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Class10

This document discusses floating point arithmetic. It begins by introducing the IEEE floating point standard and topics like rounding and floating point operations. It then discusses how fractional binary numbers are represented and the numerical format used in floating point, including normalized and denormalized values. Special values like infinity, NaN, positive/negative zero are also covered. The document concludes by discussing properties of floating point addition, multiplication, and how they differ from mathematical ideals due to rounding errors and special values.

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Floating Point Arithmetic February 15, 2001 Topics • IEEE Floating Point Standard • Rounding • Floating Point Operations • Mathematical properties • IA32 floating point class10.ppt 15-213 “The course that gives CMU its Zip!”
CS 213 S’01 – 2 – class10.ppt Floating Point Puzzles • For each of the following C expressions, either: – Argue that it is true for all argument values – Explain why not true • x == (int)(float) x • x == (int)(double) x • f == (float)(double) f • d == (float) d • f == -(-f); • 2/3 == 2/3.0 • d < 0.0  ((d*2) < 0.0) • d > f  -f < -d • d * d >= 0.0 • (d+f)-d == f int x = …; float f = …; double d = …; Assume neither d nor f is NaN
CS 213 S’01 – 3 – class10.ppt IEEE Floating Point IEEE Standard 754 • Established in 1985 as uniform standard for floating point arithmetic – Before that, many idiosyncratic formats • Supported by all major CPUs Driven by Numerical Concerns • Nice standards for rounding, overflow, underflow • Hard to make go fast – Numerical analysts predominated over hardware types in defining standard
CS 213 S’01 – 4 – class10.ppt Fractional Binary Numbers Representation • Bits to right of “binary point” represent fractional powers of 2 • Represents rational number: bi bi–1 b2 b1 b0 b–1 b–2 b–3 b–j • • • • • • . 1 2 4 2i–1 2i • • • • • • 1/2 1/4 1/8 2–j bk 2k k j i 
CS 213 S’01 – 5 – class10.ppt Fractional Binary Number Examples Value Representation 5-3/4 101.112 2-7/8 10.1112 63/64 0.1111112 Observation • Divide by 2 by shifting right • Numbers of form 0.111111…2 just below 1.0 – Use notation 1.0 –  Limitation • Can only exactly represent numbers of the form x/2k • Other numbers have repeating bit representations Value Representation 1/3 0.0101010101[01]…2 1/5 0.001100110011[0011]…2 1/10 0.0001100110011[0011]…2
CS 213 S’01 – 6 – class10.ppt Numerical Form • –1s M 2E – Sign bit s determines whether number is negative or positive – Significand M normally a fractional value in range [1.0,2.0). – Exponent E weights value by power of two Encoding • MSB is sign bit • exp field encodes E • frac field encodes M Sizes • Single precision: 8 exp bits, 23 frac bits – 32 bits total • Double precision: 11 exp bits, 52 frac bits – 64 bits total Floating Point Representation s exp frac
CS 213 S’01 – 7 – class10.ppt “Normalized” Numeric Values 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 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”
CS 213 S’01 – 8 – class10.ppt Normalized Encoding Example Value Float F = 15213.0; • 1521310 = 111011011011012 = 1.11011011011012 X 213 Significand M = 1.11011011011012 frac = 110110110110100000000002 Exponent E = 13 Bias = 127 Exp = 140 = 100011002 Floating Point Representation (Class 02): Hex: 4 6 6 D B 4 0 0 Binary: 0100 0110 0110 1101 1011 0100 0000 0000 140: 100 0110 0 15213: 1110 1101 1011 01
CS 213 S’01 – 9 – class10.ppt Denormalized Values Condition • exp = 000…0 Value • Exponent value E = –Bias + 1 • Significand value m = 0.xxx…x2 –xxx…x: bits of frac 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”
