P&H 4.51 Pipelined Control. 3. Control Hazards. Hazards. Stall => 2 Bubbles/Clocks Time (clock cycles) Control Hazard: Branching 4/15/14
|
|
- Sandra O’Connor’
- 5 years ago
- Views:
Transcription
1 P&H.51 Piplind Contol CS 61C: Gat Ida in Comput Achitctu (Machin Stuctu) Lctu 2: Piplin Paalllim Intucto: Dan Gacia int.c.bkly.du/~c61c! Hazad SituaHon that pvnt tahng th nxt logical intuchon in th nxt clock cycl 1. Stuctual hazad Rquid ouc i buy (.g., oommat tudying) 2. Data hazad Nd to wait fo pviou intuchon to complt it data ad/wit (.g., pai of ock in diffnt load). Contol hazad Dciding on contol achon dpnd on pviou intuchon (.g., how much dtgnt bad on how clan pio load tun out). Contol Hazad Banch dtmin flow of contol Ftching nxt intuchon dpnd on banch outcom Piplin can t alway ftch coct intuchon SHll woking on ID tag of banch BEQ, BNE in MIPS piplin Simpl oluhon phon 1: Stall on vy banch unhl hav nw PC valu Would add 2 bubbl/clock cycl fo vy Banch! (~ 20% of intuchon xcutd) I n t. d bq Int 1 Int 2 Int Int Stall => 2 Bubbl/Clock Tim (clock cycl) I$ Wh do w do th compa fo th banch? Rg D$ Rg Contol Hazad: Banching phmizahon #1: Int pcial banch compaato in Stag 2 A oon a intuchon i dcodd (pcod idnhfi it a a banch), immdiatly mak a dciion and t th nw valu of th PC Bnfit: inc banch i complt in Stag 2, only on unncay intuchon i ftchd, o only on no- op i ndd Sid Not: man that banch a idl in Stag, and 5 Qution: What an fficint way to implmnt th quality compaion? 1
2 I n t. d bq Int 1 Int 2 Int Int n Clock Cycl Stall Tim (clock cycl) I$ Banch compaato movd to Dcod tag. Rg D$ Rg Contol Hazad: Banching phon 2: Pdict outcom of a banch, fix up if gu wong Mut cancl all intuchon in piplin that dpndd on gu that wa wong Thi i calld fluhing th piplin Simplt hadwa if w pdict that all banch a NT takn Why? Contol Hazad: Banching phon #: Rdfin banch ld dfinihon: if w tak th banch, non of th intuchon al th banch gt xcutd by accidnt Nw dfinihon: whth o not w tak th banch, th ingl intuchon immdiatly following th banch gt xcutd (th banch- dlay lot) Dlayd Banch man w alway xcut int a8 banch Thi ophmizahon i ud with MIPS Exampl: Nondlayd v. Dlayd Banch Nondlayd Banch Dlayd Banch o $8, $9, $10! add $1, $2,$! add $1, $2, $! ub $, $5, $6! ub $, $5, $6! bq $1, $, Exit! bq $1, $, Exit! o $8, $9, $10! xo $10, $1, $11! xo $10, $1, $11! Exit: Exit: Contol Hazad: Banching Not on Banch- Dlay Slot Wot- Ca Scnaio: put a no- op in th banch- dlay lot Bm Ca: plac om intuchon pcding th banch in th banch- dlay lot a long a th changd don t affct th logic of pogam R- oding intuchon i common way to pd up pogam Compil uually find uch an intuchon 50% of Hm Jump alo hav a dlay lot Gat IntucHon- Lvl Paalllim (ILP) Dp piplin (5 => 10 => 15 tag) L wok p tag hot clock cycl MulHpl iu upcala Rplicat piplin tag mulhpl piplin Stat mulhpl intuchon p clock cycl CPI < 1, o u IntucHon P Cycl (IPC) E.g., GHz - way mulhpl- iu 16 BIPS, pak CPI = 0.25, pak IPC = But dpndnci duc thi in pachc.10 Paalllim and Advancd Intuction Lvl Paalllim 2
3 MulHpl Iu StaHc mulhpl iu Compil goup intuchon to b iud togth Packag thm into iu lot Compil dtct and avoid hazad Dynamic mulhpl iu CPU xamin intuchon tam and choo intuchon to iu ach cycl Compil can hlp by oding intuchon CPU olv hazad uing advancd tchniqu at unhm Supcala Laundy: Paalll p tag T a k 6 PM AM A B C Tim (light clothing) (dak clothing) (vy dity clothing) D (light clothing) d E (dak clothing) Mo F (vy dity clothing) ouc, HW to match mix of paalll tak? Piplin Dpth and Iu Width Intl Poco ov Tim Micopoco Ya Clock Rat Piplin Stag Iu width Co Pow Piplin Dpth and Iu Width Clock 1000 Pow i MHz W Pntium MHz W Pntium Po MHz W P Willamtt MHz W Piplin Stag Iu width Co P Chapt Pcott Th Poco MHz W StaHc MulHpl Iu Compil goup intuchon into iu packt Goup of intuchon that can b iud on a ingl cycl Dtmind by piplin ouc quid Think of an iu packt a a vy long intuchon Spcifi mulhpl concunt opahon Schduling StaHc MulHpl Iu Compil mut mov om/all hazad Rod intuchon into iu packt No dpndnci within a packt Poibly om dpndnci btwn packt Vai btwn ISA; compil mut know! Pad iu packt with nop if ncay
