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An introduction to floating point arithmetic.ppt
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An introduction to floating point arithmetic.ppt

This topic discusses the basics of floating point numbers

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Lec 14 Systems Architecture 1 Systems Architecture Lecture 14: Floating Point Arithmetic Jeremy R. Johnson Anatole D. Ruslanov William M. Mongan Some or all figures from Computer Organization and Design: The Hardware/Software Approach, Third Edition, by David Patterson and John Hennessy, are copyrighted material (COPYRIGHT 2004 MORGAN KAUFMANN PUBLISHERS, INC. ALL RIGHTS RESERVED).
Lec 14 Systems Architecture 2 Introduction • Objective: To provide hardware support for floating point arithmetic. To understand how to represent floating point numbers in the computer and how to perform arithmetic with them. Also to learn how to use floating point arithmetic in MIPS. • Approximate arithmetic – Finite Range – Limited Precision • Topics – IEEE format for single and double precision floating point numbers – Floating point addition and multiplication – Support for floating point computation in MIPS
Lec 14 Systems Architecture 3 Distribution of Floating Point Numbers • 3 bit mantissa • exponent {-1,0,1} e = -1 e = 0 e = 1 1.00 X 2^(-1) = 1/2 1.00 X 2^0 = 1 1.00 X 2^1 = 2 1.01 X 2^(-1) = 5/8 1.01 X 2^0 = 5/4 1.01 X 2^1 = 5/2 1.10 X 2^(-1) = 3/4 1.10 X 2^0 = 3/2 1.10 X 2^1= 3 1.11 X 2^(-1) = 7/8 1.11 X 2^0 = 7/4 1.11 X 2^1 = 7/2 0 1 2 3
Lec 14 Systems Architecture 4 Floating Point • An IEEE floating point representation consists of – A Sign Bit (no surprise) – An Exponent (“times 2 to the what?”) – Mantissa (“Significand”), which is assumed to be 1.xxxxx (thus, one bit of the mantissa is implied as 1) – This is called a normalized representation • So a mantissa = 0 really is interpreted to be 1.0, and a mantissa of all 1111 is interpreted to be 1.1111 • Special cases are used to represent denormalized mantissas (true mantissa = 0), NaN, etc., as will be discussed.
Lec 14 Systems Architecture 5 Floating Point Standard • Defined by IEEE Std 754-1985 • Developed in response to divergence of representations – Portability issues for scientific code • Now almost universally adopted • Two representations – Single precision (32-bit) – Double precision (64-bit)
Lec 14 Systems Architecture 6 IEEE Floating-Point Format • S: sign bit (0  non-negative, 1  negative) • Normalize significand: 1.0 ≤ |significand| < 2.0 – Always has a leading pre-binary-point 1 bit, so no need to represent it explicitly (hidden bit) – Significand is Fraction with the “1.” restored • Exponent: excess representation: actual exponent + Bias – Ensures exponent is unsigned – Single: Bias = 127; Double: Bias = 1203 S Exponent Fraction single: 8 bits double: 11 bits single: 23 bits double: 52 bits Bias) (Exponent S 2 Fraction) (1 1) ( x      
Lec 14 Systems Architecture 7 Single-Precision Range • Exponents 00000000 and 11111111 reserved • Smallest value – Exponent: 00000001  actual exponent = 1 – 127 = –126 – Fraction: 000…00  significand = 1.0 – ±1.0 × 2–126 ≈ ±1.2 × 10–38 • Largest value – exponent: 11111110  actual exponent = 254 – 127 = +127 – Fraction: 111…11  significand ≈ 2.0 – ±2.0 × 2+127 ≈ ±3.4 × 10+38
Lec 14 Systems Architecture 8 Double-Precision Range • Exponents 0000…00 and 1111…11 reserved • Smallest value – Exponent: 00000000001  actual exponent = 1 – 1023 = –1022 – Fraction: 000…00  significand = 1.0 – ±1.0 × 2–1022 ≈ ±2.2 × 10–308 • Largest value – Exponent: 11111111110  actual exponent = 2046 – 1023 = +1023 – Fraction: 111…11  significand ≈ 2.0 – ±2.0 × 2+1023 ≈ ±1.8 × 10+308
