Integer Representation Floating point Representation Other data types

Transcription

1 Chapter 2 Bits, Data Types & Operations Integer Representation Floating point Representation Other data types

2 Why do Computers use Base 2? Base 10 Number Representation Natural representation for human transactions Hard to Implement Electronically ll Hard to store ENIAC (First electronic computer) used 10 vacuum tubes / digit Hard to transmit Need dhigh h precision i to encode 10 signal levels l on single wire Messy to implement digital logic functions Addition, multiplication, etc. Binary Representation 3.5 = Specifically, the devices that make up a computer are switches that can be on or off, i.e. at high or low voltage. Easy to store with bistable elements Reliably transmitted on noisy and inaccurate wires Examples 3.5 = 1 2 Represent as Represent as Represent as [0011] 2 Binary floating point number cannot exactly represent $1.20 2

3 Data representation in binary We need a way to represent information (data) in a form that is mutually comprehensible by human and machine. We have to develop schemes for representing all conceivable types of information integers, characters, floating point numbers, language, images, actions, etc. Binary digits (Bits): sequences of 0 s and 1 s that help humans keep track of the current flows stored/processed in the computer Using base 2 provide us with two symbols to work with: we can call them on & off, or (more usefully) 0 and 1. We group bits together to allow representation of more complex information For ease of use, Computer scientists usually represent quarters of bits in Hexadecimal eg. xa13f vs Hex Binary x x x x x x x x x x xa 1010 xb 1011 xc 1100 xd 1101 xe 1110 xf

4 Unsigned Binary Integers Y = abc = a b c.2 0 (where the digits a, b, c can each take on the values of 0 or 1 only) N = number of bits Range is: 0 i < 2 N 1 Umin = 0 Umax = 2 N 1 Problem: How do we represent negative numbers? 3-bits 5-bits 8-bits

5 Signed Magnitude Leading bit is the sign bit Y = abc = (-1) a (b c.2 0 ) Range is: -2 N < i < 2 N Smin = -2 N Smax = 2 N Problems: How do we do addition/subtraction? We have two numbers for zero (+/-)!

6 Two s Complement Transformation To transform a into a, invert all bits in a and add 1 to the result Range is: -2 N-1 <i < Tmin = -2 N Tmax = 2 N Advantages: Operations need not check the sign Only one representation for zero Efficient use of all the bits

7 Manipulating Binary numbers 1 Binary to Decimal conversion & vice versa A 4 bit binary number A = a 3 a 2 a 1 a 0 corresponds to: a 3 x a 2 x a 1 x a 0 x 2 0 = a 3 x 8 + a 2 x 4 + a 1 x 2 + a 0 x 1 (where a i = 0 or 1 only) A decimal number can be broken down by iteratively determining the highest power of two that fits in the number: e.g. (13) 10 => e.g. (63) 10 => e.g. (0.63) 10 => In the 2 s complement representation, leading zeros do not affect the value of a positive binary number, and leading ones do not affect the value of a negative number. So: = = 13 and = = x0d xfb => 8 (as expected!) x08 7

8 Manipulating Binary numbers 10 Overflow If we add the two (2 s complement) 4 bit numbers representing 7 and 5 we get : 0111 => => +5 We get 4, not +12 as we would expect!! 1100 => 4 (in 4 bit 2 s comp.) We have overflowed the range of 4 bit 2s 2 s comp. ( 8 to +7), so the result is invalid. Note that if we add 16 to this result we get back 16 4 = 12 this is like stepping up to 5 bit 2 s complement representation In general, if the sum of two positive ii numbers produces a negative result, or vice versa, an overflow has occurred, and the result is invalid in that representation. 8

9 CS Reality #1 The first place where the underlying hardware abstraction can t be treated as a magic black box results in the need for data types You ve got to understand binary encodings. Can x = 20,000,000,000? Is x 2 0? Int s: * > 1,600,000, * >?? Float s: Yes! Is (x + y) + z = x + (y + z)? Unsigned & Signed Int s: Yes! Float s: Is 4/5 = 4/5.0? (1e20 + 1e20) > e20 + ( 1e ) > 0? 3? 3.1? Maybe? (int) 4/5 = 0 what is 4/5.0? 9

10 C++ Integer Puzzles Assume machine with 32 bit word size, two s complement integers For each of the following C++ expressions, either: Argue that is true for all argument values Give example where not true x * x >= 0 ux >= 0 Initialization ux > -1 int x = foo(); int y = bar(); unsigned ux = x; unsigned uy = y; x < 0 (2*x) < 0 x > y -x < -y x > 0 && y > 0 x + y > 0 x >= 0 -x <= 0 x <= 0 -x >= 0 10

11 Problems Patt & Patel, Chapter 2: 2.4, 2.8, 2.10, 2.11, 2.17,

12 Representing Strings Strings in C/C++ Represented by array of characters Each character encoded in ASCII format Standard 7 bit encoding of character set Other encodings exist, but uncommon char S[6] = "15213"; x00a6 x00a7 x31 x35 UNICODE 16 bit superset of ASCII that x00a8 x32 includes characters for non English alphabets Character 0 has code 0x30 Digit i has code 0x30+i String should be null terminated Final character = 0 Line termination, and other special characters vary x00a9 x00aa x00ab Strings are really just arrays of numbers! How can we represent numbers using bits? Hey Hey, how do I know it s a string in the first place? x31 x33 x00 Hex #49 #53 #50 #49 #51 #00 Dec 12