4 MIPS with StaHc Dual Iu Two- iu packt n /banch intuchon n load/to intuchon 6- bit alignd /banch, thn load/to Pad an unud intuchon with nop Add Intuction typ Piplin Stag n /banch IF ID EX MEM WB n + Load/to IF ID EX MEM WB n + 8 /banch IF ID EX MEM WB n + 12 Load/to IF ID EX MEM WB Hazad in th Dual- Iu MIPS Mo intuchon xcuhng in paalll EX data hazad Fowading avoidd tall with ingl- iu Now can t u ult in load/to in am packt add $t0, $0, $1 load $2, 0($t0) Split into two packt, ffchvly a tall Load- u hazad SHll on cycl u latncy, but now two intuchon Mo aggiv chduling quid n + 16 /banch IF ID EX MEM WB Schdul thi fo dual- iu MIPS Loop: lw $t0, 0($1) # $t0=aay lmnt addu $t0, $t0, $2 # add cala in $2 w $t0, 0($1) # to ult addi $1, $1, # dcmnt point bn $1, $zo, Loop # banch $1!=0 /banch Load/to cycl Loop: 1 Schdul thi fo dual- iu MIPS Loop: lw $t0, 0($1) # $t0=aay lmnt addu $t0, $t0, $2 # add cala in $2 w $t0, 0($1) # to ult addi $1, $1, # dcmnt point bn $1, $zo, Loop # banch $1!=0 /banch Load/to cycl Loop: nop lw $t0, 0($1) Schdul thi fo dual- iu MIPS Loop: lw $t0, 0($1) # $t0=aay lmnt addu $t0, $t0, $2 # add cala in $2 w $t0, 0($1) # to ult addi $1, $1, # dcmnt point bn $1, $zo, Loop # banch $1!=0 /banch Load/to cycl Loop: nop lw $t0, 0($1) 1 Schdul thi fo dual- iu MIPS Loop: lw $t0, 0($1) # $t0=aay lmnt addu $t0, $t0, $2 # add cala in $2 w $t0, 0($1) # to ult addi $1, $1, # dcmnt point bn $1, $zo, Loop # banch $1!=0 /banch Load/to cycl Loop: nop lw $t0, 0($1) 1 addi $1, $1, nop 2 addi $1, $1, nop 2 addu $t0, $t0, $2 nop
5 Schdul thi fo dual- iu MIPS Loop: lw $t0, 0($1) # $t0=aay lmnt addu $t0, $t0, $2 # add cala in $2 w $t0, 0($1) # to ult addi $1, $1, # dcmnt point bn $1, $zo, Loop # banch $1!=0 /banch Load/to cycl Loop: nop lw $t0, 0($1) 1 addi $1, $1, nop 2 addu $t0, $t0, $2 nop n IPC = 5/ = 1.25 (c.f. pak IPC = 2) Loop Unolling Rplicat loop body to xpo mo paalllim Rduc loop- contol ovhad U diffnt git p plicahon Calld git naming Avoid loop- caid anh- dpndnci Sto followd by a load of th am git Aka nam dpndnc Ru of a git nam bn $1, $zo, Loop w $t0, ($1) Loop Unolling Exampl /banch Load/to cycl Loop: addi $1, $1, 16 lw $t0, 0($1) 1 nop lw $t1, 12($1) 2 addu $t0, $t0, $2 lw $t2, 8($1) addu $t1, $t1, $2 lw $t, ($1) addu $t2, $t2, $2 w $t0, 16($1) 5 IPC = 1/8 = 1.75 Clo to 2, but at cot of git and cod iz addu $t, $t, $2 w $t1, 12($1) 6 nop w $t2, 8($1) 7 Dynamic MulHpl Iu Supcala poco CPU dcid whth to iu 0, 1, 2, ach cycl Avoiding tuctual and data hazad Avoid th nd fo compil chduling Though it may Hll hlp Cod manhc nud by th CPU bn $1, $zo, Loop w $t, ($1) 8 Dynamic Piplin Schduling Allow th CPU to xcut intuchon out of od to avoid tall But commit ult to git in od Exampl lw $t0, 20($2) addu $t1, $t0, $t2 ubu $, $, $t lti $t5, $, 20 Can tat ubu whil addu i waihng fo lw Why Do Dynamic Schduling? Why not jut lt th compil chdul cod? Not all tall a pdicabl.g., cach mi Can t alway chdul aound banch Banch outcom i dynamically dtmind Diffnt implmntahon of an ISA hav diffnt latnci and hazad 5
6 SpculaHon Gu what to do with an intuchon Stat opahon a oon a poibl Chck whth gu wa ight If o, complt th opahon If not, oll- back and do th ight thing Common to tahc and dynamic mulhpl iu Exampl Spculat on banch outcom (Banch PdicHon) Roll back if path takn i diffnt Spculat on load Roll back if locahon i updatd Piplin Hazad: Matching ock in lat load T a k d 6 PM AM A B C D E F bubbl A dpnd on D; tall inc fold Hd up; Tim ut- of- d Laundy: Don t Wait T a k 6 PM AM A B bubbl Tim C D d E F A dpnd on D; t conhnu; nd mo ouc to allow out- of- od ut f d Intl All u inc 2001 Micopoco Ya Clock Rat Piplin Stag Iu width ut-of-od/ Spculation Co i MHz 5 1 No 1 5W Pow Pntium MHz 5 2 No 1 10W Pntium Po MHz 10 Y 1 29W P Willamtt MHz 22 Y 1 75W P Pcott MHz 1 Y 1 10W Co MHz 1 Y 2 75W Do MulHpl Iu Wok? Th BIG Pictu Y, but not a much a w d lik Pogam hav al dpndnci that limit ILP Som dpndnci a had to liminat.g., point aliaing Som paalllim i had to xpo Limitd window iz duing intuchon iu Mmoy dlay and limitd bandwidth Had to kp piplin full SpculaHon can hlp if don wll And in Concluion.. Piplining i an impotant fom of ILP Challng i (a?) hazad Fowading hlp w/many data hazad Dlayd banch hlp with contol hazad in 5 tag piplin Load dlay lot / intlock ncay Mo aggiv pfomanc: Long piplin Supcala ut- of- od xcuhon SpculaHon 6