Lec 14 Systems Architecture 9 Representation of Floating Point Numbers • IEEE 754 single precision 31 30 23 22 0 Sign Biased exponent Normalized Mantissa (implicit 24th bit = 1) (-1)s  F  2E-127 Exponent Mantissa Object Represented 0 0 0 0 non-zero denormalized 1-254 anything FP number 255 0 pm infinity 255 non-zero NaN
Lec 14 Systems Architecture 10 Why biased exponent? • For faster comparisons (for sorting, etc.), allow integer comparisons of floating point numbers: • Unbiased exponent: • Biased exponent: 0 1111 1111 000 0000 0000 0000 0000 0000 0 0000 0001 000 0000 0000 0000 0000 0000 1/2 2 0 0111 1110 000 0000 0000 0000 0000 0000 0 1000 0000 000 0000 0000 0000 0000 0000 1/2 2
Lec 14 Systems Architecture 11 Basic Technique • Represent the decimal in the form +/- 1.xxxb x 2y • And “fill in the fields” – Remember biased exponent and implicit “1.” mantissa! • Examples: – 0.0: 0 00000000 00000000000000000000000 – 1.0 (1.0 x 2^0): 0 01111111 00000000000000000000000 – 0.5 (0.1 binary = 1.0 x 2^-1): 0 01111110 00000000000000000000000 – 0.75 (0.11 binary = 1.1 x 2^-1): 0 01111110 10000000000000000000000 – 3.0 (11 binary = 1.1*2^1): 0 10000000 10000000000000000000000 – -0.375 (-0.011 binary = -1.1*2^-2): 1 01111101 10000000000000000000000 – 1 10000011 01000000000000000000000 = - 1.01 * 2^4 = -20.0 http://www.math-cs.gordon.edu/courses/cs311/lectures-2003/ binary.html
Lec 14 Systems Architecture 12 Basic Technique • One can compute the mantissa just similar to the way one would convert decimal whole numbers to binary. • Take the decimal and repeatedly multiply the fractional component by 2. The whole number portion is the next binary bit. • For whole numbers, append the binary whole number to the mantissa and shift the exponent until the mantissa is in normalized form. http://www.newton.dep.anl.gov/newton/askasci/1995/math/MATH065.HTM
Lec 14 Systems Architecture 13 Floating-Point Example • Represent –0.75 – –0.75 = (–1)1 × 1.12 × 2–1 – S = 1 – Fraction = 1000…002 – Exponent = –1 + Bias • Single: –1 + 127 = 126 = 011111102 • Double: –1 + 1023 = 1022 = 011111111102 • Single: 1011111101000…00 • Double: 1011111111101000…00
Lec 14 Systems Architecture 14 Floating-Point Example • What number is represented by the single-precision float 11000000101000…00 – S = 1 – Fraction = 01000…002 – Fxponent = 100000012 = 129 • x = (–1)1 × (1 + 012) × 2(129 – 127) = (–1) × 1.25 × 22 = –5.0
Lec 14 Systems Architecture 17 Representation of Floating Point Numbers • IEEE 754 double precision 31 30 20 19 0 Sign Biased exponent Normalized Mantissa (implicit 53rd bit) (-1)s  F  2E-1023 Exponent Mantissa Object Represented 0 0 0 0 non-zero denormalized 1-2046 anything FP number 2047 0 pm infinity 2047 non-zero NaN
Lec 14 Systems Architecture 18 Floating Point Arithmetic • fl(x) = nearest floating point number to x • Relative error (precision = s digits) –|x - fl(x)|/|x|  1/21-s for  = 2, 2-s • Arithmetic –x  y = fl(x+y) = (x + y)(1 +  ) for  < u –x  y = fl(x  y)(1 +  ) for  < u ULP—Unit in the Last Place is the smallest possible increment or decrement that can be made using the machine's FP arithmetic.
Lec 14 Systems Architecture 19 Floating-Point Precision • Relative precision – all fraction bits are significant – Single: approx 2–23 • Equivalent to 23 × log102 ≈ 23 × 0.3 ≈ 6 decimal digits of precision – Double: approx 2–52 • Equivalent to 52 × log102 ≈ 52 × 0.3 ≈ 16 decimal digits of precision
Lec 14 Systems Architecture 20 Is FP addition associative? • Associativity law for addition: a + (b + c) = (a + b) + c • Let a = – 2.7 x 1023 , b = 2.7 x 1023 , and c = 1.0 • a + (b + c) = – 2.7 x 1023 + ( 2.7 x 1023 + 1.0 ) = – 2.7 x 1023 + 2.7 x 1023 = 0.0 • (a + b) + c = ( – 2.7 x 1023 + 2.7 x 1023 ) + 1.0 = 0.0 + 1.0 = 1.0 • Beware – Floating Point addition not associative! • The result is approximate… • Why the smaller number disappeared?
