Physics 331 Introduction to Numerical Techniques in Physics
|
|
- Warren Bailey
- 6 years ago
- Views:
Transcription
1 Physics 331 Introduction to Numerical Techniques in Physics Instructor: Joaquín Drut Lecture 2
2 Any logistics questions?
3 Today: Number representation Sources of error Note: typo in HW! Two parts c. Call them c1 and c2.
4 Representing numbers on a computer
5 Numbers Our understanding of nature has led us to a description in terms of real and complex numbers... What makes real numbers so special? What are their defining properties?
6 Numbers Our understanding of nature has led us to a description in terms of real and complex numbers... What makes real numbers so special? What are their defining properties? Reals form a field (6 axioms about + and. )
7 Numbers Our understanding of nature has led us to a description in terms of real and complex numbers... What makes real numbers so special? What are their defining properties? Reals form a field (6 axioms about + and. ) Reals are ordered (3 axioms allowing to define > and < )
8 Numbers Our understanding of nature has led us to a description in terms of real and complex numbers... What makes real numbers so special? What are their defining properties? Reals form a field (6 axioms about + and. ) Reals are ordered (3 axioms allowing to define > and < ) Reals are complete (every convergent sequence has a real limit)
9 Numbers Our understanding of nature has led us to a description in terms of real and complex numbers... What makes real numbers so special? What are their defining properties? Reals form a field (6 axioms about + and. ) Reals are ordered (3 axioms allowing to define > and < ) Reals are complete (every convergent sequence has a real limit) Real numbers contain rational as well as irrational numbers. This is a problem! Why?
10 Numbers Our understanding of nature has led us to a description in terms of real and complex numbers... What makes real numbers so special? What are their defining properties? Reals form a field (6 axioms about + and. ) Reals are ordered (3 axioms allowing to define > and < ) Reals are complete (every convergent sequence has a real limit) Real numbers contain rational as well as irrational numbers. This is a problem! Why? How many rationals are there? How many irrationals?
11 Numbers Our understanding of nature has led us to a description in terms of real and complex numbers but computers use a discrete and finite set of numbers. You can already imagine that the range and precision of numbers we can represent will somehow be limited. An engineering decision had to be made regarding how to manage such limitations. But there is more to consider
12 Digital computers In digital computers, everything is ultimately reduced to on and off, or 0 and 1. Numbers on such a computer are encoded in base 2: Base 10 Base bits
13 Decimal and binary representations The decimal representation of numbers is so common that we often forget why we count the way we do. (Why?)
14 Decimal and binary representations The decimal representation of numbers is so common that we often forget why we count the way we do. Decimal Take the number 23, What does this mean in the decimal representation?
15 Decimal and binary representations The decimal representation of numbers is so common that we often forget why we count the way we do. Decimal Take the number 23, What does this mean in the decimal representation? 23, x x x x x x x x 10-3
16 Decimal and binary representations Binary We only have 1 and 0, and then we move on to the next digit! You may have something like What does this mean?
17 Decimal and binary representations Binary We only have 1 and 0, and then we move on to the next digit! You may have something like What does this mean? Exercise: What number is this in decimal representation? Take 2 minutes and try to figure it out!
18 Decimal and binary representations Binary We only have 1 and 0, and then we move on to the next digit! You may have something like What does this mean? Exercise: What number is this in decimal representation? Take 2 minutes and try to figure it out! Exercise: What number is 21 in binary representation? Take 2 minutes and try to figure it out!
19 On a computer, numbers are represented in terms of bits, but that s not all there is to it
20 Floating point representations Decimal floating point representation (a.k.a. scientific notation) d.ddddddd x 10 p d: 0,1,..,9 mantissa think precision exponent think order of magnitude Example f =
21 Floating point representations Decimal floating point representation (a.k.a. scientific notation) d.ddddddd x 10 p d: 0,1,..,9 mantissa think precision exponent think order of magnitude In general To find the first digit d (from the left), and thus the mantissa as well, divide by the highest power of 10 that is less than the number you want to represent. That power is the exponent p.
