Occam 3 Reference Manual
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Programming for Engineers Pointers in C Programming: Part 02
Programming For Engineers Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff1, Ahmad Fakhri Ab. Nasir2 Faculty of Manufacturing Engineering [email protected], [email protected] PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 0.0 Chapter’s Information • Expected Outcomes – To further use pointers in C programming • Contents 1.0 Pointer and Array 2.0 Pointer and String 3.0 Pointer and dynamic memory allocation PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 1.0 Pointer and Array • We will review array data type first and later we will relate array with pointer. • Previously, we learn about basic data types such as integer, character and floating numbers. In C programming language, if we have 5 test scores and would like to average the scores, we may code in the following way. PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 1.0 Pointer and Array PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 1.0 Pointer and Array • This program is manageable if the scores are only 5. What should we do if we have 100,000 scores? In such case, we need an efficient way to represent a collection of similar data type1. In C programming, we usually use array. • Array is a fixed-size sequence of elements of the same data type.1 • In C programming, we declare an array like the following statement: PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. -
C Programming: Data Structures and Algorithms
C Programming: Data Structures and Algorithms An introduction to elementary programming concepts in C Jack Straub, Instructor Version 2.07 DRAFT C Programming: Data Structures and Algorithms, Version 2.07 DRAFT C Programming: Data Structures and Algorithms Version 2.07 DRAFT Copyright © 1996 through 2006 by Jack Straub ii 08/12/08 C Programming: Data Structures and Algorithms, Version 2.07 DRAFT Table of Contents COURSE OVERVIEW ........................................................................................ IX 1. BASICS.................................................................................................... 13 1.1 Objectives ...................................................................................................................................... 13 1.2 Typedef .......................................................................................................................................... 13 1.2.1 Typedef and Portability ............................................................................................................. 13 1.2.2 Typedef and Structures .............................................................................................................. 14 1.2.3 Typedef and Functions .............................................................................................................. 14 1.3 Pointers and Arrays ..................................................................................................................... 16 1.4 Dynamic Memory Allocation ..................................................................................................... -
Data Structure
EDUSAT LEARNING RESOURCE MATERIAL ON DATA STRUCTURE (For 3rd Semester CSE & IT) Contributors : 1. Er. Subhanga Kishore Das, Sr. Lect CSE 2. Mrs. Pranati Pattanaik, Lect CSE 3. Mrs. Swetalina Das, Lect CA 4. Mrs Manisha Rath, Lect CA 5. Er. Dillip Kumar Mishra, Lect 6. Ms. Supriti Mohapatra, Lect 7. Ms Soma Paikaray, Lect Copy Right DTE&T,Odisha Page 1 Data Structure (Syllabus) Semester & Branch: 3rd sem CSE/IT Teachers Assessment : 10 Marks Theory: 4 Periods per Week Class Test : 20 Marks Total Periods: 60 Periods per Semester End Semester Exam : 70 Marks Examination: 3 Hours TOTAL MARKS : 100 Marks Objective : The effectiveness of implementation of any application in computer mainly depends on the that how effectively its information can be stored in the computer. For this purpose various -structures are used. This paper will expose the students to various fundamentals structures arrays, stacks, queues, trees etc. It will also expose the students to some fundamental, I/0 manipulation techniques like sorting, searching etc 1.0 INTRODUCTION: 04 1.1 Explain Data, Information, data types 1.2 Define data structure & Explain different operations 1.3 Explain Abstract data types 1.4 Discuss Algorithm & its complexity 1.5 Explain Time, space tradeoff 2.0 STRING PROCESSING 03 2.1 Explain Basic Terminology, Storing Strings 2.2 State Character Data Type, 2.3 Discuss String Operations 3.0 ARRAYS 07 3.1 Give Introduction about array, 3.2 Discuss Linear arrays, representation of linear array In memory 3.3 Explain traversing linear arrays, inserting & deleting elements 3.4 Discuss multidimensional arrays, representation of two dimensional arrays in memory (row major order & column major order), and pointers 3.5 Explain sparse matrices. -
5. Data Types
