CS553 Lecture Reuse Optimization: Common SubExpr Elim 3
|
|
- Marvin Doyle
- 5 years ago
- Views:
Transcription
1 Reuse Optimizatio Last time Value umberig Today Commo subexpressio elimiatio (CSE) CS553 Lecture Reuse Optimizatio: Commo SubExpr Elim 2 Commo Subexpressio Elimiatio Fid commo subexpressios whose rage spas the same basic blocks ad elimiate uecessary re-evaluatios Leverage available expressios Recall available expressios A expressio (e.g., x+y) is available at ode if every path from the etry ode to evaluates x+y, ad there are o defiitios of x or y after the last evaluatio alog that path Strategy If a expressio is available at a poit where it is evaluated, it eed ot be recomputed CS553 Lecture Reuse Optimizatio: Commo SubExpr Elim 3
2 CSE 2 i := i + b := 4 * i i := j a := 4 * i 3 c := 4 * i 2 i := i + t := 4 * i b := t i := j t := 4 * i a := t 3 c := t Will value umberig fid this redudacy? No; value umberig operates o values CSE operates o expressios CS553 Lecture Reuse Optimizatio: Commo SubExpr Elim 4 Aother CSE Before CSE c := a + b d := m & e := b + d f := a + b g := -b h := b + a a := j + a k := m & j := b + d a := -b if m & goto L2 istructios 2 variables 9 biary operators After CSE t := a + b c := t t2 := m & d := t2 t3 := b + d e := t3 f := t g := -b h := t a := j + a k := t2 j := t3 a := -b if t2 goto L2 4 istructios 5 variables 4 biary operators CS553 Lecture Reuse Optimizatio: Commo SubExpr Elim 5 2
3 CSE Approach Notatio Avail(b) is the set of expressios available at block b Ge(b) is the set of expressios geerated ad ot killed at block b If we use e ad e Avail(b) Allocate a ew ame Search backward from b (i CFG) to fid statemets (oe for each path) that most recetly geerate e Isert copy to after geerators Problems Backward search for each use is expesive Geerates uique ame for each use ames Uses > Avail Each geerator may have may copies t := a t2 := a e := t b + c f := t2 b + c CS553 Lecture Reuse Optimizatio: Commo SubExpr Elim 6 CSE Approach 2 Reduce umber of copies by assigig a uique ame to each uique expressio e Name[e] = uassiged if we use e ad e Avail(b) if Name[e]=uassiged, allocate ew ame ad Name[e] = else = Name[e] I a subsequet traversal of block b, if e Ge(b) ad Name[e] uassiged, the isert a copy to Name[e] after the geerator of e Problem May still isert uecessary copies Requires two passes over the code t := a CS553 Lecture Reuse Optimizatio: Commo SubExpr Elim 7 3
4 CSE Approach 3 Do t worry about temporaries Create oe temporary for each uique expressio Let subsequet pass elimiate uecessary temporaries At a evaluatio of e Hash e to a ame,, i a table Isert a assigmet of e to At a use of e i b, if e Avail(b) Lookup e s ame i the hash table (call this ame ) Problems Iserts more copies tha approach 2 (but extra copies are dead) Still requires two passes (2 d pass is very geeral) CS553 Lecture Reuse Optimizatio: Commo SubExpr Elim 8 Extraeous Copies Extraeous copies degrade performace Let other trasformatios deal with them Dead code elimiatio Coalescig Coalesce assigmets to t ad t2 ito a sigle statemet t := b + c t2 := t Greatly simplifies CSE CS553 Lecture Reuse Optimizatio: Commo SubExpr Elim 9 4
5 Partial Redudacy Elimiatio (PRE) Partial Redudacy A expressio (e.g., x+y) is partially redudat at ode if some path from the etry ode to evaluates x+y, ad there are o defiitios of x or y betwee the last evaluatio of x+y ad Elimiatio Discover partially redudat expressios Covert them to fully redudat expressios Remove redudacy PRE subsumes CSE ad loop ivariat code motio CS553 Lecture Reuse Optimizatio: Commo SubExpr Elim 0 Loop Ivariace PRE removes loop ivariats A ivariat expressio is partially redudat PRE coverts this partial redudacy to full redudacy PRE removes the redudacy x := y * z x := y * z x := y * z CS553 Lecture Reuse Optimizatio: Commo SubExpr Elim 5
