Parallel and Distributed Programming. Performance Metrics


 Martha Russell
 3 years ago
 Views:
Transcription
1 Paralll and Distributd Programming Prformanc! wo main goals to b achivd with th dsign of aralll alications ar:! Prformanc: th caacity to rduc th tim to solv th roblm whn th comuting rsourcs incras;! Scalability: th caacity to incras rformanc whn th comlxity, or siz of th roblm, incrass.! h main factors limiting th rformanc and th scalability of an alication ar:! Architctural Limitations! Algorithmic Limitations
2 ! Architctural Limitations! Latncy and Bandwidth! Data ohrncy! Mmory aacity Factors Limiting Prformanc! Algorithmic Limitations! Missing Paralllism squntial cod! ommunication Frquncy! Synchronization Frquncy! Poor Schduling task granularity/load balancing 3! hr ar distinct classs of rformanc mtrics:! for Procssors: assss th rformanc of a rocssor using normally by masuring th sd or th numbr of orations that it dos in a crtain riod of tim.! of Paralll Alications: assss th rformanc of a aralll alication normally by comaring th xcution tim with multil rocssors and th xcution tim with just on rocssor.! W ar mostly intrstd in mtrics that allow th rformanc valuation of aralll alications. 4
3 for Procssors! Som of th bst known mtrics to masur rformanc of a rocssor architctur:! MIPS: Millions of Instructions Pr Scond.! FLOPS: FLoating oint Orations Pr Scond.! SPEint: SPE Standard Prformanc Evaluation ororation bnchmarks that valuat rocssor rformanc on intgr arithmtic 99.! SPEf: SPE bnchmarks that valuat rocssor rformanc on floating oint orations 000.! Whtston: synthtic bnchmarks to assss rocssor rformanc on floating oint orations 97.! Dhryston: synthtic bnchmarks to asss rocssor rformanc on intgr arithmtic for Paralll Alications! hr ar a numbr of mtrics, th bst known ar:! Sdu! Efficincy! Rdundancy! Utilization! Quality! hr also som laws/mtrics that try to xlain and assrt th otntial rformanc of a aralll alication. h bst known ar:! Amdahl Law! GustafsonBarsis Law! KarFlatt Law! Isoficincy Law 6 3
4 Sdu! Sdu is a masur of rformanc. It masurs th ration btwn th squntial xcution tim and th aralll xcution tim. S is th xcution tim with on rocssor is th xcution tim with rocssors PU PUs 4 PUs 8 PUs 6 PUs S,9 3,57 6,5 0,00 7 Efficincy! Efficincy is a masur of th usag of th comutational rsourcs. It masurs th ration btwn rformanc and th rsourcs usd to achiv that rformanc. S E S is th sdu for rocssors PU PUs 4 PUs 8 PUs 6 PUs S,9 3,57 6,5 0,00 E 0,96 0,89 0,78 0,63 8 4
5 Rdundancy! Rdundancy masurs th incras in th rquird comutation whn using mor rocssors. It masurs th ration btwn th numbr of orations rformd by th aralll xcution and by th squntial xcution. O R O O is th total numbr of orations rformd with rocssor O is th total numbr of orations rformd with rocssors PU PUs 4 PUs 8 PUs 6 PUs O R,03,0,3,50 9 Utilization! Utilization is a masur of th good us of th comutational caacity. It masurs th ratio btwn th comutational caacity utilizd during xcution and th caacity that was availabl. U R E PU PUs 4 PUs 8 PUs 6 PUs R,03,0,3,50 E 0,96 0,89 0,78 0,63 U 0,99 0,98 0,96 0,95 0 5
6 Quality! Quality is a masur of th rlvancy of using aralll comuting. S E Q R PU PUs 4 PUs 8 PUs 6 PUs S,9 3,57 6,5 0,00 E 0,96 0,89 0,78 0,63 R,03,0,3,50 Q,79,89 3,96 4,0 Amdahl Law! h comutations rformd by a aralll alication ar of 3 tys:! sq: comutations that can only b ralizd squncially.! ar: comutations that can b ralizd in aralll.! com: comutations rlatd to communication/synchronization/initialization.! Using ths 3 classs, th sdu of an alication can b dfind as: sq ar S ar sq com 6
7 Amdahl Law! Sinc com 0 thn: sq ar S ar sq! If f is th fraction of th comutation that can only b ralizd squntially, thn: f sq sq ar sq f and S! $ sq" f # ' & % sq 3 Amdahl Law! Simlifying: sq f S sq f sq S f f S f f 4 7
8 Amdahl Law! Lt 0 f b th comutation fraction that can only b ralizd squntially. h Amdahl law tlls us that th maximum sdu that a aralll alication can attain with rocssors is: S f f! h Amdahl law can also b usd to dtrmin th limit of maximum sdu that a dtrmind alication can achiv rgardlss of th numbr of rocssors uusd. 5 Amdahl Law! Suos on wants to dtrmin if it is advantagos to dvlo a aralll vrsion of a crtain squntial alication. hrough xrimntation, it was vrifid that 90% of th xcution tim is snt in rocdurs that may b aralllizabl. What is th maximum sdu that can b achivd with a aralll vrsion of th roblm xcuting on 8 rocssors? S 4,7 0, 0, 8! And th limit of th maximum sdu that can b attaind? lim 0 0, 0, 6 8
9 Limitations of th Amdahl Law! h Amdahl law ignors th cost with communication/synchronization orations associatd to th introduction of aralllism in an alication. For this rason, th Amdahl law can rsult in rdictions not vry ralistic for crtain roblms.! onsidr a aralll alication, with comlxity On, whos xcution attrn is th following, whr n is th siz of th roblm:! Excution tim of th squntial art inut and outut of data:! Excution tim of th aralll art: n 00! otal communication/synchronization oints r rocssor:! Excution tim du to communication/synchronization n0.000: n log n log n 7 Limitations of th Amdahl Law! What is th maximum sdu attainabl?! Uzing Amdahl law: f n n n 00! Uzinf th sdu masur: n n and S! 00 n n "00 n n S 00 n n 00 n 0 log n log 8 9
10 Limitations of th Amdahl Law PU PUs 4 PUs 8 PUs 6 PUs n 0.000,95 3,70 6,7,36 Amdahl law n 0.000,98 3,89 7,5 4,0 n ,99 3,94 7,7 4,8 n 0.000,6,,,57 Sdu n 0.000,87 3, 4,7 6,64 n ,93 3,55 5,89 9,9 9 GustafsonBarsis Law! onsidr again th sdu masur dfind rviously: sq ar S ar sq! If f is th fraction of th aralll comutation snt xcuting squntial comutations, thn f is th fraction of th tim snt in th aralll art: f sq sq ar and! f ar sq ar 0 0
11 ! hn:! Simlifying: S S GustafsonBarsis Law ar sq f sq ar ar f sq f f f ar sq ar sq f S f GustafsonBarsis Law! Lt 0 f b th fraction of aralll comutation snt xcuting squntial comutations. h GustafsonBarsis law tlls us that th maximum sdu that a aralll alication with rocssors can attain is: S f! Whil th Amdahl law starts from th tim of th squntial xcution to stimat th maximum sdu that can b attaind with multil rocssors, th Gustafson Barsis law dos th oosit, that is, it starts from th aralll xcution tim to stimat th maximum sdu in comarison with th squntial xcution.
