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Lecture 3 Information Representation. Lecture 3: Information Representation. In this lecture, we will study What to think about when representing information in the computer system What is the meaning of the Internal Representation and the External Representation ? Internal Representation

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Lecture 3 Information Representation

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## Lecture 3Information Representation

Information Representation

### Lecture 3: Information Representation

In this lecture, we will study

• What to think about when representing information in the computer system

• What is the meaning of the Internal Representation and the External Representation ?

• Internal Representation

• Representation of Numbers

• Fixed point representation

• Sign plus magnitude

• 1’s complement

• 2’s Complement

• Floating point representation

• Representation of Nonnumeric data

• External Representation

• ASCII, EBCDIC, 6-bit BCD

• Chinese Remainder Representation

Information Representation

Human Machines

Character, Decimal NumberCode(binary)

### Considerations forInformation Representation

• Storage Space(number of bits for a code)

• Convenience for information interchange

• Efficiency for information processing

• Simplicity for conversion from/to decimal system

• Precision of representation

Information Representation

Other

Computer

Internal

Representation

Human

CPU

Memory

Device

### Internal and External Representations

External

Representation

External

Representation

External

Representation

Information Representation

### Internal and External Representations

External Representation

• ASCII, EBCDIC, 6-bit BCD, …

Internal Representation

• Nonnumeric data

• Processing does not modify data - e.g. word processing simply exchange or ordering of letters

• Use identical internal and external representations

• no need for conversion

• Numeric Data

• Scientific Applications

• Range of number is wide - Internal representation for precision reason

• Fast Calculation - Internal representation for speed reason

• CPU bound - conversion of codes for infrequent I/O operations is acceptable

• Commercial Data Processing

• High volume I/O - Code Conversion for I/O is not acceptable

• Complex calculation is rare - external representation is acceptable

Information Representation

### Representation of Data Structures

There exists relationship between data

• Predecessor and successor

• Superior, peer, subordinate

• membership

• etc

Storage of data in a 1-dimensional memory space

• Representation of relationships in 1-dimensional memory space is not unique

• Simple linear list, string, array, and other complex structures

Information Representation

+ I II III IIII V …

I II III IIII V VI ...

II III IIII V VI VII ...

III IIII V VI VII VIII …

IIII V VI VII VIII VIIII …

V VI VII VIII VIIII X …

VI VII VIII VIIII X XI …

+ 0 1 2 3 4 5 6 7 8 9

0 0 1 2 3 4 5 6 7 8 9

1 1 2 3 4 5 6 7 8 9 10

2 2 3 4 5 6 7 8 9 10 11

3 3 4 5 6 7 8 9 10 11 12

4 4 5 6 7 8 9 10 11 12 13

5 5 6 7 8 9 10 11 12 13 14

6 6 7 8 9 10 11 12 13 14 15

7 7 8 9 10 11 12 13 14 15 16

8 8 9 10 11 12 13 14 15 16 17

9 9 10 11 12 13 14 15 16 17 18

. . . . . .

. . . . . .

. . . . . .

### Internal Representation of Numbers

Roman Number Decimal Number

- a non-positional number system - a positional number system

Inefficient

Easy to convert to/from decimal

Smaller table implies cheaper to build Adder - Binary

Information Representation

Positional Number System

Representation: AR = an an-1 … a1 a0 . a-1 … a-m

where 0 < ai < R, and R is the base or radix

n-1

Equivalent decimal value: V(AR) = S ai . Ri

i = -m

### Internal Representation of Numbers

Binary number is a positional number system which

uses the smallest number of symbols

• Binary Code(Base 2)

• Easy to convert to/from Decimal

• Cheapest code to build ALU

Information Representation

### Fixed Point Number Representation

A number representation in which the location of the fractional point is fixed

Overflow

The number to be represented is either too large, or too small(underflow) that the number cannot be precisely represented with the given number of bits.

