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Fujitsu SPARC64™ X+/X Software on Chip Overview for Developers]
White paper [Fujitsu SPARC64™ X+/X Software on Chip Overview for Developers] White paper Fujitsu SPARC64™ X+/X Software on Chip Overview for Developers Page 1 of 13 www.fujitsu.com/sparc White paper [Fujitsu SPARC64™ X+/X Software on Chip Overview for Developers] Table of Contents Table of Contents 1 Software on Chip Innovative Technology 3 2 SPARC64™ X+/X SIMD Vector Processing 4 2.1 How Oracle Databases take advantage of SPARC64™ X+/X SIMD vector processing 4 2.2 How to use of SPARC64™ X+/X SIMD instructions in user applications 5 2.3 How to check if the system and operating system is capable of SIMD execution? 6 2.4 How to check if SPARC64™ X+/X SIMD instructions indeed have been generated upon compilation of a user source code? 6 2.5 Is SPARC64™ X+/X SIMD implementation compatible with Oracle’s SPARC SIMD? 6 3 SPARC64™ X+/X Decimal Floating Point Processing 8 3.1 How Oracle Databases take advantage of SPARC64™ X+/X Decimal Floating-Point 8 3.2 Decimal Floating-Point processing in user applications 8 4 SPARC64™ X+/X Extended Floating-Point Registers 9 4.1 How Oracle Databases take advantage of SPARC64™ X+/X Decimal Floating-Point 9 5 SPARC64™ X+/X On-Chip Cryptographic Processing Capabilities 10 5.1 How to use the On-Chip Cryptographic Processing Capabilities 10 5.2 How to use the On-Chip Cryptographic Processing Capabilities in user applications 10 6 Conclusions 12 Page 2 of 13 www.fujitsu.com/sparc White paper [Fujitsu SPARC64™ X+/X Software on Chip Overview for Developers] Software on Chip Innovative Technology 1 Software on Chip Innovative Technology Fujitsu brings together innovations in supercomputing, business computing, and mainframe computing in the Fujitsu M10 enterprise server family to help organizations meet their business challenges. -
Decimal Layouts for IEEE 754 Strawman3
IEEE 754-2008 ECMA TC39/TG1 – 25 September 2008 Mike Cowlishaw IBM Fellow Overview • Summary of the new standard • Binary and decimal specifics • Support in hardware, standards, etc. • Questions? 2 Copyright © IBM Corporation 2008. All rights reserved. IEEE 754 revision • Started in December 2000 – 7.7 years – 5.8 years in committee (92 participants + e-mail) – 1.9 years in ballot (101 voters, 8 ballots, 1200+ comments) • Removes many ambiguities from 754-1985 • Incorporates IEEE 854 (radix-independent) • Recommends or requires more operations (functions) and more language support 3 Formats • Separates sets of floating-point numbers (and the arithmetic on them) from their encodings (‘interchange formats’) • Includes the 754-1985 basic formats: – binary32, 24 bits (‘single’) – binary64, 53 bits (‘double’) • Adds three new basic formats: – binary128, 113 bits (‘quad’) – decimal64, 16-digit (‘double’) – decimal128, 34-digit (‘quad’) 4 Why decimal? A web page… • Parking at Manchester airport… • £4.20 per day … … for 10 days … … calculated on-page using ECMAScript Answer shown: 5 Why decimal? A web page… • Parking at Manchester airport… • £4.20 per day … … for 10 days … … calculated on-page using ECMAScript Answer shown: £41.99 (Programmer must have truncated to two places.) 6 Where it costs real money… • Add 5% sales tax to a $ 0.70 telephone call, rounded to the nearest cent • 1.05 x 0.70 using binary double is exactly 0.734999999999999986677323704 49812151491641998291015625 (should have been 0.735) • rounds to $ 0.73, instead of $ 0.74 -
Nios II Custom Instruction User Guide
Nios II Custom Instruction User Guide Subscribe UG-20286 | 2020.04.27 Send Feedback Latest document on the web: PDF | HTML Contents Contents 1. Nios II Custom Instruction Overview..............................................................................4 1.1. Custom Instruction Implementation......................................................................... 4 1.1.1. Custom Instruction Hardware Implementation............................................... 5 1.1.2. Custom Instruction Software Implementation................................................ 6 2. Custom Instruction Hardware Interface......................................................................... 7 2.1. Custom Instruction Types....................................................................................... 7 2.1.1. Combinational Custom Instructions.............................................................. 8 2.1.2. Multicycle Custom Instructions...................................................................10 2.1.3. Extended Custom Instructions................................................................... 11 2.1.4. Internal Register File Custom Instructions................................................... 13 2.1.5. External Interface Custom Instructions....................................................... 15 3. Custom Instruction Software Interface.........................................................................16 3.1. Custom Instruction Software Examples................................................................... 16 -