12 GustafsonBarsis Law! onsidr that a crtain alication xcuts in 0 sconds in 64 rocssors. What is th maximum sdu of an alication knowing, by xrimntation, that 5% of th xcution tim is snt on squntial comutations. S 64 0, ,5 60,85! Suos that a crtain comany wants to buy a surcomutr with rocssors to achiv a sdu of in an imortant fundamntal roblm. What is th maximum fraction of th aralll xcution that can b snt in squntial comutations to attain th xctd sdu? f f f 0,084 3 GustafsonBarsis Law Limitations! Whn using th xcution tim of th aralll xcution as a starting oint, instad of th squntial xcution, th GustafsonBarsis law assums that th xcution with on rocssor is, in th worst cass, tims slowr than th xcution with rocssors.! his may not b tru if th availabl mmory for th xcution with on rocssor is insufficint whn comard to th th comutation with rocssors. For this rason, th stimatd sdu by th GustafsonBarsis law is normally dsignatd as scald sdu. 4
13 KarFlatt Mtric! Lt us considr again th dfinition of squntial xcution tim and aralll xcution tim: sq ar ar sq com! Lt b th xrimntally dtrmind squntial fraction of a aralll comutation: sq 5 KarFlatt Mtric! hn: sq ar! If on considrs that com is ngligibl thn:! On th othr hand: S S 6 3
14 4 7 KarFlatt Mtric! Simlifying: S S S S S S S S 8 KarFlatt Mtric! Lt S b th sdu of a aralll alication with > rocssors. h Kar Flatt mtric tlls us that th xrimntally dtrmind squntial fraction is:! h lss th valu th bttr th aralllization! h KarFlatt mtric is intrsting bcaus by nglting th costs with communication/synchronization/initialization orations associatd with aralllism, allows us, a ostriori, to dtrmin th rlvanc of th com comonnt in th vntual dcras of th alication s fficincy. S
15 KarFlatt Mtric! By dfinition, th xrimntally dtrmind squntial fraction is a constant valu that dos not dnd on th numbr of rocssors. sq! On th othr hand, th KarFlatt mtric is a function of th numbr of rocssors. S 9 KarFlatt Mtric! onsidring that th fficincy of an alication is a dcrasing function on th numbr of rocssors, KarFlatt mtric allows us to dtrmin th imortanc of com in that dcras.! If th valus of ar constant whn th numbr of rocssors incrass, that mans that th com comonnt is constant. hrfor, th fficincy dcras is du to th scars aralllism availabl in th alication.! If th valus of incras with th incras in th numbr of rocssors, it mans that th dcras is du to th com comonnt, that is, du to th xcssiv costs associatd with th aralll comutation communication costs, synchronization and/or comutation initialization. 30 5
16 KarFlatt Mtric! For xaml, th KarFlatt mtric allows us to dtct sourcs of infficincy not considrd by th modl, which assums that rocssors xcut th aralll art tims fastr thn whn xcuting with just on rocssor.! If w hav 5 rocssors to solv a roblm dcomosd in 0 atomic tasks, thn all rocssors can xcut 4 tasks. If all tasks tak th sam tim to xcut, thn th aralll xcution tim should b a fraction of 5.! On th othr hand, if w hav 6 rocssors to solv th sam roblm, 4 rocssors can xcut 3 tasks but th othr must ncssarily xcut 4. his maks th xcution tim again a fraction of 5 and not of 6. 3 KarFlatt Mtric! onsidr th following sdus obtaind by a crtain aralll alication: PUs 3 PUs 4 PUs 5 PUs 6 PUs 7 PUs 8 PUs S,8,50 3,08 3,57 4,00 4,38 4,7 0,099 0,00 0,00 0,00 0,00 0,00 0,00! What is th main rason for th alication to just achiv a sdu of 4,7 with 8 rocssors?! Givn that dosn t incras with th numbr of rocssors, it mans that th main rason for th small sdu is th littl aralllism avaiabl in th roblm. 3 6
17 KarFlatt Mtric! onsidr th following sdus obtaind by a crtain aralll alication: PUs 3 PUs 4 PUs 5 PUs 6 PUs 7 PUs 8 PUs S,87,6 3,3 3,73 4,4 4,46 4,7 0,070 0,075 0,079 0,085 0,090 0,095 0,00! What is th main rason for th alication to just achiv a sdu of 4,7 with 8 rocssors?! Givn that incrass slightly with th numbr of rocssors, it mans that th main rason for th small sdu ar th costs associatd to th aralll comutation. 33 Efficincy and Scalability! From rvious rsults, w can conclud that th fficincy of an alication is:! A dcrasing function of th numbr of rocssors.! yically, an incrasing function on th siz of th robm. 34 7
18 Efficincy and Scalability! An alication is said scalabl whn its fficincy is maintaind whn w incras roortionally th numbr of rocssors and th siz of th roblm.! h scalability of an alication rflcts its caacity in making us of availabl rsourcs ffctivly. PU PUs 4 PUs 8 PUs 6 PUs n ,8 0,53 0,8 0,6 Efficincy n ,94 0,80 0,59 0,4 n ,96 0,89 0,74 0,58 35 Isofficincy Mtric! h fficincy of an alication is tiically an incrasing function of th siz of th roblm sinc th comlxity of communication is, normally, smallr thn th comutation comlxity, that is, to maintain th sam lvl of fficincy whn w incras th numbr of rocssors on nds to incras th siz of th roblm. h isofficincy mtric formalizs this ida.! Lts considr again th dfinition of sdu: sq ar S ar sq com sq ar sq ar sq ar com sq ar sq com 36 8