Lec 14 Systems Architecture 21 Floating point addition • Still normalized? 4. Round the significand to the appropriate number of bits Yes Overflow or underflow? Start No Yes Done 1. Compare the exponents of the two numbers. Shift the smaller number to the right until its exponent would match the larger exponent 2. Add the significands 3. Normalize the sum, either shifting right and incrementing the exponent or shifting left and decrementing the exponent No Exception Small ALU Exponent difference Control Exponent Sign Fraction Big ALU Exponent Sign Fraction 0 1 0 1 0 1 Shift right 0 1 0 1 Increment or decrement Shift left or right Rounding hardware Exponent Sign Fraction
Lec 14 Systems Architecture 22 Floating-Point Addition • Consider a 4-digit decimal example – 9.999 × 101 + 1.610 × 10–1 • 1. Align decimal points – Shift number with smaller exponent – 9.999 × 101 + 0.016 × 101 • 2. Add significands – 9.999 × 101 + 0.016 × 101 = 10.015 × 101 • 3. Normalize result & check for over/underflow – 1.0015 × 102 • 4. Round and renormalize if necessary – 1.002 × 102
Lec 14 Systems Architecture 23 Floating-Point Addition • Now consider a 4-digit binary example – 1.0002 × 2–1 + –1.1102 × 2–2 (0.5 + –0.4375) • 1. Align binary points – Shift number with smaller exponent – 1.0002 × 2–1 + –0.1112 × 2–1 • 2. Add significands – 1.0002 × 2–1 + –0.1112 × 2– 1 = 0.0012 × 2–1 • 3. Normalize result & check for over/underflow – 1.0002 × 2–4 , with no over/underflow • 4. Round and renormalize if necessary – 1.0002 × 2–4 (no change) = 0.0625
Lec 14 Systems Architecture 24 FP Adder Hardware • Much more complex than integer adder • Doing it in one clock cycle would take too long – Much longer than integer operations – Slower clock would penalize all instructions • FP adder usually takes several cycles – Can be pipelined
Lec 14 Systems Architecture 25 FP Adder Hardware Step 1 Step 2 Step 3 Step 4
Lec 14 Systems Architecture 26 Floating Point Multiplication Algorithm
Lec 14 Systems Architecture 29 FP Arithmetic Hardware • FP multiplier is of similar complexity to FP adder – But uses a multiplier for significands instead of an adder • FP arithmetic hardware usually does – Addition, subtraction, multiplication, division, reciprocal, square-root – FP  integer conversion • Operations usually takes several cycles – Can be pipelined
Lec 14 Systems Architecture 30 FP Instructions in MIPS • FP hardware is coprocessor 1 – Adjunct processor that extends the ISA • Separate FP registers – 32 single-precision: $f0, $f1, … $f31 – Paired for double-precision: $f0/$f1, $f2/$f3, … • Release 2 of MIPs ISA supports 32 × 64-bit FP reg’s • FP instructions operate only on FP registers – Programs generally don’t do integer ops on FP data, or vice versa – More registers with minimal code-size impact • FP load and store instructions – lwc1, ldc1, swc1, sdc1 • e.g., ldc1 $f8, 32($sp)
Lec 14 Systems Architecture 31 FP Instructions in MIPS • Single-precision arithmetic – add.s, sub.s, mul.s, div.s • e.g., add.s $f0, $f1, $f6 • Double-precision arithmetic – add.d, sub.d, mul.d, div.d • e.g., mul.d $f4, $f4, $f6 • Single- and double-precision comparison – c.xx.s, c.xx.d (xx is eq, lt, le, …) – Sets or clears FP condition-code bit • e.g. c.lt.s $f3, $f4 • Branch on FP condition code true or false – bc1t, bc1f • e.g., bc1t TargetLabel
Lec 14 Systems Architecture 32 FP Example: °F to °C • C code: float f2c (float fahr) { return ((5.0/9.0)*(fahr - 32.0)); } – fahr in $f12, result in $f0, literals in global memory space • Compiled MIPS code: f2c: lwc1 $f16, const5($gp) lwc2 $f18, const9($gp) div.s $f16, $f16, $f18 lwc1 $f18, const32($gp) sub.s $f18, $f12, $f18 mul.s $f0, $f16, $f18 jr $ra
Lec 14 Systems Architecture 33 Rounding • Guard and round digits and sticky bit – When computing result, assume there are several extra digits available for shifting and computation. This improves accuracy of computation. – Guard digit: first extra digit/bit to the right of mantissa -- used for rounding addition results – Round digit: second extra digit/bit to the right of mantissa -- used for rounding multiplication results – Sticky bit: third extra digit/bit to the right of mantissa – used for resolving ties such as 0.50...00 vs. 0.50...01
