DOCSLIB.ORG

A AND, 25 Arbitrary Precision Floating-Point Arithmetic, 270

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A AND, 25 Arbitrary Precision Floating-Point Arithmetic, 270 Index A Boole, George, 25 AND, 25 Boolean algebra, 25 Arbitrary precision floating-point Bytes, 20 Arithmetic, 270 Comparing, 273 Definition, 265 C Representing, 265 Checksum, 29 Software libraries, 278 Compare instructions, 37 Trigonometric and transcendental Converting numbers functions, 274 Binary to binary-coded decimal, 70 Ariane 5 rocket explosion, 118 Binary to decimal, 13 Binary-coded decimal to binary, 72 Decimal to binary, 11 B Fixed-point to floating-point, 187 Big integers, 139 Floating-point to fixed-point, 187 Addition and subtraction, 146 Hexadecimal and binary, 10 Comba multiplication, 159 Octal and binary, 11 Comparing, 144 Others to decimal, 14 Divide-and-conquer, 165 Cryptography, 177 Division, 155 in Python, 170 Input and output, 142 D Karatsuba multiplication, 160 Decimal floating-point, 215 Knuth division, 164 Biased exponent continuation field, 216 Libraries, 167 Combination field, 216 Representation, 140 Continuation field, 216 Schönhage and Strassen multiplication, 161 Declet, 218 School method multiplication, 152 Densely packed decimal (DPD), 218 Bits, 19 in software (C), 225 Clearing, 32 in software (Python), 231 Masking, 30 Infinity, 220 Rotations, 35 Not-a-number (NaN), 220 Setting, 31 Rounding modes, 220 Shifting, 33 Storage formats, 216 Testing, 31 Storage order, 221 Toggling, 32 Diffie-Hellman key exchange, 178 © Springer International Publishing AG 2017 343 R.T. Kneusel, Numbers and Computers, DOI 10.1007/978-3-319-50508-4 344 Index Digital comparator, 37 Sine (polynomial), 198 Distribution of floating-point numbers, 82 Sine (table), 194 Double-base number system Sine (Taylor series), 196 Addition, 312 Square root (Newton’s method), 200 Definition, 307 Subtraction, 188 Discussion, 323 Trigonometric functions, 194 Graphical representation, 308 When to use, 211 Improving addition, 314 Floating-point Multiplication, 318 Addition/subtraction algorithm, 103 Python implementation, 309 Avoiding the pitfalls, 130 Real number multiplication and division, Binary coded decimal floating-point, 110 321 Comparison, 100 Real number unity approximations, 322 Floating-point number, 81 Representing integers, 308 IEEE 754 rounding modes (binary), 97 Representing real numbers, 319 IEEE addition and subtraction, 102 IEEE exceptions, 105 IEEE floating-point in hardware, 108 E IEEE infinity (binary), 91 Encryption, 28 IEEE multiplication, 103 Endianness IEEE NaN (binary), 92 Big-endian, 23 Mantissa, 82 Little-endian, 23 Multiplication algorithm, 105 Experiments Pitfalls, 117 Comparison of fixed-point trigonometric Real number, 81 functions, 199 Rules of thumb for floating-point numbers, Decimal floating-point logistic map, 226 130 Decimal floating-point to ASCII string, 221 Significand, 82 Floating-point uncertainty in comparing Subnormal numbers, 95 numbers, 125 Trapping exceptions, 106 Floating-point uncertainty in repeated Using a tool to improve floating-point subtractions, 122 calculations, 131 Floating-point uncertainty in summing an array of numbers, 123 Floating-point uncertainty in the Logistic H Map, 119 Herbie, 131 Illustrating uncertainty in floating-point calculations, 119 Using rational numbers, 176 I IBM S/360 floating-point, 85 IEEE 754 number formats, 89 F IEEE 754-2008, 81 Fixed-point numbers Integers Addition, 188 Binary addition, 41 Cosine (polynomial), 199 Binary subtraction, 43 Cosine (table), 196 Binary-code decimal subtraction, 69 Cosine (Taylor series), 197 Binary-coded decimal, 67 Division, 193 Binary-coded decimal addition, 69 DOOM (case study), 211 Densely Packed Decimal, 73 Exponential (Taylor series), 201 One’s complement numbers, 55 Machine learning, 204 Power of two