Run Time Polymorphism Against Virtual Function in Object Oriented Programming
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Improving Virtual Function Call Target Prediction Via Dependence-Based Pre-Computation
Improving Virtual Function Call Target Prediction via Dependence-Based Pre-Computation Amir Roth, Andreas Moshovos and Gurindar S. Sohi Computer Sciences Department University of Wisconsin-Madison 1210 W Dayton Street, Madison, WI 53706 {amir, moshovos, sohi}@cs.wisc.edu Abstract In this initial work, we concentrate on pre-computing virtual function call (v-call) targets. The v-call mecha- We introduce dependence-based pre-computation as a nism enables code reuse by allowing a single static func- complement to history-based target prediction schemes. tion call to transparently invoke one of multiple function We present pre-computation in the context of virtual implementations based on run-time type information. function calls (v-calls), a class of control transfers that As shown in Table 1, v-calls are currently used in is becoming increasingly important and has resisted object-oriented programs with frequencies ranging from conventional prediction. Our proposed technique 1 in 200 to 1000 instructions (4 to 20 times fewer than dynamically identifies the sequence of operations that direct calls and 30 to 150 times fewer than conditional computes a v-call’s target. When the first instruction in branches). V-call importance will increase as object-ori- such a sequence is encountered, a small execution ented languages and methodologies gain popularity. For engine speculatively and aggressively pre-executes the now, v-calls make an ideal illustration vehicle for pre- rest. The pre-computed target is stored and subse- computation techniques since their targets are both diffi- quently used when a prediction needs to be made. We cult to predict and easy to pre-compute. -
Valloy – Virtual Functions Meet a Relational Language
VAlloy – Virtual Functions Meet a Relational Language Darko Marinov and Sarfraz Khurshid MIT Laboratory for Computer Science 200 Technology Square Cambridge, MA 02139 USA {marinov,khurshid}@lcs.mit.edu Abstract. We propose VAlloy, a veneer onto the first order, relational language Alloy. Alloy is suitable for modeling structural properties of object-oriented software. However, Alloy lacks support for dynamic dis- patch, i.e., function invocation based on actual parameter types. VAlloy introduces virtual functions in Alloy, which enables intuitive modeling of inheritance. Models in VAlloy are automatically translated into Alloy and can be automatically checked using the existing Alloy Analyzer. We illustrate the use of VAlloy by modeling object equality, such as in Java. We also give specifications for a part of the Java Collections Framework. 1 Introduction Object-oriented design and object-oriented programming have become predom- inant software methodologies. An essential feature of object-oriented languages is inheritance. It allows a (sub)class to inherit variables and methods from su- perclasses. Some languages, such as Java, only support single inheritance for classes. Subclasses can override some methods, changing the behavior inherited from superclasses. We use C++ term virtual functions to refer to methods that can be overridden. Virtual functions are dynamically dispatched—the actual function to invoke is selected based on the dynamic types of parameters. Java only supports single dynamic dispatch, i.e., the function is selected based only on the type of the receiver object. Alloy [9] is a first order, declarative language based on relations. Alloy is suitable for specifying structural properties of software. Alloy specifications can be analyzed automatically using the Alloy Analyzer (AA) [8]. -
Inheritance & Polymorphism
6.088 Intro to C/C++ Day 5: Inheritance & Polymorphism Eunsuk Kang & JeanYang In the last lecture... Objects: Characteristics & responsibilities Declaring and defining classes in C++ Fields, methods, constructors, destructors Creating & deleting objects on stack/heap Representation invariant Today’s topics Inheritance Polymorphism Abstract base classes Inheritance Types A class defines a set of objects, or a type people at MIT Types within a type Some objects are distinct from others in some ways MIT professors MIT students people at MIT Subtype MIT professor and student are subtypes of MIT people MIT professors MIT students people at MIT Type hierarchy MIT Person extends extends Student Professor What characteristics/behaviors do people at MIT have in common? Type hierarchy MIT Person extends extends Student Professor What characteristics/behaviors do people at MIT have in common? �name, ID, address �change address, display profile Type hierarchy MIT Person extends extends Student Professor What things are special about students? �course number, classes taken, year �add a class taken, change course Type hierarchy MIT Person extends extends Student Professor What things are special about professors? �course number, classes taught, rank (assistant, etc.) �add a class taught, promote Inheritance A subtype inherits characteristics and behaviors of its base type. e.g. Each MIT student has Characteristics: Behaviors: name display profile ID change address address add a class taken course number change course classes taken year Base type: MITPerson -