Chapter 2 DIRECT METHODS—PART II
Chapter 2 DIRECT METHODS—PART II If we use finite precision arithmetic, the results obtained by the use of direct methods are contaminated with roundoff error, which is not always negligible. 2.1 Finite Precision Computation 2.2 Residual vs. Error 2.3 Pivoting 2.4 Scaling 2.5 Iterative Improvement 2.1 Finite Precision Computation 2.1.1 Floating-point numbers decimal floating-point numbers The essence of floating-point numbers is best illustrated by an example, such as that of a 3-digit floating-point calculator which accepts numbers like the following: 123. 50.4 −0.62 −0.02 7.00 Any such number can be expressed as e ±d1.d2d3 × 10 where e ∈{0, 1, 2}. (2.1) Here 40 t := precision = 3, [L : U] := exponent range = [0 : 2]. The exponent range is rather limited. If the calculator display accommodates scientific notation, e g., 3.46 3 −1.56 −3 then we might use [L : U]=[−9 : 9]. Some numbers have multiple representations in form (2.1), e.g., 2.00 × 101 =0.20 × 102. Hence, there is a normalization: • choose smallest possible exponent, • choose + sign for zero, e.g., 0.52 × 102 → 5.20 × 101, 0.08 × 10−8 → 0.80 × 10−9, −0.00 × 100 → 0.00 × 10−9. −9 Nonzero numbers of form ±0.d2d3 × 10 are denormalized. But for large-scale scientific computation base 2 is preferred. binary floating-point numbers This is an important matter because numbers like 0.2 do not have finite representations in base 2: 0.2=(0.001100110011 ···)2. -
Decimal Rounding
Mathematics Instructional Plan – Grade 5 Decimal Rounding Strand: Number and Number Sense Topic: Rounding decimals, through the thousandths, to the nearest whole number, tenth, or hundredth. Primary SOL: 5.1 The student, given a decimal through thousandths, will round to the nearest whole number, tenth, or hundredth. Materials Base-10 blocks Decimal Rounding activity sheet (attached) “Highest or Lowest” Game (attached) Open Number Lines activity sheet (attached) Ten-sided number generators with digits 0–9, or decks of cards Vocabulary approximate, between, closer to, decimal number, decimal point, hundredth, rounding, tenth, thousandth, whole Student/Teacher Actions: What should students be doing? What should teachers be doing? 1. Begin with a review of decimal place value: Display a decimal in the thousandths place, such as 34.726, using base-10 blocks. In pairs, have students discuss how to read the number using place-value names, and review the decimal place each digit holds. Briefly have students share their thoughts with the class by asking: What digit is in the tenths place? The hundredths place? The thousandths place? Who would like to share how to say this decimal using place value? 2. Ask, “If this were a monetary amount, $34.726, how would we determine the amount to the nearest cent?” Allow partners or small groups to discuss this question. It may be helpful if students underlined the place that indicates cents (the hundredths place) in order to keep track of the rounding place. Distribute the Open Number Lines activity sheet and display this number line on the board. Guide students to recall that the nearest cent is the same as the nearest hundredth of a dollar. -
Decimal Floating Point for Future Processors Hossam A
1 Decimal Floating Point for future processors Hossam A. H. Fahmy Electronics and Communications Department, Cairo University, Egypt Email: [email protected] Tarek ElDeeb, Mahmoud Yousef Hassan, Yasmin Farouk, Ramy Raafat Eissa SilMinds, LLC Email: [email protected] F Abstract—Many new designs for Decimal Floating Point (DFP) hard- Simple decimal fractions such as 1=10 which might ware units have been proposed in the last few years. To date, only represent a tax amount or a sales discount yield an the IBM POWER6 and POWER7 processors include internal units for infinitely recurring number if converted to a binary decimal floating point processing. We have designed and tested several representation. Hence, a binary number system with a DFP units including an adder, multiplier, divider, square root, and fused- multiply-add compliant with the IEEE 754-2008 standard. This paper finite number of bits cannot accurately represent such presents the results of using our units as part of a vector co-processor fractions. When an approximated representation is used and the anticipated gains once the units are moved on chip with the in a series of computations, the final result may devi- main processor. ate from the correct result expected by a human and required by the law [3], [4]. One study [5] shows that in a large billing application such an error may be up to 1 WHY DECIMAL HARDWARE? $5 million per year. Ten is the natural number base or radix for humans Banking, billing, and other financial applications use resulting in a decimal number system while a binary decimal extensively. -