test, 46 Multiplication, 189 Sign extension, 63 Natural logarithm (Newton’s method), 203 Sign-magnitude numbers, 54 Neural networks, 204 Signed addition, 58 Q notation, 183 Signed comparison, 56 Index 345 Signed division, 64 Egyptian numbers, 3 Signed multiplication, 60 Hexadecimal numbers, 9 Signed subtraction, 58 Mayan numbers, 7 Two’s complement numbers, 55 Octal numbers, 9 Unsigned, 21 Roman numerals, 3 Unsigned addition, 42 Unsigned division, 49 Unsigned multiplication, 47 O Unsigned square root, 52 OR, 26 Unsigned subtraction, 44 Zoned Decimal, 74 Interval arithmetic, 236 P Absolute value, 246 Parity, 29 Addition and subtraction, 239 Patriot missile failure, 118 Comparisons, 247 Place notation, 5 Dependency problem, 256 Propagation of errors, 236 in Python, 248 Monotonic functions, 253 MPFI library, 258 R Multiplication, 240 Radix point, 5 Negation, 245 Rational arithmetic, 171 Powers, 243 Addition, 174 Properties of intervals, 252 Division, 175 Reciprocal and division, 241 GCD, 172 Sine and cosine, 254 in Python, 171 Multiplication, 175 Subtraction, 174 L Receiver operating characteristics curve, 208 Least-squares curve fitting, 198 Redundant signed-digit number system, 332 Logarithmic number system, 293 Addition and subtraction, 336 Addition and subtraction, 302 Discussion, 339 Comparing numbers, 299 Redundant binary representation, 333 Discussion, 306 Residue number system, 324 Multiplication and division, 298 Addition and multiplication, 327 Representation, 294 Conversion from RNS, 330 Logical operators, 25 Conversion to RNS, 325 Discussion, 331 Representing, 324 M Subtraction, 329 Memory RSA encryption, 179 Addresses, 22 Bit order, 23 Byte order, 23 S Schleswig-Holstein parliament election, 119 Scikit Learn library, 206 N Shannon, Claude, 19 Nibbles, 19 Swift, Jonathan, 24 NOT, 26 Numbers Babylonian numbers, 5 T Binary numbers, 8 Torres y Quevedo, Leonardo, 84 Decimal numbers, 8 Truth table, 25 346 Index V X Vancouver stock exchange, 119 XOR, 26 W Z Words, 20 Zuse, Konrad, 85.
Load more

Recommended publications
  • 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
    [Show full text]
  • SPARC Assembly Language Reference Manual
    SPARC Assembly Language Reference Manual 2550 Garcia Avenue Mountain View, CA 94043 U.S.A. A Sun Microsystems, Inc. Business 1995 Sun Microsystems, Inc. 2550 Garcia Avenue, Mountain View, California 94043-1100 U.S.A. All rights reserved. This product or document is protected by copyright and distributed under licenses restricting its use, copying, distribution and decompilation. No part of this product or document may be reproduced in any form by any means without prior written authorization of Sun and its licensors, if any. Portions of this product may be derived from the UNIX® system, licensed from UNIX Systems Laboratories, Inc., a wholly owned subsidiary of Novell, Inc., and from the Berkeley 4.3 BSD system, licensed from the University of California. Third-party software, including font technology in this product, is protected by copyright and licensed from Sun’s Suppliers. RESTRICTED RIGHTS LEGEND: Use, duplication, or disclosure by the government is subject to restrictions as set forth in subparagraph (c)(1)(ii) of the Rights in Technical Data and Computer Software clause at DFARS 252.227-7013 and FAR 52.227-19. The product described in this manual may be protected by one or more U.S. patents, foreign patents, or pending applications. TRADEMARKS Sun, Sun Microsystems, the Sun logo, SunSoft, the SunSoft logo, Solaris, SunOS, OpenWindows, DeskSet, ONC, ONC+, and NFS are trademarks or registered trademarks of Sun Microsystems, Inc. in the United States and other countries. UNIX is a registered trademark in the United States and other countries, exclusively licensed through X/Open Company, Ltd. OPEN LOOK is a registered trademark of Novell, Inc.
