack of clear structure and organization.The language is inaccurate and unclear.There is little or no evidence of criticalthinking or analysis.Threshold 2: Pass (50-64%)。
The essay meets the basic requirements of the task.There is a clear structure and organization.The language is generally accurate and clear.There is some evidence of critical thinking and analysis.Threshold 3: Good (65-79%)。
作文阅卷仲裁 阈值
作文阅卷仲裁阈值英文回答：As an essay scorer, the threshold for arbitration in essay grading is an important consideration. The threshold refers to the minimum score a student must achieve in order to pass or meet the criteria for a particular level of proficiency. It serves as a benchmark to ensure consistency and fairness in the grading process.In the context of essay grading, the threshold is often determined by the specific requirements and rubrics provided by the examination board or educational institution. These requirements outline the key elements and criteria that essays should address in order to receive a passing score. They may include factors such as content, organization, language proficiency, and critical thinking skills.When it comes to arbitration, the threshold plays acrucial role in determining whether an essay should be re-evaluated or given a higher score. If an essay is on the borderline of meeting the criteria, the arbitrator will carefully review the content and language proficiency to make a fair judgment. This ensures that students are not unfairly penalized or rewarded based on slight variationsin scoring.In some cases, the threshold may be adjusted based on the difficulty level of the prompt or the overall performance of the test-takers. For example, if the prompt is particularly challenging, the threshold may be lowered to account for the increased difficulty. On the other hand, if the majority of test-takers perform exceptionally well, the threshold may be raised to maintain a certain level of rigor.Overall, the threshold for arbitration in essay grading is a crucial aspect of ensuring fairness and consistency in the evaluation process. It helps maintain the integrity of the scoring system and provides students with a fair opportunity to demonstrate their skills and knowledge.中文回答：作为一名作文评分员，阈值在作文评分的仲裁中是一个重要的考虑因素。
THESISINTRODUCTION写作内容参考
THESIS INTRODUCTION写作内容参考中部地区中小企业是否使用绩效管理体系INTRODUCTION 简介Background to the studyPerformance management is not new, despite the fact that nowadays more emphasis is being laid on it, especially in the public sector. According to Armstrong and Baron (1998), performance management is a term borrowed from the management literature. The term ‘performance management’ was first used in the 1970s, but it did not become a recognised process until the latter half of the 1980s . Generally performance management is the process of identifying, measuring, managing, and developing the perfor-mance of the human resources in an organization. This means basically we are trying to figure out how well employees perform and then to ultimately improve that performance level.In another development Weiss and Hartle (1997), opines that, Performance Management is “A process for establishing a shared understanding about what is to be achieved and how it is to be achieved, and an approach to managing people that increases the probability of achieving success”. In the contribution of Fletcher (1992) the author provides a more organizational definition of performance management as an approach to creating a shared vision of the purpose and aims of the organization, helping each individual employee understand and recognize their part in contributing to them, and in so doing manage and enhance the performance of both individuals and the organization.The concept of ‘performance management’ remains ambiguous in spite of the enormous attention it has received in academic writings (Carroll, 2000; Otley, 1999). The confusion, according to Carroll and Dewar (2002), stems from the fact that many scholars continuously use it interchangeably with ‘performance measurement’ and other forms of performance assessment including performance evaluation, performance monitoring, and performance reporting (Bruijn, 2007; Halachmi, 2005; McAdam et al., 2005; Pollitt, 2006; Talbot, 2005; Wholey, 1999).Yet, one may argue that these forms of performance assessment are part of the generic idea of performance management systems.According to Schwartz (1999), one should not confuse performance managementwith performance appraisal and evaluation. The distinction of PM consists in the fact that it has three main components: understanding and setting goals and expectations;providing ongoing feedback; and appraising performanceAccording to Briscoe and Claus (2008: 15) PM is the system through which organizations ‘setwork goals, determine performance standards, assign and evaluate work, provide performance feedback, determine training and development needs, and distribute rewards’. Performance management is, therefore, ‘conceived as a framework with system properties’ (Bouckaert and Halligan, 2008: 38).According to Carroll and Dewar (2002), four main elements make up a PM. Theseinclude: (a) deciding the desired level of performance; (b) measuring performance;(c) reporting or communicating performance information; and (d) using performanceinformation to compare actual performance to the agreed performance level (2002: 413).Today’s business world is very competitive and as a result, businesses are under pressure to survive, grow and be profitable. This has increasingly underscored the importance of Human Resource Management as an integral part of organizations. Managing people in organizations is becoming more and more important nowadays so as to produce the best result and achieve efficiency. Given current market trends, constraints and limited growth in demand, smaller firms are focusing increasingly on how to make