Calvin Lin The University of Texas at Austin
Loop Invariant Code Motion Last Time Loop invariant code motion Value numbering Today Finish value numbering More reuse optimization Common subession elimination Partial redundancy elimination Next Time
More informationSolutions to Final COMS W4115 Programming Languages and Translators Monday, May 4, :10-5:25pm, 309 Havemeyer
Departmet of Computer ciece Columbia Uiversity olutios to Fial COM W45 Programmig Laguages ad Traslators Moday, May 4, 2009 4:0-5:25pm, 309 Havemeyer Closed book, o aids. Do questios 5. Each questio is
More informationProgram Optimizations using Data-Flow Analysis
Program Optimizations using Data-Flow Analysis!Last time! Lattice theoretic framework for data-flow analysis!today! Dead-code elimination! Common sub-expression elimination (CSE)! Copy propagation! Constant
More informationCopyright 2016 Ramez Elmasri and Shamkant B. Navathe
Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 19 Query Optimizatio Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio Query optimizatio Coducted by a query optimizer i a DBMS Goal:
More informationCopyright 2016 Ramez Elmasri and Shamkant B. Navathe
Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 18 Strategies for Query Processig Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio DBMS techiques to process a query Scaer idetifies
More informationCS 11 C track: lecture 1
CS 11 C track: lecture 1 Prelimiaries Need a CMS cluster accout http://acctreq.cms.caltech.edu/cgi-bi/request.cgi Need to kow UNIX IMSS tutorial liked from track home page Track home page: http://courses.cms.caltech.edu/courses/cs11/material
More informationChapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3
More informationCOSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1
COSC 1P03 Ch 7 Recursio Itroductio to Data Structures 8.1 COSC 1P03 Recursio Recursio I Mathematics factorial Fiboacci umbers defie ifiite set with fiite defiitio I Computer Sciece sytax rules fiite defiitio,
More informationData Structures Week #5. Trees (Ağaçlar)
Data Structures Week #5 Trees Ağaçlar) Trees Ağaçlar) Toros Gökarı Avrupa Gökarı October 28, 2014 Boraha Tümer, Ph.D. 2 Trees Ağaçlar) October 28, 2014 Boraha Tümer, Ph.D. 3 Outlie Trees Deiitios Implemetatio
More informationCMSC Computer Architecture Lecture 12: Virtual Memory. Prof. Yanjing Li University of Chicago
CMSC 22200 Computer Architecture Lecture 12: Virtual Memory Prof. Yajig Li Uiversity of Chicago A System with Physical Memory Oly Examples: most Cray machies early PCs Memory early all embedded systems
More informationCOP4020 Programming Languages. Compilers and Interpreters Prof. Robert van Engelen
COP4020 mig Laguages Compilers ad Iterpreters Prof. Robert va Egele Overview Commo compiler ad iterpreter cofiguratios Virtual machies Itegrated developmet eviromets Compiler phases Lexical aalysis Sytax
More informationCS211 Fall 2003 Prelim 2 Solutions and Grading Guide
CS11 Fall 003 Prelim Solutios ad Gradig Guide Problem 1: (a) obj = obj1; ILLEGAL because type of referece must always be a supertype of type of object (b) obj3 = obj1; ILLEGAL because type of referece
More informationHow do we evaluate algorithms?
F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:
More informationBasic allocator mechanisms The course that gives CMU its Zip! Memory Management II: Dynamic Storage Allocation Mar 6, 2000.