22 Floating point representations Binary floating point representation (a.k.a. most of you have never seen this before) 1.bbbbbbb x 2 bbb b: 0,1 mantissa exponent
23 Floating point representations IEEE standard: f = s M B e E There is also quadruple precision! SP sign exponent mantissa
24 Floating point representations IEEE standard: f = s M B e E Overflow 0 Underflow Overflow Note: - There is a largest positive number! Overflow - There is a smallest positive number! Underflow - Only a finite number of exact values! - Interval between numbers depends on exponent!
25 Examples f = f = s M B e E
26 Examples f = f = s M B e E f =
27 Examples f = f = s M B e E f = f =
28 Floating point representations IEEE standard: f = s M B e E There is also quadruple precision! SP sign exponent mantissa What range of values can we represent for the exponent with 8 bits?
29 The exponent e : true exponent f = s M B e E E : bias (127 in SP; 1023 in DP)
30 Sources of error
31 Sources of error when programming... Syntax errors (aka compile errors: code does not even compile) Runtime errors (e.g. seg-fault; code compiles but fails when running) Numerical errors (more on this soon!) Physics errors (not a programming error: the code does what you want and it does it right... you just have the physics wrong!)
32 Sources of error when programming... Syntax errors (aka compile errors: code does not even compile) Runtime errors (e.g. seg-fault; code compiles but fails when running) Numerical errors (more on this soon!) Physics errors (not a programming error: the code does what you want and it does it right... you just have the physics wrong!) We will put aside discussions of the model/approach used to represent the physics. These could give... Systematic errors (controlled approximations; e.g. finite volume, finite mesh) Uncontrolled errors (the model may involve an approximation )
33 Sources of numerical error... Round-off errors (computers have finite precision) Round-off errors will propagate in iterative methods and potentially produce instabilities and give useless results.
34 Sources of numerical error... Round-off errors (computers have finite precision) Round-off errors will propagate in iterative methods and potentially produce instabilities and give useless results. Statistical error (e.g. if your method uses random numbers, like in MC) You just need more statistics - it s almost like a systematic error... but it can be much harder to deal with!
35 Statistical error / signal-to-noise problem. Example cosi loghxl x 0 x M x >
36 Statistical error / signal-to-noise problem. Example cosi loghxl x 0 x M x >
37 Sources of numerical error... Round-off errors (computers have finite precision) Round-off errors will propagate in iterative methods and potentially produce instabilities and give useless results. Statistical error (e.g. if your method uses random numbers, like in MC) You just need more statistics - it s almost like a systematic error... but it can be much harder to deal with!
38 Sources of numerical error... Round-off errors (computers have finite precision) Round-off errors will propagate in iterative methods and potentially produce instabilities and give useless results. Statistical error (e.g. if your method uses random numbers, like in MC) You just need more statistics - it s almost like a systematic error... but it can be much harder to deal with! Truncation errors (caused by the numerical method) How do you estimate the value of complicated functions, series expansions, integrals, derivatives...? Should be a systematic error, i.e. something we can treat. Why?
39 Total error Total error = True solution - Numerical solution Total relative error = True solution - Numerical solution True solution Usually we do not know the true solution; that is what we are looking for! The numerical solution can be systematically improved. Ideally the only error left is just the round-off error. We will always be limited by this!