Complement of a number A

(R-1)’s Complement(1’s Complement, when R=2)

- For each position, subtract the number from (R-1), or

- Base R representation of Rn - R-m - |A|

R’s Complement(2’s Complement, when R=2)

- Add 1 to the (R-1)’s complement, or

- If A=0, then 0, else base R representation of Rn - |A|

Information Representation

### Binary Number Representation

B2 = bn bn-1 … b1 b0 . b-1 … b-m

bn:sign bit

bn-1 … b1 b0 . b-1 … b-m :magnitude bits

bn-1 … b1 b0 : Integer part

b-1 … b-m : Fractional part

Computer systems rarely use both Integer and fractional parts, instead most computers use either Integer(Integer machine) or Fractional(Fractional machine) part only

• Sign plus magnitude representation

• (Sign plus) 1’s complement representation

• (Sign plus) 2’s complement representation

Information Representation

### Sign Plus Magnitude Representation

B = bn bn-1 … b1 b0 . b-1 … b-m

bn:sign bit

bn-1 … b1 b0 . b-1 … b-m :magnitude bits

n-1

VS(B) = (-1)bnS bi . 2i

i = -m

Easy to convert to the negative number

Range of representation;

Max:011 … 11 . 11 … 1(2n - 2-m)

Min:111 … 11 . 11 … 1-(2n - 2-m)

2 representations of zero;

+0:000 … 00 . 00 … 0

- 0:100 … 00 . 00 … 0

Information Representation

### 1’s Complement Representation

B = bn bn-1 … b1 b0 . b-1 … b-m

bn:sign bit

bn-1 … b1 b0 . b-1 … b-m :magnitude bits

n-1

V1(B) = bn(2-m - 2n) + S bi . 2i

i = -m

• Range of representation;

Max:011 … 11 . 11 … 1(2n - 2-m)

Min:100 … 00 . 00 … 0-(2n - 2-m)

• 2 representations of zero;

+0:000 … 00 . 00 … 0

- 0:111 … 11 . 11 … 1

Information Representation

### 2’s Complement Representation

B = bn bn-1 … b1 b0 . b-1 … b-m

bn:sign bit

bn-1 … b1 b0 . b-1 … b-m :magnitude bits

n-1

V2(B) = -bn 2n + S bi . 2i

i = -m

• Range of representation;

Max:011 … 11 . 11 … 1(2n - 2-m)

Min:100 … 00 . 00 … 0-2n

• Representations of zero;

000 … 00 . 00 … 0

Information Representation

### Comparison

• Easiness of negative conversion

• S+M > 1’s Complement > 2’s Complement

• Hardware

• 1’s and 2’s complement: Need only an Adder

• Speed of Arithmetic

• 2’s Complement > 1’s Complement (end-around carry)

• Recognition of zero

• 2’s Complement is fast

Information Representation

### Time Out

• 화가가 화랑 주인에게 자기 그림에 대해 관심을 보이는 사람이 없더냐고 물었다.

• 화랑 주인이 대답했다. “좋은 소식도 있고, 나쁜 소식도 있지요.”

• “좋은 소식은 내가 어떤 손님에게 당신이 죽고 나면 당신 그림 값이 크게 오를 것이라고 했더니 그 사람이 당신 그림을 몽땅 산거지요.”

• “나쁜 소식은 그 손님이 당신의 주치의라는 것입니다.”

Information Representation

bn ak ak-1 … a0bn-1 … b1 b0 . b-1 … b-m

sign exponent mantissa

### Floating Point Representation

Fixed point representation has the precision problem

19,850,000,000,000

.000,000,000,034,82

Various representations of a very large or very small numbers;

1,985 x 1010 can be represented as

0.1985 x 1014, 1.985 x 1013, 19.85 x 1012, 198.5 x 1011, …

0.3482 x 10-10can be represented as

3.482 x 10-11, 34.82 x 10-12, 348.2 x 10-13, 3,482 x 10-14, …

Floating Point Number: F

F = AB

Equivalent Decimal Value VF(F) = V(B) x 2V(A)

Information Representation

### Floating Point Number:Representation of Zero

• Representation of Zero

• Zero

• Mantissa = 0

• Real Zero

• Mantissa = 0

• Exponent: Smallest number that can be represented with given bits

• 00 … 0 is most suitable representation

• Needs to add Bias to the exponent value so that the smallest exponent can be represented with 00…0