5-23 The course that gives CM its Zip Memory Maagemet II: Dyamic Storage Allocatio Mar 6, 2000 Topics Segregated lists Buddy system Garbage collectio Mark ad Sweep Copyig eferece coutig Basic allocator
More informationHomework 1 Solutions MA 522 Fall 2017
Homework 1 Solutios MA 5 Fall 017 1. Cosider the searchig problem: Iput A sequece of umbers A = [a 1,..., a ] ad a value v. Output A idex i such that v = A[i] or the special value NIL if v does ot appear
More informationHeaps. Presentation for use with the textbook Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015
Presetatio for use with the textbook Algorithm Desig ad Applicatios, by M. T. Goodrich ad R. Tamassia, Wiley, 201 Heaps 201 Goodrich ad Tamassia xkcd. http://xkcd.com/83/. Tree. Used with permissio uder
More informationMorgan Kaufmann Publishers 26 February, COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 5
Morga Kaufma Publishers 26 February, 28 COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter 5 Set-Associative Cache Architecture Performace Summary Whe CPU performace icreases:
More informationWhat are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs
What are we goig to lear? CSC316-003 Data Structures Aalysis of Algorithms Computer Sciece North Carolia State Uiversity Need to say that some algorithms are better tha others Criteria for evaluatio Structure
More informationPseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance
Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured
More informationDesigning a learning system
CS 75 Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@cs.pitt.edu 539 Seott Square, x-5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please try
More informationEE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering
EE 4363 1 Uiversity of Miesota Midterm Exam #1 Prof. Matthew O'Keefe TA: Eric Seppae Departmet of Electrical ad Computer Egieerig Uiversity of Miesota Twi Cities Campus EE 4363 Itroductio to Microprocessors
More informationEE 459/500 HDL Based Digital Design with Programmable Logic. Lecture 13 Control and Sequencing: Hardwired and Microprogrammed Control
EE 459/500 HDL Based Digital Desig with Programmable Logic Lecture 13 Cotrol ad Sequecig: Hardwired ad Microprogrammed Cotrol Refereces: Chapter s 4,5 from textbook Chapter 7 of M.M. Mao ad C.R. Kime,
More information6.854J / J Advanced Algorithms Fall 2008
MIT OpeCourseWare http://ocw.mit.edu 6.854J / 18.415J Advaced Algorithms Fall 2008 For iformatio about citig these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 18.415/6.854 Advaced Algorithms
More informationRunning Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments
Ruig Time Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects. The
More informationCIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13
CIS Data Structures ad Algorithms with Java Sprig 08 Stacks ad Queues Moday, February / Tuesday, February Learig Goals Durig this lab, you will: Review stacks ad queues. Lear amortized ruig time aalysis
More informationRunning Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments
Ruig Time ( 3.1) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step- by- step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.
More informationAnalysis of Algorithms
Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Ruig Time Most algorithms trasform iput objects ito output objects. The
More informationBST Sequence of Operations
Splay Trees Problems with BSTs Because the shape of a BST is determied by the order that data is iserted, we ru the risk of trees that are essetially lists 12 21 20 32 24 37 15 40 55 56 77 2 BST Sequece
More informationCMPT 125 Assignment 2 Solutions
CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address
More informationLinked Lists 11/16/18. Preliminaries. Java References. Objects and references. Self references. Linking self-referential nodes
Prelimiaries Liked Lists public class StrageObject { Strig ame; StrageObject other; Arrays are ot always the optimal data structure: A array has fixed size eeds to be copied to expad its capacity Addig
More informationCIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19
CIS Data Structures ad Algorithms with Java Sprig 09 Stacks, Queues, ad Heaps Moday, February 8 / Tuesday, February 9 Stacks ad Queues Recall the stack ad queue ADTs (abstract data types from lecture.