    [Show full text]
  • X86-64 Machine-Level Programming∗
    x86-64 Machine-Level Programming∗ Randal E. Bryant David R. O'Hallaron September 9, 2005 Intel’s IA32 instruction set architecture (ISA), colloquially known as “x86”, is the dominant instruction format for the world’s computers. IA32 is the platform of choice for most Windows and Linux machines. The ISA we use today was defined in 1985 with the introduction of the i386 microprocessor, extending the 16-bit instruction set defined by the original 8086 to 32 bits. Even though subsequent processor generations have introduced new instruction types and formats, many compilers, including GCC, have avoided using these features in the interest of maintaining backward compatibility. A shift is underway to a 64-bit version of the Intel instruction set. Originally developed by Advanced Micro Devices (AMD) and named x86-64, it is now supported by high end processors from AMD (who now call it AMD64) and by Intel, who refer to it as EM64T. Most people still refer to it as “x86-64,” and we follow this convention. Newer versions of Linux and GCC support this extension. In making this switch, the developers of GCC saw an opportunity to also make use of some of the instruction-set features that had been added in more recent generations of IA32 processors. This combination of new hardware and revised compiler makes x86-64 code substantially different in form and in performance than IA32 code. In creating the 64-bit extension, the AMD engineers also adopted some of the features found in reduced-instruction set computers (RISC) [7] that made them the favored targets for optimizing compilers.
    [Show full text]
  • FPGA Based Quadruple Precision Floating Point Arithmetic for Scientific Computations
    International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-2 Number-3 Issue-5 September-2012 FPGA Based Quadruple Precision Floating Point Arithmetic for Scientific Computations 1Mamidi Nagaraju, 2Geedimatla Shekar 1Department of ECE, VLSI Lab, National Institute of Technology (NIT), Calicut, Kerala, India 2Asst.Professor, Department of ECE, Amrita Vishwa Vidyapeetham University Amritapuri, Kerala, India Abstract amounts, and fractions are essential to many computations. Floating-point arithmetic lies at the In this project we explore the capability and heart of computer graphics cards, physics engines, flexibility of FPGA solutions in a sense to simulations and many models of the natural world. accelerate scientific computing applications which Floating-point computations suffer from errors due to require very high precision arithmetic, based on rounding and quantization. Fast computers let IEEE 754 standard 128-bit floating-point number programmers write numerically intensive programs, representations. Field Programmable Gate Arrays but computed results can be far from the true results (FPGA) is increasingly being used to design high due to the accumulation of errors in arithmetic end computationally intense microprocessors operations. Implementing floating-point arithmetic in capable of handling floating point mathematical hardware can solve two separate problems. First, it operations. Quadruple Precision Floating-Point greatly speeds up floating-point arithmetic and Arithmetic is important in computational fluid calculations. Implementing a floating-point dynamics and physical modelling, which require instruction will require at a generous estimate at least accurate numerical computations. However, twenty integer instructions, many of them conditional modern computers perform binary arithmetic, operations, and even if the instructions are executed which has flaws in representing and rounding the on an architecture which goes to great lengths to numbers.