CS 213 S’01 – 10 – class10.ppt Special Values Condition • exp = 111…1 Cases • 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 =  • exp = 111…1, frac  000…0 – Not-a-Number (NaN) – Represents case when no numeric value can be determined – E.g., sqrt(–1), 
CS 213 S’01 – 11 – class10.ppt Summary of Floating Point Real Number Encodings NaN NaN +  0 +Denorm +Normalized -Denorm -Normalized +0
CS 213 S’01 – 12 – class10.ppt Tiny floating point example 8-bit Floating Point Representation • the sign bit is in the most significant bit. • the next four bits are the exponent, with a bias of 7. • the last three bits are the frac • Same General Form as IEEE Format • normalized, denormalized • representation of 0, NaN, infinity s exp frac 0 2 3 6 7
CS 213 S’01 – 13 – class10.ppt Values related to the exponent 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)
CS 213 S’01 – 14 – class10.ppt Dynamic Range 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 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 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 closest to zero largest denorm smallest norm closest to 1 below closest to 1 above largest norm Denormalized numbers Normalized numbers
CS 213 S’01 – 15 – class10.ppt Interesting Numbers Description exp frac Numeric Value Zero 00…00 00…00 0.0 Smallest Pos. Denorm. 00…00 00…01 2– {23,52} X 2– {126,1022} • Single  1.4 X 10–45 • Double  4.9 X 10–324 Largest Denormalized 00…00 11…11 (1.0 – ) X 2– {126,1022} • Single  1.18 X 10–38 • Double  2.2 X 10–308 Smallest Pos. Normalized 00…01 00…00 1.0 X 2– {126,1022} • Just larger than largest denormalized One 01…11 00…00 1.0 Largest Normalized 11…10 11…11 (2.0 – ) X 2{127,1023} • Single  3.4 X 1038 • Double  1.8 X 10308
CS 213 S’01 – 16 – class10.ppt Special Properties of Encoding FP Zero Same as Integer Zero • All bits = 0 Can (Almost) Use Unsigned Integer Comparison • Must first compare sign bits • Must consider -0 = 0 • NaNs problematic – Will be greater than any other values – What should comparison yield? • Otherwise OK – Denorm vs. normalized – Normalized vs. infinity
CS 213 S’01 – 17 – class10.ppt Floating Point Operations Conceptual View • First compute exact result • Make it fit into desired precision – Possibly overflow if exponent too large – Possibly round to fit into frac Rounding Modes (illustrate with $ rounding) $1.40 $1.60 $1.50 $2.50 –$1.50 • Zero $1.00 $1.00 $1.00 $2.00 –$1.00 • Round down (-) $1.00 $1.00 $1.00 $2.00 –$2.00 • Round up (+) $2.00 $2.00 $2.00 $3.00 –$1.00 • Nearest Even (default) $1.00 $2.00 $2.00 $2.00 –$2.00 Note: 1. Round down: rounded result is close to but no greater than true result. 2. Round up: rounded result is close to but no less than true result.
CS 213 S’01 – 18 – class10.ppt A Closer Look at Round-To-Even Default Rounding Mode • Hard to get any other kind without dropping into assembly • All others are statistically biased – Sum of set of positive numbers will consistently be over- or under- estimated Applying to Other Decimal Places • When exactly halfway between two possible values – Round so that least significant digit is even • E.g., round to nearest hundredth 1.2349999 1.23 (Less than half way) 1.2350001 1.24 (Greater than half way) 1.2350000 1.24 (Half way—round up) 1.2450000 1.24 (Half way—round down)
CS 213 S’01 – 19 – class10.ppt Rounding Binary Numbers Binary Fractional Numbers • “Even” when least significant bit is 0 • Half way when bits to right of rounding position = 100…2 Examples • Round to nearest 1/4 (2 bits right of binary point) Value Binary Rounded Action Rounded Value 2 3/32 10.000112 10.002 (<1/2—down) 2 2 3/16 10.001102 10.012 (>1/2—up) 2 1/4 2 7/8 10.111002 11.002 (1/2—up) 3 2 5/8 10.101002 10.102 (1/2—down) 2 1/2