19 9 37 Isofficincy Mtric! Lt 0 b th xcution tim snt by rocssors on th aralll algorithm rforming comutations not don in squntial algorithm:! Simlifying: 0 com sq ar sq ar sq ar sq E ar sq ar sq S 38 Isofficincy Mtric! hn:! If on wants to maintain th sam lvl of fficincy whn w incras th numbr of rocssors, thn: E E E E E 0 c c E E
20 Isofficincy Mtric! Lt E b th fficincy of a aralll alication with rocssors. h isofficincy mtric tlls us that to maintain th sam lvl of fficincy whn w incras th numbr of rocssors, thn th siz of th roblm must b incrasd so that th following inquality is satisfid: with c E # E! c " 0 and 0 #"sq "com! h alicability of th isofficincy mtric may dnd on th availabl mmory, considring th maximum siz of th roblm that can b solvd is limitd by that quantity. 39 Isofficincy Mtric! Suos that th isofficincy mtric for a roblm siz n is givn as a function on th numbr of rocssors : n f! If Mn dsignats th quantity of rquird mmory to solv a roblm of siz n thn: f M n M! hat is, to maintain th sam lvl of fficincy, th quantity of rquird mmory r rocssor is: f M n M 40 0
21 Isofficincy Mtric c log Mmory r rocssor Efficincy can not b Maintaind and should dcras Effcincy can c c log Mmory limit B maintaind c Numbr of rocssors 4 Isofficincy Mtric! onsidr that th squntial vrsion of a crtain alication has comlxity On 3, and that th xcution tim snt by ach of th rocssors of th aralll vrsion in communication/synchronization orations is On log. If th amount of mmory ncssary to rrsnt a roblm of siz n is n, what is th scalability of th alication in trms of mmory? 3 n c n log n c log M n n M c log c log c log! hn, th scalability of th alication is low. 4
22 Surlinar Sdu! h sdu is said to b surlinar whn th ratio btwn th squntial xcution tim and th aralll xcution tim with rocssors is gratr than.! Som factors that may mak th sdu surlinar ar:! omunication/synchronization/initialization costs ar almost inxistnt.! olrancy to communication latncy.! Incras th mmory caacity th roblm may hav to fit all in mmory.! Subdivisions of th roblma smallr tasks may gnrat lss cach misss.! omutation randomnss in otimization roblms or with multil solutions. 43 Surlinar Sdu If just on comutr rocssador can solv a roblm in N sconds, could N comutrs rocssors Solv th sam roblm in scond? 44
QUANTITATIVE METHODS CLASSES WEEK SEVEN
QUANTITATIVE METHODS CLASSES WEEK SEVEN Th rgrssion modls studid in prvious classs assum that th rspons variabl is quantitativ. Oftn, howvr, w wish to study social procsss that lad to two diffrnt outcoms.
More informationQuestion 3: How do you find the relative extrema of a function?
ustion 3: How do you find th rlativ trma of a function? Th stratgy for tracking th sign of th drivativ is usful for mor than dtrmining whr a function is incrasing or dcrasing. It is also usful for locating
More informationby John Donald, Lecturer, School of Accounting, Economics and Finance, Deakin University, Australia
Studnt Nots Cost Volum Profit Analysis by John Donald, Lcturr, School of Accounting, Economics and Financ, Dakin Univrsity, Australia As mntiond in th last st of Studnt Nots, th ability to catgoris costs
More informationEFFECT OF GEOMETRICAL PARAMETERS ON HEAT TRANSFER PERFORMACE OF RECTANGULAR CIRCUMFERENTIAL FINS
25 Vol. 3 () JanuaryMarch, pp.375/tripathi EFFECT OF GEOMETRICAL PARAMETERS ON HEAT TRANSFER PERFORMACE OF RECTANGULAR CIRCUMFERENTIAL FINS *Shilpa Tripathi Dpartmnt of Chmical Enginring, Indor Institut
More informationForeign Exchange Markets and Exchange Rates
Microconomics Topic 1: Explain why xchang rats indicat th pric of intrnational currncis and how xchang rats ar dtrmind by supply and dmand for currncis in intrnational markts. Rfrnc: Grgory Mankiw s Principls
More informationThe example is taken from Sect. 1.2 of Vol. 1 of the CPN book.
Rsourc Allocation Abstract This is a small toy xampl which is wllsuitd as a first introduction to Cnts. Th CN modl is dscribd in grat dtail, xplaining th basic concpts of Cnts. Hnc, it can b rad by popl
More informationEcon 371: Answer Key for Problem Set 1 (Chapter 1213)
con 37: Answr Ky for Problm St (Chaptr 23) Instructor: Kanda Naknoi Sptmbr 4, 2005. (2 points) Is it possibl for a country to hav a currnt account dficit at th sam tim and has a surplus in its balanc
More information7 Timetable test 1 The Combing Chart
7 Timtabl tst 1 Th Combing Chart 7.1 Introduction 7.2 Tachr tams two workd xampls 7.3 Th Principl of Compatibility 7.4 Choosing tachr tams workd xampl 7.5 Ruls for drawing a Combing Chart 7.6 Th Combing
More informationKeywords Cloud Computing, Service level agreement, cloud provider, business level policies, performance objectives.