Floating Point Arithmetic
Floating Point Arithmetic CS 365 Floating-Point What can be represented in N bits? Unsigned 0 to 2 N 2s Complement -2 N-1 to 2 N-1-1 But, what about? very large numbers? 9,349,398,989,787,762,244,859,087,678
More informationScientific Computing. Error Analysis
ECE257 Numerical Methods and Scientific Computing Error Analysis Today s s class: Introduction to error analysis Approximations Round-Off Errors Introduction Error is the difference between the exact solution
More informationChapter Three. Arithmetic
Chapter Three 1 Arithmetic Where we've been: Performance (seconds, cycles, instructions) Abstractions: Instruction Set Architecture Assembly Language and Machine Language What's up ahead: Implementing
More informationChapter 3. Errors and numerical stability
Chapter 3 Errors and numerical stability 1 Representation of numbers Binary system : micro-transistor in state off 0 on 1 Smallest amount of stored data bit Object in memory chain of 1 and 0 10011000110101001111010010100010
More informationFinite arithmetic and error analysis
Finite arithmetic and error analysis Escuela de Ingeniería Informática de Oviedo (Dpto de Matemáticas-UniOvi) Numerical Computation Finite arithmetic and error analysis 1 / 45 Outline 1 Number representation:
More informationData Representation Floating Point
Data Representation Floating Point CSCI 2400 / ECE 3217: Computer Architecture Instructor: David Ferry Slides adapted from Bryant & O Hallaron s slides via Jason Fritts Today: Floating Point Background:
More informationFLOATING POINT NUMBERS
Exponential Notation FLOATING POINT NUMBERS Englander Ch. 5 The following are equivalent representations of 1,234 123,400.0 x 10-2 12,340.0 x 10-1 1,234.0 x 10 0 123.4 x 10 1 12.34 x 10 2 1.234 x 10 3
More informationData Representation Floating Point
Data Representation Floating Point CSCI 2400 / ECE 3217: Computer Architecture Instructor: David Ferry Slides adapted from Bryant & O Hallaron s slides via Jason Fritts Today: Floating Point Background:
More informationIEEE-754 floating-point
IEEE-754 floating-point Real and floating-point numbers Real numbers R form a continuum - Rational numbers are a subset of the reals - Some numbers are irrational, e.g. π Floating-point numbers are an
More informationComputational Economics and Finance
Computational Economics and Finance Part I: Elementary Concepts of Numerical Analysis Spring 2015 Outline Computer arithmetic Error analysis: Sources of error Error propagation Controlling the error Rates
More informationFloating Point Representation. CS Summer 2008 Jonathan Kaldor
Floating Point Representation CS3220 - Summer 2008 Jonathan Kaldor Floating Point Numbers Infinite supply of real numbers Requires infinite space to represent certain numbers We need to be able to represent
More informationNumerical computing. How computers store real numbers and the problems that result
Numerical computing How computers store real numbers and the problems that result The scientific method Theory: Mathematical equations provide a description or model Experiment Inference from data Test
More informationFloating Point Representation in Computers
Floating Point Representation in Computers Floating Point Numbers - What are they? Floating Point Representation Floating Point Operations Where Things can go wrong What are Floating Point Numbers? Any
More informationCSCI 402: Computer Architectures. Arithmetic for Computers (3) Fengguang Song Department of Computer & Information Science IUPUI.
CSCI 402: Computer Architectures Arithmetic for Computers (3) Fengguang Song Department of Computer & Information Science IUPUI 3.5 Today s Contents Floating point numbers: 2.5, 10.1, 100.2, etc.. How
More information2 Computation with Floating-Point Numbers
2 Computation with Floating-Point Numbers 2.1 Floating-Point Representation The notion of real numbers in mathematics is convenient for hand computations and formula manipulations. However, real numbers
More information2.1.1 Fixed-Point (or Integer) Arithmetic
x = approximation to true value x error = x x, relative error = x x. x 2.1.1 Fixed-Point (or Integer) Arithmetic A base 2 (base 10) fixed-point number has a fixed number of binary (decimal) places. 1.
More informationWhat we need to know about error: Class Outline. Computational Methods CMSC/AMSC/MAPL 460. Errors in data and computation
Class Outline Computational Methods CMSC/AMSC/MAPL 460 Errors in data and computation Representing numbers in floating point Ramani Duraiswami, Dept. of Computer Science Computations should be as accurate
More informationUp next. Midterm. Today s lecture. To follow
Up next Midterm Next Friday in class Exams page on web site has info + practice problems Excited for you to rock the exams like you have been the assignments! Today s lecture Back to numbers, bits, data
More informationNumber Systems. Binary Numbers. Appendix. Decimal notation represents numbers as powers of 10, for example
Appendix F Number Systems Binary Numbers Decimal notation represents numbers as powers of 10, for example 1729 1 103 7 102 2 101 9 100 decimal = + + + There is no particular reason for the choice of 10,
