Course: Programming Languages and Paradigms:
A brief introduction to imperative programming principles: history, von neumann, BNF, variables (r-values, l-values), modifiable data structures, order of evaluation, static and dynamic scopes, referencing environments, call by value, control flow (sequencing, selection, iteration), ...
The document discusses programming languages and their evolution. It begins by explaining that a programming language allows communication between humans and computers by telling computers what tasks to perform. It then summarizes the main types of programming languages:
1) Machine language uses binary and is directly understood by computers but is difficult for humans.
2) Assembly language uses mnemonics to make machine language more readable for programmers.
3) High-level languages use words and concepts familiar to humans, making programming accessible to non-experts. However, programs must be translated before computers can execute them.
4) Fourth generation languages provide even more human-friendly features like built-in database support.
This document provides an overview of compiler design, including:
- The history and importance of compilers in translating high-level code to machine-level code.
- The main components of a compiler including the front-end (analysis), back-end (synthesis), and tools used in compiler construction.
- Key phases of compilation like lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
- Types of translators like interpreters, assemblers, cross-compilers and their functions.
- Compiler construction tools that help generate scanners, parsers, translation engines, code generators, and data flow analysis.
Machine language uses binary to directly instruct the computer but is tedious for programmers. Assembly language replaces machine code with mnemonics like ADD and SUB making it easier. High-level languages are the easiest for programmers being English-like but require compilation to machine code, making them slower than low-level languages.
The document discusses compilers and their role in translating high-level programming languages into machine-readable code. It notes that compilers perform several key functions: lexical analysis, syntax analysis, generation of an intermediate representation, optimization of the intermediate code, and finally generation of assembly or machine code. The compiler allows programmers to write code in a high-level language that is easier for humans while still producing efficient low-level code that computers can execute.
The document provides an introduction to programming languages. It discusses the different levels of programming languages including low-level languages like machine language and assembly language that are close to hardware, and high-level languages like C++, Java, and Python that are more abstract. It also covers procedural languages which specify steps to complete tasks and object-oriented languages which model real-world objects. Examples are given of popular languages from each paradigm like C, Pascal, and PHP for procedural and C++, Java, Ruby for object-oriented.
This document discusses programming languages and paradigms. It begins by describing the evolution of programming languages from machine language to high-level languages. It then discusses four programming paradigms - procedural, object-oriented, functional, and declarative. For each paradigm, it provides examples of languages that use that paradigm and describes key concepts like objects, classes, inheritance, and polymorphism. It concludes by describing common concepts found in many procedural and object-oriented languages like variables, data types, expressions, statements, and subprograms.
This PPT is for First year engineering student,It covered all about C Programming according to Rajastha Technical University Kota.
flowchart, pseudo code, Programming Languages and Language Translators, Identifiers, Constants, Variables, Basic Data Types, Operators, Expressions, type casting, Input / Output Statement, Scope Rules and Storage classes, Preprocessor and Macro Substitution.
This document provides information about programming fundamentals including definitions of computer hardware, software, operating systems, compilers, interpreters, source code, and text editors. It discusses how computer hardware refers to physical components like the monitor, keyboard, and CPU, and how software includes programs that direct the computer's processor. It also summarizes the differences between compilers and interpreters in processing source code.
The document discusses programming languages and their evolution. It begins by explaining that a programming language allows communication between humans and computers by telling computers what tasks to perform. It then summarizes the main types of programming languages:
1) Machine language uses binary and is directly understood by computers but is difficult for humans.
2) Assembly language uses mnemonics to make machine language more readable for programmers.
3) High-level languages use words and concepts familiar to humans, making programming accessible to non-experts. However, programs must be translated before computers can execute them.
4) Fourth generation languages provide even more human-friendly features like built-in database support.
This document provides an overview of compiler design, including:
- The history and importance of compilers in translating high-level code to machine-level code.
- The main components of a compiler including the front-end (analysis), back-end (synthesis), and tools used in compiler construction.