Volum 3, Issu 6, Jun 2013 ISSN: 2277 128X Intrnational Journal of Advancd Rsarch in Computr Scinc and Softwar Enginring Rsarch Papr Availabl onlin at: wwwijarcsscom Dynamic Ranking and Slction of Cloud
More informationAdverse Selection and Moral Hazard in a Model With 2 States of the World
Advrs Slction and Moral Hazard in a Modl With 2 Stats of th World A modl of a risky situation with two discrt stats of th world has th advantag that it can b natly rprsntd using indiffrnc curv diagrams,
More informationAP Calculus AB 2008 Scoring Guidelines
AP Calculus AB 8 Scoring Guidlins Th Collg Board: Conncting Studnts to Collg Succss Th Collg Board is a notforprofit mmbrship association whos mission is to connct studnts to collg succss and opportunity.
More informationITIL & Service Predictability/Modeling. 2006 Plexent
ITIL & Srvic Prdictability/Modling 1 2 Plxnt Th Company 2001 Foundd Plxnt basd on an Expandd ITIL Architctur, CMMI, ISO, and BS15000  itdna 2003 Launchd itdna Srvic Offring 2003 John Groom, past Dirctor
More informationReview on KVM Hypervisor
Intrnational Journal of Rcnt chnolog and Enginring (IJRE) ISSN: 22773878, Volum3 Issu4 Stmbr, 2014 Riw on KVM Hrisor Pankaj R Kadam, Nilsh V Alon AbstractIn toda s world th boom of cloud comuting is
More informationNonHomogeneous Systems, Euler s Method, and Exponential Matrix
NonHomognous Systms, Eulr s Mthod, and Exponntial Matrix W carry on nonhomognous firstordr linar systm of diffrntial quations. W will show how Eulr s mthod gnralizs to systms, giving us a numrical approach
More informationTHE FUNDAMENTALS OF CURRENT SENSE TRANSFORMER DESIGN. Patrick A. Cattermole, Senior Applications Engineer MMG 10 Vansco Road, Toronto Ontario Canada
, Snior Alications nginr MMG 10 Vansco Road, Toronto Ontario Canada Abstract Th following ar will first rviw th basic rincils of oration of a Currnt Sns Transformr and thn follow a simlifid dsign rocdur.
More informationInstallation Saving Spaceefficient Panel Enhanced Physical Durability Enhanced Performance Warranty The IRR Comparison
Contnts Tchnology Nwly Dvlopd Cllo Tchnology Cllo Tchnology : Improvd Absorption of Light Doublsidd Cll Structur Cllo Tchnology : Lss Powr Gnration Loss Extrmly Low LID Clls 3 3 4 4 4 Advantag Installation
More informationMathematics. Mathematics 3. hsn.uk.net. Higher HSN23000
hsn uknt Highr Mathmatics UNIT Mathmatics HSN000 This documnt was producd spcially for th HSNuknt wbsit, and w rquir that any copis or drivativ works attribut th work to Highr Still Nots For mor dtails
More informationThe Matrix Exponential
Th Matrix Exponntial (with xrciss) 92.222  Linar Algbra II  Spring 2006 by D. Klain prliminary vrsion Corrctions and commnts ar wlcom! Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial
More informationNoble gas configuration. Atoms of other elements seek to attain a noble gas electron configuration. Electron configuration of ions
Valnc lctron configuration dtrmins th charactristics of lmnts in a group Nobl gas configuration Th nobl gass (last column in th priodic tabl) ar charactrizd by compltly filld s and p orbitals this is a
More informationJournal of Engineering and Natural Sciences Mühendislik ve Fen Bilimleri Dergisi
Journal of Enginring and Natural Scincs Mühndisli v Fn Bilimlri Drgisi Sigma 4/ Invitd Rviw Par OPTIMAL DESIGN OF NONLINEAR MAGNETIC SYSTEMS USING FINITE ELEMENTS Lvnt OVACIK * Istanbul Tchnical Univrsity,
More informationLong run: Law of one price Purchasing Power Parity. Short run: Market for foreign exchange Factors affecting the market for foreign exchange
Lctur 6: Th Forign xchang Markt xchang Rats in th long run CON 34 Mony and Banking Profssor Yamin Ahmad xchang Rats in th Short Run Intrst Parity Big Concpts Long run: Law of on pric Purchasing Powr Parity
More informationhttp://www.wwnorton.com/chemistry/tutorials/ch14.htm Repulsive Force
ctivation nrgis http://www.wwnorton.com/chmistry/tutorials/ch14.htm (back to collision thory...) Potntial and Kintic nrgy during a collision + + ngativly chargd lctron cloud Rpulsiv Forc ngativly chargd
More informationTraffic Flow Analysis (2)
Traffic Flow Analysis () Statistical Proprtis. Flow rat distributions. Hadway distributions. Spd distributions by Dr. GangLn Chang, Profssor Dirctor of Traffic safty and Oprations Lab. Univrsity of Maryland,
More informationFACULTY SALARIES FALL 2004. NKU CUPA Data Compared To Published National Data
FACULTY SALARIES FALL 2004 NKU CUPA Data Compard To Publishd National Data May 2005 Fall 2004 NKU Faculty Salaris Compard To Fall 2004 Publishd CUPA Data In th fall 2004 Northrn Kntucky Univrsity was among