effective use of their limited resources.There is growing recognition of the important role Micro, Small and Medium Scale Enterprises (SME‟s) play in economic development. They are often described as efficient and prolific job creators, the seeds of big businesses and the fuel of national economic engines. Even in the developed industrial economies, SME‟s Sector is the largest employer of workers. Interest in the role of SME‟s in the development process continues to be in the forefront of policy debates in most countries. Governments at all levels have undertaken initiatives to promote the growth of SME‟s (Feeney and Riding, 1997: Carsamer, 2009).Given their resource limitations, small and medium-sized firms have fewer options than their larger counterparts to improve performance.. Consequently, to develop their knowledge and skill and driving their performance toward the organization’s goal, organizations are becoming more conscious than ever in implementing Performance Management System.However, one resource that is common to all organisations, which has been the focus of increasing theoretical, empirical and practical attention in small and medium sized enterprises (SMEs), is that of human resources. Sheehan,(2013)When used correctly, performance management is a systematic analysis and measurement of worker performance (including communication of that assessment to the individual) that we use to improve performance over time.In recent years, many organizations are trying to create a ‘performance culture’, which is incorporated of several strategies in order to develop individuals’ contribution to the overall success of the organization.Armstrong (2006) asserts that, the aim of performance management is to establish a high performance culture in which individuals and team takes reasonability for the continuous improvement of business process and for their own skills and contributions within a framework provided effective leadership. On the other hand, it can be said that performance management system could have multiple objectives and multiple contents. The system might be linked to business objectives and to developing employees’ skills and competences. At the same time, the system could include planning, training and development and succession planning, setting and reviewing objectives and competencies as well. Armstrong and Baron (1998) claim, performance management has the capacity to match the organization’s overall strategy and if it is not the case, performance management will not reach its full potential. (as cited in Millmore et al, 2007).Micro, Small and Medium Enterprises in Ghana are said to be a characteristic feature of the production landscape and have been noted to provide about 85% of manufacturing employment of Ghana (Aryeetey, 2001). SME‟s are also believed to contribute about 70% to Ghana‟s Gross Domestic Product (GDP) and account for about 92% of businesses in Ghana yet much has not being done about their human resource activities more so that of the very small private ones notably the SME's amongst them. The role played by these companies are in many folds as they pay income taxes of the workers and also corporate taxes which goes directly to the government. They also pay salaries to their workers which they also use in taking care of their families bearing in mind that in Ghana a worker has at least about four dependents on them thereby also trying to curb the unemployment in the country and minimizing hardship.With the crucial role played by SME's in our society it is therefore imperative on them to be constantly performing at their best to maintain the successes they chalked hence the implementation of performance management systems at the work place. The need for an efficient and effective performance management systems (PMS) has increased over the last decade. This is because it has been shown that the use of PMS improves the performance and overall quality of an organization (Linge and Schiemann, 1996; Lawson et al., 2003; de Waal and Coevert, 2007).Statement of the problemMicro, Small and Medium Scale Enterprises contributes a colossal percentage to Gross Domestic Product (GDP) in ensuring economic growth, employment, income stability and poverty reduction in most developing countries like Ghana. For these Small-Medium Enterprises to survive, grow and be profitable in this era of business there is need to work towards a common goal, have a clear understanding of job expectations, provide a regular feedback about performance, give advice and steps for improving performance and provide rewards for good performance. However, reality on the grounds appears the Small Scale Business are not ready and prepared to face the challenges associated with the changing corporate world of business especially with regards to the measuring of performance of their business.The achievement of corporate goals is reliant on the quality and calibre of human resources an organisation possesses. The human resources are the reservoir of skills and knowledge which mustbe managed for the survival of the organisation and nation as whole. To gain competitive advantage over competitors requires efficiency and effectiveness in the performance of the organisation.The fundamental issue here is to what extent has small businesses in the Cape Coast metro area benefited from performance management , are any of the small firms in the catchment area practicing it at the moment. When organisations concerned are not having form of training regarding performance management sysytems for its employees what form of result or achievements is expected from the employees.Past studies have not adequately explored or examined the effect of a best run performance management system on small businesses especially in Africa .Over the years, however, little attention has been paid to the difficulties facing these firms in