CS 61C: Great Ideas in Computer Architecture (Machine Structures) Instruc(on Level Parallelism: Mul(ple Instruc(on Issue
CS 61C: Gat Ida in Comput Achitctu (Machin Stuctu) Intuc(on Lvl Paalllim: Mul(pl Intuc(on Iu Intucto: Randy H. Katz David A. PaGon hgp://int.c.bkly.du/~c61c/fa10 1 Paalll Rqut Aignd to comput.g., Sach
More informationInstruction Execution
MIPS Piplining Cpt280 D Cuti Nlon Intuction Excution C intuction: x = a + b; Ambly intuction: a a,b,x Stp 1: Stp 2: Stp 3: Stp : Stp 5: Stp 6: Ftch th intuction Dtmin it i an a intuction Ftch th ata a
More informationCOMP303 Computer Architecture Lecture 11. An Overview of Pipelining
COMP303 Compute Achitectue Lectue 11 An Oveview of Pipelining Pipelining Pipelining povides a method fo executing multiple instuctions at the same time. Laundy Example: Ann, Bian, Cathy, Dave each have
More informationGreat Idea #4: Parallelism. CS 61C: Great Ideas in Computer Architecture. Pipelining Hazards. Agenda. Review of Last Lecture
CS 61C: Gat das i Comput Achitctu Pipliig Hazads Gu Lctu: Jui Hsia 4/12/2013 Spig 2013 Lctu #31 1 Gat da #4: Paalllism Softwa Paalll Rqus Assigd to comput.g. sach Gacia Paalll Thads Assigd to co.g. lookup,
More informationAgenda. Single Cycle Performance Assume >me for ac>ons are 100ps for register read or write; 200ps for other events. Review: Single- cycle Processor
Agna CS 61C: Gat Ia in Comput Achitctu (Machin Stuctu) Intuc>on Lvl Paalllim Intucto: Rany H. Katz Davi A. PaJon hjp://int.c.bkly.u/~c61c/fa1 Rviw Piplin Excu>on Piplin Datapath Aminitivia Piplin Haza
More informationRevision MIPS Pipelined Architecture
Rviion MIPS Piplin Achitctu D. Eng. Am T. Abl-Hami ELECT 1002 Sytm-n-a-Chip Dign Sping 2009 MIPS: A "Typical" RISC ISA 32-bit fix fomat intuction (3 fomat) 32 32-bit GPR (R0 contain zo, DP tak pai) 3-a,
More informationCOMPSCI 230 Discrete Math Trees March 21, / 22
COMPSCI 230 Dict Math Mach 21, 2017 COMPSCI 230 Dict Math Mach 21, 2017 1 / 22 Ovviw 1 A Simpl Splling Chck Nomnclatu 2 aval Od Dpth-it aval Od Badth-it aval Od COMPSCI 230 Dict Math Mach 21, 2017 2 /
More informationIn Review: A Single Cycle Datapath We have everything! Now we just need to know how to BUILD CONTROL
S6 L2 PU ign: ontol II n Piplining I () int.c.bly.u/~c6c S6 : Mchin Stuctu Lctu 2 PU ign: ontol II & Piplining I Noh Johnon 2-7-26 In Rviw: Singl ycl tpth W hv vything! Now w jut n to now how to UIL NRL
More informationPhysics 111. Lecture 38 (Walker: ) Phase Change Latent Heat. May 6, The Three Basic Phases of Matter. Solid Liquid Gas
Physics 111 Lctu 38 (Walk: 17.4-5) Phas Chang May 6, 2009 Lctu 38 1/26 Th Th Basic Phass of Matt Solid Liquid Gas Squnc of incasing molcul motion (and ngy) Lctu 38 2/26 If a liquid is put into a sald contain
More informationSTRIPLINES. A stripline is a planar type transmission line which is well suited for microwave integrated circuitry and photolithographic fabrication.
STIPLINES A tiplin i a plana typ tanmiion lin hih i ll uitd fo mioav intgatd iuity and photolithogaphi faiation. It i uually ontutd y thing th nt onduto of idth, on a utat of thikn and thn oving ith anoth
More informationPartial Fraction Expansion
Paial Facion Expanion Whn ying o find h inv Laplac anfom o inv z anfom i i hlpfl o b abl o bak a complicad aio of wo polynomial ino fom ha a on h Laplac Tanfom o z anfom abl. W will illa h ing Laplac anfom.
More informationEXAMPLES 4/12/2018. The MIPS Pipeline. Hazard Summary. Show the pipeline diagram. Show the pipeline diagram. Pipeline Datapath and Control
The MIPS Pipeline CSCI206 - Computer Organization & Programming Pipeline Datapath and Control zybook: 11.6 Developed and maintained by the Bucknell University Computer Science Department - 2017 Hazard
More informationWhat Makes Production System Design Hard?
