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Integer Operations Overview: Arithmetic & Overflow in Integers, Utilizing Shift for Multiply/Divide

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tkessler
72 diapositives · 786 Views · Jan 05, 2020

Explore integer operations like arithmetic computations, overflow handling, signed and unsigned addition, and multiplication techniques. Learn how to utilize shift operations for power-of-2 multiply and divide. Understand mathematical properties of signed and unsigned addition in integers.

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Integer Operations Overview: Arithmetic & Overflow in Integers, Utilizing Shift for Multiply/Divide
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  1. Integer Operations

  2. Outline • Arithmetic Operations • overflow • Unsigned addition, multiplication • Signed addition, negation, multiplication • Using Shift to perform power-of-2 multiply/divide • Suggested reading • Chap 2.3 Negation：取反

  3. • • • • • • u Operands: w bits • • • • • • + v True Sum: w+1 bits u + v UAddw(u , v) Discard Carry: w bits Unsigned Addition

  4. Unsigned Addition • Standard Addition Function • Ignores carry output • Implements Modular Arithmetic • s = UAddw(u , v) = (u + v) mod 2w P67 (2.9)

  5. Overflow UAdd4(u , v) True Sum 2w+1 Overflow 2w v 0 Modular Sum u Visualizing Unsigned Addition P68 Figure 2.16 • Wraps Around • If true sum ≥ 2w • At most once • Module: 取模

  6. Unsigned Addition Forms an Abelian Group P68 • Closed under addition • 0   UAddw(u , v)     2w –1 • Commutative （交换律） • UAddw(u , v) = UAddw(v , u) • Associative （结合律） • UAddw(t, UAddw (u,v)) = UAddw(UAddw (t, u ), v)

  7. Unsigned Addition Forms an Abelian Group • 0 is additive identity • UAddw(u , 0)  =  u • Every element has additive inverse • Let UCompw(u )  = 2w – u • UAddw(u , UCompw(u ))  =  0 P68 （2.10）

  8. Signed Addition • Functionality • True sum requires w+1 bits • Drop off MSB • Treat remaining bits as 2’s comp. integer P70 （2.12） PosOver：Positive Overflow NegOver：Negative Overflow

  9. True Sum 0 111…1 2w–1 PosOver TAdd Result 0 100…0 2w –1 011…1 0 000…0 0 000…0 PosOver TAdd(u , v) 1 100…0 –2w –1 100…0 > 0 –2w NegOver 1 000…0 v < 0 < 0 > 0 u NegOver Signed Addition P70 Figure 2.17

  10. Visualizing 2’s Comp. Addition • Values • 4-bit two’s comp. • Range from -8 to +7 • Wraps Around • If sum  2w-1 • Becomes negative • If sum < –2w–1 • Becomes positive

  11. NegOver TAdd4(u , v) v u PosOver Visualizing 2’s Comp. Addition P72 Figure 2.19

  12. 2w–1 PosOver 2w –1 0 NegOver Detecting Tadd Overflow P71 • Task • Given s = TAddw(u , v) • Determine if s =Addw(u , v) • Claim • Overflow iff either: • u, v < 0, s 0 (NegOver) • u, v 0, s < 0 (PosOver) • ovf = (u<0 == v<0) && (u<0 != s<0);

  13. Mathematical Properties of TAdd • Two’s Complement Under TAdd Forms a Group • Closed, Commutative, Associative, 0 is additive identity • Every element has additive inverse • Let • TAddw(u , TCompw(u ))  =  0 P73 （2.13）

  14. Mathematical Properties of TAdd • Isomorphic Algebra to UAdd • TAddw (u , v) = U2T (UAddw(T2U(u ), T2U(v))) • Since both have identical bit patterns • T2U(TAddw (u , v)) = UAddw(T2U(u ), T2U(v)) Isomorphic：同构

  15. x -1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 + ~x 0 1 1 0 0 0 1 0 Negating with Complement & Increment P73 • In C • ~x + 1 == -x • Complement • Observation: ~x + x == 1111…111 == -1 ~x：Complement

  16. Signed Addition • Increment • ~x + 1 = ~x +[x + (-x)] +1 • (~x + x) + -x + 1 == -1 + (-x + 1) == -x • So, • ~x + 1 == -x

  17. Multiplication P75 • Computing Exact Product of w-bit numbers x, y • Either signed or unsigned • Ranges • Unsigned: 0 ≤ x * y ≤ (2w – 1) 2 = 22w – 2w+1 + 1 • Up to 2w bits • Two’s complement min: x *y ≥–2w–1*(2w–1–1) = –22w–2 + 2w–1 • Up to 2w–1 bits • Two’s complement max: x * y ≤ (–2w–1) 2 = 22w–2 • Up to 2w bits, but only for TMinw2