CS 213 S’01 – 20 – class10.ppt FP Multiplication Operands (–1)s1 M1 2E1 (–1)s2 M2 2E2 Exact Result (–1)s M 2E • Sign s: s1 ^ s2 • Significand M: M1 * M2 • Exponent E: E1 + E2 Fixing • If M ≥ 2, shift M right, increment E • If E out of range, overflow • Round M to fit frac precision Implementation • Biggest chore is multiplying significands
CS 213 S’01 – 21 – class10.ppt FP Addition Operands (–1)s1 M1 2E1 (–1)s2 M2 2E2 • Assume E1 > E2 Exact Result (–1)s M 2E • Sign s, significand M: – Result of signed align & add • Exponent E: E1 Fixing • If M ≥ 2, shift M right, increment E • if M < 1, shift M left k positions, decrement E by k • Overflow if E out of range • Round M to fit frac precision (–1)s1 m1 (–1)s2 m2 E1–E2 + (–1)s m
CS 213 S’01 – 22 – class10.ppt Mathematical Properties of FP Add Compare to those of Abelian Group • Closed under addition? YES – But may generate infinity or NaN • Commutative? YES • Associative? NO – Overflow and inexactness of rounding • 0 is additive identity? YES • Every element has additive inverse ALMOST – Except for infinities & NaNs Montonicity • a ≥ b  a+c ≥ b+c? ALMOST – Except for infinities & NaNs
CS 213 S’01 – 23 – class10.ppt Algebraic Properties of FP Mult Compare to Commutative Ring • Closed under multiplication? YES – But may generate infinity or NaN • Multiplication Commutative? YES • Multiplication is Associative? NO – Possibility of overflow, inexactness of rounding • 1 is multiplicative identity? YES • Multiplication distributes over addtion? NO – Possibility of overflow, inexactness of rounding Montonicity • a ≥ b & c ≥ 0  a *c ≥ b *c? ALMOST – Except for infinities & NaNs
CS 213 S’01 – 24 – class10.ppt Floating Point in 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
CS 213 S’01 – 25 – class10.ppt Answers to Floating Point Puzzles • x == (int)(float) x No: 24 bit significand • x == (int)(double) x Yes: 53 bit significand • f == (float)(double) f Yes: increases precision • d == (float) d No: loses precision • f == -(-f); Yes: Just change sign bit • 2/3 == 2/3.0 No: 2/3 == 0 • d < 0.0 ((d*2) < 0.0) Yes! • d > f -f < -d Yes! • d * d >= 0.0 Yes! • (d+f)-d == f No: Not associative int x = …; float f = …; double d = …; Assume neither d nor f is NAN
CS 213 S’01 – 26 – class10.ppt IA32 Floating Point History • 8086: first computer to implement IEEE FP – separate 8087 FPU (floating point unit) • 486: merged FPU and Integer Unit onto one chip Summary • Hardware to add, multiply, and divide • Floating point data registers • Various control & status registers Floating Point Formats • single precision (C float): 32 bits • double precision (C double): 64 bits • extended precision (C long double): 80 bits Instruction decoder and sequencer FPU Integer Unit Data Bus
CS 213 S’01 – 27 – class10.ppt FPU Data Register Stack FPU register format (extended precision) s exp frac 0 63 64 78 79 R7 R6 R5 R4 R3 R2 R1 R0 st(0) st(1) st(2) st(3) st(4) st(5) st(6) st(7) Top FPU register stack • stack grows down – wraps around from R0 -> R7 • FPU registers are typically referenced relative to top of stack – st(0) is top of stack (Top) – followed by st(1), st(2),… • push: increment Top, load • pop: store, decrement Top stack grows down absolute view stack view
CS 213 S’01 – 28 – class10.ppt FPU instructions Large number of floating point instructions and formats • ~50 basic instruction types • load, store, add, multiply • sin, cos, tan, arctan, and log! Sampling of instructions: Instruction Effect Description fldz push 0.0 Load zero flds S push S Load single precision real fmuls S st(0) <- st(0)*S Multiply faddp st(1) <- st(0)+st(1); pop Add and pop