13 Real numbers Most numbers are not integer! Range: The magnitude of the numbers we can represent is determined by how many bits we use: Precision: e.g. with 32 bits the largest number we can represent is about +/ 2 billion, far too small for many purposes. The exactness with which we can specify a number: e.g. a 32 bit number gives us 31 bits of precision, or roughly 9 figure precision in decimal representation Our decimal system handles non integer real numbers by adding yet another symbol the decimal point (.) to make a fixed point notation: e.g. 3, = The floating point, or scientific, notation allows us to represent very large and very small numbers (integer or real), with as much or as little precision as needed. 13

14 Real numbers in a fixed space What if you only have 8 digits to represent a real number? Do you prefer... a low precision number with a large range? or a high precision number with a smaller range? Wouldn t it be nice if we could float the decimal point to allow for either case? 14

15 Scientific notation In binary, we only have 0 and 1, how can we represent the decimal point? In scientific notation, it s implicit. In other words: ( 10 0 ) = = = We can represent any number with the decimal after the first digit by floating the decimal point to the left (or right) = 15

16 Real numbers in binary We mimic the decimal floating point notation to create a hybrid binary floating point number: We first use a binary point to separate whole numbers from fractional numbers to make a fixed point notation: e.g = = (2 1 = 0.5 and 2 2 = 0.25, etc.) We then float the binary point: => x 2 4 mantissa = , exponent = 4 Now we have to express this without the extra symbols ( x, 2,. ) by convention, we divide the available bits into three fields: sign, mantissa, exponent 16

17 32 bits: IEEE 754 fp numbers 32 bit float Sign: 1 bit 1 8 bits 23 bits s biased exp. fraction N = (-1) s x 1.fraction x 2 (biased exp. 127) Mantissa: 23 bits We normalize the mantissa by dropping the leading 1 and recording only its fractional part (why?) Exponent: 8 bits In order to handle both + and exponents, we add 127 to the actual exponent to create a biased exponent (this provides a lexographic order!) => biased exponent = (= 0) 2 0 => biased exponent = (= 127) => biased exponent = (= 254) 17

18 IEEE 754 fp numbers example Example: => => x 2 4 sign bit = 0 (+) normalized mantissa (fraction) = biased exponent = = 131 => so => => x41ce0000 Special values represented by convention: Infinity (+ and ): exponent = 255 ( ) and mantissa = 0 NaN (not a number): exponent = 255 and mantissa 0 Zero (0): exponent = 0 and mantissa = 0 note: special case => fraction is de normalized, i.e no hidden 1 18

19 IEEE 754 fp numbers 64 bit double Double precision (64 bit) floating point 64 bits: 1 11 bits 52 bits s biased exp. fraction N = (-1) s x 1.fraction x 2 (biased exp. 1023) Range & Precision: i 32 bit: mantissa of 23 bits + 1 => approx. 7 digits decimal 2 +/-127 => approx. 10 +/ bit: mantissa of 52 bits + 1 => approx. 15 digits decimal 2 +/-1023 => approx. 10 +/

20 Floating point in C/C++ C Guarantees Two Levels float single precision double double precision Conversions Casting between int,, float,, and double changes numeric values Double or float to int Truncates fractional part Like rounding toward zero Not defined when out of range Generally saturates to TMin or TMax int to double Exact conversion, as long as int has 53 bit word size int to float Will round according to rounding mode 20

21 Floating point puzzles in C++ int x = ; float f = ; double d = ; Assume neither d nor f is NAN x == (int)(float) x x == (int)(double) x f == (float)(double) f d == (float) d f == -(-f); 2/3 == 2/3.0 d < 0.00 ((d*2) < 0.0) 0) d > f -f > -d d * d >= 0.0 (d+f)-d d == f 21

22 Another floating point puzzle #include <iostream> using namespace std; int main() { float i; for (i=0.0;i<25.0;i+=0.1) { cout << i << endl; } } [...] [...] 22

23 Floating point number line 32 bits can represent 2 32 unique values How are those values distributed differently between integers and floats? What are the implications? b 0 +2b 23

24 Ariane 5 Payload rocket 7 billion (Euro) in design alone Danger! Computed horizontal velocity as floating point number Converted to 16 bit integer Worked OK for Ariane 4 Overflowed for Ariane 5 Result same software Exploded 37 seconds after liftoff Cargo worth $500 million 24

25 Other Data Types Other numeric data types e.g. BCD Bit vectors & masks sometimes we want to deal with the individual bits themselves Logic values True (non zero) or False (0) Instructions Misc Output as data from a compiler Grapics, sounds, 25

26 Practice Problems Patt & Patel 2.39, 2.40, 2.41, 2.42,