    [Show full text]
  • X86 Intrinsics Cheat Sheet Jan Finis [email protected]
    x86 Intrinsics Cheat Sheet Jan Finis [email protected] Bit Operations Conversions Boolean Logic Bit Shifting & Rotation Packed Conversions Convert all elements in a packed SSE register Reinterpet Casts Rounding Arithmetic Logic Shift Convert Float See also: Conversion to int Rotate Left/ Pack With S/D/I32 performs rounding implicitly Bool XOR Bool AND Bool NOT AND Bool OR Right Sign Extend Zero Extend 128bit Cast Shift Right Left/Right ≤64 16bit ↔ 32bit Saturation Conversion 128 SSE SSE SSE SSE Round up SSE2 xor SSE2 and SSE2 andnot SSE2 or SSE2 sra[i] SSE2 sl/rl[i] x86 _[l]rot[w]l/r CVT16 cvtX_Y SSE4.1 cvtX_Y SSE4.1 cvtX_Y SSE2 castX_Y si128,ps[SSE],pd si128,ps[SSE],pd si128,ps[SSE],pd si128,ps[SSE],pd epi16-64 epi16-64 (u16-64) ph ↔ ps SSE2 pack[u]s epi8-32 epu8-32 → epi8-32 SSE2 cvt[t]X_Y si128,ps/d (ceiling) mi xor_si128(mi a,mi b) mi and_si128(mi a,mi b) mi andnot_si128(mi a,mi b) mi or_si128(mi a,mi b) NOTE: Shifts elements right NOTE: Shifts elements left/ NOTE: Rotates bits in a left/ NOTE: Converts between 4x epi16,epi32 NOTE: Sign extends each NOTE: Zero extends each epi32,ps/d NOTE: Reinterpret casts !a & b while shifting in sign bits. right while shifting in zeros. right by a number of bits 16 bit floats and 4x 32 bit element from X to Y. Y must element from X to Y. Y must from X to Y. No operation is SSE4.1 ceil NOTE: Packs ints from two NOTE: Converts packed generated.
    [Show full text]
  • Powerpc User Instruction Set Architecture Book I Version 2.01
    PowerPC User Instruction Set Architecture Book I Version 2.01 September 2003 Manager: Joe Wetzel/Poughkeepsie/IBM Technical Content: Ed Silha/Austin/IBM Cathy May/Watson/IBM Brad Frey/Austin/IBM The following paragraph does not apply to the United Kingdom or any country or state where such provisions are inconsistent with local law. The specifications in this manual are subject to change without notice. This manual is provided “AS IS”. Interna- tional Business Machines Corp. makes no warranty of any kind, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. International Business Machines Corp. does not warrant that the contents of this publication or the accompanying source code examples, whether individually or as one or more groups, will meet your requirements or that the publication or the accompanying source code examples are error-free. This publication could include technical inaccuracies or typographical errors. Changes are periodically made to the information herein; these changes will be incorporated in new editions of the publication. Address comments to IBM Corporation, Internal Zip 9630, 11400 Burnett Road, Austin, Texas 78758-3493. IBM may use or distribute whatever information you supply in any way it believes appropriate without incurring any obligation to you. The following terms are trademarks of the International Business Machines Corporation in the United States and/or other countries: IBM PowerPC RISC/System 6000 POWER POWER2 POWER4 POWER4+ IBM System/370 Notice to U.S. Government Users—Documentation Related to Restricted Rights—Use, duplication or disclosure is subject to restrictions set fourth in GSA ADP Schedule Contract with IBM Corporation.
