Overview ILP32 and LP64 Data Models Data Type Sizes Char Short
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Type-Safe Composition of Object Modules*
International Conference on Computer Systems and Education I ISc Bangalore Typ esafe Comp osition of Ob ject Mo dules Guruduth Banavar Gary Lindstrom Douglas Orr Department of Computer Science University of Utah Salt LakeCity Utah USA Abstract Intro duction It is widely agreed that strong typing in We describ e a facility that enables routine creases the reliability and eciency of soft typ echecking during the linkage of exter ware However compilers for statically typ ed nal declarations and denitions of separately languages suchasC and C in tradi compiled programs in ANSI C The primary tional nonintegrated programming environ advantage of our serverstyle typ echecked ments guarantee complete typ esafety only linkage facility is the ability to program the within a compilation unit but not across comp osition of ob ject mo dules via a suite of suchunits Longstanding and widely avail strongly typ ed mo dule combination op era able linkers comp ose separately compiled tors Such programmability enables one to units bymatching symb ols purely byname easily incorp orate programmerdened data equivalence with no regard to their typ es format conversion stubs at linktime In ad Such common denominator linkers accom dition our linkage facility is able to automat mo date ob ject mo dules from various source ically generate safe co ercion stubs for com languages by simply ignoring the static se patible encapsulated data mantics of the language Moreover com monly used ob ject le formats are not de signed to incorp orate source language typ e -
Faster Ffts in Medium Precision
Faster FFTs in medium precision Joris van der Hoevena, Grégoire Lecerfb Laboratoire d’informatique, UMR 7161 CNRS Campus de l’École polytechnique 1, rue Honoré d’Estienne d’Orves Bâtiment Alan Turing, CS35003 91120 Palaiseau a. Email: [email protected] b. Email: [email protected] November 10, 2014 In this paper, we present new algorithms for the computation of fast Fourier transforms over complex numbers for “medium” precisions, typically in the range from 100 until 400 bits. On the one hand, such precisions are usually not supported by hardware. On the other hand, asymptotically fast algorithms for multiple precision arithmetic do not pay off yet. The main idea behind our algorithms is to develop efficient vectorial multiple precision fixed point arithmetic, capable of exploiting SIMD instructions in modern processors. Keywords: floating point arithmetic, quadruple precision, complexity bound, FFT, SIMD A.C.M. subject classification: G.1.0 Computer-arithmetic A.M.S. subject classification: 65Y04, 65T50, 68W30 1. Introduction Multiple precision arithmetic [4] is crucial in areas such as computer algebra and cryptography, and increasingly useful in mathematical physics and numerical analysis [2]. Early multiple preci- sion libraries appeared in the seventies [3], and nowadays GMP [11] and MPFR [8] are typically very efficient for large precisions of more than, say, 1000 bits. However, for precisions which are only a few times larger than the machine precision, these libraries suffer from a large overhead. For instance, the MPFR library for arbitrary precision and IEEE-style standardized floating point arithmetic is typically about a factor 100 slower than double precision machine arithmetic. -
Truffle/C Interpreter
JOHANNES KEPLER UNIVERSITAT¨ LINZ JKU Faculty of Engineering and Natural Sciences Truffle/C Interpreter Master’s Thesis submitted in partial fulfillment of the requirements for the academic degree Diplom-Ingenieur in the Master’s Program Computer Science Submitted by Manuel Rigger, BSc. At the Institut f¨urSystemsoftware Advisor o.Univ.-Prof. Dipl.-Ing. Dr.Dr.h.c. Hanspeter M¨ossenb¨ock Co-advisor Dipl.-Ing. Lukas Stadler Dipl.-Ing. Dr. Thomas W¨urthinger Xiamen, April 2014 Contents I Contents 1 Introduction 3 1.1 Motivation . .3 1.2 Goals and Scope . .4 1.3 From C to Java . .4 1.4 Structure of the Thesis . .6 2 State of the Art 9 2.1 Graal . .9 2.2 Truffle . 10 2.2.1 Rewriting and Specialization . 10 2.2.2 Truffle DSL . 11 2.2.3 Control Flow . 12 2.2.4 Profiling and Inlining . 12 2.2.5 Partial Evaluation and Compilation . 12 2.3 Clang . 13 3 Architecture 14 3.1 From Clang to Java . 15 3.2 Node Construction . 16 3.3 Runtime . 16 4 The Truffle/C File 17 4.1 Truffle/C File Format Goals . 17 4.2 Truffle/C File Format 1 . 19 4.2.1 Constant Pool . 19 4.2.2 Function Table . 20 4.2.3 Functions and Attributes . 20 4.3 Truffle/C File Considerations and Comparison . 21 4.3.1 Java Class File and Truffle/C File . 21 4.3.2 ELF and Truffle/C File . 22 4.4 Clang Modification Truffle/C File . 23 Contents II 5 Truffle/C Data Types 25 5.1 Data Type Hierarchy: Boxing, Upcasts and Downcasts . -