More informationAn Adaptive Clustering MAP Algorithm to Filter Speckle in Multilook SAR Images
An Adaptiv Clustring MAP Algorithm to Filtr Spckl in Multilook SAR Imags FÁTIMA N. S. MEDEIROS 1,3 NELSON D. A. MASCARENHAS LUCIANO DA F. COSTA 1 1 Cybrntic Vision Group IFSC Univrsity of São Paulo Caia
More informationLecture 3: Diffusion: Fick s first law
Lctur 3: Diffusion: Fick s first law Today s topics What is diffusion? What drivs diffusion to occur? Undrstand why diffusion can surprisingly occur against th concntration gradint? Larn how to dduc th
More informationSUBATOMIC PARTICLES AND ANTIPARTICLES AS DIFFERENT STATES OF THE SAME MICROCOSM OBJECT. Eduard N. Klenov* RostovonDon. Russia
SUBATOMIC PARTICLES AND ANTIPARTICLES AS DIFFERENT STATES OF THE SAME MICROCOSM OBJECT Eduard N. Klnov* RostovonDon. Russia Th distribution law for th valus of pairs of th consrvd additiv quantum numbrs
More informationPerformance Evaluation
Prformanc Evaluation ( ) Contnts lists availabl at ScincDirct Prformanc Evaluation journal hompag: www.lsvir.com/locat/pva Modling Baylik rputation systms: Analysis, charactrization and insuranc mchanism
More informationUpper Bounding the Price of Anarchy in Atomic Splittable Selfish Routing
Uppr Bounding th Pric of Anarchy in Atomic Splittabl Slfish Routing Kamyar Khodamoradi 1, Mhrdad Mahdavi, and Mohammad Ghodsi 3 1 Sharif Univrsity of Tchnology, Thran, Iran, khodamoradi@c.sharif.du Sharif
More informationMedia Considerations Related to Puerto Rico s Fiscal Situation
CUNY Graduat School of Journalism Jun, Mdia Considrations Rlatd to Purto Rico s Fiscal Situation Alan Schankl Managing Dirctor Municial Stratgy and Rsarch Economy is Stagnant and Dbt Continus to Grow.%.%.%
More informationCombinatorial Analysis of Network Security
Combinatorial Analysis of Ntwork Scurity Stvn Nol a, Brian O Brry a, Charls Hutchinson a, Sushil Jajodia a, Lynn Kuthan b, and Andy Nguyn b a Gorg Mason Univrsity Cntr for Scur Information Systms b Dfns
More information(Analytic Formula for the European Normal Black Scholes Formula)
(Analytic Formula for th Europan Normal Black Schols Formula) by Kazuhiro Iwasawa Dcmbr 2, 2001 In this short summary papr, a brif summary of Black Schols typ formula for Normal modl will b givn. Usually
More informationSTATEMENT OF INSOLVENCY PRACTICE 3.2
STATEMENT OF INSOLVENCY PRACTICE 3.2 COMPANY VOLUNTARY ARRANGEMENTS INTRODUCTION 1 A Company Voluntary Arrangmnt (CVA) is a statutory contract twn a company and its crditors undr which an insolvncy practitionr
More informationHardware Modules of the RSA Algorithm
SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol. 11, No. 1, Fbruary 2014, 121131 UDC: 004.3`142:621.394.14 DOI: 10.2298/SJEE140114011S Hardwar Moduls of th RSA Algorithm Vlibor Škobić 1, Branko Dokić 1,
More informationRural and Remote Broadband Access: Issues and Solutions in Australia
Rural and Rmot Broadband Accss: Issus and Solutions in Australia Dr Tony Warrn Group Managr Rgulatory Stratgy Tlstra Corp Pag 1 Tlstra in confidnc Ovrviw Australia s gographical siz and population dnsity
More informationChapter 10 Function of a Matrix
EE448/58 Vrsion. John Stnsby Chatr Function of a atrix t f(z) b a comlxvalud function of a comlx variabl z. t A b an n n comlxvalud matrix. In this chatr, w giv a dfinition for th n n matrix f(a). Also,
More informationDeer: Predation or Starvation
: Prdation or Starvation National Scinc Contnt Standards: Lif Scinc: s and cosystms Rgulation and Bhavior Scinc in Prsonal and Social Prspctiv s, rsourcs and nvironmnts Unifying Concpts and Procsss Systms,
More informationImproving Managerial Accounting and Calculation of Labor Costs in the Context of Using Standard Cost
Economy Transdisciplinarity Cognition www.ugb.ro/tc Vol. 16, Issu 1/2013 5054 Improving Managrial Accounting and Calculation of Labor Costs in th Contxt of Using Standard Cost Lucian OCNEANU, Constantin
More informationEROS SYSTEM SATELLITE ORBIT AND CONSTELLATION DESIGN
EROS SYSTEM SATELLITE ORBIT AND CONSTELLATION DESIGN Dr. Mosh BarLv, Dr. Lonid Shchrbina, Mr. Vola Lvin Dr. Mosh BarLv, Prsidnt ImagSat Intrnational N. V. 2 Kaufman Strt, TlAviv 61500 Isral. Tl: 9723796
More informationME 612 Metal Forming and Theory of Plasticity. 6. Strain
Mtal Forming and Thory of Plasticity mail: azsnalp@gyt.du.tr Makin Mühndisliği Bölümü Gbz Yüksk Tknoloji Enstitüsü 6.1. Uniaxial Strain Figur 6.1 Dfinition of th uniaxial strain (a) Tnsil and (b) Comprssiv.