IEEE FOR THE FUNCTIONAL VERIFICATION LANGUAGE e Std 1647-2011 5. Data types The e language has a number of predefined data types, including the integer and Boolean scalar types common to most programming languages. In addition, new scalar data types (enumerated types) that are appropriate for programming, modeling hardware, and interfacing with hardware simulators can be created. The e language also provides a powerful mechanism for defining OO hierarchical data structures (structs) and ordered collections of elements of the same type (lists). The following subclauses provide a basic explanation of e data types. 5.1 e data types Most e expressions have an explicit data type, as follows: — Scalar types — Scalar subtypes — Enumerated scalar types — Casting of enumerated types in comparisons — Struct types — Struct subtypes — Referencing fields in when constructs — List types — The set type — The string type — The real type — The external_pointer type — The “untyped” pseudo type Certain expressions, such as HDL objects, have no explicit data type. See 5.2 for information on how these expressions are handled. 5.1.1 Scalar types Scalar types in e are one of the following: numeric, Boolean, or enumerated. Table 17 shows the predefined numeric and Boolean types. Both signed and unsigned integers can be of any size and, thus, of any range. See 5.1.2 for information on how to specify the size and range of a scalar field or variable explicitly. See also Clause 4. 5.1.2 Scalar subtypes A scalar subtype can be named and created by using a scalar modifier to specify the range or bit width of a scalar type. -
Lecture 2: Variables and Primitive Data Types
Lecture 2: Variables and Primitive Data Types MIT-AITI Kenya 2005 1 In this lecture, you will learn… • What a variable is – Types of variables – Naming of variables – Variable assignment • What a primitive data type is • Other data types (ex. String) MIT-Africa Internet Technology Initiative 2 ©2005 What is a Variable? • In basic algebra, variables are symbols that can represent values in formulas. • For example the variable x in the formula f(x)=x2+2 can represent any number value. • Similarly, variables in computer program are symbols for arbitrary data. MIT-Africa Internet Technology Initiative 3 ©2005 A Variable Analogy • Think of variables as an empty box that you can put values in. • We can label the box with a name like “Box X” and re-use it many times. • Can perform tasks on the box without caring about what’s inside: – “Move Box X to Shelf A” – “Put item Z in box” – “Open Box X” – “Remove contents from Box X” MIT-Africa Internet Technology Initiative 4 ©2005 Variables Types in Java • Variables in Java have a type. • The type defines what kinds of values a variable is allowed to store. • Think of a variable’s type as the size or shape of the empty box. • The variable x in f(x)=x2+2 is implicitly a number. • If x is a symbol representing the word “Fish”, the formula doesn’t make sense. MIT-Africa Internet Technology Initiative 5 ©2005 Java Types • Integer Types: – int: Most numbers you’ll deal with. – long: Big integers; science, finance, computing. – short: Small integers. -
Occam 2.1 Reference Manual
occam 2.1 reference manual SGS-THOMSON Microelectronics Limited May 12, 1995 iv occam 2.1 REFERENCE MANUAL SGS-THOMSON Microelectronics Limited First published 1988 by Prentice Hall International (UK) Ltd as the occam 2 Reference Manual. SGS-THOMSON Microelectronics Limited 1995. SGS-THOMSON Microelectronics reserves the right to make changes in specifications at any time and without notice. The information furnished by SGS-THOMSON Microelectronics in this publication is believed to be accurate, but no responsibility is assumed for its use, nor for any infringement of patents or other rights of third parties resulting from its use. No licence is granted under any patents, trademarks or other rights of SGS-THOMSON Microelectronics. The INMOS logo, INMOS, IMS and occam are registered trademarks of SGS-THOMSON Microelectronics Limited. Document number: 72 occ 45 03 All rights reserved. No part of this publication may be reproduced, stored in a retrival system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior permission, in writing, from SGS-THOMSON Microelectronics Limited. Contents Contents v Contents overview ix Preface xi Introduction 1 Syntax and program format 3 1 Primitive processes 5 1.1 Assignment 5 1.2 Communication 6 1.3 SKIP and STOP 7 2 Constructed processes 9 2.1 Sequence 9 2.2 Conditional 11 2.3 Selection 13 2.4 WHILE loop 14 2.5 Parallel 15 2.6 Alternation 19 2.7 Processes 24 3 Data types 25 3.1 Primitive data types 25 3.2 Named data types 26 3.3 Literals -
Source Code Auditing: Day 2
