# Floating point. Today! IEEE Floating Point Standard! Rounding! Floating Point Operations! Mathematical properties. Next time. !

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1 Floating point Today! IEEE Floating Point Standard! Rounding! Floating Point Operations! Mathematical properties Next time! The machine model Chris Riesbeck, Fall 2011

2 Checkpoint

3 IEEE Floating point Floating point representations Encodes rational numbers of the form V=x*(2 y ) Useful for very large numbers or numbers close to zero IEEE Standard 754 (IEEE floating point) Established in 1985 as uniform standard for floating point arithmetic (started as an Intel s sponsored effort) 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 3

4 Fractional binary numbers Representation #1: Place notation like decimals, Bits to right of binary point represent fractional powers of 2 Represents rational number: b i b i 1 b 2 b 1 b 0. b 1 b 2 b 3 b j 1/2 1/4 1/8 2 i 2 i j 4

5 Fractional binary number examples Value Representation 5-3/ / / Observations Divide by 2 by shifting right (the point moves to the left) Multiply by 2 by shifting left (the point moves to the right) Numbers of form represent those just below 1.0 1/2 + 1/4 + 1/ /2 i +! 1.0 We use notation 1.0! to represent them 5

6 Representable numbers Limitation Can only exactly represent numbers of the form x/2 k Other numbers have repeating bit representations Value Representation 1/ [01] 2 1/ [0011] 2 1/ [0011] 2 Wastes bits with very big ( ) and very small ( ) numbers Wasted bits means fewer representable numbers 6

7 Floating point representation Representation #2: Scientific notation, like x 10 2 Numerical form V = ( 1) s * M * 2 E 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 (note: encode!= is) frac field encodes M s exp frac 7

8 Floating point precisions Encoding s exp frac Sign bit; exp (encodes E): k-bit; frac (encodes M): n-bit Sizes Single precision: k = 8 exp bits, n= 23 frac bits 32 bits total Double precision: k = 11 exp bits, n = 52 frac bits 64 bits total Extended precision: k = 15 exp bits, n = 63 frac bits Only found in Intel-compatible machines Stored in 80 bits 1 bit wasted Value encoded three different cases, depending on value of exp 8

9 Normalized numeric values Condition exp " and exp " Exponent coded as biased value E = Exp Bias Exp : unsigned value denoted by exp Bias : Bias value Single precision: 127 (Exp: 1 254, E: ) Double precision: 1023 (Exp: , E: ) in general: Bias = 2 k-1-1, where k is number of exponent bits Significand coded with implied leading 1 M = 1.xxx x 2 (1+f & f = 0.xxx 2 ) xxx x: bits of frac Minimum when (M = 1.0) Maximum when (M = 2.0!) Get extra leading bit for free 9

10 Normalized encoding example Value Float F = ; = = X 2 13 Significand M = frac = Exponent E = 13 Bias = 127 exp = 140 = Floating Point Representation: Hex: D B Binary: : :

11 Denormalized values Condition exp = Value Exponent value E = 1 - Bias Note: not simply E= Bias Significand value M = 0.xxx x 2 (0.f) Cases xxx x: bits of frac exp = 000 0, frac = Represents value 0 Note that have distinct values +0 and 0 exp = 000 0, frac " Numbers very close to

12 Special values Condition exp = Cases exp = 111 1, frac = 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 " Not-a-Number (NaN) Represents case when no numeric value can be determined E.g., sqrt(-1), - ("-") 12

13 Checkpoint

14 Dynamic range s exp frac E Value Denormalized numbers Normalized numbers /8*1/64 = 1/ /8*1/64 = 2/ /8*1/64 = 6/ /8*1/64 = 7/ /8*1/64 = 8/ /8*1/64 = 9/ /8*1/2 = 14/ /8*1/2 = 15/ /8*1 = /8*1 = 9/ /8*1 = 10/ /8*128 = /8*128 = n/a inf closest to zero largest denorm smallest norm closest to 1 below closest to 1 above largest norm 14