    [Show full text]
  • UM0434 E200z3 Powerpc Core Reference Manual
    UM0434 e200z3 PowerPC core Reference manual Introduction The primary objective of this user’s manual is to describe the functionality of the e200z3 embedded microprocessor core for software and hardware developers. This book is intended as a companion to the EREF: A Programmer's Reference Manual for Freescale Book E Processors (hereafter referred to as EREF). Book E is a PowerPC™ architecture definition for embedded processors that ensures binary compatibility with the user-instruction set architecture (UISA) portion of the PowerPC architecture as it was jointly developed by Apple, IBM, and Motorola (referred to as the AIM architecture). This document distinguishes among the three levels of the architectural and implementation definition, as follows: ● The Book E architecture—Book E defines a set of user-level instructions and registers that are drawn from the user instruction set architecture (UISA) portion of the AIM definition PowerPC architecture. Book E also includes numerous supervisor-level registers and instructions as they were defined in the AIM version of the PowerPC architecture for the virtual environment architecture (VEA) and the operating environment architecture (OEA). Because the operating system resources (such as the MMU and interrupts) defined by Book E differ greatly from those defined by the AIM architecture, Book E introduces many new registers and instructions. ● Freescale Book E implementation standards (EIS)—In many cases, the Book E architecture definition provides a general framework, leaving specific details up to the implementation. To ensure consistency among its Book E implementations, Freescale has defined implementation standards that provide an additional layer of architecture between Book E and the actual devices.
    [Show full text]
  • Why Transcendentals and Arbitrary Precision?
    Why transcendentals and arbitrary precision? Paul Zimmermann, December 15th, 2005 Why transcendentals and arbitrary precision? IEEE 754 Revision Committee, December 15th, 2005 Why Transcendentals? Why transcendentals and arbitrary precision? IEEE 754 Revision Committee, December 15th, 2005 Some transcendentals today Opteron, Linux 2.6.12, gcc 4.0.1, libc 2.3.5: Testing function atan for exponent 0. rounding mode GMP_RNDU: 1.507141 ulp(s) for x=5.27348750514293418412e-01 wrong DR: x=8.71159292701253917812e-01 [-0.505215] Testing function cbrt for exponent 0. rounding mode GMP_RNDN: wrong monotonicity for x=8.90550497574918109578e-01 f(x-)=9.62098454219197263271e-01 not <= f(x)=9.62098454219197152248e-01 Why transcendentals and arbitrary precision? IEEE 754 Revision Committee, December 15th, 2005 Sparc, SunOS 5.7, cc Sun WorkShop 6: Testing function exp for exponent 0. rounding mode GMP_RNDN: 0.659120 ulp(s) for x=9.43344491255437844757e-01 rounding mode GMP_RNDU: wrong DR: x=5.33824498679617898134e-01 [-0.295496] Testing function pow for exponents 0 and 0. rounding mode GMP_RNDN: -0.522792 ulp(s) for x=9.91109071895216686698e-01 t=6.06627312254989226048e-01 Testing function tanh for exponent 0. rounding mode GMP_RNDN: 1.771299 ulp(s) for x=5.19240368581155742334e-01 Why transcendentals and arbitrary precision? IEEE 754 Revision Committee, December 15th, 2005 MIPS R16000, IRIX64 6.5, gcc 3.3: Testing function tan for exponent 10. rounding mode GMP_RNDZ: -6.143332 ulp(s) for x=5.25427198389763360000e+02 wrong DR: x=7.56078520967298570000e+02 [-4.523771] Itanium 1, Linux 2.4.20, gcc 3.2.3, libc 2.2.5: Testing function gamma for exponent 7.
    [Show full text]
  • Fpnew: an Open-Source Multi-Format Floating-Point Unit Architecture For
    1 FPnew: An Open-Source Multi-Format Floating-Point Unit Architecture for Energy-Proportional Transprecision Computing Stefan Mach, Fabian Schuiki, Florian Zaruba, Student Member, IEEE, and Luca Benini, Fellow, IEEE Abstract—The slowdown of Moore’s law and the power wall Internet of Things (IoT) domain. In this environment, achiev- necessitates a shift towards finely tunable precision (a.k.a. trans- ing high energy efficiency in numerical computations requires precision) computing to reduce energy footprint. Hence, we need architectures and circuits which are fine-tunable in terms of circuits capable of performing floating-point operations on a wide range of precisions with high energy-proportionality. We precision and performance. Such circuits can minimize the present FPnew, a highly configurable open-source transprecision energy cost per operation by adapting both performance and floating-point unit (TP-FPU) capable of supporting a wide precision to the application requirements in an agile way. The range of standard and custom FP formats. To demonstrate the paradigm of “transprecision computing” [1] aims at creating flexibility and efficiency of FPnew in general-purpose processor a holistic framework ranging from algorithms and software architectures, we extend the RISC-V ISA with operations on half-precision, bfloat16, and an 8bit FP format, as well as SIMD down to hardware and circuits which offer many knobs to vectors and multi-format operations. Integrated into a 32-bit fine-tune workloads. RISC-V core, our TP-FPU can speed up execution of mixed- The most flexible and dynamic way of performing nu- precision applications by 1.67x w.r.t.