  18. Multiplication • Maintaining Exact Results • Would need to keep expanding word size with each product computed • Done in software by “arbitrary precision” arithmetic packages

  19. • • • • • • k u Operands: w bits * 2k 0 ••• 0 1 0 ••• 0 0 True Product: w+k bits u · 2k 0 ••• 0 0 UMultw(u , 2k) ••• 0 ••• 0 0 Discard k bits: w bits TMultw(u , 2k) Power-of-2 Multiply with Shift

  20. Power-of-2 Multiply with Shift • Operation • u << k gives u * 2k • Both signed and unsigned • Examples • u << 3 == u * 8 • u << 5 - u << 3 == u * 24 • Most machines shift and add much faster than multiply • Compiler will generate this code automatically

  21. k u Binary Point ••• ••• ••• Operands: / 2k 0 ••• 0 1 0 ••• 0 0 Division: u / 2k . 0 ••• 0 0 ••• Quotient: u / 2k 0 ••• 0 0 ••• Unsigned Power-of-2 Divide with Shift • Quotient of Unsigned by Power of 2 • u >> k gives  u / 2k • Uses logical shift

  22. k ••• u Binary Point ••• Operands: / 2k 0 ••• 0 1 0 ••• 0 0 Division: u / 2k . 0 ••• ••• ••• Result: RoundDown(u / 2k) 0 ••• ••• 2’s Comp Power-of-2 Divide with Shift P77 • Quotient of Signed by Power of 2 • u >> k gives  u / 2k • Uses arithmetic shift • Rounds wrong direction when u < 0

  23. Correct Power-of-2 Divide • Quotient of Negative Number by Power of 2 • Want  u / 2k (Round Toward 0) • Compute as  (u+2k-1)/ 2k • In C: (u + (1<<k)-1) >> k • Biases divided toward 0 Quotient：商

  24. Case 1: No rounding k Dividend: u 1 ••• 0 ••• 0 0 +2k +–1 0 ••• 0 0 1 ••• 1 1 Binary Point 1 ••• 1 ••• 1 1 Divisor: / 2k 0 ••• 0 1 0 ••• 0 0  u / 2k  . 0 1 ••• 1 1 1 ••• 1 ••• 1 1 • Biasing has no effect Correct Power-of-2 Divide

  25. Case 2: Rounding k Dividend: u 1 ••• ••• +2k +–1 0 ••• 0 0 1 ••• 1 1 1 ••• ••• Incremented by 1 Binary Point Divisor: / 2k 0 ••• 0 1 0 ••• 0 0  u / 2k  . 1 0 ••• 1 1 1 ••• ••• Incremented by 1 • Biasing adds 1 to final result Correct Power-of-2 Divide

  26. Floating Point

  27. Topics • Fractional Binary Numbers • IEEE 754 Standard • Rounding Mode • FP Operations • Floating Point in C • Suggested Reading: Chap 2.4

  28. Encoding Rational Numbers P80 • Form V = • Very useful when >> 0 or <<1 • An Approximation to real arithmetic • From programmer’s perspective • Uninteresting • Arcane and incomprehensive * Arcane：神秘的 * Incomprehensive: 不可理解的

  29. Encoding Rational Numbers • Until 1980s • Many idiosyncratic formats, fast speed, easy implementation, less accuracy • IEEE 754 • Designed by W. Kahan for Intel processors • Based on a small and consistent set of principles, elegant, understandable, hard to make go fast Idiosyncratic: 特殊的 Elegant：雅致的

  30. 2m 2m–1 4 • • • 2 1 bm bm–1 • • • b2 b1 b0 . b–1 b–2 b–3 • • • b–n 1/2 • • • 1/4 1/8 2–n Fractional Binary Numbers

  31. Fractional Binary Numbers • Bits to right of “binary point” represent fractional powers of 2 • Represents rational number: 2i P81 （2.17）

  32. Fractional Numbers to Binary Bits unsigned result_bits=0, current_bit=0x80000000 for (i=0;i<32;i++) { x *= 2 if ( x>= 1 ) { result_bits |= current_bit ; if ( x == 1) break ; x -= 1 ; } current_bit >> 1 ; }

  33. Fraction Binary Number Examples Value Binary Fraction 0.2 0.00110011[0011] • Observations: • The form 0.11111…11 represent numbers just below 1.0 which is noted as 1.0- • Binary Fractions can only exactly represent x/2k • Others have repeated bit patterns