CS 213 S’01 – 29 – class10.ppt Floating Point Code Example Compute Inner Product of Two Vectors • Single precision arithmetic • Scientific computing and signal processing workhorse float ipf (float x[], float y[], int n) { int i; float result = 0.0; for (i = 0; i < n; i++) { result += x[i] * y[i]; } return result; } pushl %ebp # setup movl %esp,%ebp pushl %ebx movl 8(%ebp),%ebx # %ebx=&x movl 12(%ebp),%ecx # %ecx=&y movl 16(%ebp),%edx # %edx=n fldz # push +0.0 xorl %eax,%eax # i=0 cmpl %edx,%eax # if i>=n done jge .L3 .L5: flds (%ebx,%eax,4) # push x[i] fmuls (%ecx,%eax,4) # st(0)*=y[i] faddp # st(1)+=st(0); pop incl %eax # i++ cmpl %edx,%eax # if i<n repeat jl .L5 .L3: movl -4(%ebp),%ebx # finish leave ret # st(0) = result
CS 213 S’01 – 30 – class10.ppt Inner product stack trace 0 st(0) 1. fldz 2. flds (%ebx,%eax,4) 0 st(0) x[0] st(1) 3. fmuls (%ecx,%eax,4) 0 st(0) x[0]*y[0] st(1) 4. faddp %st,%st(1) 0 + x[0]*y[0] st(0) 5. flds (%ebx,%eax,4) 0 + x[0]*y[0] st(0) x[1] 6. fmuls (%ecx,%eax,4) 0 + x[0]*y[0] st(1) x[1]*y[1] st(0) 7. faddp %st,%st(1) 0 + x[0]*y[0] + x[1]*y[1] st(0) st(1)
CS 213 S’01 – 31 – class10.ppt Summary IEEE Floating Point Has Clear Mathematical Properties • Represents numbers of form M X 2E • Can reason about operations independent of implementation – As if computed with perfect precision and then rounded • Not the same as real arithmetic – Violates associativity/distributivity – Makes life difficult for compilers & serious numerical applications programmers IA32 Floating Point is a Mess • Ill-conceived, pseudo-stack architecture • Covered in notes

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Class10

  • 1.
    Floating Point Arithmetic February15, 2001 Topics • IEEE Floating Point Standard • Rounding • Floating Point Operations • Mathematical properties • IA32 floating point class10.ppt 15-213 “The course that gives CMU its Zip!”
  • 2.
    CS 213 S’01 –2 – class10.ppt Floating Point Puzzles • For each of the following C expressions, either: – Argue that it is true for all argument values – Explain why not true • x == (int)(float) x • x == (int)(double) x • f == (float)(double) f • d == (float) d • f == -(-f); • 2/3 == 2/3.0 • d < 0.0  ((d*2) < 0.0) • d > f  -f < -d • d * d >= 0.0 • (d+f)-d == f int x = …; float f = …; double d = …; Assume neither d nor f is NaN
  • 3.
    CS 213 S’01 –3 – class10.ppt IEEE Floating Point IEEE Standard 754 • Established in 1985 as uniform standard for floating point arithmetic – Before that, many idiosyncratic formats • Supported by all major CPUs Driven by Numerical Concerns • Nice standards for rounding, overflow, underflow • Hard to make go fast – Numerical analysts predominated over hardware types in defining standard
  • 4.
    CS 213 S’01 –4 – class10.ppt Fractional Binary Numbers Representation • Bits to right of “binary point” represent fractional powers of 2 • Represents rational number: bi bi–1 b2 b1 b0 b–1 b–2 b–3 b–j • • • • • • . 1 2 4 2i–1 2i • • • • • • 1/2 1/4 1/8 2–j bk 2k k j i 
  • 5.
    CS 213 S’01 –5 – class10.ppt Fractional Binary Number Examples Value Representation 5-3/4 101.112 2-7/8 10.1112 63/64 0.1111112 Observation • Divide by 2 by shifting right • Numbers of form 0.111111…2 just below 1.0 – Use notation 1.0 –  Limitation • Can only exactly represent numbers of the form x/2k • Other numbers have repeating bit representations Value Representation 1/3 0.0101010101[01]…2 1/5 0.001100110011[0011]…2 1/10 0.0001100110011[0011]…2
  • 6.