Lec 14 Systems Architecture 34 Rounding examples • An example without guard and round digits – Add 9.76 x 1025 and 2.59 x 1024 assuming 3 digit mantissa • Shift mantissa of the smaller number to the right: 0.25 x 1025 • Add mantissas: 10.01x 1025 • Check and normalize mantissa if necessary: 1.00x 1026 • An example with guard and round digits – Add 9.76 x 1025 and 2.59 x 1024 assuming 3 digit mantissa • Internal registers have extra two digits: 9.7600 x 1025 and 2.5900 x 1024 • Shift mantissa of the smaller number to the right: 0.2590 x 1025 • Add mantissas: 10.0190 x 1025 • Check and normalize mantissa if necessary: 1.0019 x 1026 • Round the result: 1.00 x 1026
Lec 14 Systems Architecture 35 Rounding examples • An example without guard and round digits – Add 9.78 x 1025 and 8.79 x 1024 assuming 3 digit mantissa • Shift mantissa of the smaller number to the right: 0.87 x 1025 • Add mantissas: 10.65 x 1025 • Normalize mantissa if necessary: 1.06 x 1026 • An example with guard and round digits – Add 9.78 x 1025 and 8.79 x 1024 assuming 3 digit mantissa • Internal registers have extra two digits: 9.7800 x 1025 and 8.7900 x 1024 • Shift mantissa of the smaller number to the right: 0.8790 x 1025 • Add mantissas (note extra digit on the left): 10.6590 x 1025 • Check and normalize mantissa if necessary: 1.0659 x 1026 • Round the result: 1.07 x 1026
Lec 14 Systems Architecture 36 IEEE Rounding Modes 1. Round toward – Infinity: always round toward the smaller number 2. Round toward + Infinity: always round toward the larger number 3. Round to Zero: always round toward the smallest absolute (truncate) 4. Round toward Nearest Even: always round so that least significant bit (lsb) is zero 1.40 1.60 1.50 2.50 –1.50 Zero 1.00 2.00 1.00 2.00 –1.00  1.00 2.00 1.00 2.00 –2.00  1.00 2.00 2.00 3.00 –1.00 Nearest Even (default) 1.00 2.00 2.00 2.00 –2.00 • When rounding a binary fraction, the least significant digit of rounded result will be either 1 or 0. Nearest even mode always rounds the number so that the lsb is 0. Hence, the name. (If we omit the binary point, the rounded number would be even.) • It can be shown that if we assume uniform distribution of digits, rounding to nearest mode tends to have mean error = 0.
Lec 14 Systems Architecture 37 FP Instructions in MIPS • Floating point operations are slower than integer operations • Data is rarely converted from integers to float within the same procedure • 1980’s solution – place FP processing unit in a separate chip • Today’s solution – imbed FP processing unit in processor chip • Co-processor 1 features: – Contains 32 single precision floating point registers: $f0, $f1, … $f31 – These registers can also act as 16 double precision registers: $f0/$f1, $f2/$f3, … , $f30/$f31 (only the first one is specified in the instructions) – Uses special floating point instructions, which are similar (in format) to integer instructions but have .s or .d attached to signify that they work on fp numbers – Several special instructions to move between “regular” registers and the co- processor registers
Lec 14 Systems Architecture 38 FP Instructions in MIPS • lwc1 / swc1 – load/store word coprocessor 1 • Move instructions (between processors) mfc1 rt, rd Move floating point register rd to CPU register rt mtc1 rd, rt Move CPU register rt to floating point register rd mfc1.d rdest, frsrc1 Move frsrc1 & frsrc1 + 1 to regs rdest & rdest + 1 • Single and double precision arithmetic instructions Single add.s, sub.s, mul.s, div.s, c.lt.s Double add.d, sub.d, mul.d, div.d, c.lt.d • Examples: add.s $f0, $f1, $f2 sub.d $f0, $f2, $f4

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An introduction to floating point arithmetic.ppt

  • 1.
    Lec 14 SystemsArchitecture 1 Systems Architecture Lecture 14: Floating Point Arithmetic Jeremy R. Johnson Anatole D. Ruslanov William M. Mongan Some or all figures from Computer Organization and Design: The Hardware/Software Approach, Third Edition, by David Patterson and John Hennessy, are copyrighted material (COPYRIGHT 2004 MORGAN KAUFMANN PUBLISHERS, INC. ALL RIGHTS RESERVED).