GNU MPFR the Multiple Precision Floating-Point Reliable Library Edition 4.1.0 July 2020
GNU MPFR The Multiple Precision Floating-Point Reliable Library Edition 4.1.0 July 2020 The MPFR team [email protected] This manual documents how to install and use the Multiple Precision Floating-Point Reliable Library, version 4.1.0. Copyright 1991, 1993-2020 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 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, with no Front-Cover Texts, and with no Back- Cover Texts. A copy of the license is included in Appendix A [GNU Free Documentation License], page 59. 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 :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 4 2.3 Build Problems :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 4 2.4 Getting the Latest Version of MPFR ::::::::::::::::::::::::::::::::::::::::::::::: 4 3 Reporting Bugs::::::::::::::::::::::::::::::::::::::::::::::::: 5 4 MPFR Basics ::::::::::::::::::::::::::::::::::::::::::::::::::: 6 4.1 Headers and Libraries :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 6 -
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 -
Effectiveness of Floating-Point Precision on the Numerical Approximation by Spectral Methods
Mathematical and Computational Applications Article Effectiveness of Floating-Point Precision on the Numerical Approximation by Spectral Methods José A. O. Matos 1,2,† and Paulo B. Vasconcelos 1,2,∗,† 1 Center of Mathematics, University of Porto, R. Dr. Roberto Frias, 4200-464 Porto, Portugal; [email protected] 2 Faculty of Economics, University of Porto, R. Dr. Roberto Frias, 4200-464 Porto, Portugal * Correspondence: [email protected] † These authors contributed equally to this work. Abstract: With the fast advances in computational sciences, there is a need for more accurate compu- tations, especially in large-scale solutions of differential problems and long-term simulations. Amid the many numerical approaches to solving differential problems, including both local and global methods, spectral methods can offer greater accuracy. The downside is that spectral methods often require high-order polynomial approximations, which brings numerical instability issues to the prob- lem resolution. In particular, large condition numbers associated with the large operational matrices, prevent stable algorithms from working within machine precision. Software-based solutions that implement arbitrary precision arithmetic are available and should be explored to obtain higher accu- racy when needed, even with the higher computing time cost associated. In this work, experimental results on the computation of approximate solutions of differential problems via spectral methods are detailed with recourse to quadruple precision arithmetic. Variable precision arithmetic was used in Tau Toolbox, a mathematical software package to solve integro-differential problems via the spectral Tau method. Citation: Matos, J.A.O.; Vasconcelos, Keywords: floating-point arithmetic; variable precision arithmetic; IEEE 754-2008 standard; quadru- P.B. -
Direct to SOM
Direct to SOM Contents General Description Header Requirements Language Restrictions MacSOM Pragmas General Description MrCpp and SCpp support Direct-To-SOM programming in C++. You can write MacSOM based classes directly using C++, that is, without using the IDL language or the IDL compiler. To use the compiler’s Direct-To-SOM feature, combine the replacement SOMObjects headers (described below) from CIncludes, and the MrCpp/SCpp tools from the Tools found in the folder Past&Future:PreRelease:Direct SOMObjects for Mac OS, with an MPW installation that already has SOM 2.0.8 (or greater) installed. Look in the SOMExamples folder for build scripts called DTS.build.script . The -som command line option enables Direct-To-SOM support. When this flag is specified, classes which are derived (directly or indirectly) from the special class named SOMObject will be processed differently than ordinary C++ classes—for these classes, the compiler will generate the MacSOM