    CS 213 S’01 –6 – class10.ppt Numerical Form • –1s M 2E – Sign bit s determines whether number is negative or positive – Significand M normally a fractional value in range [1.0,2.0). – Exponent E weights value by power of two Encoding • MSB is sign bit • exp field encodes E • frac field encodes M Sizes • Single precision: 8 exp bits, 23 frac bits – 32 bits total • Double precision: 11 exp bits, 52 frac bits – 64 bits total Floating Point Representation s exp frac
  • 7.
    CS 213 S’01 –7 – class10.ppt “Normalized” Numeric Values 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 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”
  • 8.
    CS 213 S’01 –8 – class10.ppt Normalized Encoding Example Value Float F = 15213.0; • 1521310 = 111011011011012 = 1.11011011011012 X 213 Significand M = 1.11011011011012 frac = 110110110110100000000002 Exponent E = 13 Bias = 127 Exp = 140 = 100011002 Floating Point Representation (Class 02): Hex: 4 6 6 D B 4 0 0 Binary: 0100 0110 0110 1101 1011 0100 0000 0000 140: 100 0110 0 15213: 1110 1101 1011 01
  • 9.
    CS 213 S’01 –9 – class10.ppt Denormalized Values Condition • exp = 000…0 Value • Exponent value E = –Bias + 1 • Significand value m = 0.xxx…x2 –xxx…x: bits of frac 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”
  • 10.
    CS 213 S’01 –10 – class10.ppt Special Values Condition • exp = 111…1 Cases • 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 =  • exp = 111…1, frac  000…0 – Not-a-Number (NaN) – Represents case when no numeric value can be determined – E.g., sqrt(–1), 
  • 11.
    CS 213 S’01 –11 – class10.ppt Summary of Floating Point Real Number Encodings NaN NaN +  0 +Denorm +Normalized -Denorm -Normalized +0
  • 12.
    CS 213 S’01 –12 – class10.ppt Tiny floating point example 8-bit Floating Point Representation • the sign bit is in the most significant bit. • the next four bits are the exponent, with a bias of 7. • the last three bits are the frac • Same General Form as IEEE Format • normalized, denormalized • representation of 0, NaN, infinity s exp frac 0 2 3 6 7
  • 13.
    CS 213 S’01 –13 – class10.ppt Values related to the exponent 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)
  • 14.
    CS 213 S’01 –14 – class10.ppt Dynamic Range 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 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 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 closest to zero largest denorm smallest norm closest to 1 below closest to 1 above largest norm Denormalized numbers Normalized numbers
  • 15.
    CS 213 S’01 –15 – class10.ppt Interesting Numbers Description exp frac Numeric Value Zero 00…00 00…00 0.0 Smallest Pos. Denorm. 00…00 00…01 2– {23,52} X 2– {126,1022} • Single  1.4 X 10–45 • Double  4.9 X 10–324 Largest Denormalized 00…00 11…11 (1.0 – ) X 2– {126,1022} • Single  1.18 X 10–38 • Double  2.2 X 10–308 Smallest Pos. Normalized 00…01 00…00 1.0 X 2– {126,1022} • Just larger than largest denormalized One 01…11 00…00 1.0 Largest Normalized 11…10 11…11 (2.0 – ) X 2{127,1023} • Single  3.4 X 1038 • Double  1.8 X 10308
  • 16.
    CS 213 S’01 –16 – class10.ppt Special Properties of Encoding FP Zero Same as Integer Zero • All bits = 0 Can (Almost) Use Unsigned Integer Comparison • Must first compare sign bits • Must consider -0 = 0 • NaNs problematic – Will be greater than any other values – What should comparison yield? • Otherwise OK – Denorm vs. normalized – Normalized vs. infinity
  • 17.