More information811ISD Economic Considerations of Heat Transfer on Sheet Metal Duct
Air Handling Systms Enginring & chnical Bulltin 811ISD Economic Considrations of Hat ransfr on Sht Mtal Duct Othr bulltins hav dmonstratd th nd to add insulation to cooling/hating ducts in ordr to achiv
More informationCloud and Big Data Summer School, Stockholm, Aug., 2015 Jeffrey D. Ullman
Cloud and Big Data Summr Scool, Stockolm, Aug., 2015 Jffry D. Ullman Givn a st of points, wit a notion of distanc btwn points, group t points into som numbr of clustrs, so tat mmbrs of a clustr ar clos
More informationStatistical Machine Translation
Statistical Machin Translation Sophi Arnoult, Gidon Mailltt d Buy Wnnigr and Andra Schuch Dcmbr 7, 2010 1 Introduction All th IBM modls, and Statistical Machin Translation (SMT) in gnral, modl th problm
More informationProjections  3D Viewing. Overview Lecture 4. Projection  3D viewing. Projections. Projections Parallel Perspective
Ovrviw Lctur 4 Projctions  3D Viwing Projctions Paralll Prspctiv 3D Viw Volum 3D Viwing Transformation Camra Modl  Assignmnt 2 OFF fils 3D mor compl than 2D On mor dimnsion Displa dvic still 2D Analog
More informationThe Constrained SkiRental Problem and its Application to Online Cloud Cost Optimization
3 Procdings IEEE INFOCOM Th Constraind SkiRntal Problm and its Application to Onlin Cloud Cost Optimization Ali Khanafr, Murali Kodialam, and Krishna P. N. Puttaswam Coordinatd Scinc Laborator, Univrsit
More informationBasis risk. When speaking about forward or futures contracts, basis risk is the market
Basis risk Whn spaking about forward or futurs contracts, basis risk is th markt risk mismatch btwn a position in th spot asst and th corrsponding futurs contract. Mor broadly spaking, basis risk (also
More informationA tutorial for laboratory determination of Planck s constant from the Planck radiation law
A tutorial for laboratory dtrmination of Planck s constant from th Planck radiation law Adam Usman, John Dogari, M. idwan Enuwa and sa Sambo Dartmnt of Physics, Fdral Univrsity of Tchnology, P. M. B. 076,
More informationGAME THEORY SOLUTIONS USING NEURAL NETWORKS FOR MISSILE GUIDANCE
GAME THEORY SOLUTIONS USING NEURAL NETWORKS FOR MISSILE GUIDANCE Vnkat Durbha * and S.N. Balakrishnan ** vdkc7@umr.du, bala@umr.du Dartmnt of Mchanical and Arosac Enginring Univrsity of MissouriRolla,
More informationA Loadable Task Execution Recorder for Hierarchical Scheduling in Linux
A Loadabl Task Excution Rcordr for Hirarchical Schduling in Linux Mikal Åsbrg and Thomas Nolt MRTC/Mälardaln Univrsity PO Box 883, SE721 23, Västrås, Swdn {mikalasbrg,thomasnolt@mdhs Shinpi Kato Carngi
More informationFetch. Decode. Execute. Memory. PC update
nwpc PC Nw PC valm Mmory Mm. control rad writ Data mmory data out rmmovl ra, D(rB) Excut Bch CC ALU A vale ALU Addr ALU B Data vala ALU fun. valb dste dstm srca srcb dste dstm srca srcb Ftch Dcod Excut
More informationEntityRelationship Model
EntityRlationship Modl Kuanghua Chn Dpartmnt of Library and Information Scinc National Taiwan Univrsity A Company Databas Kps track of a company s mploys, dpartmnts and projcts Aftr th rquirmnts collction
More informationExpertMediated Search
ExprtMdiatd Sarch Mnal Chhabra Rnsslar Polytchnic Inst. Dpt. of Computr Scinc Troy, NY, USA chhabm@cs.rpi.du Sanmay Das Rnsslar Polytchnic Inst. Dpt. of Computr Scinc Troy, NY, USA sanmay@cs.rpi.du David
More informationA MultiHeuristic GA for Schedule Repair in Precast Plant Production
From: ICAPS03 Procdings. Copyright 2003, AAAI (www.aaai.org). All rights rsrvd. A MultiHuristic GA for Schdul Rpair in Prcast Plant Production WngTat Chan* and Tan Hng W** *Associat Profssor, Dpartmnt
More informationCategory 11: Use of Sold Products
11 Catgory 11: Us of Sold Products Catgory dscription T his catgory includs missions from th us of goods and srvics sold by th rporting company in th rporting yar. A rporting company s scop 3 missions
More informationESCI 241 Meteorology Lesson 6 Humidity
ESCI 41 Mtorology Lsson 6 Humiity Raing: MT Chatr 5 PARTIAL PRESSURE In a mixtur of gass, ach gas scis contributs to th total rssur. ο Th rssur xrt by a singl gas scis is known as th artial rssur for that
More informationBusiness rules FATCA V. 02/11/2015
Elmnt Attribut Siz InputTyp Rquirmnt BUSINESS RULES TYPE ERROR ACK Xpath I.Mssag Hadr FATCA_OECD Vrsion xsd: string = Validation WrongVrsion ftc:fatca_oecd/vrsion SndingCompanyIN Unlimit d xsd: string
More informationCPU. Rasterization. Per Vertex Operations & Primitive Assembly. Polynomial Evaluator. Frame Buffer. Per Fragment. Display List.
Elmntary Rndring Elmntary rastr algorithms for fast rndring Gomtric Primitivs Lin procssing Polygon procssing Managing OpnGL Stat OpnGL uffrs OpnGL Gomtric Primitivs ll gomtric primitivs ar spcifid by
More informationIntermediate Macroeconomic Theory / Macroeconomic Analysis (ECON 3560/5040) Final Exam (Answers)
Intrmdiat Macroconomic Thory / Macroconomic Analysis (ECON 3560/5040) Final Exam (Answrs) Part A (5 points) Stat whthr you think ach of th following qustions is tru (T), fals (F), or uncrtain (U) and brifly
More informationCARE QUALITY COMMISSION ESSENTIAL STANDARDS OF QUALITY AND SAFETY. Outcome 10 Regulation 11 Safety and Suitability of Premises
CARE QUALITY COMMISSION ESSENTIAL STANDARDS OF QUALITY AND SAFETY Outcom 10 Rgulation 11 Safty and Suitability of Prmiss CQC Rf 10A 10A(1) Lad Dirctor / Lad Officr Rspons Impact Liklihood Lvl of Concrn
More information5 2 index. e e. Prime numbers. Prime factors and factor trees. Powers. worked example 10. base. power
Prim numbrs W giv spcial nams to numbrs dpnding on how many factors thy hav. A prim numbr has xactly two factors: itslf and 1. A composit numbr has mor than two factors. 1 is a spcial numbr nithr prim
More informationFactorials! Stirling s formula
Author s not: This articl may us idas you havn t larnd yt, and might sm ovrly complicatd. It is not. Undrstanding Stirling s formula is not for th faint of hart, and rquirs concntrating on a sustaind mathmatical
More informationTIME MANAGEMENT. 1 The Process for Effective Time Management 2 Barriers to Time Management 3 SMART Goals 4 The POWER Model e. Section 1.