enabling class meta-data instead of standard C++ vtables. Also when -som is specified on the command line, the preprocessor symbol __SOM_ENABLED__ is defined as 1. MrCpp and SCpp ship with new, replacement MacSOM header files. The header files have been upgraded to support Direct-To-SOM. Two new header files are of special interest: Header Requirements MrCpp and SCpp ship with new, replacement MacSOM header files. The header files have been upgraded to support Direct-To-SOM. Two new header files are of special interest: • somobj.hh Defines the root MacSOM class SOMObject. It should be included when subclassing from SOMObject. If you are converting from IDL with C++ to Direct-To-SOM C++, then this file can be thought of as a replacement for both somobj.idl and somobj.xh . -
DATA and C a Sample Program
03 0672326965 CH03 10/19/04 1:53 PM Page 49 CHAPTER 3 DATA AND C You will learn about the following in this chapter: • Keywords: • The distinctions between integer int, short, long, unsigned, char, types and floating-point types float, double, _Bool, _Complex, • Writing constants and declaring _Imaginary variables of those types • Operator: • How to use the printf() and sizeof scanf() functions to read and • Function: write values of different types scanf() • The basic data types that C uses rograms work with data. You feed numbers, letters, and words to the computer, and you expect it to do something with the data. For example, you might want the com- puter to calculate an interest payment or display a sorted list of vintners. In this chap- ter,P you do more than just read about data; you practice manipulating data, which is much more fun. This chapter explores the two great families of data types: integer and floating point. C offers several varieties of these types. This chapter tells you what the types are, how to declare them, and how and when to use them. Also, you discover the differences between constants and variables, and as a bonus, your first interactive program is coming up shortly. A Sample Program Once again, you begin with a sample program. As before, you’ll find some unfamiliar wrinkles that we’ll soon iron out for you. The program’s general intent should be clear, so try compiling and running the source code shown in Listing 3.1. To save time, you can omit typing the com- ments. -
C Programming Tutorial
C Programming Tutorial C PROGRAMMING TUTORIAL Simply Easy Learning by tutorialspoint.com tutorialspoint.com i COPYRIGHT & DISCLAIMER NOTICE All the content and graphics on this tutorial are the property of tutorialspoint.com. Any content from tutorialspoint.com or this tutorial may not be redistributed or reproduced in any way, shape, or form without the written permission of tutorialspoint.com. Failure to do so is a violation of copyright laws. This tutorial may contain inaccuracies or errors and tutorialspoint provides no guarantee regarding the accuracy of the site or its contents including this tutorial. If you discover that the tutorialspoint.com site or this tutorial content contains some errors, please contact us at [email protected] ii Table of Contents C Language Overview .............................................................. 1 Facts about C ............................................................................................... 1 Why to use C ? ............................................................................................. 2 C Programs .................................................................................................. 2 C Environment Setup ............................................................... 3 Text Editor ................................................................................................... 3 The C Compiler ............................................................................................ 3 Installation on Unix/Linux ............................................................................ -
C Language Features