  • 2.
    Lec 14 SystemsArchitecture 2 Introduction • Objective: To provide hardware support for floating point arithmetic. To understand how to represent floating point numbers in the computer and how to perform arithmetic with them. Also to learn how to use floating point arithmetic in MIPS. • Approximate arithmetic – Finite Range – Limited Precision • Topics – IEEE format for single and double precision floating point numbers – Floating point addition and multiplication – Support for floating point computation in MIPS
  • 3.
    Lec 14 SystemsArchitecture 3 Distribution of Floating Point Numbers • 3 bit mantissa • exponent {-1,0,1} e = -1 e = 0 e = 1 1.00 X 2^(-1) = 1/2 1.00 X 2^0 = 1 1.00 X 2^1 = 2 1.01 X 2^(-1) = 5/8 1.01 X 2^0 = 5/4 1.01 X 2^1 = 5/2 1.10 X 2^(-1) = 3/4 1.10 X 2^0 = 3/2 1.10 X 2^1= 3 1.11 X 2^(-1) = 7/8 1.11 X 2^0 = 7/4 1.11 X 2^1 = 7/2 0 1 2 3
  • 4.
    Lec 14 SystemsArchitecture 4 Floating Point • An IEEE floating point representation consists of – A Sign Bit (no surprise) – An Exponent (“times 2 to the what?”) – Mantissa (“Significand”), which is assumed to be 1.xxxxx (thus, one bit of the mantissa is implied as 1) – This is called a normalized representation • So a mantissa = 0 really is interpreted to be 1.0, and a mantissa of all 1111 is interpreted to be 1.1111 • Special cases are used to represent denormalized mantissas (true mantissa = 0), NaN, etc., as will be discussed.
  • 5.
    Lec 14 SystemsArchitecture 5 Floating Point Standard • Defined by IEEE Std 754-1985 • Developed in response to divergence of representations – Portability issues for scientific code • Now almost universally adopted • Two representations – Single precision (32-bit) – Double precision (64-bit)
  • 6.
    Lec 14 SystemsArchitecture 6 IEEE Floating-Point Format • S: sign bit (0  non-negative, 1  negative) • Normalize significand: 1.0 ≤ |significand| < 2.0 – Always has a leading pre-binary-point 1 bit, so no need to represent it explicitly (hidden bit) – Significand is Fraction with the “1.” restored • Exponent: excess representation: actual exponent + Bias – Ensures exponent is unsigned – Single: Bias = 127; Double: Bias = 1203 S Exponent Fraction single: 8 bits double: 11 bits single: 23 bits double: 52 bits Bias) (Exponent S 2 Fraction) (1 1) ( x      
  • 7.
    Lec 14 SystemsArchitecture 7 Single-Precision Range • Exponents 00000000 and 11111111 reserved • Smallest value – Exponent: 00000001  actual exponent = 1 – 127 = –126 – Fraction: 000…00  significand = 1.0 – ±1.0 × 2–126 ≈ ±1.2 × 10–38 • Largest value – exponent: 11111110  actual exponent = 254 – 127 = +127 – Fraction: 111…11  significand ≈ 2.0 – ±2.0 × 2+127 ≈ ±3.4 × 10+38
  • 8.
    Lec 14 SystemsArchitecture 8 Double-Precision Range • Exponents 0000…00 and 1111…11 reserved • Smallest value – Exponent: 00000000001  actual exponent = 1 – 1023 = –1022 – Fraction: 000…00  significand = 1.0 – ±1.0 × 2–1022 ≈ ±2.2 × 10–308 • Largest value – Exponent: 11111111110  actual exponent = 2046 – 1023 = +1023 – Fraction: 111…11  significand ≈ 2.0 – ±2.0 × 2+1023 ≈ ±1.8 × 10+308
  • 9.
    Lec 14 SystemsArchitecture 9 Representation of Floating Point Numbers • IEEE 754 single precision 31 30 23 22 0 Sign Biased exponent Normalized Mantissa (implicit 24th bit = 1) (-1)s  F  2E-127 Exponent Mantissa Object Represented 0 0 0 0 non-zero denormalized 1-254 anything FP number 255 0 pm infinity 255 non-zero NaN
  • 10.