Prsonal Dvlopmnt Track Sction 1 TIME MANAGEMENT Ky Points 1 Th Procss for Effctiv Tim Managmnt 2 Barrirs to Tim Managmnt 3 SMART Goals 4 Th POWER Modl In th Army, w spak of rsourcs in trms of th thr M
More informationLecture notes: 160B revised 9/28/06 Lecture 1: Exchange Rates and the Foreign Exchange Market FT chapter 13
Lctur nots: 160B rvisd 9/28/06 Lctur 1: xchang Rats and th Forign xchang Markt FT chaptr 13 Topics: xchang Rats Forign xchang markt Asst approach to xchang rats Intrst Rat Parity Conditions 1) Dfinitions
More informationC H A P T E R 1 Writing Reports with SAS
C H A P T E R 1 Writing Rports with SAS Prsnting information in a way that s undrstood by th audinc is fundamntally important to anyon s job. Onc you collct your data and undrstand its structur, you nd
More informationthe socalled KOBOS system. 1 with the exception of a very small group of the most active stocks which also trade continuously through
Liquidity and InformationBasd Trading on th Ordr Drivn Capital Markt: Th Cas of th Pragu tock Exchang Libor 1ÀPH³HN Cntr for Economic Rsarch and Graduat Education, Charls Univrsity and Th Economic Institut
More informationWhy MarketValuationIndifferent Indexing Works
Volum 61 Numbr 5 005, CFA Institut PERSPECTIVES Why MarktValuationIndirnt Indxing Works Jack Trynor By th nd o th 0th cntury, vn casual invstors had bcom comortabl with th ida o indx unds. Th ida o a
More informationNew Basis Functions. Section 8. Complex Fourier Series
Nw Basis Functions Sction 8 Complx Fourir Sris Th complx Fourir sris is prsntd first with priod 2, thn with gnral priod. Th connction with th ralvalud Fourir sris is xplaind and formula ar givn for convrting
More informationFinancial Mathematics
Financial Mathatics A ractical Guid for Actuaris and othr Businss rofssionals B Chris Ruckan, FSA & Jo Francis, FSA, CFA ublishd b B rofssional Education Solutions to practic qustions Chaptr 7 Solution
More informationThe Normal Distribution: A derivation from basic principles
Th Normal Distribution: A drivation from basic principls Introduction Dan Tagu Th North Carolina School of Scinc and Mathmatics Studnts in lmntary calculus, statistics, and finit mathmatics classs oftn
More informationDehumidifiers: A Major Consumer of Residential Electricity
Dhumidifirs: A Major Consumr of Rsidntial Elctricity Laurn Mattison and Dav Korn, Th Cadmus Group, Inc. ABSTRACT An stimatd 19% of U.S. homs hav dhumidifirs, and thy can account for a substantial portion
More informationThe Fourier Transform
Th Fourir Transfor Larning outcos Us th Discrt Fourir Transfor to prfor frquncy analysis on a discrt (digital) signal Eplain th significanc of th Fast Fourir Transfor algorith; Eplain why windowing is
More informationUse a highlevel conceptual data model (ER Model). Identify objects of interest (entities) and relationships between these objects
Chaptr 3: Entity Rlationship Modl Databas Dsign Procss Us a highlvl concptual data modl (ER Modl). Idntify objcts of intrst (ntitis) and rlationships btwn ths objcts Idntify constraints (conditions) End
More informationLG has introduced the NeON 2, with newly developed Cello Technology which improves performance and reliability. Up to 320W 300W
Cllo Tchnology LG has introducd th NON 2, with nwly dvlopd Cllo Tchnology which improvs prformanc and rliability. Up to 320W 300W Cllo Tchnology Cll Connction Elctrically Low Loss Low Strss Optical Absorption
More informationGlobal Sourcing: lessons from lean companies to improve supply chain performances
3 rd Intrnational Confrnc on Industrial Enginring and Industrial Managmnt XIII Congrso d Ingniría d Organización BarclonaTrrassa, Sptmbr 2nd4th 2009 Global Sourcing: lssons from lan companis to improv
More informationCategory 7: Employee Commuting
7 Catgory 7: Employ Commuting Catgory dscription This catgory includs missions from th transportation of mploys 4 btwn thir homs and thir worksits. Emissions from mploy commuting may aris from: Automobil
More informationAP Calculus MultipleChoice Question Collection 1969 1998. connect to college success www.collegeboard.com
AP Calculus MultiplChoic Qustion Collction 969 998 connct to collg succss www.collgboard.com Th Collg Board: Conncting Studnts to Collg Succss Th Collg Board is a notforprofit mmbrship association whos
More information1754 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 6, NO. 5, MAY 2007
1754 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 6, NO. 5, MAY 007 On th Fasibility of Distributd Bamforming in Wirlss Ntworks R. Mudumbai, Studnt Mmbr, IEEE, G. Barriac, Mmbr, IEEE, and U. Madhow,
More informationTheoretical aspects of investment demand for gold
Victor Sazonov (Russia), Dmitry Nikolav (Russia) Thortical aspcts of invstmnt dmand for gold Abstract Th main objctiv of this articl is construction of a thortical modl of invstmnt in gold. Our modl is
More informationCPS 220 Theory of Computation REGULAR LANGUAGES. Regular expressions
CPS 22 Thory of Computation REGULAR LANGUAGES Rgular xprssions Lik mathmatical xprssion (5+3) * 4. Rgular xprssion ar built using rgular oprations. (By th way, rgular xprssions show up in various languags:
More informationAbstract. Introduction. Statistical Approach for Analyzing Cell Phone Handoff Behavior. Volume 3, Issue 1, 2009
Volum 3, Issu 1, 29 Statistical Approach for Analyzing Cll Phon Handoff Bhavior Shalini Saxna, Florida Atlantic Univrsity, Boca Raton, FL, shalinisaxna1@gmail.com Sad A. Rajput, Farquhar Collg of Arts
More informationESCI 341 Atmospheric Thermodynamics Lesson 14 Humidity Dr. DeCaria