15 Summary of FP real number encodings \$# -Normalized -Denorm +Denorm +Normalized +# NaN \$0 +0 NaN 15

16 Distribution of values 6-bit IEEE-like format e = 3 exponent bits f = 2 fraction bits Bias is 3 Notice how the distribution gets denser toward zero Denormalized Normalized Infinity 16

17 Distribution of values (close-up view) 6-bit IEEE-like format e = 3 exponent bits f = 2 fraction bits Bias is 3 Note: Smooth transition between normalized and denormalized numbers due to definition E = 1 - Bias for denormalized values Denormalized Normalized Infinity 17

18 Interesting numbers Description exp frac Numeric Value Zero Smallest Pos. Denorm {23,52} X 2 {126,1022} Single ~ 1.4 X Double ~ 4.9 X Largest Denormalized (1.0!) X 2 {126,1022} Single ~ 1.18 X Double ~ 2.2 X Smallest Pos. Normalized X 2 {126,1022} Just larger than largest denormalized One Largest Normalized (2.0!) X 2 {127,1023} Single ~ 3.4 X Double ~ 1.8 X

19 Values related to the exponent Exp exp E 2 E Normalized E = e - Bias Denormalized E = 1 - Bias /64 (denorms) / / / / / / n/a (inf, NaN) 19

20 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 \$1 \$1 \$2 \$1 Round down (-") \$1 \$1 \$1 \$2 \$2 Round up (+") \$2 \$2 \$2 \$3 \$1 Nearest Even (default) \$1 \$2 \$2 \$2 \$2 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. 20

21 Closer look at round-to-even Default rounding mode All others are statistically biased Sum of set of positive numbers will consistently be overor under- estimated Applying to other decimal places / bit positions When exactly halfway between two possible values Round so that least significant digit is even E.g., round to nearest hundredth (Less than half way) (Greater than half way) (Half way round up) (Half way round down) 21

22 Rounding binary numbers Binary fractional numbers Even when least significant bit is 0 Half way when bits to right of rounding position = Examples Round to nearest 1/4 (2 bits right of binary point) Value Binary Rounded Action Rounded Value 2 3/ (<1/2 down) 2 2 3/ (>1/2 up) 2 1/4 2 7/ (1/2 up) 3 2 5/ (1/2 down) 2 1/2 22

23 FP multiplication Operands ( 1) s1 M 1 2 E1 * ( 1) s2 M 2 2 E2 Exact result ( 1) s M 2 E 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 23

24 FP addition Operands ( 1) s1 M1 2 E1 E1 E2 ( 1) s2 M2 2 E2 Assume E1 > E2 Exact Result ( 1) s M 2 E 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) s M ( 1) s2 M2 24

25 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 (3.14+1e10)-1e10=0 (rounding) 3.14+(1e10-1e10)= is additive identity? YES Every element has additive inverse ALMOST Except for infinities & NaNs Monotonicity a # b % a+c # b+c? ALMOST Except for NaNs 25

26 Math. properties of FP multiplication 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 addition? NO Possibility of overflow, inexactness of rounding Monotonicity a # b & c # 0 % a *c # b *c? ALMOST Except for NaNs 26

27 Floating point in C C guarantees two levels float single precision double double precision Conversions int \$ float : maybe rounded int \$ double : exact value preserved (double has greater range and higher precision) float \$ double : exact value preserved (double has greater range and higher precision) double \$ float : may overflow or be rounded double \$ int : truncated toward zero ( \$ -1) float \$ int : truncated toward zero No standard methods to change rounding or get special values like -0, inf and NaN. 27

28 Summary IEEE Floating point has clear mathematical properties Represents numbers of form M X 2 E Not the same as real arithmetic Violates associativity/distributivity Makes life difficult for compilers & serious numerical applications programmers 28

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