Source Code Auditing: Day 2 Penetration Testing & Vulnerability Analysis Brandon Edwards [email protected] Data Types Continued Data Type Signedness Remember, by default all data types are signed unless specifically declared otherwise But many functions which accept size arguments take unsigned values What is the difference of the types below? char y; unsigned char x; x = 255; y = -1; 3 Data Type Signedness These types are the same size (8-bits) char y; unsigned char x; 4 Data Type Signedness A large value in the unsigned type (highest bit set) is a negative value in the signed type char y; unsigned char x; 5 Data Type Bugs Same concept applies to 16 and 32 bit data types What are the implications of mixing signed & unsigned types ? #define MAXSOCKBUF 4096 int readNetworkData(int sock) { char buf[MAXSOCKBUF]; int length; read(sock, (char *)&length, 4); if (length < MAXSOCKBUF) { read(sock, buf, length); } } 6 Data Type Signedness The check is between two signed values… #define MAXSOCKBUF 4096 if (length < MAXSOCKBUF) So if length is negative (highest bit / signed bit set), it will evaluate as less than MAXSOCKBUF But the read() function takes only unsigned values for it’s size Remember, the highest bit (or signed bit is set), and the compiler implicitly converts the length to unsigned for read() 7 Data Type Signedness So what if length is -1 (or 0xFFFFFFFF in hex)? #define MAXSOCKBUF 4096 if (length < MAXSOCKBUF) { read(sock, buf, length); } When the length check is performed, it is asking if -1 is less than 4096 When the length is passed to read, it is converted to unsigned and becomes the unsigned equivalent of -1, which for 32bits is 4294967295 8 Data Type Bugs Variation in data type sizes can also introduce bugs Remember the primitive data type sizes? (x86): An integer type is 32bits A short type is 16bits A char type is 8 bits Sometimes code is written without considering differences between these. -
Programming the Capabilities of the PC Have Changed Greatly Since the Introduction of Electronic Computers
1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in INTRODUCTION TO PROGRAMMING LANGUAGE Topic Objective: At the end of this topic the student will be able to understand: History of Computer Programming C++ Definition/Overview: Overview: A personal computer (PC) is any general-purpose computer whose original sales price, size, and capabilities make it useful for individuals, and which is intended to be operated directly by an end user, with no intervening computer operator. Today a PC may be a desktop computer, a laptop computer or a tablet computer. The most common operating systems are Microsoft Windows, Mac OS X and Linux, while the most common microprocessors are x86-compatible CPUs, ARM architecture CPUs and PowerPC CPUs. Software applications for personal computers include word processing, spreadsheets, databases, games, and myriad of personal productivity and special-purpose software. Modern personal computers often have high-speed or dial-up connections to the Internet, allowing access to the World Wide Web and a wide range of other resources. Key Points: 1. History of ComputeWWW.BSSVE.INr Programming The capabilities of the PC have changed greatly since the introduction of electronic computers. By the early 1970s, people in academic or research institutions had the opportunity for single-person use of a computer system in interactive mode for extended durations, although these systems would still have been too expensive to be owned by a single person. The introduction of the microprocessor, a single chip with all the circuitry that formerly occupied large cabinets, led to the proliferation of personal computers after about 1975. Early personal computers - generally called microcomputers - were sold often in Electronic kit form and in limited volumes, and were of interest mostly to hobbyists and technicians. -
Floating-Point Exception Handing
ISO/IEC JTC1/SC22/WG5 N1372 Working draft of ISO/IEC TR 15580, second edition Information technology ± Programming languages ± Fortran ± Floating-point exception handing This page to be supplied by ISO. No changes from first edition, except for for mechanical things such as dates. i ISO/IEC TR 15580 : 1998(E) Draft second edition, 13th August 1999 ISO/IEC Foreword To be supplied by ISO. No changes from first edition, except for for mechanical things such as dates. ii ISO/IEC Draft second edition, 13th August 1999 ISO/IEC TR 15580: 1998(E) Introduction Exception handling is required for the development of robust and efficient numerical software. In particular, it is necessary in order to be able to write portable scientific libraries. In numerical Fortran programming, current practice is to employ whatever exception handling mechanisms are provided by the system/vendor. This clearly inhibits the production of fully portable numerical libraries and programs. It is particularly frustrating now that IEEE arithmetic (specified by IEEE 754-1985 Standard for binary floating-point arithmetic, also published as IEC 559:1989, Binary floating-point arithmetic for microprocessor systems) is so widely used, since built into it are the five conditions: overflow, invalid, divide-by-zero, underflow, and inexact. Our aim is to provide support for these conditions. We have taken the opportunity to provide support for other aspects of the IEEE standard through a set of elemental functions that are applicable only to IEEE data types. This proposal involves three standard modules: IEEE_EXCEPTIONS contains a derived type, some named constants of this type, and some simple procedures. -
UML Profile for Communicating Systems a New UML Profile for the Specification and Description of Internet Communication and Signaling Protocols