the development and implementation of PM programmes. Where there have been some discussions, they have generally been lumped together with other reform policies or with reforms in general creating a gap in the literature on what has gone wrong and what is to be done to reverse this trend.It is for these reasons that the researcher considers it vital to conduct this study .Objective of the studyThe general objective of the study is to ascertain whether Small-Medium Enterprises in the Central Region, especially Cape Coast Metropolis uses Performance Management System. However, the specific objectives of the study are as follows;1. To examine the kind of system used by the Small-scale Businesses to check performance.2. To find out if the Small-Medium Enterprises need Performance Management System.3. To identify the challenges of implementing Performance Management System by small scale businesses4. To make recommendations for improving performance management for small businesses.Research questionsThe research questions of the study were used to determine the extent to which performance management systems have affected the performance of small businesses and whether the use of it will improve the performance of such firms.The research questions were.1. What kind of performance management systems are the Small and Medium sized Enterprises currently using?2. What do theory and research say about Performance Management?3. Which performance management systems have already been applied in the small medium enterprise industry4. How can a new model which is more relevant and applicable for SMEs in Cape Coast be developed?Significance of the studyThe questions asked in the research questions area will be addressed as they are relevant in order to understand how performance management is perceived by practitioners. Furthermore it is necessary to examine the environment in which the business operates to comprehend the rationale behind and identify possible weaknesses in the current performance management not mentioned hitherto.It is also extremely relevant since it intends to build a strong theoretical basis that will be applied later on to improve the performance management systems in the aforesaid small business enterprise. Therefore, a literature review concerning Performance Management will be carried out, discussing the most relevant frameworks and performance management philosophies.Another part of the thesis seeks to investigate which performance management systems philosophies have already been applied into the small medium enterprises. A case review is done, discussing the rationale behind of these systems as well as their feasibility into the SMEs context. The final goal of this research is to propose a model or framework that will be suitable for Small and Medium sized companies aligning their operations with strategies at the same time enabling them to measure its overall performance beyond financial numbers. Furthermore, this model will help them to monitor their strategies not only in a short term but also in a medium to long term perspective.Overall the work will add to the body of knowledge in the field of human resource with specific emphasis on Performance Management System. Also, the study will enlighten Small-Medium Enterprises of the need and benefits of Performance Management System. It will also serve as a reference material for future researchers in their quest to do research on Performance Management System.Scope of the studyThe study will be limited to Small-Medium Enterprises in the Central Region with particular reference to Cape Coast but for time constraints and proximity, the study would have covered all Small-Scale Businesses in Ghana.Study organisationThe study is presented in five chapters with Chapter One as the introductory chapter wich comprises the background to the study, statement of the problem, research questions, objectives if the study, significance of the study and the organisation of the study. Chapter Two dealt with the literature review on the performance management systems , the systems concept, how small medium enterprises are defined in the Ghanaian context . Chapter Three was devoted to the research design or methodology. Issues discussed include the population, the sample and the sampling procedures, the research instruments and their administration , data collection and data analyses. While Chapter four focused on data processing and analysis from the field and the last chapter which is five dealt with the summary of main findings, conclusions and recommendations for policy makers. The summary emphasizes the study's special characteristics, particularly its contributions to the advancement of knowledge in performance management systems.。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
Abstract: Photovoltaic (PV) systems, especially the ones installed in urban and suburban areas, operate often under nonuniform distribution of solar irradiance and PV cells temperature over the PV array. At this purpose, an architecture of the PV system based on distributed DC–DC converters, that performs the maximum power point tracking algorithm (DMPPT) can effectively counteract the reduction of power efficiency because of electrical, thermal and irradiance mismatch phenomena. Aim of this study is to experimentally assess the effect of the use of DC–DC boost converters with MPPT capability, directly applied at the substring level of a single PV module. The novelty of the approach here proposed with respect to the state-of-the-art is that the outputs of the converters are connected in parallel. Comparing the power efficiency of a conventional PV module with that of the considered prototype where the three substrings are connected to a dedicated DC–DC converter, a remarkable improvement of the extracted power ranging from 11 to 25% under nonuniform solar radiation was found.