C Language Features 5.4 SUPPORTED DATA TYPES AND VARIABLES 5.4.1 Identifiers A C variable identifier (the following is also true for function identifiers) is a sequence of letters and digits, where the underscore character “_” counts as a letter. Identifiers cannot start with a digit. Although they can start with an underscore, such identifiers are reserved for the compiler’s use and should not be defined by your programs. Such is not the case for assembly domain identifiers, which often begin with an underscore, see Section 5.12.3.1 “Equivalent Assembly Symbols”. Identifiers are case sensitive, so main is different to Main. Not every character is significant in an identifier. The maximum number of significant characters can be set using an option, see Section 4.8.8 “-N: Identifier Length”. If two identifiers differ only after the maximum number of significant characters, then the compiler will consider them to be the same symbol. 5.4.2 Integer Data Types The MPLAB XC8 compiler supports integer data types with 1, 2, 3 and 4 byte sizes as well as a single bit type. Table 5-1 shows the data types and their corresponding size and arithmetic type. The default type for each type is underlined. TABLE 5-1: INTEGER DATA TYPES Type Size (bits) Arithmetic Type bit 1 Unsigned integer signed char 8 Signed integer unsigned char 8 Unsigned integer signed short 16 Signed integer unsigned short 16 Unsigned integer signed int 16 Signed integer unsigned int 16 Unsigned integer signed short long 24 Signed integer unsigned short long 24 Unsigned integer signed long 32 Signed integer unsigned long 32 Unsigned integer signed long long 32 Signed integer unsigned long long 32 Unsigned integer The bit and short long types are non-standard types available in this implementa- tion. -
Static Reflection
N3996- Static reflection Document number: N3996 Date: 2014-05-26 Project: Programming Language C++, SG7, Reflection Reply-to: Mat´uˇsChochl´ık([email protected]) Static reflection How to read this document The first two sections are devoted to the introduction to reflection and reflective programming, they contain some motivational examples and some experiences with usage of a library-based reflection utility. These can be skipped if you are knowledgeable about reflection. Section3 contains the rationale for the design decisions. The most important part is the technical specification in section4, the impact on the standard is discussed in section5, the issues that need to be resolved are listed in section7, and section6 mentions some implementation hints. Contents 1. Introduction4 2. Motivation and Scope6 2.1. Usefullness of reflection............................6 2.2. Motivational examples.............................7 2.2.1. Factory generator............................7 3. Design Decisions 11 3.1. Desired features................................. 11 3.2. Layered approach and extensibility...................... 11 3.2.1. Basic metaobjects........................... 12 3.2.2. Mirror.................................. 12 3.2.3. Puddle.................................. 12 3.2.4. Rubber................................. 13 3.2.5. Lagoon................................. 13 3.3. Class generators................................ 14 3.4. Compile-time vs. Run-time reflection..................... 16 4. Technical Specifications 16 4.1. Metaobject Concepts............................. -
Names for Standardized Floating-Point Formats
Names WORK IN PROGRESS April 19, 2002 11:05 am Names for Standardized Floating-Point Formats Abstract I lack the courage to impose names upon floating-point formats of diverse widths, precisions, ranges and radices, fearing the damage that can be done to one’s progeny by saddling them with ill-chosen names. Still, we need at least a list of the things we must name; otherwise how can we discuss them without talking at cross-purposes? These things include ... Wordsize, Width, Precision, Range, Radix, … all of which must be both declarable by a computer programmer, and ascertainable by a program through Environmental Inquiries. And precision must be subject to truncation by Coercions. Conventional Floating-Point Formats Among the names I have seen in use for floating-point formats are … float, double, long double, real, REAL*…, SINGLE PRECISION, DOUBLE PRECISION, double extended, doubled double, TEMPREAL, quad, … . Of these floating-point formats the conventional ones include finite numbers x all of the form x = (–1)s·m·ßn+1–P in which s, m, ß, n and P are integers with the following significance: s is the Sign bit, 0 or 1 according as x ≥ 0 or x ≤ 0 . ß is the Radix or Base, two for Binary and ten for Decimal. (I exclude all others). P is the Precision measured in Significant Digits of the given radix. m is the Significand or Coefficient or (wrongly) Mantissa of | x | running in the range 0 ≤ m ≤ ßP–1 . ≤ ≤ n is the Exponent, perhaps Biased, confined to some interval Nmin n Nmax . Other terms are in use.