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Lecture 3 Information Representation

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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  1. Lecture 3Information Representation Information Representation

  2. 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

  3. 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

  4. Other Computer Internal Representation Human CPU Memory Device Internal and External Representations External Representation External Representation External Representation Information Representation

  5. 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

  6. 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

  7. + 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 Addition Table is infinite Inefficient Impossible to build an Adder Addition Table is finite Easy to convert to/from decimal Possible to build an Adder Smaller table implies cheaper to build Adder - Binary Information Representation

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. Comparison • Easiness of negative conversion • S+M > 1’s Complement > 2’s Complement • Hardware • S+M: Need an Adder and a Subtractor for addition • 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

  15. 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

  16. 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

  17. 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 +3 011 +2 010 +1 001 +0 000 -0 111 -1 110 -2 101 -3 100 111 110 101 100 011 010 001 +100 100+ bias =000 Bias = 100 Information Representation

  18. 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

  19. BCD Decimal 0000 0 0001 1 0010 2 0011 3 0100 4 0101 5 0110 6 0111 7 1000 8 1001 9 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

  20. Decimal BCD 2421 84-2-1 excess 3 0 0000 0000 0000 0011 1 0001 0001 0111 0100 2 0010 0010 0110 0101 3 0011 0011 0101 0110 4 0100 0100 0100 0111 9’s complement 5 0101 1011 1011 1000 representation 6 0110 1100 1010 1001 7 0111 1101 1001 1010 8 1000 1110 1000 1011 9 1001 1111 1111 1100 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

  21. Totem in Sunrise Information Representation

  22. 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

  23. 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

  24. 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

  25. 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

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