What Maks Poduction Systm Dsign Had? 1. Things not always wh you want thm whn you want thm wh tanspot and location logistics whn invntoy schduling and poduction planning 2. Rsoucs a lumpy minimum ffctiv
More informationChapter 6 Folding. Folding
Chaptr 6 Folding Wintr 1 Mokhtar Abolaz Folding Th folding transformation is usd to systmatically dtrmin th control circuits in DSP architctur whr multipl algorithm oprations ar tim-multiplxd to a singl
More informationPipelining. Traditional Execution. CS 365 Lecture 12 Prof. Yih Huang. add ld beq CS CS 365 2
Pipelining CS 365 Lecture 12 Prof. Yih Huang CS 365 1 Traditional Execution 1 2 3 4 1 2 3 4 5 1 2 3 add ld beq CS 365 2 1 Pipelined Execution 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
More informationLecture 2: Frequency domain analysis, Phasors. Announcements
EECS 5 SPRING 24, ctu ctu 2: Fquncy domain analyi, Phao EECS 5 Fall 24, ctu 2 Announcmnt Th cou wb it i http://int.c.bkly.du/~5 Today dicuion ction will mt Th Wdnday dicuion ction will mo to Tuday, 5:-6:,
More informationCDS 101/110: Lecture 7.1 Loop Analysis of Feedback Systems
CDS 11/11: Lctu 7.1 Loop Analysis of Fdback Systms Novmb 7 216 Goals: Intoduc concpt of loop analysis Show how to comput closd loop stability fom opn loop poptis Dscib th Nyquist stability cition fo stability
More informationCS 61C: Great Ideas in Computer Architecture Control and Pipelining, Part II. Anything can be represented as a number, i.e., data or instrucwons
CS 61C: Ga a i Compu Achicu Cool a Pipliig, Pa 10/29/12 uco: K Aaovic, Ray H. Kaz hdp://i.c.bkly.u/~c61c/fa12 Fall 2012 - - Lcu #28 1 Paalll Rqu Aig o compu.g., Sach Kaz Paalll Tha Aig o co.g., Lookup,
More informationCSCI-564 Advanced Computer Architecture
CSCI-564 Advanced Computer Architecture Lecture 8: Handling Exceptions and Interrupts / Superscalar Bo Wu Colorado School of Mines Branch Delay Slots (expose control hazard to software) Change the ISA
More informationExtinction Ratio and Power Penalty
Application Not: HFAN-.. Rv.; 4/8 Extinction Ratio and ow nalty AVALABLE Backgound Extinction atio is an impotant paamt includd in th spcifications of most fib-optic tanscivs. h pupos of this application
More informationICS 233 Computer Architecture & Assembly Language
ICS 233 Computer Architecture & Assembly Language Assignment 6 Solution 1. Identify all of the RAW data dependencies in the following code. Which dependencies are data hazards that will be resolved by
More informationMOS transistors (in subthreshold)
MOS tanito (in ubthhold) Hitoy o th Tanito Th tm tanito i a gnic nam o a olid-tat dvic with 3 o mo tminal. Th ild-ct tanito tuctu wa it dcibd in a patnt by J. Lilinld in th 193! t took about 4 ya bo MOS
More informationE F. and H v. or A r and F r are dual of each other.
A Duality Thom: Consid th following quations as an xampl = A = F μ ε H A E A = jωa j ωμε A + β A = μ J μ A x y, z = J, y, z 4π E F ( A = jω F j ( F j β H F ωμε F + β F = ε M jβ ε F x, y, z = M, y, z 4π
More informationGRAVITATION 4) R. max. 2 ..(1) ...(2)
GAVITATION PVIOUS AMCT QUSTIONS NGINING. A body is pojctd vtically upwads fom th sufac of th ath with a vlocity qual to half th scap vlocity. If is th adius of th ath, maximum hight attaind by th body
More informationLecture 3.2: Cosets. Matthew Macauley. Department of Mathematical Sciences Clemson University
Lctu 3.2: Costs Matthw Macauly Dpatmnt o Mathmatical Scincs Clmson Univsity http://www.math.clmson.du/~macaul/ Math 4120, Modn Algba M. Macauly (Clmson) Lctu 3.2: Costs Math 4120, Modn Algba 1 / 11 Ovviw
More informationHelping you learn to save. Pigby s tips and tricks
Hlpg yu lan t av Pigby tip and tick Hlpg vy littl av Pigby ha bn tachg hi find all abut ny and hw t av f what ty want. Tuffl i avg f a nw tappy bubbl d and Pi can t wait t b abl t buy nw il pat. Pigby
More informationCDS 101: Lecture 7.1 Loop Analysis of Feedback Systems
CDS : Lct 7. Loop Analsis of Fback Sstms Richa M. Ma Goals: Show how to compt clos loop stabilit fom opn loop poptis Dscib th Nqist stabilit cition fo stabilit of fback sstms Dfin gain an phas magin an
More information(1) Then we could wave our hands over this and it would become:
MAT* K285 Spring 28 Anthony Bnoit 4/17/28 Wk 12: Laplac Tranform Rading: Kohlr & Johnon, Chaptr 5 to p. 35 HW: 5.1: 3, 7, 1*, 19 5.2: 1, 5*, 13*, 19, 45* 5.3: 1, 11*, 19 * Pla writ-up th problm natly and
More informationMid Year Examination F.4 Mathematics Module 1 (Calculus & Statistics) Suggested Solutions
Mid Ya Eamination 3 F. Matmatics Modul (Calculus & Statistics) Suggstd Solutions Ma pp-: 3 maks - Ma pp- fo ac qustion: mak. - Sam typ of pp- would not b countd twic fom wol pap. - In any cas, no pp maks
More informationComputer Architecture ELEC2401 & ELEC3441
Last Time Pipeline Hazard Computer Architecture ELEC2401 & ELEC3441 Lecture 8 Pipelining (3) Dr. Hayden Kwok-Hay So Department of Electrical and Electronic Engineering Structural Hazard Hazard Control
More informationLecture 37 (Schrödinger Equation) Physics Spring 2018 Douglas Fields
Lctur 37 (Schrödingr Equation) Physics 6-01 Spring 018 Douglas Filds Rducd Mass OK, so th Bohr modl of th atom givs nrgy lvls: E n 1 k m n 4 But, this has on problm it was dvlopd assuming th acclration