More information5.3 Recursive definitions and structural induction
/8/05 5.3 Recursive defiitios ad structural iductio CSE03 Discrete Computatioal Structures Lecture 6 A recursively defied picture Recursive defiitios e sequece of powers of is give by a = for =0,,, Ca
More informationPython Programming: An Introduction to Computer Science
Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to
More informationCS Polygon Scan Conversion. Slide 1
CS 112 - Polygo Sca Coversio Slide 1 Polygo Classificatio Covex All iterior agles are less tha 180 degrees Cocave Iterior agles ca be greater tha 180 degrees Degeerate polygos If all vertices are colliear
More informationPar4All. From Convex Array Regions to Heterogeneous Computing
Par4All From Covex Array Regios to Heterogeeous Computig Mehdi Amii, Béatrice Creusillet, Stéphaie Eve, Roa Keryell, Oig Goubier, Serge Guelto, Jaice Oaia McMaho, Fraçois Xavier Pasquier, Grégoire Péa,
More informationComputer Architecture ELEC3441
CPU-Memory Bottleeck Computer Architecture ELEC44 CPU Memory Lecture 8 Cache Dr. Hayde Kwok-Hay So Departmet of Electrical ad Electroic Egieerig Performace of high-speed computers is usually limited by
More informationAlgorithm Efficiency
Algorithm Effiiey Exeutig ime Compariso of algorithms to determie whih oe is better approah implemet algorithms & reord exeutio time Problems with this approah there are may tasks ruig ourretly o a omputer
More informationLecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming
Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis
More informationAbstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup Note:
Chapter 4 Computatio Bjare Stroustrup www.stroustrup.com/programmig Abstract Today, I ll preset the basics of computatio. I particular, we ll discuss expressios, how to iterate over a series of values
More informationDesigning a learning system
CS 75 Itro to Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@pitt.edu 539 Seott Square, -5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please
More informationAnalysis of Algorithms
Aalysis of Algorithms Ruig Time of a algorithm Ruig Time Upper Bouds Lower Bouds Examples Mathematical facts Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite
More informationThe golden search method: Question 1
1. Golde Sectio Search for the Mode of a Fuctio The golde search method: Questio 1 Suppose the last pair of poits at which we have a fuctio evaluatio is x(), y(). The accordig to the method, If f(x())
More informationAnalysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis
Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems
More informationThe Magma Database file formats
The Magma Database file formats Adrew Gaylard, Bret Pikey, ad Mart-Mari Breedt Johaesburg, South Africa 15th May 2006 1 Summary Magma is a ope-source object database created by Chris Muller, of Kasas City,
More informationAdministrative UNSUPERVISED LEARNING. Unsupervised learning. Supervised learning 11/25/13. Final project. No office hours today
Admiistrative Fial project No office hours today UNSUPERVISED LEARNING David Kauchak CS 451 Fall 2013 Supervised learig Usupervised learig label label 1 label 3 model/ predictor label 4 label 5 Supervised
More informationA Boolean Query Processing with a Result Cache in Mediator Systems
A Boolea Query Processig with a Result Cache i Mediator Systems Jae-heo Cheog ad Sag-goo Lee * Departmet of Computer Sciece Seoul Natioal Uiversity Sa 56-1 Shillim-dog Kwaak-gu, Seoul Korea {cjh, sglee}cygus.su.ac.kr
More informationCS 111: Program Design I Lecture 21: Network Analysis. Robert H. Sloan & Richard Warner University of Illinois at Chicago April 10, 2018
CS 111: Program Desig I Lecture 21: Network Aalysis Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago April 10, 2018 NETWORK ANALYSIS Which displays a graph i the sese of graph/etwork aalysis?
