8 HLA Data Types
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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. -
Create Union Variables Difference Between Unions and Structures
Union is a user-defined data type similar to a structure in C programming. How to define a union? We use union keyword to define unions. Here's an example: union car { char name[50]; int price; }; The above code defines a user define data type union car. Create union variables When a union is defined, it creates a user-define type. However, no memory is allocated. To allocate memory for a given union type and work with it, we need to create variables. Here's how we create union variables: union car { char name[50]; int price; } car1, car2, *car3; In both cases, union variables car1, car2, and a union pointer car3 of union car type are created. How to access members of a union? We use . to access normal variables of a union. To access pointer variables, we use ->operator. In the above example, price for car1 can be accessed using car1.price price for car3 can be accessed using car3->price Difference between unions and structures Let's take an example to demonstrate the difference between unions and structures: #include <stdio.h> union unionJob { //defining a union char name[32]; float salary; int workerNo; } uJob; struct structJob { char name[32]; float salary; int workerNo; } sJob; main() { printf("size of union = %d bytes", sizeof(uJob)); printf("\nsize of structure = %d bytes", sizeof(sJob)); } Output size of union = 32 size of structure = 40 Why this difference in size of union and structure variables? The size of structure variable is 40 bytes. It's because: size of name[32] is 32 bytes size of salary is 4 bytes size of workerNo is 4 bytes However, the size of union variable is 32 bytes. -
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 ..................................................................................................... -
A Hybrid Static Type Inference Framework with Neural
HiTyper: A Hybrid Static Type Inference Framework with Neural Prediction Yun Peng Zongjie Li Cuiyun Gao∗ The Chinese University of Hong Kong Harbin Institute of Technology Harbin Institute of Technology Hong Kong, China Shenzhen, China Shenzhen, China [email protected] [email protected] [email protected] Bowei Gao David Lo Michael Lyu Harbin Institute of Technology Singapore Management University The Chinese University of Hong Kong Shenzhen, China Singapore Hong Kong, China [email protected] [email protected] [email protected] ABSTRACT also supports type annotations in the Python Enhancement Pro- Type inference for dynamic programming languages is an impor- posals (PEP) [21, 22, 39, 43]. tant yet challenging task. By leveraging the natural language in- Type prediction is a popular task performed by most attempts. formation of existing human annotations, deep neural networks Traditional static type inference techniques [4, 9, 14, 17, 36] and outperform other traditional techniques and become the state-of- type inference tools such as Pytype [34], Pysonar2 [33], and Pyre the-art (SOTA) in this task. However, they are facing some new Infer [31] can predict sound results for the variables with enough challenges, such as fixed type set, type drift, type correctness, and static constraints, e.g., a = 1, but are unable to handle the vari- composite type prediction. ables with few static constraints, e.g. most function arguments. To mitigate the challenges, in this paper, we propose a hybrid On the other hand, dynamic type inference techniques [3, 37] and type inference framework named HiTyper, which integrates static type checkers simulate the workflow of functions and solve types inference into deep learning (DL) models for more accurate type according to input cases and typing rules. -
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. -
[Note] C/C++ Compiler Package for RX Family (No.55-58)
RENESAS TOOL NEWS [Note] R20TS0649EJ0100 Rev.1.00 C/C++ Compiler Package for RX Family (No.55-58) Jan. 16, 2021 Overview When using the CC-RX Compiler package, note the following points. 1. Using rmpab, rmpaw, rmpal or memchr intrinsic functions (No.55) 2. Performing the tail call optimization (No.56) 3. Using the -ip_optimize option (No.57) 4. Using multi-dimensional array (No.58) Note: The number following the note is an identification number for the note. 1. Using rmpab, rmpaw, rmpal or memchr intrinsic functions (No.55) 1.1 Applicable products CC-RX V2.00.00 to V3.02.00 1.2 Details The execution result of a program including the intrinsic function rmpab, rmpaw, rmpal, or the standard library function memchr may not be as intended. 1.3 Conditions This problem may arise if all of the conditions from (1) to (3) are met. (1) One of the following calls is made: (1-1) rmpab or __rmpab is called. (1-2) rmpaw or __rmpaw is called. (1-3) rmpal or __rmpal is called. (1-4) memchr is called. (2) One of (1-1) to (1-3) is met, and neither -optimize=0 nor -noschedule option is specified. (1-4) is met, and both -size and -avoid_cross_boundary_prefetch (Note 1) options are specified. (3) Memory area that overlaps with the memory area (Note2) read by processing (1) is written in a single function. (This includes a case where called function processing is moved into the caller function by inline expansion.) Note 1: This is an option added in V2.07.00. -
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. -
ACDT: Architected Composite Data Types Trading-In Unfettered Data Access for Improved Execution