More informationThe angle between L and the z-axis is found from
Poblm 6 This is not a ifficult poblm but it is a al pain to tansf it fom pap into Mathca I won't giv it to you on th quiz, but know how to o it fo th xam Poblm 6 S Figu 6 Th magnitu of L is L an th z-componnt
More informationTEST 1 REVIEW. Lectures 1-5
TEST 1 REVIEW Lectures 1-5 REVIEW Test 1 will cover lectures 1-5. There are 10 questions in total with the last being a bonus question. The questions take the form of short answers (where you are expected
More informationSolid state physics. Lecture 3: chemical bonding. Prof. Dr. U. Pietsch
Solid stat physics Lctu 3: chmical bonding Pof. D. U. Pitsch Elcton chag dnsity distibution fom -ay diffaction data F kp ik dk h k l i Fi H p H; H hkl V a h k l Elctonic chag dnsity of silicon Valnc chag
More informationMor Tutorial at www.dumblittldoctor.com Work th problms without a calculator, but us a calculator to chck rsults. And try diffrntiating your answrs in part III as a usful chck. I. Applications of Intgration
More informationEE 361L Fall 2010 Pipelined MIPS L0 (PMIPS L0) and Pipelined MIPS L (PMIPS L)
EE 361L Fall 2010 iplind S L0 (S L0) and iplind S L (S L) Last updatd: Novmbr 8, 2010 1. ntroduction S L0 and S L ar piplind vrsions of SL (for S Lit). Appndix A has a dscription of th SL procssor. S L0
More informationu x v x dx u x v x v x u x dx d u x v x u x v x dx u x v x dx Integration by Parts Formula
7. Intgration by Parts Each drivativ formula givs ris to a corrsponding intgral formula, as w v sn many tims. Th drivativ product rul yilds a vry usful intgration tchniqu calld intgration by parts. Starting
More informationCHAPTER 5 CIRCULAR MOTION
CHAPTER 5 CIRCULAR MOTION and GRAVITATION 5.1 CENTRIPETAL FORCE It is known that if a paticl mos with constant spd in a cicula path of adius, it acquis a cntiptal acclation du to th chang in th diction
More informationGRAVITATION. (d) If a spring balance having frequency f is taken on moon (having g = g / 6) it will have a frequency of (a) 6f (b) f / 6
GVITTION 1. Two satllits and o ound a plant P in cicula obits havin adii 4 and spctivly. If th spd of th satllit is V, th spd of th satllit will b 1 V 6 V 4V V. Th scap vlocity on th sufac of th ath is
More information(, ) which is a positively sloping curve showing (Y,r) for which the money market is in equilibrium. The P = (1.4)
ots lctu Th IS/LM modl fo an opn conomy is basd on a fixd pic lvl (vy sticky pics) and consists of a goods makt and a mony makt. Th goods makt is Y C+ I + G+ X εq (.) E SEK wh ε = is th al xchang at, E
More informationCBSE-XII-2013 EXAMINATION (MATHEMATICS) The value of determinant of skew symmetric matrix of odd order is always equal to zero.
CBSE-XII- EXAMINATION (MATHEMATICS) Cod : 6/ Gnal Instuctions : (i) All qustions a compulso. (ii) Th qustion pap consists of 9 qustions dividd into th sctions A, B and C. Sction A compiss of qustions of
More informationPeriod vs. Length of a Pendulum
Gaphcal Mtho n Phc Gaph Intptaton an Lnazaton Pat 1: Gaphng Tchnqu In Phc w u a vat of tool nclung wo, quaton, an gaph to mak mol of th moton of objct an th ntacton btwn objct n a tm. Gaph a on of th bt
More informationEstimation and Confidence Intervals: Additional Topics
Chapte 8 Etimation and Confidence Inteval: Additional Topic Thi chapte imply follow the method in Chapte 7 fo foming confidence inteval The text i a bit dioganized hee o hopefully we can implify Etimation:
More informationIntegration by Parts
Intgration by Parts Intgration by parts is a tchniqu primarily for valuating intgrals whos intgrand is th product of two functions whr substitution dosn t work. For ampl, sin d or d. Th rul is: u ( ) v'(
More informationCS:APP Chapter 4 Computer Architecture Pipelined Implementation
CS:APP Chaptr 4 Computr Architctur Piplind Implmntation CS:APP2 Ovrviw Gnral Principls of Piplinin n Goal n Difficultis Cratin a Piplind Y86 Procssor n arranin SEQ n Insrtin piplin ristrs n Problms with
More informationThe Language of SOCIAL MEDIA. Christine Dugan
Th Languag f SOCIAL MEDIA Christin Dugan Tabl f Cntnts Gt th Wrd Out...4 A Nw Kind f Languag...6 Scial Mdia Talk...12 Cnncting with Othrs...28 Changing th Dictinary...36 Glssary...42 Indx...44 Chck It
More informationORBITAL TO GEOCENTRIC EQUATORIAL COORDINATE SYSTEM TRANSFORMATION. x y z. x y z GEOCENTRIC EQUTORIAL TO ROTATING COORDINATE SYSTEM TRANSFORMATION
ORITL TO GEOCENTRIC EQUTORIL COORDINTE SYSTEM TRNSFORMTION z i i i = (coωcoω in Ωcoiinω) (in Ωcoω + coωcoiinω) iniinω ( coωinω in Ωcoi coω) ( in Ωinω + coωcoicoω) in icoω in Ωini coωini coi z o o o GEOCENTRIC
More informationLecture 3, Performance
Lecture 3, Performance Repeating some definitions: CPI Clocks Per Instruction MHz megahertz, millions of cycles per second MIPS Millions of Instructions Per Second = MHz / CPI MOPS Millions of Operations
More informationHydrogen Atom and One Electron Ions
Hydrogn Atom and On Elctron Ions Th Schrödingr quation for this two-body problm starts out th sam as th gnral two-body Schrödingr quation. First w sparat out th motion of th cntr of mass. Th intrnal potntial