More informationComputational Methods CMSC/AMSC/MAPL 460. Representing numbers in floating point and associated issues. Ramani Duraiswami, Dept. of Computer Science
Computational Methods CMSC/AMSC/MAPL 460 Representing numbers in floating point and associated issues Ramani Duraiswami, Dept. of Computer Science Class Outline Computations should be as accurate and as
More informationFloating-point representations
Lecture 10 Floating-point representations Methods of representing real numbers (1) 1. Fixed-point number system limited range and/or limited precision results must be scaled 100101010 1111010 100101010.1111010
More informationFloating-point representations
Lecture 10 Floating-point representations Methods of representing real numbers (1) 1. Fixed-point number system limited range and/or limited precision results must be scaled 100101010 1111010 100101010.1111010
More informationChapter 03: Computer Arithmetic. Lesson 09: Arithmetic using floating point numbers
Chapter 03: Computer Arithmetic Lesson 09: Arithmetic using floating point numbers Objective To understand arithmetic operations in case of floating point numbers 2 Multiplication of Floating Point Numbers
More informationComputational Economics and Finance
Computational Economics and Finance Part I: Elementary Concepts of Numerical Analysis Spring 2016 Outline Computer arithmetic Error analysis: Sources of error Error propagation Controlling the error Rates
More informationCOMP Overview of Tutorial #2
COMP 1402 Winter 2008 Tutorial #2 Overview of Tutorial #2 Number representation basics Binary conversions Octal conversions Hexadecimal conversions Signed numbers (signed magnitude, one s and two s complement,
More informationMAT128A: Numerical Analysis Lecture Two: Finite Precision Arithmetic
MAT128A: Numerical Analysis Lecture Two: Finite Precision Arithmetic September 28, 2018 Lecture 1 September 28, 2018 1 / 25 Floating point arithmetic Computers use finite strings of binary digits to represent
More informationChapter 3: Arithmetic for Computers
Chapter 3: Arithmetic for Computers Objectives Signed and Unsigned Numbers Addition and Subtraction Multiplication and Division Floating Point Computer Architecture CS 35101-002 2 The Binary Numbering
More informationMATH 353 Engineering mathematics III
MATH 353 Engineering mathematics III Instructor: Francisco-Javier Pancho Sayas Spring 2014 University of Delaware Instructor: Francisco-Javier Pancho Sayas MATH 353 1 / 20 MEET YOUR COMPUTER Instructor:
More information1.3 Floating Point Form
Section 1.3 Floating Point Form 29 1.3 Floating Point Form Floating point numbers are used by computers to approximate real numbers. On the surface, the question is a simple one. There are an infinite
More informationCS321. Introduction to Numerical Methods
CS31 Introduction to Numerical Methods Lecture 1 Number Representations and Errors Professor Jun Zhang Department of Computer Science University of Kentucky Lexington, KY 40506 0633 August 5, 017 Number
More informationCS101 Lecture 04: Binary Arithmetic
CS101 Lecture 04: Binary Arithmetic Binary Number Addition Two s complement encoding Briefly: real number representation Aaron Stevens (azs@bu.edu) 25 January 2013 What You ll Learn Today Counting in binary
More information1.2 Round-off Errors and Computer Arithmetic
1.2 Round-off Errors and Computer Arithmetic 1 In a computer model, a memory storage unit word is used to store a number. A word has only a finite number of bits. These facts imply: 1. Only a small set
More informationRoundoff Errors and Computer Arithmetic
Jim Lambers Math 105A Summer Session I 2003-04 Lecture 2 Notes These notes correspond to Section 1.2 in the text. Roundoff Errors and Computer Arithmetic In computing the solution to any mathematical problem,
More informationLecture Objectives. Structured Programming & an Introduction to Error. Review the basic good habits of programming
Structured Programming & an Introduction to Error Lecture Objectives Review the basic good habits of programming To understand basic concepts of error and error estimation as it applies to Numerical Methods
More information3.5 Floating Point: Overview
3.5 Floating Point: Overview Floating point (FP) numbers Scientific notation Decimal scientific notation Binary scientific notation IEEE 754 FP Standard Floating point representation inside a computer
More informationVariables and Data Representation
You will recall that a computer program is a set of instructions that tell a computer how to transform a given set of input into a specific output. Any program, procedural, event driven or object oriented
More informationFloating Point Numbers
Floating Point Numbers Summer 8 Fractional numbers Fractional numbers fixed point Floating point numbers the IEEE 7 floating point standard Floating point operations Rounding modes CMPE Summer 8 Slides
More informationFloating point. Today! IEEE Floating Point Standard! Rounding! Floating Point Operations! Mathematical properties. Next time. !