UML Profile for Communicating Systems A New UML Profile for the Specification and Description of Internet Communication and Signaling Protocols Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultäten der Georg-August-Universität zu Göttingen vorgelegt von Constantin Werner aus Salzgitter-Bad Göttingen 2006 D7 Referent: Prof. Dr. Dieter Hogrefe Korreferent: Prof. Dr. Jens Grabowski Tag der mündlichen Prüfung: 30.10.2006 ii Abstract This thesis presents a new Unified Modeling Language 2 (UML) profile for communicating systems. It is developed for the unambiguous, executable specification and description of communication and signaling protocols for the Internet. This profile allows to analyze, simulate and validate a communication protocol specification in the UML before its implementation. This profile is driven by the experience and intelligibility of the Specification and Description Language (SDL) for telecommunication protocol engineering. However, as shown in this thesis, SDL is not optimally suited for specifying communication protocols for the Internet due to their diverse nature. Therefore, this profile features new high-level language concepts rendering the specification and description of Internet protocols more intuitively while abstracting from concrete implementation issues. Due to its support of several concrete notations, this profile is designed to work with a number of UML compliant modeling tools. In contrast to other proposals, this profile binds the informal UML semantics with many semantic variation points by defining formal constraints for the profile definition and providing a mapping specification to SDL by the Object Constraint Language. In addition, the profile incorporates extension points to enable mappings to many formal description languages including SDL. To demonstrate the usability of the profile, a case study of a concrete Internet signaling protocol is presented. -
Mark Vie Controller Standard Block Library for Public Disclosure Contents
GEI-100682AC Mark* VIe Controller Standard Block Library These instructions do not purport to cover all details or variations in equipment, nor to provide for every possible contingency to be met during installation, operation, and maintenance. The information is supplied for informational purposes only, and GE makes no warranty as to the accuracy of the information included herein. Changes, modifications, and/or improvements to equipment and specifications are made periodically and these changes may or may not be reflected herein. It is understood that GE may make changes, modifications, or improvements to the equipment referenced herein or to the document itself at any time. This document is intended for trained personnel familiar with the GE products referenced herein. Public – This document is approved for public disclosure. GE may have patents or pending patent applications covering subject matter in this document. The furnishing of this document does not provide any license whatsoever to any of these patents. GE provides the following document and the information included therein as is and without warranty of any kind, expressed or implied, including but not limited to any implied statutory warranty of merchantability or fitness for particular purpose. For further assistance or technical information, contact the nearest GE Sales or Service Office, or an authorized GE Sales Representative. Revised: July 2018 Issued: Sept 2005 © 2005 – 2018 General Electric Company. ___________________________________ * Indicates a trademark of General -
Adaptivfloat: a Floating-Point Based Data Type for Resilient Deep Learning Inference
ADAPTIVFLOAT:AFLOATING-POINT BASED DATA TYPE FOR RESILIENT DEEP LEARNING INFERENCE Thierry Tambe 1 En-Yu Yang 1 Zishen Wan 1 Yuntian Deng 1 Vijay Janapa Reddi 1 Alexander Rush 2 David Brooks 1 Gu-Yeon Wei 1 ABSTRACT Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequately capture the wide data distributions commonly seen in sequence transduction models. We present AdaptivFloat, a floating-point inspired number representation format for deep learning that dynamically maximizes and optimally clips its available dynamic range, at a layer granularity, in order to create faithful encoding of neural network parameters. AdaptivFloat consistently produces higher inference accuracies compared to block floating-point, uniform, IEEE-like float or posit encodings at very low precision (≤ 8-bit) across a diverse set of state-of-the-art neural network topologies. And notably, AdaptivFloat is seen surpassing baseline FP32 performance by up to +0.3 in BLEU score and -0.75 in word error rate at weight bit widths that are ≤ 8-bit. Experimental results on a deep neural network (DNN) hardware accelerator, exploiting AdaptivFloat logic in its computational datapath, demonstrate per-operation energy and area that is 0.9× and 1.14×, respectively, that of equivalent bit width integer-based accelerator variants. (a) ResNet-50 Weight Histogram (b) Inception-v3 Weight Histogram 1 INTRODUCTION 105 Max Weight: 1.32 105 Max Weight: 1.27 Min Weight: -0.78 Min Weight: -1.20 4 4 Deep learning approaches have transformed representation 10 10 103 103 learning in a multitude of tasks.