CSE 307: Principles of Programming Languages Classes and Inheritance
OOP Introduction Type & Subtype Inheritance Overloading and Overriding CSE 307: Principles of Programming Languages Classes and Inheritance R. Sekar 1 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Topics 1. OOP Introduction 3. Inheritance 2. Type & Subtype 4. Overloading and Overriding 2 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Section 1 OOP Introduction 3 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding OOP (Object Oriented Programming) So far the languages that we encountered treat data and computation separately. In OOP, the data and computation are combined into an “object”. 4 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Benefits of OOP more convenient: collects related information together, rather than distributing it. Example: C++ iostream class collects all I/O related operations together into one central place. Contrast with C I/O library, which consists of many distinct functions such as getchar, printf, scanf, sscanf, etc. centralizes and regulates access to data. If there is an error that corrupts object data, we need to look for the error only within its class Contrast with C programs, where access/modification code is distributed throughout the program 5 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Benefits of OOP (Continued) Promotes reuse. by separating interface from implementation. We can replace the implementation of an object without changing client code. Contrast with C, where the implementation of a data structure such as a linked list is integrated into the client code by permitting extension of new objects via inheritance. Inheritance allows a new class to reuse the features of an existing class. -
Inheritance | Data Abstraction Z Information Hiding, Adts | Encapsulation | Type Extensibility for : COP 3330
OOP components Inheritance | Data Abstraction z Information Hiding, ADTs | Encapsulation | Type Extensibility For : COP 3330. z Operator Overloading Object oriented Programming (Using C++) | Inheritance http://www.compgeom.com/~piyush/teach/3330 z Code Reuse | Polymorphism Piyush Kumar “Is a” Vs “Has a” Another Example UML Vehicle Inheritance class Car : public Vehicle { public: Considered an “Is a” class relationship // ... Car }; e.g.: An HourlyEmployee “is a” Employee A Convertible “is a” Automobile We state the above relationship in several ways: * Car is "a kind of a" Vehicle A class contains objects of another class * Car is "derived from" Vehicle as it’s member data * Car is "a specialized" Vehicle * Car is the "subclass" of Vehicle Considered a “Has a” class relationship * Vehicle is the "base class" of Car * Vehicle is the "superclass" of Car (this not as common in the C++ community) e.g.: One class “has a” object of another class as it’s data Virtual Functions Virtual Destructor rule | Virtual means “overridable” | If a class has one virtual function, you | Runtime system automatically invokes want to have a virtual destructor. the proper member function. | A virtual destructor causes the compiler to use dynamic binding when calling the destructor. | Costs 10% to 20% extra overhead compared to calling a non-virtual | Constructors: Can not be virtual. You function call. should think of them as static member functions that create objects. 1 Pure virtual member Pure virtual. functions. | A pure virtual member function is a | Specified by writing =0 after the member function that the base class function parameter list. forces derived classes to provide. -
Polymorphism
Polymorphism A closer look at types.... Chap 8 polymorphism º comes from Greek meaning ‘many forms’ In programming: Def: A function or operator is polymorphic if it has at least two possible types. Polymorphism i) OverloaDing Def: An overloaDeD function name or operator is one that has at least two Definitions, all of Different types. Example: In Java the ‘+’ operator is overloaDeD. String s = “abc” + “def”; +: String * String ® String int i = 3 + 5; +: int * int ® int Polymorphism Example: Java allows user DefineD polymorphism with overloaDeD function names. bool f (char a, char b) { return a == b; f : char * char ® bool } bool f (int a, int b) { f : int * int ® bool return a == b; } Note: ML Does not allow function overloaDing Polymorphism ii) Parameter Coercion Def: An implicit type conversion is calleD a coercion. Coercions usually exploit the type-subtype relationship because a wiDening type conversion from subtype to supertype is always DeemeD safe ® a compiler can insert these automatically ® type coercions. Example: type coercion in Java Double x; x = 2; the value 2 is coerceD from int to Double by the compiler Polymorphism Parameter coercion is an implicit type conversion on parameters. Parameter coercion makes writing programs easier – one function can be applieD to many subtypes. Example: Java voiD f (Double a) { ... } int Ì double float Ì double short Ì double all legal types that can be passeD to function ‘f’. byte Ì double char Ì double Note: ML Does not perform type coercion (ML has no notion of subtype). Polymorphism iii) Parametric Polymorphism Def: A function exhibits parametric polymorphism if it has a type that contains one or more type variables. -