More informationBig-O Analysis. Asymptotics
Big-O Aalysis 1 Defiitio: Suppose that f() ad g() are oegative fuctios of. The we say that f() is O(g()) provided that there are costats C > 0 ad N > 0 such that for all > N, f() Cg(). Big-O expresses
More informationCOP4020 Programming Languages. Subroutines and Parameter Passing Prof. Robert van Engelen
COP4020 Programmig Laguages Subrouties ad Parameter Passig Prof. Robert va Egele Overview Parameter passig modes Subroutie closures as parameters Special-purpose parameters Fuctio returs COP4020 Fall 2016
More informationCS 683: Advanced Design and Analysis of Algorithms
CS 683: Advaced Desig ad Aalysis of Algorithms Lecture 6, February 1, 2008 Lecturer: Joh Hopcroft Scribes: Shaomei Wu, Etha Feldma February 7, 2008 1 Threshold for k CNF Satisfiability I the previous lecture,
More informationMaster Informatics Eng. 2017/18. A.J.Proença. Memory Hierarchy. (most slides are borrowed) AJProença, Advanced Architectures, MiEI, UMinho, 2017/18 1
Advaced Architectures Master Iformatics Eg. 2017/18 A.J.Proeça Memory Hierarchy (most slides are borrowed) AJProeça, Advaced Architectures, MiEI, UMiho, 2017/18 1 Itroductio Programmers wat ulimited amouts
More informationNumerical Methods Lecture 6 - Curve Fitting Techniques
Numerical Methods Lecture 6 - Curve Fittig Techiques Topics motivatio iterpolatio liear regressio higher order polyomial form expoetial form Curve fittig - motivatio For root fidig, we used a give fuctio
More informationDATA STRUCTURES. amortized analysis binomial heaps Fibonacci heaps union-find. Data structures. Appetizer. Appetizer
Data structures DATA STRUCTURES Static problems. Give a iput, produce a output. Ex. Sortig, FFT, edit distace, shortest paths, MST, max-flow,... amortized aalysis biomial heaps Fiboacci heaps uio-fid Dyamic
More informationHash Tables. Presentation for use with the textbook Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015.
Presetatio for use with the textbook Algorithm Desig ad Applicatios, by M. T. Goodrich ad R. Tamassia, Wiley, 2015 Hash Tables xkcd. http://xkcd.com/221/. Radom Number. Used with permissio uder Creative
More informationData Structures and Algorithms. Analysis of Algorithms
Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output
More informationThe isoperimetric problem on the hypercube
The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose
More informationimplement language system
Outlie Priciples of programmig laguages Lecture 3 http://few.vu.l/~silvis/ppl/2007 Part I. Laguage systems Part II. Fuctioal programmig. First look at ML. Natalia Silvis-Cividjia e-mail: silvis@few.vu.l
More informationMath Section 2.2 Polynomial Functions
Math 1330 - Sectio. Polyomial Fuctios Our objectives i workig with polyomial fuctios will be, first, to gather iformatio about the graph of the fuctio ad, secod, to use that iformatio to geerate a reasoably
More informationSolving Fuzzy Assignment Problem Using Fourier Elimination Method
Global Joural of Pure ad Applied Mathematics. ISSN 0973-768 Volume 3, Number 2 (207), pp. 453-462 Research Idia Publicatios http://www.ripublicatio.com Solvig Fuzzy Assigmet Problem Usig Fourier Elimiatio
More informationMassachusetts Institute of Technology Lecture : Theory of Parallel Systems Feb. 25, Lecture 6: List contraction, tree contraction, and
Massachusetts Istitute of Techology Lecture.89: Theory of Parallel Systems Feb. 5, 997 Professor Charles E. Leiserso Scribe: Guag-Ie Cheg Lecture : List cotractio, tree cotractio, ad symmetry breakig Work-eciet
More informationToday s objectives. CSE401: Introduction to Compiler Construction. What is a compiler? Administrative Details. Why study compilers?
CSE401: Itroductio to Compiler Costructio Larry Ruzzo Sprig 2004 Today s objectives Admiistrative details Defie compilers ad why we study them Defie the high-level structure of compilers Associate specific
More informationMorgan Kaufmann Publishers 26 February, COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 5.