Floating point Today! IEEE Floating Point Standard! Rounding! Floating Point Operations! Mathematical properties Next time! The machine model Chris Riesbeck, Fall 2011 Checkpoint IEEE Floating point Floating
More informationHomework 1 graded and returned in class today. Solutions posted online. Request regrades by next class period. Question 10 treated as extra credit
Announcements Homework 1 graded and returned in class today. Solutions posted online. Request regrades by next class period. Question 10 treated as extra credit Quiz 2 Monday on Number System Conversions
More informationFloating-Point Arithmetic
Floating-Point Arithmetic Raymond J. Spiteri Lecture Notes for CMPT 898: Numerical Software University of Saskatchewan January 9, 2013 Objectives Floating-point numbers Floating-point arithmetic Analysis
More informationComputational Methods CMSC/AMSC/MAPL 460. Representing numbers in floating point and associated issues. Ramani Duraiswami, Dept. of Computer Science
Computational Methods CMSC/AMSC/MAPL 460 Representing numbers in floating point and associated issues Ramani Duraiswami, Dept. of Computer Science Class Outline Computations should be as accurate and as
More informationFloating-Point Data Representation and Manipulation 198:231 Introduction to Computer Organization Lecture 3
Floating-Point Data Representation and Manipulation 198:231 Introduction to Computer Organization Instructor: Nicole Hynes nicole.hynes@rutgers.edu 1 Fixed Point Numbers Fixed point number: integer part
More informationCS 6210 Fall 2016 Bei Wang. Lecture 4 Floating Point Systems Continued
CS 6210 Fall 2016 Bei Wang Lecture 4 Floating Point Systems Continued Take home message 1. Floating point rounding 2. Rounding unit 3. 64 bit word: double precision (IEEE standard word) 4. Exact rounding
More informationReview of Calculus, cont d
Jim Lambers MAT 460/560 Fall Semester 2009-10 Lecture 4 Notes These notes correspond to Sections 1.1 1.2 in the text. Review of Calculus, cont d Taylor s Theorem, cont d We conclude our discussion of Taylor
More informationComputer Arithmetic Floating Point
Computer Arithmetic Floating Point Chapter 3.6 EEC7 FQ 25 About Floating Point Arithmetic Arithmetic basic operations on floating point numbers are: Add, Subtract, Multiply, Divide Transcendental operations
More informationFloating Point : Introduction to Computer Systems 4 th Lecture, May 25, Instructor: Brian Railing. Carnegie Mellon
Floating Point 15-213: Introduction to Computer Systems 4 th Lecture, May 25, 2018 Instructor: Brian Railing Today: Floating Point Background: Fractional binary numbers IEEE floating point standard: Definition
More informationClasses of Real Numbers 1/2. The Real Line
Classes of Real Numbers All real numbers can be represented by a line: 1/2 π 1 0 1 2 3 4 real numbers The Real Line { integers rational numbers non-integral fractions irrational numbers Rational numbers
More informationFloating Point. The World is Not Just Integers. Programming languages support numbers with fraction
1 Floating Point The World is Not Just Integers Programming languages support numbers with fraction Called floating-point numbers Examples: 3.14159265 (π) 2.71828 (e) 0.000000001 or 1.0 10 9 (seconds in
More informationFloating Point. CSE 351 Autumn Instructor: Justin Hsia
Floating Point CSE 351 Autumn 2016 Instructor: Justin Hsia Teaching Assistants: Chris Ma Hunter Zahn John Kaltenbach Kevin Bi Sachin Mehta Suraj Bhat Thomas Neuman Waylon Huang Xi Liu Yufang Sun http://xkcd.com/899/
More informationFloating-Point Numbers in Digital Computers
POLYTECHNIC UNIVERSITY Department of Computer and Information Science Floating-Point Numbers in Digital Computers K. Ming Leung Abstract: We explain how floating-point numbers are represented and stored
More informationData Representation Floating Point
Data Representation Floating Point CSCI 224 / ECE 317: Computer Architecture Instructor: Prof. Jason Fritts Slides adapted from Bryant & O Hallaron s slides Today: Floating Point Background: Fractional
More informationFloating-Point Numbers in Digital Computers
POLYTECHNIC UNIVERSITY Department of Computer and Information Science Floating-Point Numbers in Digital Computers K. Ming Leung Abstract: We explain how floating-point numbers are represented and stored