C/C++ Program Design CS205 Week 10
C/C++ Program Design CS205 Week 10 Prof. Shiqi Yu (于仕琪) <[email protected]> Prof. Feng (郑锋) <[email protected]> Operators for cv::Mat Function overloading More convenient to code as follows Mat add(Mat A, Mat B); Mat A, B; Mat add(Mat A, float b); float a, b; Mat add(float a, Mat B); //… Mat C = A + B; Mat mul(Mat A, Mat B); Mat D = A * B; Mat mul(Mat A, float b); Mat E = a * A; Mat mul(float a, Mat B); ... operators for cv::Mat #include <iostream> #include <opencv2/opencv.hpp> using namespace std; int main() { float a[6]={1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f}; float b[6]={1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}; cv::Mat A(2, 3, CV_32FC1, a); cv::Mat B(3, 2, CV_32FC1, b); cv::Mat C = A * B; cout << "Matrix C = " << endl << C << endl; return 0; } The slides are based on the book <Stephen Prata, C++ Primer Plus, 6th Edition, Addison-Wesley Professional, 2011> Operator Overloading Overloading • Function overloading Ø Let you use multiple functions sharing the same name Ø Relationship to others: üDefault arguments üFunction templates • * operator (An operator overloading example) Ø Applied to an address, yield the value stored at that address Ø Applied two numbers, yield the product of the values Operator Function • Operator function Ø Keyword: operator for C++ Ø To overload an operator, you use a special function Ø Function header has the form: üoperator+() overloads the + operator üoperator*() overloads the * operator üoperator[]() overloads the [] operator Ø The compiler, recognizing the operands as belonging to the class, replaces the -
CHAPTER-3 Function Overloading SHORT ANSWER QUESTIONS 1
CHAPTER-3 Function Overloading SHORT ANSWER QUESTIONS 1. How does the compiler interpret more than one definitions having same name? What steps does it follow to distinguish these? Ans. The compiler will follow the following steps to interpret more than one definitions having same name: (i) if the signatures of subsequent functions match the previous function’s, then the second is treated as a re- declaration of the first. (ii) if the signature of the two functions match exactly but the return type differ, the second declaration is treated as an erroneous re-declaration of the first and is flagged at compile time as an error. (iii) if the signature of the two functions differ in either the number or type of their arguments, the two functions are considered to be overloaded. 2. Discuss how the best match is found when a call to an overloaded function is encountered? Give example(s) to support your answer. Ans. In order to find the best possible match, the compiler follows the following steps: 1. Search for an exact match is performed. If an exact match is found, the function is invoked. For example, 2. If an exact match is not found, a match trough promotion is searched for. Promotion means conversion of integer types char, short, enumeration and int into int or unsigned int and conversion of float into double. 3. If first two steps fail then a match through application of C++ standard conversion rules is searched for. 4. If all the above mentioned steps fail, a match through application of user-defined conversions and built-in conversion is searched for. -
Structure of Programming Languages – Lecture 2A
Structure of Programming Languages { Lecture 2a CSCI 6636 { 4536 February 4, 2020 CSCI 6636 { 4536 Lecture 2a. 1/19 February 4, 2020 1 / 19 Outline 1 Overview of Languages 2 Language Properties 3 Good or Bad: Fundamental Considerations 4 Homework CSCI 6636 { 4536 Lecture 2a. 2/19 February 4, 2020 2 / 19 Outline The Language Landscape Languages Come in Many Flavors. Possible Design Goals Design Examples Representation Issues CSCI 6636 { 4536 Lecture 2a. 3/19 February 4, 2020 3 / 19 Overview of Languages What is a Program? We can view a program two ways: Developer's view: A program is the implementation of a design (a model) for a piece of software. Coder's view: A program is a description of a set of actions that we want a computer to carry out on some data. Similarly, we can view a language more than one way: High level: It permits us to express a model. Low level: It permits us to define a correct set of instructions for the computer. CSCI 6636 { 4536 Lecture 2a. 4/19 February 4, 2020 4 / 19 Overview of Languages Aspects of a Language. To use a language effectively we must learn these four aspects: Syntax is the set legal forms that a sentence or program unit may take. During translation, if the syntax of a program unit is legal (correct), then we can talk about the semantics of the unit. Semantics is the science of meaning. Operational semantics is the set of actions when a program is executed. Style is a set of human factors. -
Comparative Studies of 10 Programming Languages Within 10 Diverse Criteria Revision 1.0