More informationph People Grade Level: basic Duration: minutes Setting: classroom or field site
ph Popl Adaptd from: Whr Ar th Frogs? in Projct WET: Curriculum & Activity Guid. Bozman: Th Watrcours and th Council for Environmntal Education, 1995. ph Grad Lvl: basic Duration: 10 15 minuts Stting:
More information(( ) ( ) ( ) ( ) ( 1 2 ( ) ( ) ( ) ( ) Two Stage Cluster Sampling and Random Effects Ed Stanek
Two ag ampling and andom ffct 8- Two Stag Clu Sampling and Random Effct Ed Stank FTE POPULATO Fam Labl Expctd Rpon Rpon otation and tminology Expctd Rpon: y = and fo ach ; t = Rpon: k = y + Wk k = indx
More informationMichela Taufer CS:APP
Michla Taufr CS:APP Powrpoint Lctur Nots for Computr Systms: A Prorammr's Prspctiv,. Bryant and D. O'Hallaron, Prntic Hall, 2003 Ovrviw 2 CISC 360 Faʼ08 al-world Piplins: Car Washs Squntial Paralll Piplind
More informationPhysics 202, Lecture 5. Today s Topics. Announcements: Homework #3 on WebAssign by tonight Due (with Homework #2) on 9/24, 10 PM
Physics 0, Lctu 5 Today s Topics nnouncmnts: Homwok #3 on Wbssign by tonight Du (with Homwok #) on 9/4, 10 PM Rviw: (Ch. 5Pat I) Elctic Potntial Engy, Elctic Potntial Elctic Potntial (Ch. 5Pat II) Elctic
More informationFall 2011 Prof. Hyesoon Kim
Fall 2011 Prof. Hyesoon Kim Add: 2 cycles FE_stage add r1, r2, r3 FE L ID L EX L MEM L WB L add add sub r4, r1, r3 sub sub add add mul r5, r2, r3 mul sub sub add add mul sub sub add add mul sub sub add
More information3 2x. 3x 2. Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB
Math B Intgration Rviw (Solutions) Do ths intgrals. Solutions ar postd at th wbsit blow. If you hav troubl with thm, sk hlp immdiatly! () 8 d () 5 d () d () sin d (5) d (6) cos d (7) d www.clas.ucsb.du/staff/vinc
More informationComputing and Communications -- Network Coding
89 90 98 00 Computing and Communications -- Ntwork Coding Dr. Zhiyong Chn Institut of Wirlss Communications Tchnology Shanghai Jiao Tong Univrsity China Lctur 5- Nov. 05 0 Classical Information Thory Sourc
More informationAakash. For Class XII Studying / Passed Students. Physics, Chemistry & Mathematics
Aakash A UNIQUE PPRTUNITY T HELP YU FULFIL YUR DREAMS Fo Class XII Studying / Passd Studnts Physics, Chmisty & Mathmatics Rgistd ffic: Aakash Tow, 8, Pusa Road, Nw Dlhi-0005. Ph.: (0) 4763456 Fax: (0)
More informationRandom Access Techniques: ALOHA (cont.)
Random Accss Tchniqus: ALOHA (cont.) 1 Exampl [ Aloha avoiding collision ] A pur ALOHA ntwork transmits a 200-bit fram on a shard channl Of 200 kbps at tim. What is th rquirmnt to mak this fram collision
More information[ ] 1+ lim G( s) 1+ s + s G s s G s Kacc SYSTEM PERFORMANCE. Since. Lecture 10: Steady-state Errors. Steady-state Errors. Then
SYSTEM PERFORMANCE Lctur 0: Stady-tat Error Stady-tat Error Lctur 0: Stady-tat Error Dr.alyana Vluvolu Stady-tat rror can b found by applying th final valu thorm and i givn by lim ( t) lim E ( ) t 0 providd
More informationLecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t
Cla ot fo EE6318/Phy 6383 Spg 001 Th doumt fo tutoal u oly ad may ot b opd o dtbutd outd of EE6318/Phy 6383 tu 7 Dffuo Ou flud quato that w dvlopd bfo a: f ( )+ v v m + v v M m v f P+ q E+ v B 13 1 4 34
More informationOutcomes. Spiral 1 / Unit 2. Boolean Algebra BOOLEAN ALGEBRA INTRO. Basic Boolean Algebra Logic Functions Decoders Multiplexers
-2. -2.2 piral / Unit 2 Basic Boolean Algebra Logic Functions Decoders Multipleers Mark Redekopp Outcomes I know the difference between combinational and sequential logic and can name eamples of each.
More informationCS152 Computer Architecture and Engineering Lecture 12. Introduction to Pipelining
CS152 Comput chitctu a Egiig Lctu 12 Itouctio to Pipliig ctob 11, 1999 Joh Kubiatowicz (http.c.bkly.u/~kubito) lctu li: http://www-it.c.bkly.u/~c152/ Rcap: Micopogammig Micopogammig i a covit mtho fo implmtig
More informationHydrogen atom. Energy levels and wave functions Orbital momentum, electron spin and nuclear spin Fine and hyperfine interaction Hydrogen orbitals
Hydogn atom Engy lvls and wav functions Obital momntum, lcton spin and nucla spin Fin and hypfin intaction Hydogn obitals Hydogn atom A finmnt of th Rydbg constant: R ~ 109 737.3156841 cm -1 A hydogn mas
More informationLaplace Transformation
Univerity of Technology Electromechanical Department Energy Branch Advance Mathematic Laplace Tranformation nd Cla Lecture 6 Page of 7 Laplace Tranformation Definition Suppoe that f(t) i a piecewie continuou
More informationLecture 3, Performance
Repeating some definitions: Lecture 3, Performance CPI MHz MIPS MOPS Clocks Per Instruction megahertz, millions of cycles per second Millions of Instructions Per Second = MHz / CPI Millions of Operations
More informationEECE 301 Signals & Systems Prof. Mark Fowler
EECE 301 Signals & Systms Prof. Mark Fowlr ot St #21 D-T Signals: Rlation btwn DFT, DTFT, & CTFT 1/16 W can us th DFT to implmnt numrical FT procssing This nabls us to numrically analyz a signal to find
More informationWhere k is either given or determined from the data and c is an arbitrary constant.
