Data Representa+on: Floa+ng Point Numbers
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1 Data Representa+on: Floa+ng Point Numbers Csci Machine Architecture and Organiza+on Professor Pen- Chung Yew With sides from Randy Bryant and Dave O Hallaron 1
2 Floa+ng Point Numbers Background: Frac+onal binary numbers IEEE floa+ng point standard: Defini+on Example and proper+es Rounding, addi+on, mul+plica+on Floa+ng point in C Summary 2
3 Frac+onal binary numbers What is ? 3
4 Frac+onal Binary Numbers 2 i 2 i bi bi- 1 b2 b1 b0 b- 1 b- 2 b- 3 b- j 1/2 1/4 1/8 Representa+on 2 - j Bits to right of binary point represent fracgonal powers of 2 Represents ragonal number: 4
5 Frac+onal Binary Numbers: Examples Value Representa+on 5 3/ / / Observa+ons Divide by 2 by shining right (unsigned) Mul+ply by 2 by shining leu Numbers of form are just below 1.0 1/2 + 1/4 + 1/ /2 i Use notagon 1.0 ε 5
6 Representable Numbers Limita+on #1 Can only exactly represent numbers of the form x/2 k Other ragonal numbers may have repeagng bit representagons Value Representa+on 1/ [01] 2 1/ [0011] 2 1/ [0011] 2 Limita+on #2 Just one se]ng of binary point within the w bits Limited range of numbers (very small values? very large?) 6
7 Floa+ng Point Numbers Background: Frac+onal binary numbers IEEE floa+ng point standard: Defini+on Example and proper+es Rounding, addi+on, mul+plica+on Floa+ng point in C Summary 7
8 IEEE Floa+ng Point IEEE Standard 754 Established in 1985 as uniform standard for floagng point arithmegc Before that, many idiosyncragc formats Supported by all major CPU vendors Driven by numerical concerns Nice standards for rounding, overflow, underflow Hard to make fast in hardware Numerical analysts predominated over hardware designers in defining standard 8
9 Announcement 2/3/2016 Prof. Yew s office hour on Friday 2/5/206 has been extended by ½ hour, i.e. from 10am- 11:30am Data Lab due 11:55pm, next Monday 2/8/2016 Check Data Lab Forum for common Q&As, or post your problems there. Homework Assignment #1 has been issued on Monday 2/1/2016 Download from Moodle class web page Due date before class Wednesday 2/10/2016 (check class schedule) 9
10 Review: Representable Numbers Limita+on #1 Can only exactly represent numbers of the form x/2 k Other ragonal numbers may have repeagng bit representagons Value Representa+on 1/ [01] 2 1/ [0011] 2 1/ [0011] 2 Limita+on #2 Just one se]ng of binary point within the w bits Limited range of numbers (very small values? very large?) 10
11 Floa+ng Point Representa+on Numerical Form: ( 1) s M 2 E Sign bit s determines whether number is negagve or posigve Significand M normally a frac+onal value in range [1.0,2.0). Exponent E weights value by power of two Encoding Most significant bit (MSB) is sign bit s exp field encodes E (but is not equal to E) frac field encodes M (but is not equal to M) Three different encoding schemes: (1) Normalized (exp , exp ), (2) Denormalized (exp= ), (3) Not- a- Number (NaN) (exp = ) s exp frac 11
12 Precision op+ons Single precision: 32 bits s exp frac 1 8-bits 23-bits Double precision: 64 bits s exp frac 1 11-bits 52-bits Extended precision: 80 bits (Intel only, internal to hardware) s exp frac 1 15-bits 63 or 64-bits 12
13 (1) Normalized Values When: exp and exp s exp v = ( 1) s M 2 E frac Exponent coded as a biased value: Exp = E + Bias Exp: unsigned value of exp field Bias = 2 k- 1-1, where k is the number of exponent bits Single precision: 127 (Exp: 1 254, E: ) Double precision: 1023 (Exp: , E: ) Significand coded with implied leading 1: M = 1.xxx x 2 xxx x: bits of frac field Minimum when frac=000 0 (M = 1.0) Maximum when frac=111 1 (M = 2.0 ε) Get extra leading bit for free 13