Example: Assume 3-bit exponent

+3011

+2010

+1001

+0000

-0111

-1110

-2101

-3100

111

110

101

100

011

010

001

+100

100+ bias =000

Bias = 100

Information Representation

S Exponent Mantissa

Significant bit(s)

is(are) wasted

Normal Form

37.26 0.3726 x 102 , 0.0372 x 103, 0.0037 x 104, 0.0003 x 105

### Floating Point Number:Normalization

• Normal Form

• Unified representation makes the comparison of numbers easy

• A representation that represents the given number most precisely by utilizing the most significant bit of mantissa

• e.g. Mantissa: Fractional representation with 4 decimal digits

+ 2 .3726

+ 3 .0372

+ 4 .0037

+ 5 .0003

. . .

Information Representation

BCD Decimal

00000

00011

00102

00113

01004

01015

01106

01117

10008

10019

1010-

1011-

1100-

1101-

1110-

1111-

• 1 digit log2 10 = 4 4-bit BCD

• Unused code space

• Slow arithmetic

### Internal Number Representation:Decimal Representation

• Arithmetic with Decimal Numbers

• No need to convert between decimal and other representations

• Commercial data processing applications

Information Representation

DecimalBCD242184-2-1excess 3

00000000000000011

10001000101110100

20010001001100101

30011001101010110

40100010001000111 9’s complement

50101101110111000 representation

60110110010101001

70111110110011010

81000111010001011

91001111111111100

Decimal Number Representation:BCD

BCD(Binary Coded Decimal)

• 4-bit code with 8, 4, 2, 1 weight on each bit (8421 code)

• Difficult to convert to 9’s complement for the negative number representation 2421 code, 84-2-1 code, excess 3 code for this purpose

Information Representation

Totem in Sunrise

Information Representation

### Nonnumeric Representation

• 6-bit BCD

• 4-bit BCD + 2 bits

• Representation of numeric as well as nonnumeric symbols

• Capital and Small English letters are not distinguishable

• Suitable for computers with the word length of multiples of 6

• ASCII

• Representation of 128 letters with 7 bits

• 8-bit ASCII <= 7-bit ASCII + 1 parity bit

• EBCDIC

• 8-bit code

Information Representation

Chinese Remainder Theorem

A set of n relatively prime numbers: m1, m2, … , mn

A set of remainders {a1, a2, … , an} uniquely determines an integer A in the range of 0 < A < M, where

n

M = P mi

i=1

Conversion to decimal

n

Let Nj = P mi

i=1, i= j

n

A = | S Ni | ai / Ni |mi |M , where |X|M represents X modulo M

i=1

### Internal Representation of Numbers:Chinese Remainder Theorem

Information Representation

0 < 23 < 30

A = (1, 2, 3)

### Chinese Remainder Representation

m1 = 2, m2 = 3, m3 = 5;

M = 2 x 3 x 5 = 30

A=23;

a1 = |23|2 = 1

a2 = |23|3 = 2

a3 = |23|5 = 3

Conversion to decimal:

N1 = m2 x m3 = 3 x 5 = 15

N2 = m1 x m3 = 2 x 5 = 10

N3 = m1 x m2 = 2 x 3 = 6

A = |(15 x |1/15|2) + 10 x (|2/10|3) + 6 x (|3/6|5) |30

= |(15 x |1/1|2) + 10 x (|2/1|3) + 6 x (|3/1|5) |30

= |15 + 20 + 18|30 = |53|30 = 23

Information Representation

### Significance of Chinese Remainder Representation

• Very compact code - mi-remainders

• Allows to calculate with smaller numbers

• Faster with smaller numbers

We have a 32-bit computer.

Assume that we have to deal with a very large fixed point number which can not be fitted into a 32-bit register.

If we select a set of prime numbers that can be represented with 32 bits,

the number that we have to deal with can be represented with a code consists of a set of 32-bit numbers.

• mi-complement arithmetic is possible

• Difficult to compare values of numbers

• No carries to propagate between mi-remainders (although we have to consider the carry propagation within an mi-remainder)

• Division is extremely difficult

Information Representation