The MPFR Team [email protected] This Manual Documents How to Install and Use the Multiple Precision Floating-Point Reliable Library, Version 2.2.0
MPFR The Multiple Precision Floating-Point Reliable Library Edition 2.2.0 September 2005 The MPFR team [email protected] This manual documents how to install and use the Multiple Precision Floating-Point Reliable Library, version 2.2.0. Copyright 1991, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation; with no Invariant Sections, with the Front-Cover Texts being “A GNU Manual”, and with the Back-Cover Texts being “You have freedom to copy and modify this GNU Manual, like GNU software”. A copy of the license is included in Appendix A [GNU Free Documentation License], page 30. i Table of Contents MPFR Copying Conditions ................................ 1 1 Introduction to MPFR ................................. 2 1.1 How to use this Manual ........................................................ 2 2 Installing MPFR ....................................... 3 2.1 How to install ................................................................. 3 2.2 Other make targets ............................................................ 3 2.3 Known Build Problems ........................................................ 3 2.4 Getting the Latest Version of MPFR ............................................ 4 3 Reporting Bugs ........................................ 5 4 MPFR Basics ......................................... -
IEEE Std 754-1985 Revision of Reaffirmed1990 IEEE Standard for Binary Floating-Point Arithmetic
Recognized as an American National Standard (ANSI) IEEE Std 754-1985 An American National Standard IEEE Standard for Binary Floating-Point Arithmetic Sponsor Standards Committee of the IEEE Computer Society Approved March 21, 1985 Reaffirmed December 6, 1990 IEEE Standards Board Approved July 26, 1985 Reaffirmed May 21, 1991 American National Standards Institute © Copyright 1985 by The Institute of Electrical and Electronics Engineers, Inc 345 East 47th Street, New York, NY 10017, USA No part of this publication may be reproduced in any form, in an electronic retrieval system or otherwise, without the prior written permission of the publisher. IEEE Standards documents are developed within the Technical Committees of the IEEE Societies and the Standards Coordinating Committees of the IEEE Standards Board. Members of the committees serve voluntarily and without compensation. They are not necessarily members of the Institute. The standards developed within IEEE represent a consensus of the broad expertise on the subject within the Institute as well as those activities outside of IEEE which have expressed an interest in participating in the development of the standard. Use of an IEEE Standard is wholly voluntary. The existence of an IEEE Standard does not imply that there are no other ways to produce, test, measure, purchase, market, or provide other goods and services related to the scope of the IEEE Standard. Furthermore, the viewpoint expressed at the time a standard is approved and issued is subject to change brought about through developments in the state of the art and comments received from users of the standard. Every IEEE Standard is subjected to review at least once every five years for revision or reaffirmation. -
Floating Point Representation (Unsigned) Fixed-Point Representation
Floating point representation (Unsigned) Fixed-point representation The numbers are stored with a fixed number of bits for the integer part and a fixed number of bits for the fractional part. Suppose we have 8 bits to store a real number, where 5 bits store the integer part and 3 bits store the fractional part: 1 0 1 1 1.0 1 1 $ 2& 2% 2$ 2# 2" 2'# 2'$ 2'% Smallest number: Largest number: (Unsigned) Fixed-point representation Suppose we have 64 bits to store a real number, where 32 bits store the integer part and 32 bits store the fractional part: "# "% + /+ !"# … !%!#!&. (#(%(" … ("% % = * !+ 2 + * (+ 2 +,& +,# "# "& & /# % /"% = !"#× 2 +!"&× 2 + ⋯ + !&× 2 +(#× 2 +(%× 2 + ⋯ + ("%× 2 0 ∞ (Unsigned) Fixed-point representation Range: difference between the largest and smallest numbers possible. More bits for the integer part ⟶ increase range Precision: smallest possible difference between any two numbers More bits for the fractional part ⟶ increase precision "#"$"%. '$'#'( # OR "$"%. '$'#'(') # Wherever we put the binary point, there is a trade-off between the amount of range and precision. It can be hard to decide how much you need of each! Scientific Notation In scientific notation, a number can be expressed in the form ! = ± $ × 10( where $ is a coefficient in the range 1 ≤ $ < 10 and + is the exponent. 1165.7 = 0.0004728 = Floating-point numbers A floating-point number can represent numbers of different order of magnitude (very large and very small) with the same number of fixed bits. In general, in the binary system, a floating number can be expressed as ! = ± $ × 2' $ is the significand, normally a fractional value in the range [1.0,2.0) . -