    Lec 14 SystemsArchitecture 10 Why biased exponent? • For faster comparisons (for sorting, etc.), allow integer comparisons of floating point numbers: • Unbiased exponent: • Biased exponent: 0 1111 1111 000 0000 0000 0000 0000 0000 0 0000 0001 000 0000 0000 0000 0000 0000 1/2 2 0 0111 1110 000 0000 0000 0000 0000 0000 0 1000 0000 000 0000 0000 0000 0000 0000 1/2 2
  • 11.
    Lec 14 SystemsArchitecture 11 Basic Technique • Represent the decimal in the form +/- 1.xxxb x 2y • And “fill in the fields” – Remember biased exponent and implicit “1.” mantissa! • Examples: – 0.0: 0 00000000 00000000000000000000000 – 1.0 (1.0 x 2^0): 0 01111111 00000000000000000000000 – 0.5 (0.1 binary = 1.0 x 2^-1): 0 01111110 00000000000000000000000 – 0.75 (0.11 binary = 1.1 x 2^-1): 0 01111110 10000000000000000000000 – 3.0 (11 binary = 1.1*2^1): 0 10000000 10000000000000000000000 – -0.375 (-0.011 binary = -1.1*2^-2): 1 01111101 10000000000000000000000 – 1 10000011 01000000000000000000000 = - 1.01 * 2^4 = -20.0 http://www.math-cs.gordon.edu/courses/cs311/lectures-2003/ binary.html
  • 12.
    Lec 14 SystemsArchitecture 12 Basic Technique • One can compute the mantissa just similar to the way one would convert decimal whole numbers to binary. • Take the decimal and repeatedly multiply the fractional component by 2. The whole number portion is the next binary bit. • For whole numbers, append the binary whole number to the mantissa and shift the exponent until the mantissa is in normalized form. http://www.newton.dep.anl.gov/newton/askasci/1995/math/MATH065.HTM
  • 13.
    Lec 14 SystemsArchitecture 13 Floating-Point Example • Represent –0.75 – –0.75 = (–1)1 × 1.12 × 2–1 – S = 1 – Fraction = 1000…002 – Exponent = –1 + Bias • Single: –1 + 127 = 126 = 011111102 • Double: –1 + 1023 = 1022 = 011111111102 • Single: 1011111101000…00 • Double: 1011111111101000…00
  • 14.
    Lec 14 SystemsArchitecture 14 Floating-Point Example • What number is represented by the single-precision float 11000000101000…00 – S = 1 – Fraction = 01000…002 – Fxponent = 100000012 = 129 • x = (–1)1 × (1 + 012) × 2(129 – 127) = (–1) × 1.25 × 22 = –5.0
  • 15.
    Lec 14 SystemsArchitecture 17 Representation of Floating Point Numbers • IEEE 754 double precision 31 30 20 19 0 Sign Biased exponent Normalized Mantissa (implicit 53rd bit) (-1)s  F  2E-1023 Exponent Mantissa Object Represented 0 0 0 0 non-zero denormalized 1-2046 anything FP number 2047 0 pm infinity 2047 non-zero NaN
  • 16.
    Lec 14 SystemsArchitecture 18 Floating Point Arithmetic • fl(x) = nearest floating point number to x • Relative error (precision = s digits) –|x - fl(x)|/|x|  1/21-s for  = 2, 2-s • Arithmetic –x  y = fl(x+y) = (x + y)(1 +  ) for  < u –x  y = fl(x  y)(1 +  ) for  < u ULP—Unit in the Last Place is the smallest possible increment or decrement that can be made using the machine's FP arithmetic.
  • 17.
    Lec 14 SystemsArchitecture 19 Floating-Point Precision • Relative precision – all fraction bits are significant – Single: approx 2–23 • Equivalent to 23 × log102 ≈ 23 × 0.3 ≈ 6 decimal digits of precision – Double: approx 2–52 • Equivalent to 52 × log102 ≈ 52 × 0.3 ≈ 16 decimal digits of precision
  • 18.
    Lec 14 SystemsArchitecture 20 Is FP addition associative? • Associativity law for addition: a + (b + c) = (a + b) + c • Let a = – 2.7 x 1023 , b = 2.7 x 1023 , and c = 1.0 • a + (b + c) = – 2.7 x 1023 + ( 2.7 x 1023 + 1.0 ) = – 2.7 x 1023 + 2.7 x 1023 = 0.0 • (a + b) + c = ( – 2.7 x 1023 + 2.7 x 1023 ) + 1.0 = 0.0 + 1.0 = 1.0 • Beware – Floating Point addition not associative! • The result is approximate… • Why the smaller number disappeared?