Morga Kaufma Publishers 26 February, 208 COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter 5 Virtual Memory Review: The Memory Hierarchy Take advatage of the priciple
More informationGraphs. Minimum Spanning Trees. Slides by Rose Hoberman (CMU)
Graphs Miimum Spaig Trees Slides by Rose Hoberma (CMU) Problem: Layig Telephoe Wire Cetral office 2 Wirig: Naïve Approach Cetral office Expesive! 3 Wirig: Better Approach Cetral office Miimize the total
More information1 Graph Sparsfication
CME 305: Discrete Mathematics ad Algorithms 1 Graph Sparsficatio I this sectio we discuss the approximatio of a graph G(V, E) by a sparse graph H(V, F ) o the same vertex set. I particular, we cosider
More informationStructuring Redundancy for Fault Tolerance. CSE 598D: Fault Tolerant Software
Structurig Redudacy for Fault Tolerace CSE 598D: Fault Tolerat Software What do we wat to achieve? Versios Damage Assessmet Versio 1 Error Detectio Iputs Versio 2 Voter Outputs State Restoratio Cotiued
More informationAnnouncements TREES II. Comparing Data Structures. Binary Search Trees. Red-Black Trees. Red-Black Trees 3/13/18
//8 Aoucemets Prelim is Toight, brig your studet ID :PM EXAM OLH: etids startig aa to dh OLH: etids startig di to ji PHL: etids startig jj to ks (Plus studets who switched from the 7: exam) TREES II Lecture
More informationCS : Programming for Non-Majors, Summer 2007 Programming Project #3: Two Little Calculations Due by 12:00pm (noon) Wednesday June
CS 1313 010: Programmig for No-Majors, Summer 2007 Programmig Project #3: Two Little Calculatios Due by 12:00pm (oo) Wedesday Jue 27 2007 This third assigmet will give you experiece writig programs that
More informationOutline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis
Outlie ad Readig Aalysis of Algorithms Iput Algorithm Output Ruig time ( 3.) Pseudo-code ( 3.2) Coutig primitive operatios ( 3.3-3.) Asymptotic otatio ( 3.6) Asymptotic aalysis ( 3.7) Case study Aalysis
More informationA Generalized Set Theoretic Approach for Time and Space Complexity Analysis of Algorithms and Functions
Proceedigs of the 10th WSEAS Iteratioal Coferece o APPLIED MATHEMATICS, Dallas, Texas, USA, November 1-3, 2006 316 A Geeralized Set Theoretic Approach for Time ad Space Complexity Aalysis of Algorithms
More informationCIS 121. Introduction to Trees
CIS 121 Itroductio to Trees 1 Tree ADT Tree defiitio q A tree is a set of odes which may be empty q If ot empty, the there is a distiguished ode r, called root ad zero or more o-empty subtrees T 1, T 2,
More informationCS200: Hash Tables. Prichard Ch CS200 - Hash Tables 1
CS200: Hash Tables Prichard Ch. 13.2 CS200 - Hash Tables 1 Table Implemetatios: average cases Search Add Remove Sorted array-based Usorted array-based Balaced Search Trees O(log ) O() O() O() O(1) O()
More informationCMSC Computer Architecture Lecture 11: More Caches. Prof. Yanjing Li University of Chicago
CMSC 22200 Computer Architecture Lecture 11: More Caches Prof. Yajig Li Uiversity of Chicago Lecture Outlie Caches 2 Review Memory hierarchy Cache basics Locality priciples Spatial ad temporal How to access
More informationJava Expressions & Flow Control
Java Expressios & Flow Cotrol Rui Moreira Expressio Separators:. [ ] ( ), ; Dot used as decimal separator or to access attributes ad methods double d = 2.6; Poto poto = ew Poto(2, 3); it i = poto.x; it