Exponntial growth and dcay applications W wish to solv an quation that has a drivativ. dy ky k > dx This quation says that th rat of chang of th function is proportional to th function. Th solution is
More informationSTATISTICAL MECHANICS OF DIATOMIC GASES
Pof. D. I. ass Phys54 7 -Ma-8 Diatomic_Gas (Ashly H. Cat chapt 5) SAISICAL MECHAICS OF DIAOMIC GASES - Fo monatomic gas whos molculs hav th dgs of fdom of tanslatoy motion th intnal u 3 ngy and th spcific
More informationENEE350 Lecture Notes-Weeks 14 and 15
Pipelining & Amdahl s Law ENEE350 Lecture Notes-Weeks 14 and 15 Pipelining is a method of processing in which a problem is divided into a number of sub problems and solved and the solu8ons of the sub problems
More informationSection 25 Describing Rotational Motion
Section 25 Decibing Rotational Motion What do object do and wh do the do it? We have a ve thoough eplanation in tem of kinematic, foce, eneg and momentum. Thi include Newton thee law of motion and two
More informationHow!do!humans!combine!sounds!into!an! infinite!number!of!utterances? How!do!they!use!these!utterances!!to! communicate!and!express!meaning?
Linguistics How!o!humans!combin!s!into!an! H h bi i infinit!numb!of!uttancs? Supcomputing an Linguistics Kis Hyln Univsity of Luvn RU Quantitativ Lxicology an Vaiational Linguistics Linguistics Linguistics
More information8 - GRAVITATION Page 1
8 GAVITATION Pag 1 Intoduction Ptolmy, in scond cntuy, gav gocntic thoy of plantay motion in which th Eath is considd stationay at th cnt of th univs and all th stas and th plants including th Sun volving
More informationMicroprocessor Power Analysis by Labeled Simulation
Microprocessor Power Analysis by Labeled Simulation Cheng-Ta Hsieh, Kevin Chen and Massoud Pedram University of Southern California Dept. of EE-Systems Los Angeles CA 989 Outline! Introduction! Problem
More information/ : Computer Architecture and Design
16.482 / 16.561: Computer Architecture and Design Summer 2015 Homework #5 Solution 1. Dynamic scheduling (30 points) Given the loop below: DADDI R3, R0, #4 outer: DADDI R2, R1, #32 inner: L.D F0, 0(R1)
More informationChapter 19 Webassign Help Problems
Chapte 9 Webaign Help Poblem 4 5 6 7 8 9 0 Poblem 4: The pictue fo thi poblem i a bit mileading. They eally jut give you the pictue fo Pat b. So let fix that. Hee i the pictue fo Pat (a): Pat (a) imply
More informationPartial Derivatives: Suppose that z = f(x, y) is a function of two variables.
Chaptr Functions o Two Variabls Applid Calculus 61 Sction : Calculus o Functions o Two Variabls Now that ou hav som amiliarit with unctions o two variabls it s tim to start appling calculus to hlp us solv
More informationDealing with quantitative data and problem solving life is a story problem! Attacking Quantitative Problems
Daling with quantitati data and problm soling lif is a story problm! A larg portion of scinc inols quantitati data that has both alu and units. Units can sa your butt! Nd handl on mtric prfixs Dimnsional
More informationCOHORT MBA. Exponential function. MATH review (part2) by Lucian Mitroiu. The LOG and EXP functions. Properties: e e. lim.
MTH rviw part b Lucian Mitroiu Th LOG and EXP functions Th ponntial function p : R, dfind as Proprtis: lim > lim p Eponntial function Y 8 6 - -8-6 - - X Th natural logarithm function ln in US- log: function
More informationShor s Algorithm. Motivation. Why build a classical computer? Why build a quantum computer? Quantum Algorithms. Overview. Shor s factoring algorithm
Motivation Sho s Algoith It appas that th univs in which w liv is govnd by quantu chanics Quantu infoation thoy givs us a nw avnu to study & tst quantu chanics Why do w want to build a quantu coput? Pt
More informationSimple Instruction-Pipelining. Pipelined Harvard Datapath
6.823, L8--1 Simple ruction-pipelining Laboratory for Computer Science M.I.T. http://www.csg.lcs.mit.edu/6.823 Pipelined Harvard path 6.823, L8--2. I fetch decode & eg-fetch execute memory Clock period
More informationCS152 Computer Architecture and Engineering Lecture 12. Introduction to Pipelining
CS152 Comput chitctu a Egiig Lctu 12 Itouctio to Pipliig a 10, 1999 Joh Kubiatowicz (http.c.bkly.u/~kubito) lctu li: http://www-it.c.bkly.u/~c152/ Rcap: icopogammig icopogammig i a covit mtho fo implmtig
More informationThe tight-binding method
Th tight-idig thod Wa ottial aoach: tat lcto a a ga of aly f coductio lcto. ow aout iulato? ow aout d-lcto? d Tight-idig thod: gad a olid a a collctio of wa itactig utal ato. Ovla of atoic wav fuctio i
More informationCMP N 301 Computer Architecture. Appendix C
CMP N 301 Computer Architecture Appendix C Outline Introduction Pipelining Hazards Pipelining Implementation Exception Handling Advanced Issues (Dynamic Scheduling, Out of order Issue, Superscalar, etc)
More informationUser s Guide. Electronic Crossover Network. XM66 Variable Frequency. XM9 24 db/octave. XM16 48 db/octave. XM44 24/48 db/octave. XM26 24 db/octave Tube
U Guid Elctnic Cv Ntwk XM66 Vaiabl Fquncy XM9 24 db/ctav XM16 48 db/ctav XM44 24/48 db/ctav XM26 24 db/ctav Tub XM46 24 db/ctav Paiv Lin Lvl XM126 24 db/ctav Tub Machand Elctnic Inc. Rcht, NY (585) 423
More informationSolutions to Supplementary Problems
Solution to Supplmntay Poblm Chapt Solution. Fomula (.4): g d G + g : E ping th void atio: G d 2.7 9.8 0.56 (56%) 7 mg Fomula (.6): S Fomula (.40): g d E ping at contnt: S m G 0.56 0.5 0. (%) 2.7 + m E
More informationAddition of angular momentum
Addition of angular momntum April, 0 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat th
More informationWant to go a-courtin but not allowed to dance? Well then, try this once popular party game as a way to get to know each other.