More informationCOMP2611: Computer Organization. Data Representation
COMP2611: Computer Organization Comp2611 Fall 2015 2 1. Binary numbers and 2 s Complement Numbers 3 Bits: are the basis for binary number representation in digital computers What you will learn here: How
More informationECE232: Hardware Organization and Design
ECE232: Hardware Organization and Design Lecture 11: Floating Point & Floating Point Addition Adapted from Computer Organization and Design, Patterson & Hennessy, UCB Last time: Single Precision Format
More informationFloating-point representation
Lecture 3-4: Floating-point representation and arithmetic Floating-point representation The notion of real numbers in mathematics is convenient for hand computations and formula manipulations. However,
More informationCS 61C: Great Ideas in Computer Architecture Performance and Floating Point Arithmetic
CS 61C: Great Ideas in Computer Architecture Performance and Floating Point Arithmetic Instructors: Bernhard Boser & Randy H. Katz http://inst.eecs.berkeley.edu/~cs61c/ 10/25/16 Fall 2016 -- Lecture #17
More informationNumbers and Computers. Debdeep Mukhopadhyay Assistant Professor Dept of Computer Sc and Engg IIT Madras
Numbers and Computers Debdeep Mukhopadhyay Assistant Professor Dept of Computer Sc and Engg IIT Madras 1 Think of a number between 1 and 15 8 9 10 11 12 13 14 15 4 5 6 7 12 13 14 15 2 3 6 7 10 11 14 15
More informationIntroduction to Scientific Computing Lecture 1
Introduction to Scientific Computing Lecture 1 Professor Hanno Rein Last updated: September 10, 2017 1 Number Representations In this lecture, we will cover two concept that are important to understand
More informationFoundations of Computer Systems
18-600 Foundations of Computer Systems Lecture 4: Floating Point Required Reading Assignment: Chapter 2 of CS:APP (3 rd edition) by Randy Bryant & Dave O Hallaron Assignments for This Week: Lab 1 18-600
More information2 Computation with Floating-Point Numbers
2 Computation with Floating-Point Numbers 2.1 Floating-Point Representation The notion of real numbers in mathematics is convenient for hand computations and formula manipulations. However, real numbers
More informationFloating Point. CSE 351 Autumn Instructor: Justin Hsia
Floating Point CSE 351 Autumn 2017 Instructor: Justin Hsia Teaching Assistants: Lucas Wotton Michael Zhang Parker DeWilde Ryan Wong Sam Gehman Sam Wolfson Savanna Yee Vinny Palaniappan Administrivia Lab
More informationFloating Point. CSE 351 Autumn Instructor: Justin Hsia
Floating Point CSE 351 Autumn 2017 Instructor: Justin Hsia Teaching Assistants: Lucas Wotton Michael Zhang Parker DeWilde Ryan Wong Sam Gehman Sam Wolfson Savanna Yee Vinny Palaniappan http://xkcd.com/571/
More informationRepresenting and Manipulating Floating Points. Jo, Heeseung
Representing and Manipulating Floating Points Jo, Heeseung The Problem How to represent fractional values with finite number of bits? 0.1 0.612 3.14159265358979323846264338327950288... 2 Fractional Binary
More informationCS321 Introduction To Numerical Methods
CS3 Introduction To Numerical Methods Fuhua (Frank) Cheng Department of Computer Science University of Kentucky Lexington KY 456-46 - - Table of Contents Errors and Number Representations 3 Error Types
More informationComputational Methods. Sources of Errors
Computational Methods Sources of Errors Manfred Huber 2011 1 Numerical Analysis / Scientific Computing Many problems in Science and Engineering can not be solved analytically on a computer Numeric solutions
More informationFloating Point (with contributions from Dr. Bin Ren, William & Mary Computer Science)
Floating Point (with contributions from Dr. Bin Ren, William & Mary Computer Science) Floating Point Background: Fractional binary numbers IEEE floating point standard: Definition Example and properties
More informationOutline. What is Performance? Restating Performance Equation Time = Seconds. CPU Performance Factors
CS 61C: Great Ideas in Computer Architecture Performance and Floating-Point Arithmetic Instructors: Krste Asanović & Randy H. Katz http://inst.eecs.berkeley.edu/~cs61c/fa17 Outline Defining Performance
More informationCS 61C: Great Ideas in Computer Architecture Performance and Floating-Point Arithmetic