IEEE Standard 754 for Binary Floating-Point Arithmetic
Work in Progress: Lecture Notes on the Status of IEEE 754 October 1, 1997 3:36 am Lecture Notes on the Status of IEEE Standard 754 for Binary Floating-Point Arithmetic Prof. W. Kahan Elect. Eng. & Computer Science University of California Berkeley CA 94720-1776 Introduction: Twenty years ago anarchy threatened floating-point arithmetic. Over a dozen commercially significant arithmetics boasted diverse wordsizes, precisions, rounding procedures and over/underflow behaviors, and more were in the works. “Portable” software intended to reconcile that numerical diversity had become unbearably costly to develop. Thirteen years ago, when IEEE 754 became official, major microprocessor manufacturers had already adopted it despite the challenge it posed to implementors. With unprecedented altruism, hardware designers had risen to its challenge in the belief that they would ease and encourage a vast burgeoning of numerical software. They did succeed to a considerable extent. Anyway, rounding anomalies that preoccupied all of us in the 1970s afflict only CRAY X-MPs — J90s now. Now atrophy threatens features of IEEE 754 caught in a vicious circle: Those features lack support in programming languages and compilers, so those features are mishandled and/or practically unusable, so those features are little known and less in demand, and so those features lack support in programming languages and compilers. To help break that circle, those features are discussed in these notes under the following headings: Representable Numbers, Normal and Subnormal, Infinite -
IEEE 754 Format (From Last Time)
IEEE 754 Format (from last time) ● Single precision format S Exponent Significand 1 8 23 The exponent is represented in bias 127 notation. Why? ● Example: 52.25 = 00110100.010000002 5 Normalize: 001.10100010000002 x 2 (127+5) 0 10000100 10100010000000000000000 0100 0010 0101 0001 0000 0000 0000 0000 52.25 = 0x42510000 08/29/2017 Comp 411 - Fall 2018 1 IEEE 754 Limits and Features ● SIngle precision limitations ○ A little more than 7 decimal digits of precision ○ Minimum positive normalized value: ~1.18 x 10-38 ○ Maximum positive normalized value: ~3.4 x 1038 ● Inaccuracies become evident after multiple single precision operations ● Double precision format 08/29/2017 Comp 411 - Fall 2018 2 IEEE 754 Special Numbers ● Zero - ±0 A floating point number is considered zero when its exponent and significand are both zero. This is an exception to our “hidden 1” normalization trick. There are also a positive and negative zeros. S 000 0000 0 000 0000 0000 0000 0000 0000 ● Infinity - ±∞ A floating point number with a maximum exponent (all ones) is considered infinity which can also be positive or negative. S 111 1111 1 000 0000 0000 0000 0000 0000 ● Not a Number - NaN for ±0/±0, ±∞/±∞, log(-42), etc. S 111 1111 1 non-zero 08/29/2017 Comp 411 - Fall 2018 3 A Quick Wake-up exercise What decimal value is represented by 0x3f800000, when interpreted as an IEEE 754 single precision floating point number? 08/29/2017 Comp 411 - Fall 2018 4 Bits You can See The smallest element of a visual display is called a “pixel”. -
A Decimal Floating-Point Speciftcation
A Decimal Floating-point Specification Michael F. Cowlishaw, Eric M. Schwarz, Ronald M. Smith, Charles F. Webb IBM UK IBM Server Division P.O. Box 31, Birmingham Rd. 2455 South Rd., MS:P310 Warwick CV34 5JL. UK Poughkeepsie, NY 12601 USA [email protected] [email protected] Abstract ing is required. In the fixed point formats, rounding must be explicitly applied in software rather than be- Even though decimal arithmetic is pervasive in fi- ing provided by the hardware. To address these and nancial and commercial transactions, computers are other limitations, we propose implementing a decimal stdl implementing almost all arithmetic calculations floating-point format. But what should this format be? using binary arithmetic. As chip real estate becomes This paper discusses the issues of defining a decimal cheaper it is becoming likely that more computer man- floating-point format. ufacturers will provide processors with decimal arith- First, we consider the goals of the specification. It metic engines. Programming languages and databases must be compliant with standards already in place. are expanding the decimal data types available whale One standard we consider is the ANSI X3.274-1996 there has been little change in the base hardware. As (Programming Language REXX) [l]. This standard a result, each language and application is defining a contains a definition of an integrated floating-point and different arithmetic and few have considered the efi- integer decimal arithmetic which avoids the need for ciency of hardware implementations when setting re- two distinct data types and representations. The other quirements. relevant standard is the ANSI/IEEE 854-1987 (Radix- In this paper, we propose a decimal format which Independent Floating-point Arithmetic) [a].