fiddl, piano party gam, circl danc Want to go a-courtin but not allowd to danc? Wll thn, try this onc popular party gam as a way to gt to know ach othr. Ky G, first not do(g) Squar/Circl Danc d d d d d
More informationEEO 401 Digital Signal Processing Prof. Mark Fowler
EEO 401 Digital Signal Procssing Prof. Mark Fowlr Dtails of th ot St #19 Rading Assignmnt: Sct. 7.1.2, 7.1.3, & 7.2 of Proakis & Manolakis Dfinition of th So Givn signal data points x[n] for n = 0,, -1
More informationCENG 3420 Computer Organization and Design. Lecture 07: Pipeline Review. Bei Yu
CENG 3420 Compu gaizaio a Dig Lcu 07: Pipli Rviw Bi Yu CEG3420 L07.1 Spig 2016 Rviw: Sigl Cycl Diavaag & Avaag q U h clock cycl ifficily h clock cycl mu b im o accommoa h low i pcially poblmaic fo mo complx
More informationMeasurement & Performance
Measurement & Performance Timers Performance measures Time-based metrics Rate-based metrics Benchmarking Amdahl s law Topics 2 Page The Nature of Time real (i.e. wall clock) time = User Time: time spent
More informationLast Lecture Summary ADALINE
Lat Lctu Summa ADALIN Analtical Solution Gadint Bad Laning Batch Laning Incmntal Laning Laning Rat Adatation Statitical Inttation Aland Bnadino al@i.it.utl.t Machin Laning 9/ ADALIN N l l l T Aland Bnadino
More informationMeasurement & Performance
Measurement & Performance Topics Timers Performance measures Time-based metrics Rate-based metrics Benchmarking Amdahl s law 2 The Nature of Time real (i.e. wall clock) time = User Time: time spent executing
More informationProject Two RISC Processor Implementation ECE 485
Project Two RISC Processor Implementation ECE 485 Chenqi Bao Peter Chinetti November 6, 2013 Instructor: Professor Borkar 1 Statement of Problem This project requires the design and test of a RISC processor
More informationECE 361 Computer Architecture Lecture 13: Designing a Pipeline Processor
ECE 361 Compu Achicu Lcu 13: Digig a Pipli Poco 361 haza.1 Rviw: A Pipli Daapah Clk fch Rg/Dc Exc Mm W RgW Exp p Bach PC 1 0 PC+4 A Ui F/D Rgi PC+4 mm16 R Ra Rb R RFil R Rw Di R D/Ex Rgi 0 1 PC+4 mm16
More informationECE 3401 Lecture 23. Pipeline Design. State Table for 2-Cycle Instructions. Control Unit. ISA: Instruction Specifications (for reference)
ECE 3401 Lecture 23 Pipeline Design Control State Register Combinational Control Logic New/ Modified Control Word ISA: Instruction Specifications (for reference) P C P C + 1 I N F I R M [ P C ] E X 0 PC
More informationpriority queue ADT heaps 1
COMP 250 Lctur 23 priority quu ADT haps 1 Nov. 1/2, 2017 1 Priority Quu Li a quu, but now w hav a mor gnral dinition o which lmnt to rmov nxt, namly th on with highst priority..g. hospital mrgncy room
More informationMon. Tues. 6.2 Field of a Magnetized Object 6.3, 6.4 Auxiliary Field & Linear Media HW9
Fi. on. Tus. 6. Fild of a agntid Ojct 6.3, 6.4 uxiliay Fild & Lina dia HW9 Dipol t fo a loop Osvation location x y agntic Dipol ont Ia... ) ( 4 o I I... ) ( 4 I o... sin 4 I o Sa diction as cunt B 3 3
More informationStructural Hazard #1: Single Memory (1/2)! Structural Hazard #1: Single Memory (2/2)! Review! Pipelining is a BIG idea! Optimal Pipeline! !
S61 L21 PU ig: Pipliig (1)! i.c.bly.u/~c61c S61 : Mchi Sucu Lcu 21 PU ig: Pipliig 2010-07-27!!!uco Pul Pc! G GE FLL SESN KES NW! Foobll Su So ic ow o-l o icomig Fhm, f, Gu u (hʼ m!). h i o log xcu fo yo
More informationcycle that does not cross any edges (including its own), then it has at least
W prov th following thorm: Thorm If a K n is drawn in th plan in such a way that it has a hamiltonian cycl that dos not cross any dgs (including its own, thn it has at last n ( 4 48 π + O(n crossings Th
More informationExam 2 Solutions. Jonathan Turner 4/2/2012. CS 542 Advanced Data Structures and Algorithms
CS 542 Avn Dt Stutu n Alotm Exm 2 Soluton Jontn Tun 4/2/202. (5 ont) Con n oton on t tton t tutu n w t n t 2 no. Wt t mllt num o no tt t tton t tutu oul ontn. Exln you nw. Sn n mut n you o u t n t, t n
More information