Comparative Studies of 10 Programming Languages within 10 Diverse Criteria Revision 1.0 Rana Naim∗ Mohammad Fahim Nizam† Concordia University Montreal, Concordia University Montreal, Quebec, Canada Quebec, Canada [email protected] [email protected] Sheetal Hanamasagar‡ Jalal Noureddine§ Concordia University Montreal, Concordia University Montreal, Quebec, Canada Quebec, Canada [email protected] [email protected] Marinela Miladinova¶ Concordia University Montreal, Quebec, Canada [email protected] Abstract This is a survey on the programming languages: C++, JavaScript, AspectJ, C#, Haskell, Java, PHP, Scala, Scheme, and BPEL. Our survey work involves a comparative study of these ten programming languages with respect to the following criteria: secure programming practices, web application development, web service composition, OOP-based abstractions, reflection, aspect orientation, functional programming, declarative programming, batch scripting, and UI prototyping. We study these languages in the context of the above mentioned criteria and the level of support they provide for each one of them. Keywords: programming languages, programming paradigms, language features, language design and implementation 1 Introduction Choosing the best language that would satisfy all requirements for the given problem domain can be a difficult task. Some languages are better suited for specific applications than others. In order to select the proper one for the specific problem domain, one has to know what features it provides to support the requirements. Different languages support different paradigms, provide different abstractions, and have different levels of expressive power. Some are better suited to express algorithms and others are targeting the non-technical users. The question is then what is the best tool for a particular problem. -
Multiparadigm Programming with Python 3 Chapter 5
Multiparadigm Programming with Python 3 Chapter 5 H. Conrad Cunningham 7 September 2018 Contents 5 Python 3 Types 2 5.1 Chapter Introduction . .2 5.2 Type System Concepts . .2 5.2.1 Types and subtypes . .2 5.2.2 Constants, variables, and expressions . .2 5.2.3 Static and dynamic . .3 5.2.4 Nominal and structural . .3 5.2.5 Polymorphic operations . .4 5.2.6 Polymorphic variables . .5 5.3 Python 3 Type System . .5 5.3.1 Objects . .6 5.3.2 Types . .7 5.4 Built-in Types . .7 5.4.1 Singleton types . .8 5.4.1.1 None .........................8 5.4.1.2 NotImplemented ..................8 5.4.2 Number types . .8 5.4.2.1 Integers (int)...................8 5.4.2.2 Real numbers (float)...............9 5.4.2.3 Complex numbers (complex)...........9 5.4.2.4 Booleans (bool)..................9 5.4.2.5 Truthy and falsy values . 10 5.4.3 Sequence types . 10 5.4.3.1 Immutable sequences . 10 5.4.3.2 Mutable sequences . 12 5.4.4 Mapping types . 13 5.4.5 Set Types . 14 5.4.5.1 set ......................... 14 5.4.5.2 frozenset ..................... 14 5.4.6 Other object types . 15 1 5.5 What Next? . 15 5.6 Exercises . 15 5.7 Acknowledgements . 15 5.8 References . 16 5.9 Terms and Concepts . 16 Copyright (C) 2018, H. Conrad Cunningham Professor of Computer and Information Science University of Mississippi 211 Weir Hall P.O. Box 1848 University, MS 38677 (662) 915-5358 Browser Advisory: The HTML version of this textbook requires a browser that supports the display of MathML. -
Object-Oriented Type Inference
Object-Oriented Type Inference Jens Palsberg and Michael I. Schwartzbach [email protected] and [email protected] Computer Science Department, Aarhus University Ny Munkegade, DK-8000 Arh˚ us C, Denmark Abstract The algorithm guarantees that all messages are un- derstood, annotates the program with type infor- We present a new approach to inferring types in un- mation, allows polymorphic methods, and can be typed object-oriented programs with inheritance, used as the basis of an optimizing compiler. Types assignments, and late binding. It guarantees that are finite sets of classes and subtyping is set in- all messages are understood, annotates the pro- clusion. Given a concrete program, the algorithm gram with type information, allows polymorphic methods, and can be used as the basis of an op- constructs a finite graph of type constraints. The timizing compiler. Types are finite sets of classes program is typable if these constraints are solvable. and subtyping is set inclusion. Using a trace graph, The algorithm then computes the least solution in our algorithm constructs a set of conditional type worst-case exponential time. The graph contains constraints and computes the least solution by least all type information that can be derived from the fixed-point derivation. program without keeping track of nil values or flow analyzing the contents of instance variables. This 1 Introduction makes the algorithm capable of checking most com- mon programs; in particular, it allows for polymor- phic methods. The algorithm is similar to previous Untyped object-oriented languages with assign- work on type inference [18, 14, 27, 1, 2, 19, 12, 10, 9] ments and late binding allow rapid prototyping be- in using type constraints, but it differs in handling cause classes inherit implementation and not spec- late binding by conditional constraints and in re- ification.