More informationCOP4020 Programming Languages. Functional Programming Prof. Robert van Engelen
COP4020 Programmig Laguages Fuctioal Programmig Prof. Robert va Egele Overview What is fuctioal programmig? Historical origis of fuctioal programmig Fuctioal programmig today Cocepts of fuctioal programmig
More informationBig-O Analysis. Asymptotics
Big-O Aalysis 1 Defiitio: Suppose that f() ad g() are oegative fuctios of. The we say that f() is O(g()) provided that there are costats C > 0 ad N > 0 such that for all > N, f() Cg(). Big-O expresses
More informationUniversity of Waterloo Department of Electrical and Computer Engineering ECE 250 Algorithms and Data Structures
Uiversity of Waterloo Departmet of Electrical ad Computer Egieerig ECE 250 Algorithms ad Data Structures Midterm Examiatio ( pages) Istructor: Douglas Harder February 7, 2004 7:30-9:00 Name (last, first)
More informationOperating System Concepts. Operating System Concepts
Chapter 4: Mass-Storage Systems Logical Disk Structure Logical Disk Structure Disk Schedulig Disk Maagemet RAID Structure Disk drives are addressed as large -dimesioal arrays of logical blocks, where the
More informationMath 10C Long Range Plans
Math 10C Log Rage Plas Uits: Evaluatio: Homework, projects ad assigmets 10% Uit Tests. 70% Fial Examiatio.. 20% Ay Uit Test may be rewritte for a higher mark. If the retest mark is higher, that mark will
More informationPython Programming: An Introduction to Computer Science
Pytho Programmig: A Itroductio to Computer Sciece Chapter 1 Computers ad Programs 1 Objectives To uderstad the respective roles of hardware ad software i a computig system. To lear what computer scietists
More informationA graphical view of big-o notation. c*g(n) f(n) f(n) = O(g(n))
ca see that time required to search/sort grows with size of We How do space/time eeds of program grow with iput size? iput. time: cout umber of operatios as fuctio of iput Executio size operatio Assigmet:
More informationCopyright 2016 Ramez Elmasri and Shamkant B. Navathe
Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 26 Ehaced Data Models: Itroductio to Active, Temporal, Spatial, Multimedia, ad Deductive Databases Copyright 2016 Ramez Elmasri ad Shamkat B.
More informationChapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 1 Itroductio to Computers ad C++ Programmig Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 1.1 Computer Systems 1.2 Programmig ad Problem Solvig 1.3 Itroductio to C++ 1.4 Testig
More informationExamples and Applications of Binary Search
Toy Gog ITEE Uiersity of Queeslad I the secod lecture last week we studied the biary search algorithm that soles the problem of determiig if a particular alue appears i a sorted list of iteger or ot. We
More informationCOMP Parallel Computing. PRAM (1): The PRAM model and complexity measures
COMP 633 - Parallel Computig Lecture 2 August 24, 2017 : The PRAM model ad complexity measures 1 First class summary This course is about parallel computig to achieve high-er performace o idividual problems
More informationComputers and Scientific Thinking
Computers ad Scietific Thikig David Reed, Creighto Uiversity Chapter 15 JavaScript Strigs 1 Strigs as Objects so far, your iteractive Web pages have maipulated strigs i simple ways use text box to iput
More information! Given the following Structure: ! We can define a pointer to a structure. ! Now studentptr points to the s1 structure.