    CS 213 S’01 –17 – class10.ppt Floating Point Operations Conceptual View • First compute exact result • Make it fit into desired precision – Possibly overflow if exponent too large – Possibly round to fit into frac Rounding Modes (illustrate with $ rounding) $1.40 $1.60 $1.50 $2.50 –$1.50 • Zero $1.00 $1.00 $1.00 $2.00 –$1.00 • Round down (-) $1.00 $1.00 $1.00 $2.00 –$2.00 • Round up (+) $2.00 $2.00 $2.00 $3.00 –$1.00 • Nearest Even (default) $1.00 $2.00 $2.00 $2.00 –$2.00 Note: 1. Round down: rounded result is close to but no greater than true result. 2. Round up: rounded result is close to but no less than true result.
  • 18.
    CS 213 S’01 –18 – class10.ppt A Closer Look at Round-To-Even Default Rounding Mode • Hard to get any other kind without dropping into assembly • All others are statistically biased – Sum of set of positive numbers will consistently be over- or under- estimated Applying to Other Decimal Places • When exactly halfway between two possible values – Round so that least significant digit is even • E.g., round to nearest hundredth 1.2349999 1.23 (Less than half way) 1.2350001 1.24 (Greater than half way) 1.2350000 1.24 (Half way—round up) 1.2450000 1.24 (Half way—round down)
  • 19.
    CS 213 S’01 –19 – class10.ppt Rounding Binary Numbers Binary Fractional Numbers • “Even” when least significant bit is 0 • Half way when bits to right of rounding position = 100…2 Examples • Round to nearest 1/4 (2 bits right of binary point) Value Binary Rounded Action Rounded Value 2 3/32 10.000112 10.002 (<1/2—down) 2 2 3/16 10.001102 10.012 (>1/2—up) 2 1/4 2 7/8 10.111002 11.002 (1/2—up) 3 2 5/8 10.101002 10.102 (1/2—down) 2 1/2
  • 20.
    CS 213 S’01 –20 – class10.ppt FP Multiplication Operands (–1)s1 M1 2E1 (–1)s2 M2 2E2 Exact Result (–1)s M 2E • Sign s: s1 ^ s2 • Significand M: M1 * M2 • Exponent E: E1 + E2 Fixing • If M ≥ 2, shift M right, increment E • If E out of range, overflow • Round M to fit frac precision Implementation • Biggest chore is multiplying significands
  • 21.
    CS 213 S’01 –21 – class10.ppt FP Addition Operands (–1)s1 M1 2E1 (–1)s2 M2 2E2 • Assume E1 > E2 Exact Result (–1)s M 2E • Sign s, significand M: – Result of signed align & add • Exponent E: E1 Fixing • If M ≥ 2, shift M right, increment E • if M < 1, shift M left k positions, decrement E by k • Overflow if E out of range • Round M to fit frac precision (–1)s1 m1 (–1)s2 m2 E1–E2 + (–1)s m
  • 22.
    CS 213 S’01 –22 – class10.ppt Mathematical Properties of FP Add Compare to those of Abelian Group • Closed under addition? YES – But may generate infinity or NaN • Commutative? YES • Associative? NO – Overflow and inexactness of rounding • 0 is additive identity? YES • Every element has additive inverse ALMOST – Except for infinities & NaNs Montonicity • a ≥ b  a+c ≥ b+c? ALMOST – Except for infinities & NaNs
  • 23.
    CS 213 S’01 –23 – class10.ppt Algebraic Properties of FP Mult Compare to Commutative Ring • Closed under multiplication? YES – But may generate infinity or NaN • Multiplication Commutative? YES • Multiplication is Associative? NO – Possibility of overflow, inexactness of rounding • 1 is multiplicative identity? YES • Multiplication distributes over addtion? NO – Possibility of overflow, inexactness of rounding Montonicity • a ≥ b & c ≥ 0  a *c ≥ b *c? ALMOST – Except for infinities & NaNs
  • 24.
    CS 213 S’01 –24 – class10.ppt Floating Point in 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
  • 25.