14 Normalized Encoding Example Value: float F = ; = = x 2 13 v = ( 1) s M 2 E E = Exp Bias Significand M = frac= Exponent E = 13 Bias = 127 Exp = 140 = Result: s exp frac 14
15 (2) Denormalized Values Condi+on: exp = v = ( 1) s M 2 E E = 1 Bias Exponent value: E = 1 Bias (instead of E = 0 Bias) Significand coded with implied leading 0: M = 0.xxx x2 xxx x: bits of frac Allows gradual underflow Cases exp = 000 0, frac = Represents zero value Note disgnct values: +0 and 0 (why?) exp = 000 0, frac Numbers closest to 0.0 Equi- spaced s exp frac 15
16 (3) Special Values Condi+on: exp = Case: exp = 111 1, frac = Represents value (infinity) OperaGon that overflows Both posigve and negagve E.g., 1.0/0.0 = 1.0/ 0.0 = +, 1.0/ 0.0 = Case: exp = 111 1, frac Not- a- Number (NaN) Represents case when no numeric value can be determined E.g., sqrt( 1),, 0 s exp frac 16
17 Visualiza+on: Floa+ng Point Encodings Normalized Denorm +Denorm +Normalized + NaN NaN 17
18 Floa+ng Point Numbers Background: Frac+onal binary numbers IEEE floa+ng point standard: Defini+on Example and proper+es Rounding, addi+on, mul+plica+on Floa+ng point in C Summary 18
19 Tiny Floa+ng Point Example s exp frac 1 4- bits 3- bits 8- bit Floa+ng Point Representa+on Sign bit is in the most significant bit Next four bits are the exponent, with a bias of 7 Last three bits are the frac Same general form as IEEE Format Normalized, denormalized RepresentaGon of 0, NaN, infinity 19
20 Dynamic Range (Posi+ve Only) Denormalized numbers Normalized numbers s exp frac E Value /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 University of Minnesota v = ( 1) s M 2 E n: E = Exp Bias = Exp 7 d: E = 1 Bias = 1 7 = - 6 closest to zero largest denorm smallest norm closest to 1 below closest to 1 above largest norm 20
21 Distribu+on of Values 6- bit IEEE- like format e = 3 exponent bits f = 2 fracgon bits Bias is = 3 s exp frac 1 3- bits 2- bits No+ce how the distribu+on gets denser toward zero. 8 values Denormalized Normalized Infinity 21
22 Distribu+on of Values (close- up view) 6- bit IEEE- like format e = 3 exponent bits f = 2 fracgon bits Bias is 3 s exp frac 1 3- bits 2- bits Denormalized Normalized Infinity 22
23 Special Proper+es of the IEEE 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 problemagc Will be greater than any other values What should comparison yield? Otherwise OK Denorm vs. normalized Normalized vs. infinity NegaGve is smaller than posigve (not so in 2 s complement) 23
24 Floa+ng Point Numbers Background: Frac+onal binary numbers IEEE floa+ng point standard: Defini+on Example and proper+es Rounding, addi+on, mul+plica+on Floa+ng point in C Summary 24
25 Floa+ng Point Opera+ons: Basic Idea x +f y = Round(x + y) x f y = Round(x y) Basic idea First compute exact result Make it fit into desired precision Possibly overflow if exponent too large Possibly round to fit into frac 25
26 Rounding Rounding Modes (illustrate with $ rounding) $1.4 $1.6 $1.5 $2.5 $1.5 Towards 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 26
27 Closer Look at Round- To- Even Default Rounding Mode Round to Even Need to use assembly programming to get other rounding modes All others are sta+s+cally biased Sum of set of posigve numbers will consistently be over- or under- esgmated Applying to Other Decimal Places / Bit Posi+ons 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) 27
28 Rounding Binary Numbers Binary Frac+onal Numbers Even when least significant bit is 0 Half way when bits to right of rounding posigon = Examples Round to nearest 1/4 (2 bits right of binary point) Value Binary Rounded Ac+on 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 28
29 FP Mul+plica+on ( 1) s1 M1 2 E1 ( 1) s2 M2 2 E2 Exact Result: ( 1) s M 2 E Sign s: s1 ^ s2 Significand M: M1 x M2 Exponent E: E1 + E2 Fixing If M 2, shin M right, increment E If E out of range, overflow Round M to fit frac precision Implementa+on Biggest chore is mulgplying significands 29