  34. IEEE Floating-Point Representation P83 • Numeric form • V=(-1)sM  2E • Sign bit s determines whether number is negative or positive • Significand Mnormally a fractional value in range [1.0,2.0). • Exponent E weights value by power of two

  35. s exp frac IEEE Floating-Point Representation • Encoding • s is sign bit • exp field encodes E • frac field encodes M • Sizes • Single precision (32 bits): 8 exp bits, 23 frac bits • Double precision (64 bits): 11 exp bits, 52 frac bits

  36. Normalize Values P84 • Condition • exp000…0 and exp111…1 • Exponent coded as biased value • E = Exp – Bias • Exp : unsigned value denoted by exp • Bias : Bias value • Single precision: 127 (Exp: 1…254, E : -126…127) • Double precision: 1023 (Exp: 1…2046, E : -1022 …1023) • In general: Bias = 2m-1 - 1, where m is the number of exponent bits

  37. Normalize Values • Significand coded with implied leading 1 • m =1.xxx…x2 • xxx…x: bits of frac • Minimum when 000…0 (M = 1.0) • Maximum when 111…1 (M = 2.0 – ) • Get extra leading bit for “free”

  38. Normalized Encoding Examples • Value: 12345 (Hex: 0x3039) • Binary bits: 11000000111001 • Fraction representation: 1.1000000111001*213 • M: 10000001110010000000000 • E: 10001100 (140) • Binary Encoding • 0100 0110 0100 0000 1110 0100 0000 0000 • 4640E400

  39. Denormalized Values P84 • Condition • exp=000…0 • Values • Exponent Value: E = 1 – Bias • Significant Value m =0.xxx…x2 • xxx…x: bits of frac

  40. Denormalized Values • Cases • exp = 000…0, frac = 000…0 • Represents value 0 • Note that have distinct values +0 and –0 • exp = 000…0, frac000…0 • Numbers very close to 0.0 • Lose precision as get smaller • “Gradual underflow”

  41. Special Values P85 • Condition • exp = 111…1

  42. Special Values • exp = 111…1, frac = 000…0 • Represents value(infinity) • Operation that overflows • Both positive and negative • E.g., 1.0/0.0 = 1.0/0.0 = +, 1.0/0.0 = 

  43. Special Values • exp = 111…1, frac000…0 • Not-a-Number (NaN) • Represents case when no numeric value can be determined • E.g., sqrt(–1), 

  44.  + -Normalized +Denorm +Normalized -Denorm NaN NaN 0 +0 Summary of Real Number Encodings P85 Figure 2.22

  45. 0 7 6 3 2 s exp frac 8-bit Floating-Point Representations

  46. 8-bit Floating-Point Representations • Exp exp E 2E • 0 0000 -6 1/64 (denorms) • 1 0001 -6 1/64 • 2 0010 -5 1/32 • 3 0011 -4 1/16 • 4 0100 -3 1/8 • 5 0101 -2 1/4 • 6 0110 -1 1/2 • 7 0111 0 1 • 8 1000 +1 2 • 9 1001 +2 4 • 10 1010 +3 8 • 11 1011 +4 16 • 12 1100 +5 32 • 13 1101 +6 64 • 14 1110 +7 128 • 15 1111 n/a (inf, NaN)

  47. Dynamic Range (Denormalized numbers) P86Figure 2.23 • s exp frac E Value • 0 0000 000 -6 0 • 0 0000 001 -6 1/8*1/64 = 1/512 • 0 0000 010 -6 2/8*1/64 = 2/512 • … • 0 0000 110 -6 6/8*1/64 = 6/512 • 0 0000 111 -6 7/8*1/64 = 7/512

  48. Dynamic Range • s exp frac E Value • 0 0001 000 -6 8/8*1/64 = 8/512 • 0 0001 001 -6 9/8*1/64 = 9/512 • … • 0 0110 110 -1 14/8*1/2 = 14/16 • 0 0110 111 -1 15/8*1/2 = 15/16 • 0 0111 000 0 8/8*1 = 1 • 0 0111 001 0 9/8*1 = 9/8

  49. Dynamic Range (Denormalized numbers) • s exp frac E Value • 0 0111 010 0 10/8*1 = 10/8 • … • 0 1110 110 7 14/8*128 = 224 • 0 1110 111 7 15/8*128 = 240 • 0 1111 000 n/a inf

  50. Distribution of Representable Values • 6-bit IEEE-like format • K = 3 exponent bits • n = 2 significand bits • Bias is 3 • Notice how the distribution gets denser toward zero.