Liked Lists Uit 5 Sectios 11.9 & 18.1-2 CS 2308 Fall 2018 Jill Seama 11.9: Poiters to Structures! Give the followig Structure: struct Studet { strig ame; // Studet s ame it idnum; // Studet ID umber it
More informationReliable Transmission. Spring 2018 CS 438 Staff - University of Illinois 1
Reliable Trasmissio Sprig 2018 CS 438 Staff - Uiversity of Illiois 1 Reliable Trasmissio Hello! My computer s ame is Alice. Alice Bob Hello! Alice. Sprig 2018 CS 438 Staff - Uiversity of Illiois 2 Reliable
More informationMR-2010I %MktBSize Macro 989. %MktBSize Macro
MR-2010I %MktBSize Macro 989 %MktBSize Macro The %MktBSize autocall macro suggests sizes for balaced icomplete block desigs (BIBDs). The sizes that it reports are sizes that meet ecessary but ot sufficiet
More informationWYSE Academic Challenge Sectional Computer Science 2005 SOLUTION SET
WYSE Academic Challege Sectioal Computer Sciece 2005 SOLUTION SET 1. Correct aswer: a. Hz = cycle / secod. CPI = 2, therefore, CPI*I = 2 * 28 X 10 8 istructios = 56 X 10 8 cycles. The clock rate is 56
More informationImage Segmentation EEE 508
Image Segmetatio Objective: to determie (etract) object boudaries. It is a process of partitioig a image ito distict regios by groupig together eighborig piels based o some predefied similarity criterio.
More informationReview of Truth Tables and De Morgan s Rules. Bradley Kjell (Revised 03/30/2010)
Review of ruth ables ad De Morga s Rules. Bradley Kjell (Revised 03/30/2010) 1. Complete the followig truth table: Operad Operad Expressio x 100 x 100 Decide from the table what the value
More informationMessage Integrity and Hash Functions. TELE3119: Week4
Message Itegrity ad Hash Fuctios TELE3119: Week4 Outlie Message Itegrity Hash fuctios ad applicatios Hash Structure Popular Hash fuctios 4-2 Message Itegrity Goal: itegrity (ot secrecy) Allows commuicatig
More informationParametric curves. Reading. Parametric polynomial curves. Mathematical curve representation. Brian Curless CSE 457 Spring 2015
Readig Required: Agel 0.-0.3, 0.5., 0.6-0.7, 0.9 Parametric curves Bria Curless CSE 457 Sprig 05 Optioal Bartels, Beatty, ad Barsy. A Itroductio to Splies for use i Computer Graphics ad Geometric Modelig,
More informationOnes Assignment Method for Solving Traveling Salesman Problem
Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:
More informationEvaluation scheme for Tracking in AMI
A M I C o m m u i c a t i o A U G M E N T E D M U L T I - P A R T Y I N T E R A C T I O N http://www.amiproject.org/ Evaluatio scheme for Trackig i AMI S. Schreiber a D. Gatica-Perez b AMI WP4 Trackig:
More information. Written in factored form it is easy to see that the roots are 2, 2, i,
CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or
More informationLecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions
U.C. Berkeley CS170 : Algorithms Midterm 1 Solutios Lecturers: Sajam Garg ad Prasad Raghavedra Feb 1, 017 Midterm 1 Solutios 1. (4 poits) For the directed graph below, fid all the strogly coected compoets
More informationModern Systems Analysis and Design Seventh Edition
Moder Systems Aalysis ad Desig Seveth Editio Jeffrey A. Hoffer Joey F. George Joseph S. Valacich Desigig Databases Learig Objectives ü Cocisely defie each of the followig key database desig terms: relatio,
More informationIntroduction to GAMIT/GLOBK Applications of GLOBK. Lecture 11 OVERVIEW
Itroductio to GAMIT/GLOBK Applicatios of GLOBK Lecture 11 GAMIT/GLOBK Lec11 1 OVERVIEW o I this lecture we cover: o Basic types of aalyses with globk l Velocity ad repeatability rus o GLOBK acillary programs
More informationThe Graphs of Polynomial Functions
Sectio 4.3 The Graphs of Polyomial Fuctios Objective 1: Uderstadig the Defiitio of a Polyomial Fuctio Defiitio Polyomial Fuctio 1 2 The fuctio ax a 1x a 2x a1x a0 is a polyomial fuctio of degree where
More information