30 Floa+ng Point Addi+on ( 1) s1 M1 2 E1 + (- 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 (the larger of E 1 and E 2 ) Get binary points lined up E1 E2 ( 1) s1 M1 ( 1) s2 M2 ( 1) s M Fixing If M 2, shin M right, increment E if M < 1, shin M len k posigons, decrement E by k Overflow if E out of range Round M to fit frac precision 30
31 31
32 Review: Floa+ng Point Encodings Normalized Denorm +Denorm +Normalized + NaN NaN 32
33 Review: Distribu+on of Values 6- bit IEEE- like format e = 3 exponent bits f = 2 fracgon bits Bias is = 3 s exp frac 1 3- bits 2- bits No+ce how the distribu+on gets denser toward zero. 8 values Denormalized Normalized Infinity 33
34 Review: A Close- up View 6- bit IEEE- like format e = 3 exponent bits f = 2 fracgon bits Bias is 3 s exp frac 1 3- bits 2- bits Denormalized Normalized Infinity 34
35 Review: Posi+ve Only Denormalized numbers Normalized numbers s exp frac E Value /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 University of Minnesota v = ( 1) s M 2 E n: E = Exp Bias = Exp 7 d: E = 1 Bias = 1 7 = - 6 closest to zero largest denorm smallest norm closest to 1 below closest to 1 above largest norm 35
36 Review: Floa+ng Point Addi+on ( 1) s1 M1 2 E1 + (- 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 (the larger of E 1 and E 2 ) Get binary points lined up E1 E2 ( 1) s1 M1 ( 1) s2 M2 ( 1) s M Fixing If M 2, shin M right, increment E if M < 1, shin M len k posigons, decrement E by k Overflow if E out of range Round M to fit frac precision 36
37 Mathema+cal Proper+es of FP Add Mathema+cal Proper+es CommutaGve? AssociaGve? Overflow and inexactness of rounding (3.14+1e10)-1e10 = 0, 3.14+(1e10-1e10) = is addigve idengty? Yes Every element has addigve inverse? Yes, except for infiniges & NaNs Monotonicity a b a+c b+c? Except for infiniges & NaNs Yes No Almost Almost 37
38 Mathema+cal Proper+es of FP Mult Mathema+cal Proper+es MulGplicaGon CommutaGve? MulGplicaGon is AssociaGve? Possibility of overflow, inexactness of rounding Ex: (1e20*1e20)*1e-20= inf, 1e20*(1e20*1e-20)= 1e20 1 is mulgplicagve idengty? Yes MulGplicaGon distributes over addigon? No Possibility of overflow, inexactness of rounding 1e20*(1e20-1e20)= 0.0, 1e20*1e20 1e20*1e20 = NaN Monotonicity a b & c 0 a * c b *c? Except for infiniges & NaNs Yes No Almost 39
39 Floa+ng Point Numbers Background: Frac+onal binary numbers IEEE floa+ng point standard: Defini+on Example and proper+es Rounding, addi+on, mul+plica+on Floa+ng point in C Summary 40
40 Floa+ng Point in C C Guarantees Two Levels float single precision double double precision Conversions/Cas+ng Type cas+ng between int, float, and double changes bit representa+on double/float int Truncates fracgonal part Like rounding toward zero Not defined when out of range or NaN: Generally sets to TMin int double Exact conversion, as long as int has 53 bit word size int float Will round according to rounding mode 41
41 Summary IEEE Floa+ng Point has clear mathema+cal proper+es Represents numbers of form M x 2 E One can reason about opera+ons independent of implementa+on As if computed with perfect precision and then rounded Not the same as real arithme+c Violates associagvity/distribugvity Makes life difficult for compilers & serious numerical applicagons programmers 43
42 Interes+ng Numbers {single,double} Descrip7on 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 ε) 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 ε) x 2 {127,1023} Single 3.4 x Double 1.8 x Denormalized Normalized Infinity 48
43 Machine- Level Data Representa+on (Done) to Machine- Level Program Representa+on (Next) 49
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