  • 19.
    Lec 14 SystemsArchitecture 21 Floating point addition • Still normalized? 4. Round the significand to the appropriate number of bits Yes Overflow or underflow? Start No Yes Done 1. Compare the exponents of the two numbers. Shift the smaller number to the right until its exponent would match the larger exponent 2. Add the significands 3. Normalize the sum, either shifting right and incrementing the exponent or shifting left and decrementing the exponent No Exception Small ALU Exponent difference Control Exponent Sign Fraction Big ALU Exponent Sign Fraction 0 1 0 1 0 1 Shift right 0 1 0 1 Increment or decrement Shift left or right Rounding hardware Exponent Sign Fraction
  • 20.
    Lec 14 SystemsArchitecture 22 Floating-Point Addition • Consider a 4-digit decimal example – 9.999 × 101 + 1.610 × 10–1 • 1. Align decimal points – Shift number with smaller exponent – 9.999 × 101 + 0.016 × 101 • 2. Add significands – 9.999 × 101 + 0.016 × 101 = 10.015 × 101 • 3. Normalize result & check for over/underflow – 1.0015 × 102 • 4. Round and renormalize if necessary – 1.002 × 102
  • 21.
    Lec 14 SystemsArchitecture 23 Floating-Point Addition • Now consider a 4-digit binary example – 1.0002 × 2–1 + –1.1102 × 2–2 (0.5 + –0.4375) • 1. Align binary points – Shift number with smaller exponent – 1.0002 × 2–1 + –0.1112 × 2–1 • 2. Add significands – 1.0002 × 2–1 + –0.1112 × 2– 1 = 0.0012 × 2–1 • 3. Normalize result & check for over/underflow – 1.0002 × 2–4 , with no over/underflow • 4. Round and renormalize if necessary – 1.0002 × 2–4 (no change) = 0.0625
  • 22.
    Lec 14 SystemsArchitecture 24 FP Adder Hardware • Much more complex than integer adder • Doing it in one clock cycle would take too long – Much longer than integer operations – Slower clock would penalize all instructions • FP adder usually takes several cycles – Can be pipelined
  • 23.
    Lec 14 SystemsArchitecture 25 FP Adder Hardware Step 1 Step 2 Step 3 Step 4
  • 24.
    Lec 14 SystemsArchitecture 26 Floating Point Multiplication Algorithm
  • 25.
    Lec 14 SystemsArchitecture 29 FP Arithmetic Hardware • FP multiplier is of similar complexity to FP adder – But uses a multiplier for significands instead of an adder • FP arithmetic hardware usually does – Addition, subtraction, multiplication, division, reciprocal, square-root – FP  integer conversion • Operations usually takes several cycles – Can be pipelined
  • 26.
    Lec 14 SystemsArchitecture 30 FP Instructions in MIPS • FP hardware is coprocessor 1 – Adjunct processor that extends the ISA • Separate FP registers – 32 single-precision: $f0, $f1, … $f31 – Paired for double-precision: $f0/$f1, $f2/$f3, … • Release 2 of MIPs ISA supports 32 × 64-bit FP reg’s • FP instructions operate only on FP registers – Programs generally don’t do integer ops on FP data, or vice versa – More registers with minimal code-size impact • FP load and store instructions – lwc1, ldc1, swc1, sdc1 • e.g., ldc1 $f8, 32($sp)
  • 27.
    Lec 14 SystemsArchitecture 31 FP Instructions in MIPS • Single-precision arithmetic – add.s, sub.s, mul.s, div.s • e.g., add.s $f0, $f1, $f6 • Double-precision arithmetic – add.d, sub.d, mul.d, div.d • e.g., mul.d $f4, $f4, $f6 • Single- and double-precision comparison – c.xx.s, c.xx.d (xx is eq, lt, le, …) – Sets or clears FP condition-code bit • e.g. c.lt.s $f3, $f4 • Branch on FP condition code true or false – bc1t, bc1f • e.g., bc1t TargetLabel
  • 28.
    Lec 14 SystemsArchitecture 32 FP Example: °F to °C • C code: float f2c (float fahr) { return ((5.0/9.0)*(fahr - 32.0)); } – fahr in $f12, result in $f0, literals in global memory space • Compiled MIPS code: f2c: lwc1 $f16, const5($gp) lwc2 $f18, const9($gp) div.s $f16, $f16, $f18 lwc1 $f18, const32($gp) sub.s $f18, $f12, $f18 mul.s $f0, $f16, $f18 jr $ra
  • 29.