PARIAL PRESSURE ESCI 341 Atmoshric hrmoynamics Lsson 14 Humiity Dr. DCaria In a mixtur of gass, ach gas scis contributs to th total rssur. ο h rssur xrt by a singl gas scis is known as th artial rssur
More information[ ] These are the motor parameters that are needed: Motor voltage constant. J total (lbinsec^2)
MEASURING MOOR PARAMEERS Fil: Motor paramtrs hs ar th motor paramtrs that ar ndd: Motor voltag constant (voltssc/rad Motor torqu constant (lbin/amp Motor rsistanc R a (ohms Motor inductanc L a (Hnris
More informationACCURACY OF DISTRIBUTION CURRENT TRANSFORMERS UNDER NONSINUSOIDEAL EXCITATION
ACCURACY OF DSTRBUTON CURRENT TRANSFORMERS UNDER NONSNUSODEAL EXCTATON Astract K. Dnath School of Enginring Univrsity of Tasmania Hoart, Australia 7000 Th accuracy of CTs usd for mtring uross ar rquird
More informationA Theoretical Model of Public Response to the Homeland Security Advisory System
A Thortical Modl of Public Rspons to th Homland Scurity Advisory Systm Amy (Wnxuan) Ding Dpartmnt of Information and Dcision Scincs Univrsity of Illinois Chicago, IL 60607 wxding@uicdu Using a diffrntial
More informationFACILITY MANAGEMENT SCHEMES FOR SCHOOLS IN THE UK:A STUDY OF VARIATIONS IN SUPPORT SERVICES COSTS AND CAPITAL EFFICIENCY RATIOS
FACILITY MANAGEMENT SCHEMES FOR SCHOOLS IN THE UK:A STUDY OF VARIATIONS IN SUPPORT SERVICES COSTS AND CAPITAL EFFICIENCY RATIOS By Rui PdroPrira Magalhas 1 Sptmbr 2013 A Dissrtation submittd in part fulfilmnt
More informationThe price of liquidity in constant leverage strategies. Marcos Escobar, Andreas Kiechle, Luis Seco and Rudi Zagst
RACSAM Rv. R. Acad. Cin. Sri A. Mat. VO. 103 2, 2009, pp. 373 385 Matmática Aplicada / Applid Mathmatics Th pric of liquidity in constant lvrag stratgis Marcos Escobar, Andras Kichl, uis Sco and Rudi Zagst
More informationGOAL SETTING AND PERSONAL MISSION STATEMENT
Prsonal Dvlopmnt Track Sction 4 GOAL SETTING AND PERSONAL MISSION STATEMENT Ky Points 1 Dfining a Vision 2 Writing a Prsonal Mission Statmnt 3 Writing SMART Goals to Support a Vision and Mission If you
More informationIn the previous two chapters, we clarified what it means for a problem to be decidable or undecidable.
Chaptr 7 Computational Complxity 7.1 Th Class P In th prvious two chaptrs, w clarifid what it mans for a problm to b dcidabl or undcidabl. In principl, if a problm is dcidabl, thn thr is an algorithm (i..,
More informationLogo Design/Development 1on1
Logo Dsign/Dvlopmnt 1on1 If your company is looking to mak an imprssion and grow in th marktplac, you ll nd a logo. Fortunatly, a good graphic dsignr can crat on for you. Whil th pric tags for thos famous
More informationMETHODS FOR HANDLING TIED EVENTS IN THE COX PROPORTIONAL HAZARD MODEL
STUDIA OECONOMICA POSNANIENSIA 204, vol. 2, no. 2 (263 Jadwiga Borucka Warsaw School of Economics, Institut of Statistics and Dmography, Evnt History and Multilvl Analysis Unit jadwiga.borucka@gmail.com
More informationTopic: Introduction to Brayton Cycle
MAE41Enrgy Systm Prsntation Topic: Introduction to Brayton Cycl Prpard by: L Lng Fng 1. Gas Turb powr Plant.. History of Brayton Cycl.. Air standard Brayton Cycl 4. Work and Hat Transfr Brayton cycl.
More informationTheoretical approach to algorithm for metrological comparison of two photothermal methods for measuring of the properties of materials
Rvista Invstigación Cintífica, ol. 4, No. 3, Nuva época, sptimbr dicimbr 8, IN 187 8196 Thortical approach to algorithm for mtrological comparison of two photothrmal mthods for masuring of th proprtis
More informationFree ACA SOLUTION (IRS 1094&1095 Reporting)
Fr ACA SOLUTION (IRS 1094&1095 Rporting) Th Insuranc Exchang (301) 2791062 ACA Srvics Transmit IRS Form 1094 C for mployrs Print & mail IRS Form 1095C to mploys HR Assist 360 will gnrat th 1095 s for
More informationDefining Retirement Success for Defined Contribution Plan Sponsors: Begin with the End in Mind
Dfining Rtirmnt Succss for Dfind Contribution Plan Sponsors: Bgin with th End in Mind David Blanchtt, CFA, CFP, AIFA Had of Rtirmnt Rsarch Morningstar Invstmnt Managmnt david.blanchtt@morningstar.com Nathan
More informationOn the moments of the aggregate discounted claims with dependence introduced by a FGM copula
On th momnts of th aggrgat discountd claims with dpndnc introducd by a FGM copula  Mathiu BARGES Univrsité Lyon, Laboratoir SAF, Univrsité Laval  Hélèn COSSETTE Ecol Actuariat, Univrsité Laval, Québc,
More informationIncomplete 2Port Vector Network Analyzer Calibration Methods
Incomplt Port Vctor Ntwork nalyzr Calibration Mthods. Hnz, N. Tmpon, G. Monastrios, H. ilva 4 RF Mtrology Laboratory Instituto Nacional d Tcnología Industrial (INTI) Bunos irs, rgntina ahnz@inti.gov.ar
More informationAn Broad outline of Redundant Array of Inexpensive Disks Shaifali Shrivastava 1 Department of Computer Science and Engineering AITR, Indore
Intrnational Journal of mrging Tchnology and dvancd nginring Wbsit: www.ijta.com (ISSN 22502459, Volum 2, Issu 4, pril 2012) n road outlin of Rdundant rray of Inxpnsiv isks Shaifali Shrivastava 1 partmnt
More informationSharp bounds for Sándor mean in terms of arithmetic, geometric and harmonic means
Qian t al. Journal of Inqualitis and Applications (015) 015:1 DOI 10.1186/s166001507411 R E S E A R C H Opn Accss Sharp bounds for Sándor man in trms of arithmtic, gomtric and harmonic mans WiMao Qian
More informationIncorporating Statistical Process Control and Statistical Quality Control Techniques into a Quality Assurance Program
Incooating Statistical Pocss Contol and Statistical Quality Contol Tchniqus into a Quality Assuanc Pogam Robyn Sikis U.S. Cnsus Buau Puos Incooat SPC and SQC mthods into quality assuanc ogam Monito and
More information