The cause may be the shadow of clouds or the shadow of one solar array on the other, trees, booms, neighbour’s houses etc. Moreover, in the framework of building integration, concentration PV systems are often used to increase the power density [9]. Moreover for these systems, shadowing and the non-uniform solar radiation distribution is a crucial problem if a double axis tracking system is not adopted. Actually, maximum power point tracking is a difficult operation when a PV array is partially shaded or works under non-uniform or rapidly changeable irradiance conditions, because two or more local maximum power points may appear [10]. Since in many cases these causes of efficiency losses cannot be removed, it is important to minimise the effect of such mismatching phenomena on the energy production [11]. To reduce the effects of these problems, distributed DC–DC converters that performs the maximum power point tracking algorithm (DMPPT) techniques were recently studied [12]. These approaches are based on the adoption of a DC–DC converter dedicated to the maximum power point tracking (MPPT) of each PV module or group of modules by eventually moving part of the electronics from theThere are a number of studies in the literature concerning distributed architectures [6, 14–25]. Moreover, a PV system can be used to increase the autonomy of a mobile systems, for instance electric vehicles or moving robots [26], in such applications the DMPPT approach was also applied [27, 28]. Using distributed PV architectures leads to higher costs compared with conventional approaches. Therefore a trade-off between costs and advantages in terms of output power of the PV system should be investigated for each application. Under partially shaded or non-uniform solar radiation conditions, the use of distributed power electronics can recover between 10 and 30% of annual performance loss or more depending on the system configuration and type of used devices [6, 29]. Of course, when the impact, in terms of duration and extension, of the aforementioned negative phenomena is limited, the use of distributed power electronics does not appear economic but in case of BIPV systems, characterised by shading conditions or low concentrating PV systems, it is expected that a DMPPT could lead to great advantages.
Alfio Dario Grasso 1, Salvatore Pennisi 1, Massimiliano Ragusa 2, Giuseppe Marco Tina 1 ✉, Cristina Ventura 1
Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Viale Andrea Doria n. 6, 95125, Catania, Italy STMicroelectronics, Catania, Italy ✉ E-mail: giuseppe.tina@dieei.unict.it
IET Renewable Power Generation Research Article
Performance evaluation of a multistring photovoltaic module with distributed DC–DC converters
1 2
ISSN 1752-1416 Received on 22nd July 2014 Revised on 6th March 2015 Accepted on 4th May 2015 doi: 10.1049/iet-rpg.2014.0246
1
Introduction
With the continuous fall in the price of photovoltaic modules, the rising concern on the greenhouse gas emissions and increasing costs for some generation alternatives, solar energy is rapidly becoming a competitive solution for the production of clean energy. As an example, large drops in solar module prices have helped spur record levels of deployment, which increased 54% over the previous year to 28.7 GW in 2011. This is ten times the new build level of 2007 [1]. With a global PV modules overcapacity that is less acute than one or two years ago, process has stabilised in 2013, and the return to profitability should allow companies to invest again [2]. Technologically, PV systems are relatively easy to install, very safe, almost maintenance free and more importantly, environment friendly. Moreover, a very important feature of PV systems is that, because of their modular structure, incremental power additions are easily accommodated. Therefore because of all these advantages and their medium and long term economic prospects, large PV power systems are being installed worldwide. Meanwhile, unused space, such as rooftops of homes, factories and large buildings can be effectively utilised to harvest solar energy as demonstrated by the success of the building integrated PV (BIPV) initiatives in various countries [3–5]. The PV power conversion efficiency depends on several causes, such as weather conditions, surrounding obstacles, geographic location, ageing and also design strategies; some of these factors can cause non-uniformity of irradiance and temperature on PV modules, which can play an important role in the whole efficiency. The problem of thermal mismatch has a minimal impact when cells and module are connected in series, on the contrary the irradiance mismatch has the greatest impact in this case [6]. One of the most significant causes of uneven distribution of solar radiation is the partially shading of PV arrays [7]. Although a planar PV array in an isolated landscape would be exposed to uniform illumination, the complex designs and landscapes of PV systems in urban and suburban environments often create situations where PV systems on buildings receive a non-uniform radiation, resulting in decreased power conversion efficiency [8].