    [Show full text]
  • SPARC Joint Programming Specification 1 Implementation Supplement: Sun Ultrasparc III
    SPARC Joint Programming Specification 1 Implementation Supplement: Sun UltraSPARC III Sun Microsystems Proprietary/Need-to-Know JRC Contributed Material Working Draft 1.0.5, 10 Sep 2002 Part No.: 806-6754-1 Working Draft 1.0.5, 10 Sep 2002 Copyright 2001 Sun Microsystems, Inc., 901 San Antonio Road, Palo Alto, California 94303 U.S.A. All rights reserved. Portions of this document are protected by copyright 1994 SPARC International, Inc. This product or document is protected by copyright and distributed under licenses restricting its use, copying, distribution, and decompilation. No part of this product or document may be reproduced in any form by any means without prior written authorization of Sun and its licensors, if any. Third-party software, including font technology, is copyrighted and licensed from Sun suppliers. Parts of the product may be derived from Berkeley BSD systems, licensed from the University of California. UNIX is a registered trademark in the U.S. and other countries, exclusively licensed through X/Open Company, Ltd. Sun, Sun Microsystems, the Sun logo, SunSoft, SunDocs, SunExpress, and Solaris are trademarks, registered trademarks, or service marks of Sun Microsystems, Inc. in the U.S. and other countries. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. in the U.S. and other countries. Products bearing SPARC trademarks are based upon an architecture developed by Sun Microsystems, Inc. The OPEN LOOK and Sun™ Graphical User Interface was developed by Sun Microsystems, Inc. for its users and licensees. Sun acknowledges the pioneering efforts of Xerox in researching and developing the concept of visual or graphical user interfaces for the computer industry.
    [Show full text]
  • Release Notes
    Interactive Session Recorder Release Notes Release 6.2 F20208-05 January 2021 Interactive Session Recorder Release Notes, Release 6.2 F20208-05 Copyright © 2014, 2021, Oracle and/or its affiliates. This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or "commercial computer software documentation" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, reproduction, duplication, release, display, disclosure, modification, preparation of derivative works, and/or adaptation of i) Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs), ii) Oracle computer documentation and/or iii) other Oracle data, is subject to the rights and limitations specified in the license contained in the applicable contract.
    [Show full text]
  • Floating Point Exception Tracking and NAN Propagation
    Floating point exception tracking and NAN propagation By Agner Fog. Technical University of Denmark. © 2018-2020 CC-BY. Last updated 2020-04-27. Abstract The most common methods for detecting floating point errors are based on exception trapping or a global status register. These methods are inefficient in modern systems that use out-of-order parallelism and single-instruction-multiple-data (SIMD) parallelism for improving performance. It is argued that a method based on NAN propagation is more efficient and deterministic. Problems with NAN propagation in current systems are discussed. Examples of implementation in the C++ vector class library and in an experimental instruction set named ForwardCom are presented. The IEEE-754 standard for floating point arithmetic may need adjustment to accommodate the needs of modern forms of parallelism. Contents 1 Introduction ....................................................................................................................... 1 2 Exception trapping ............................................................................................................. 2 3 Using a status register ....................................................................................................... 3 4 Using errno ........................................................................................................................ 3 5 Parallel error detection ...................................................................................................... 3 6 Propagation of INF and NAN
    [Show full text]