CS 61C: Great Ideas in Computer Architecture Performance and Floating-Point Arithmetic Instructors: Krste Asanović & Randy H. Katz http://inst.eecs.berkeley.edu/~cs61c/fa17 10/24/17 Fall 2017-- Lecture
More informationInf2C - Computer Systems Lecture 2 Data Representation
Inf2C - Computer Systems Lecture 2 Data Representation Boris Grot School of Informatics University of Edinburgh Last lecture Moore s law Types of computer systems Computer components Computer system stack
More informationNumerical Representations On The Computer: Negative And Rational Numbers
Numerical Representations On The Computer: Negative And Rational Numbers How are negative and rational numbers represented on the computer? How are subtractions performed by the computer? Subtraction In
More informationNumber Systems. Decimal numbers. Binary numbers. Chapter 1 <1> 8's column. 1000's column. 2's column. 4's column
1's column 10's column 100's column 1000's column 1's column 2's column 4's column 8's column Number Systems Decimal numbers 5374 10 = Binary numbers 1101 2 = Chapter 1 1's column 10's column 100's
More informationIntroduction to Computers and Programming. Numeric Values
Introduction to Computers and Programming Prof. I. K. Lundqvist Lecture 5 Reading: B pp. 47-71 Sept 1 003 Numeric Values Storing the value of 5 10 using ASCII: 00110010 00110101 Binary notation: 00000000
More informationNumerical Representations On The Computer: Negative And Rational Numbers
Numerical Representations On The Computer: Negative And Rational Numbers How are negative and rational numbers represented on the computer? How are subtractions performed by the computer? Subtraction In
More information15213 Recitation 2: Floating Point
15213 Recitation 2: Floating Point 1 Introduction This handout will introduce and test your knowledge of the floating point representation of real numbers, as defined by the IEEE standard. This information
More informationFloating Point Arithmetic
Floating Point Arithmetic Clark N. Taylor Department of Electrical and Computer Engineering Brigham Young University clark.taylor@byu.edu 1 Introduction Numerical operations are something at which digital
More informationModule 2: Computer Arithmetic
Module 2: Computer Arithmetic 1 B O O K : C O M P U T E R O R G A N I Z A T I O N A N D D E S I G N, 3 E D, D A V I D L. P A T T E R S O N A N D J O H N L. H A N N E S S Y, M O R G A N K A U F M A N N
More informationSigned umbers. Sign/Magnitude otation
Signed umbers So far we have discussed unsigned number representations. In particular, we have looked at the binary number system and shorthand methods in representing binary codes. With m binary digits,
More informationFloating-Point Arithmetic
Floating-Point Arithmetic 1 Numerical Analysis a definition sources of error 2 Floating-Point Numbers floating-point representation of a real number machine precision 3 Floating-Point Arithmetic adding
More informationDivide: Paper & Pencil
Divide: Paper & Pencil 1001 Quotient Divisor 1000 1001010 Dividend -1000 10 101 1010 1000 10 Remainder See how big a number can be subtracted, creating quotient bit on each step Binary => 1 * divisor or
More informationFloating Point Numbers
Floating Point Numbers Computer Systems Organization (Spring 2016) CSCI-UA 201, Section 2 Instructor: Joanna Klukowska Slides adapted from Randal E. Bryant and David R. O Hallaron (CMU) Mohamed Zahran
More informationFloating Point Numbers
Floating Point Numbers Computer Systems Organization (Spring 2016) CSCI-UA 201, Section 2 Fractions in Binary Instructor: Joanna Klukowska Slides adapted from Randal E. Bryant and David R. O Hallaron (CMU)
More informationLecture 13: (Integer Multiplication and Division) FLOATING POINT NUMBERS
Lecture 13: (Integer Multiplication and Division) FLOATING POINT NUMBERS Lecture 13 Floating Point I (1) Fall 2005 Integer Multiplication (1/3) Paper and pencil example (unsigned): Multiplicand 1000 8
More informationIEEE Standard 754 Floating Point Numbers
IEEE Standard 754 Floating Point Numbers Steve Hollasch / Last update 2005-Feb-24 IEEE Standard 754 floating point is the most common representation today for real numbers on computers, including Intel-based
More informationDeclaration. Fundamental Data Types. Modifying the Basic Types. Basic Data Types. All variables must be declared before being used.