ACDT: Architected Composite Data Types Trading-in Unfettered Data Access for Improved Execution Andres Marquez∗, Joseph Manzano∗, Shuaiwen Leon Song∗, Benoˆıt Meistery Sunil Shresthaz, Thomas St. Johnz and Guang Gaoz ∗Pacific Northwest National Laboratory fandres.marquez,joseph.manzano,[email protected] yReservoir Labs [email protected] zUniversity of Delaware fshrestha,stjohn,[email protected] Abstract— reduction approaches associated with improved data locality, obtained through optimized data and computation distribution. With Exascale performance and its challenges in mind, one ubiquitous concern among architects is energy efficiency. In the SW-stack we foresee the runtime system to have a Petascale systems projected to Exascale systems are unsustainable particular important role to contribute to the solution of the at current power consumption rates. One major contributor power challenge. It is here where the massive concurrency is to system-wide power consumption is the number of memory managed and where judicious data layouts [11] and data move- operations leading to data movement and management techniques ments are orchestrated. With that in mind, we set to investigate applied by the runtime system. To address this problem, we on how to improve efficiency of a massively multithreaded present the concept of the Architected Composite Data Types adaptive runtime system in managing and moving data, and the (ACDT) framework. The framework is made aware of data trade-offs an improved data management efficiency requires. composites, assigning them a specific layout, transformations Specifically, in the context of run-time system (RTS), we and operators. Data manipulation overhead is amortized over a explore the power efficiency potential that data compression larger number of elements and program performance and power efficiency can be significantly improved. -
Chapter 5: Substitutes for C Constructs
CHAPTER 5 Substitutes for C Constructs THE Java programming language shares many similarities with the C program- ming language, but several C constructs have been omitted. In most cases, it’s obvi- ous why a C construct was omitted and how to make do without it. This chapter suggests replacements for several omitted C constructs whose replacements are not so obvious. The common thread that connects the items in this chapter is that all of the omitted constructs are data-oriented rather than object-oriented. The Java pro- gramming language provides a powerful type system, and the suggested replace- ments take full advantage of that type system to deliver a higher quality abstraction than the C constructs they replace. Even if you choose to skip this chapter, it’s probably worth reading Item 21, which discusses the typesafe enum pattern, a replacement for C’s enum construct. This pattern is not widely known at the time of this writing, and it has several advan- tages over the methods currently in common use. Item 19: Replace structures with classes The C struct construct was omitted from the Java programming language because a class does everything a structure does and more. A structure merely groups multi- ple data fields into a single object; a class associates operations with the resulting object and allows the data fields to be hidden from users of the object. In other words, a class can encapsulate its data into an object that is accessed solely by its methods, allowing the implementor the freedom to change the representation over time (Item 12). -
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. -
Coredx DDS Type System
CoreDX DDS Type System Designing and Using Data Types with X-Types and IDL 4 2017-01-23 Table of Contents 1Introduction........................................................................................................................1 1.1Overview....................................................................................................................2 2Type Definition..................................................................................................................2 2.1Primitive Types..........................................................................................................3 2.2Collection Types.........................................................................................................4 2.2.1Enumeration Types.............................................................................................4 2.2.1.1C Language Mapping.................................................................................5 2.2.1.2C++ Language Mapping.............................................................................6 2.2.1.3C# Language Mapping...............................................................................6 2.2.1.4Java Language Mapping.............................................................................6 2.2.2BitMask Types....................................................................................................7 2.2.2.1C Language Mapping.................................................................................8 2.2.2.2C++ Language Mapping.............................................................................8 -
Julia's Efficient Algorithm for Subtyping Unions and Covariant
Julia’s Efficient Algorithm for Subtyping Unions and Covariant Tuples Benjamin Chung Northeastern University, Boston, MA, USA [email protected] Francesco Zappa Nardelli Inria of Paris, Paris, France [email protected] Jan Vitek Northeastern University, Boston, MA, USA Czech Technical University in Prague, Czech Republic [email protected] Abstract The Julia programming language supports multiple dispatch and provides a rich type annotation language to specify method applicability. When multiple methods are applicable for a given call, Julia relies on subtyping between method signatures to pick the correct method to invoke. Julia’s subtyping algorithm is surprisingly complex, and determining whether it is correct remains an open question. In this paper, we focus on one piece of this problem: the interaction between union types and covariant tuples. Previous work normalized unions inside tuples to disjunctive normal form. However, this strategy has two drawbacks: complex type signatures induce space explosion, and interference between normalization and other features of Julia’s type system. In this paper, we describe the algorithm that Julia uses to compute subtyping between tuples and unions – an algorithm that is immune to space explosion and plays well with other features of the language. We prove this algorithm correct and complete against a semantic-subtyping denotational model in Coq. 2012 ACM Subject Classification Theory of computation → Type theory Keywords and phrases Type systems, Subtyping, Union types Digital Object Identifier 10.4230/LIPIcs.ECOOP.2019.24 Category Pearl Supplement Material ECOOP 2019 Artifact Evaluation approved artifact available at https://dx.doi.org/10.4230/DARTS.5.2.8 Acknowledgements The authors thank Jiahao Chen for starting us down the path of understanding Julia, and Jeff Bezanson for coming up with Julia’s subtyping algorithm.