    Lec 14 SystemsArchitecture 33 Rounding • Guard and round digits and sticky bit – When computing result, assume there are several extra digits available for shifting and computation. This improves accuracy of computation. – Guard digit: first extra digit/bit to the right of mantissa -- used for rounding addition results – Round digit: second extra digit/bit to the right of mantissa -- used for rounding multiplication results – Sticky bit: third extra digit/bit to the right of mantissa – used for resolving ties such as 0.50...00 vs. 0.50...01
  • 30.
    Lec 14 SystemsArchitecture 34 Rounding examples • An example without guard and round digits – Add 9.76 x 1025 and 2.59 x 1024 assuming 3 digit mantissa • Shift mantissa of the smaller number to the right: 0.25 x 1025 • Add mantissas: 10.01x 1025 • Check and normalize mantissa if necessary: 1.00x 1026 • An example with guard and round digits – Add 9.76 x 1025 and 2.59 x 1024 assuming 3 digit mantissa • Internal registers have extra two digits: 9.7600 x 1025 and 2.5900 x 1024 • Shift mantissa of the smaller number to the right: 0.2590 x 1025 • Add mantissas: 10.0190 x 1025 • Check and normalize mantissa if necessary: 1.0019 x 1026 • Round the result: 1.00 x 1026
  • 31.
    Lec 14 SystemsArchitecture 35 Rounding examples • An example without guard and round digits – Add 9.78 x 1025 and 8.79 x 1024 assuming 3 digit mantissa • Shift mantissa of the smaller number to the right: 0.87 x 1025 • Add mantissas: 10.65 x 1025 • Normalize mantissa if necessary: 1.06 x 1026 • An example with guard and round digits – Add 9.78 x 1025 and 8.79 x 1024 assuming 3 digit mantissa • Internal registers have extra two digits: 9.7800 x 1025 and 8.7900 x 1024 • Shift mantissa of the smaller number to the right: 0.8790 x 1025 • Add mantissas (note extra digit on the left): 10.6590 x 1025 • Check and normalize mantissa if necessary: 1.0659 x 1026 • Round the result: 1.07 x 1026
  • 32.
    Lec 14 SystemsArchitecture 36 IEEE Rounding Modes 1. Round toward – Infinity: always round toward the smaller number 2. Round toward + Infinity: always round toward the larger number 3. Round to Zero: always round toward the smallest absolute (truncate) 4. Round toward Nearest Even: always round so that least significant bit (lsb) is zero 1.40 1.60 1.50 2.50 –1.50 Zero 1.00 2.00 1.00 2.00 –1.00  1.00 2.00 1.00 2.00 –2.00  1.00 2.00 2.00 3.00 –1.00 Nearest Even (default) 1.00 2.00 2.00 2.00 –2.00 • When rounding a binary fraction, the least significant digit of rounded result will be either 1 or 0. Nearest even mode always rounds the number so that the lsb is 0. Hence, the name. (If we omit the binary point, the rounded number would be even.) • It can be shown that if we assume uniform distribution of digits, rounding to nearest mode tends to have mean error = 0.
  • 33.
    Lec 14 SystemsArchitecture 37 FP Instructions in MIPS • Floating point operations are slower than integer operations • Data is rarely converted from integers to float within the same procedure • 1980’s solution – place FP processing unit in a separate chip • Today’s solution – imbed FP processing unit in processor chip • Co-processor 1 features: – Contains 32 single precision floating point registers: $f0, $f1, … $f31 – These registers can also act as 16 double precision registers: $f0/$f1, $f2/$f3, … , $f30/$f31 (only the first one is specified in the instructions) – Uses special floating point instructions, which are similar (in format) to integer instructions but have .s or .d attached to signify that they work on fp numbers – Several special instructions to move between “regular” registers and the co- processor registers
  • 34.
    Lec 14 SystemsArchitecture 38 FP Instructions in MIPS • lwc1 / swc1 – load/store word coprocessor 1 • Move instructions (between processors) mfc1 rt, rd Move floating point register rd to CPU register rt mtc1 rd, rt Move CPU register rt to floating point register rd mfc1.d rdest, frsrc1 Move frsrc1 & frsrc1 + 1 to regs rdest & rdest + 1 • Single and double precision arithmetic instructions Single add.s, sub.s, mul.s, div.s, c.lt.s Double add.d, sub.d, mul.d, div.d, c.lt.d • Examples: add.s $f0, $f1, $f2 sub.d $f0, $f2, $f4