Declaration Fundamental Data Types All variables must be declared before being used. Tells compiler to set aside an appropriate amount of space in memory to hold a value. Enables the compiler to perform
More informationComputer (Literacy) Skills. Number representations and memory. Lubomír Bulej KDSS MFF UK
Computer (Literacy Skills Number representations and memory Lubomír Bulej KDSS MFF UK Number representations? What for? Recall: computer works with binary numbers Groups of zeroes and ones 8 bits (byte,
More informationOperations On Data CHAPTER 4. (Solutions to Odd-Numbered Problems) Review Questions
CHAPTER 4 Operations On Data (Solutions to Odd-Numbered Problems) Review Questions 1. Arithmetic operations interpret bit patterns as numbers. Logical operations interpret each bit as a logical values
More informationFloating Point Numbers. Lecture 9 CAP
Floating Point Numbers Lecture 9 CAP 3103 06-16-2014 Review of Numbers Computers are made to deal with numbers What can we represent in N bits? 2 N things, and no more! They could be Unsigned integers:
More information4.1 QUANTIZATION NOISE
DIGITAL SIGNAL PROCESSING UNIT IV FINITE WORD LENGTH EFFECTS Contents : 4.1 Quantization Noise 4.2 Fixed Point and Floating Point Number Representation 4.3 Truncation and Rounding 4.4 Quantization Noise
More informationFloating Point Puzzles. Lecture 3B Floating Point. IEEE Floating Point. Fractional Binary Numbers. Topics. IEEE Standard 754
Floating Point Puzzles Topics Lecture 3B Floating Point IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties For each of the following C expressions, either: Argue that
More informationScientific Computing: An Introductory Survey
Scientific Computing: An Introductory Survey Chapter 1 Scientific Computing Prof. Michael T. Heath Department of Computer Science University of Illinois at Urbana-Champaign Copyright c 2002. Reproduction
More informationLecture Notes: Floating-Point Numbers
Lecture Notes: Floating-Point Numbers CS227-Scientific Computing September 8, 2010 What this Lecture is About How computers represent numbers How this affects the accuracy of computation Positional Number
More informationIn this lesson you will learn: how to add and multiply positive binary integers how to work with signed binary numbers using two s complement how fixed and floating point numbers are used to represent
More informationRepresenting and Manipulating Floating Points
Representing and Manipulating Floating Points Jin-Soo Kim (jinsookim@skku.edu) Computer Systems Laboratory Sungkyunkwan University http://csl.skku.edu The Problem How to represent fractional values with
More informationFloating point. Today. IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties Next time.
Floating point Today IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties Next time The machine model Fabián E. Bustamante, Spring 2010 IEEE Floating point Floating point
More informationComputers and programming languages introduction
Computers and programming languages introduction Eugeniy E. Mikhailov The College of William & Mary Lecture 01 Eugeniy Mikhailov (W&M) Practical Computing Lecture 01 1 / 19 Class goals and structure Primary
More informationData Representation 1
1 Data Representation Outline Binary Numbers Adding Binary Numbers Negative Integers Other Operations with Binary Numbers Floating Point Numbers Character Representation Image Representation Sound Representation
More informationComputer Numbers and their Precision, I Number Storage
Computer Numbers and their Precision, I Number Storage Learning goal: To understand how the ways computers store numbers lead to limited precision and how that introduces errors into calculations. Learning
More informationBryant and O Hallaron, Computer Systems: A Programmer s Perspective, Third Edition. Carnegie Mellon
Carnegie Mellon Floating Point 15-213/18-213/14-513/15-513: Introduction to Computer Systems 4 th Lecture, Sept. 6, 2018 Today: Floating Point Background: Fractional binary numbers IEEE floating point
More informationTopic Notes: Bits and Bytes and Numbers
Computer Science 220 Assembly Language & Comp Architecture Siena College Fall 2010 Topic Notes: Bits and Bytes and Numbers Binary Basics At least some of this will be review, but we will go over it for
More informationSection 1.4 Mathematics on the Computer: Floating Point Arithmetic
Section 1.4 Mathematics on the Computer: Floating Point Arithmetic Key terms Floating point arithmetic IEE Standard Mantissa Exponent Roundoff error Pitfalls of floating point arithmetic Structuring computations
More information