    CS 213 S’01 –25 – class10.ppt Answers to Floating Point Puzzles • x == (int)(float) x No: 24 bit significand • x == (int)(double) x Yes: 53 bit significand • f == (float)(double) f Yes: increases precision • d == (float) d No: loses precision • f == -(-f); Yes: Just change sign bit • 2/3 == 2/3.0 No: 2/3 == 0 • d < 0.0 ((d*2) < 0.0) Yes! • d > f -f < -d Yes! • d * d >= 0.0 Yes! • (d+f)-d == f No: Not associative int x = …; float f = …; double d = …; Assume neither d nor f is NAN
  • 26.
    CS 213 S’01 –26 – class10.ppt IA32 Floating Point History • 8086: first computer to implement IEEE FP – separate 8087 FPU (floating point unit) • 486: merged FPU and Integer Unit onto one chip Summary • Hardware to add, multiply, and divide • Floating point data registers • Various control & status registers Floating Point Formats • single precision (C float): 32 bits • double precision (C double): 64 bits • extended precision (C long double): 80 bits Instruction decoder and sequencer FPU Integer Unit Data Bus
  • 27.
    CS 213 S’01 –27 – class10.ppt FPU Data Register Stack FPU register format (extended precision) s exp frac 0 63 64 78 79 R7 R6 R5 R4 R3 R2 R1 R0 st(0) st(1) st(2) st(3) st(4) st(5) st(6) st(7) Top FPU register stack • stack grows down – wraps around from R0 -> R7 • FPU registers are typically referenced relative to top of stack – st(0) is top of stack (Top) – followed by st(1), st(2),… • push: increment Top, load • pop: store, decrement Top stack grows down absolute view stack view
  • 28.
    CS 213 S’01 –28 – class10.ppt FPU instructions Large number of floating point instructions and formats • ~50 basic instruction types • load, store, add, multiply • sin, cos, tan, arctan, and log! Sampling of instructions: Instruction Effect Description fldz push 0.0 Load zero flds S push S Load single precision real fmuls S st(0) <- st(0)*S Multiply faddp st(1) <- st(0)+st(1); pop Add and pop
  • 29.
    CS 213 S’01 –29 – class10.ppt Floating Point Code Example Compute Inner Product of Two Vectors • Single precision arithmetic • Scientific computing and signal processing workhorse float ipf (float x[], float y[], int n) { int i; float result = 0.0; for (i = 0; i < n; i++) { result += x[i] * y[i]; } return result; } pushl %ebp # setup movl %esp,%ebp pushl %ebx movl 8(%ebp),%ebx # %ebx=&x movl 12(%ebp),%ecx # %ecx=&y movl 16(%ebp),%edx # %edx=n fldz # push +0.0 xorl %eax,%eax # i=0 cmpl %edx,%eax # if i>=n done jge .L3 .L5: flds (%ebx,%eax,4) # push x[i] fmuls (%ecx,%eax,4) # st(0)*=y[i] faddp # st(1)+=st(0); pop incl %eax # i++ cmpl %edx,%eax # if i<n repeat jl .L5 .L3: movl -4(%ebp),%ebx # finish leave ret # st(0) = result
  • 30.
    CS 213 S’01 –30 – class10.ppt Inner product stack trace 0 st(0) 1. fldz 2. flds (%ebx,%eax,4) 0 st(0) x[0] st(1) 3. fmuls (%ecx,%eax,4) 0 st(0) x[0]*y[0] st(1) 4. faddp %st,%st(1) 0 + x[0]*y[0] st(0) 5. flds (%ebx,%eax,4) 0 + x[0]*y[0] st(0) x[1] 6. fmuls (%ecx,%eax,4) 0 + x[0]*y[0] st(1) x[1]*y[1] st(0) 7. faddp %st,%st(1) 0 + x[0]*y[0] + x[1]*y[1] st(0) st(1)
  • 31.
    CS 213 S’01 –31 – class10.ppt Summary IEEE Floating Point Has Clear Mathematical Properties • Represents numbers of form M X 2E • Can reason about operations independent of implementation – As if computed with perfect precision and then rounded • Not the same as real arithmetic – Violates associativity/distributivity – Makes life difficult for compilers & serious numerical applications programmers IA32 Floating Point is a Mess • Ill-conceived, pseudo-stack architecture • Covered in notes