- Key phases of compilation like lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
- Types of translators like interpreters, assemblers, cross-compilers and their functions.
- Compiler construction tools that help generate scanners, parsers, translation engines, code generators, and data flow analysis.
Machine language uses binary to directly instruct the computer but is tedious for programmers. Assembly language replaces machine code with mnemonics like ADD and SUB making it easier. High-level languages are the easiest for programmers being English-like but require compilation to machine code, making them slower than low-level languages.
The document discusses compilers and their role in translating high-level programming languages into machine-readable code. It notes that compilers perform several key functions: lexical analysis, syntax analysis, generation of an intermediate representation, optimization of the intermediate code, and finally generation of assembly or machine code. The compiler allows programmers to write code in a high-level language that is easier for humans while still producing efficient low-level code that computers can execute.
The document provides an introduction to programming languages. It discusses the different levels of programming languages including low-level languages like machine language and assembly language that are close to hardware, and high-level languages like C++, Java, and Python that are more abstract. It also covers procedural languages which specify steps to complete tasks and object-oriented languages which model real-world objects. Examples are given of popular languages from each paradigm like C, Pascal, and PHP for procedural and C++, Java, Ruby for object-oriented.
This document discusses programming languages and paradigms. It begins by describing the evolution of programming languages from machine language to high-level languages. It then discusses four programming paradigms - procedural, object-oriented, functional, and declarative. For each paradigm, it provides examples of languages that use that paradigm and describes key concepts like objects, classes, inheritance, and polymorphism. It concludes by describing common concepts found in many procedural and object-oriented languages like variables, data types, expressions, statements, and subprograms.
This PPT is for First year engineering student,It covered all about C Programming according to Rajastha Technical University Kota.
flowchart, pseudo code, Programming Languages and Language Translators, Identifiers, Constants, Variables, Basic Data Types, Operators, Expressions, type casting, Input / Output Statement, Scope Rules and Storage classes, Preprocessor and Macro Substitution.
This document provides information about programming fundamentals including definitions of computer hardware, software, operating systems, compilers, interpreters, source code, and text editors. It discusses how computer hardware refers to physical components like the monitor, keyboard, and CPU, and how software includes programs that direct the computer's processor. It also summarizes the differences between compilers and interpreters in processing source code.
This document discusses the evolution of programming languages from early machine languages to modern higher-level languages. It begins with an introduction to human and computer languages. It then covers the development of machine languages, assembly languages, and higher-level languages like FORTRAN and COBOL. The document discusses the advantages of each generation of languages and examples of languages from the 1950s to modern times.
This document outlines several criteria for evaluating programming languages:
Readability is affected by a language's simplicity, structure, and syntax and determines how easily programs can be understood. Writability, which influences how easily programs can be created, is impacted by simplicity, structure, and abstraction. Reliability, the ability of a program to perform as specified, depends on type checking, exception handling, aliasing, and readability and writability. Cost is determined by factors like training, development, compilation, and maintenance, and is most dependent on a language's readability and writability.
Compiler vs Interpreter-Compiler design ppt.Md Hossen
This document presents a comparison between compilers and interpreters. It discusses that both compilers and interpreters translate high-level code into machine-readable code, but they differ in their execution process. Compilers translate entire programs at once during compilation, while interpreters translate code line-by-line at runtime. As a result, compiled code generally runs faster but cannot be altered as easily during execution as interpreted code. The document provides examples of compiler and interpreter code and outlines advantages of each approach.
Assembly language is a low-level programming language that corresponds directly to a processor's machine language instructions. It uses symbolic codes that are assembled into machine-readable object code. Assembly languages are commonly used when speed, compact code size, or direct hardware interaction is important. Assemblers translate assembly language into binary machine code that can be directly executed by processors.
The document provides an overview of compilers by discussing:
1. Compilers translate source code into executable target code by going through several phases including lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
2. An interpreter directly executes source code statement by statement while a compiler produces target code as translation. Compiled code generally runs faster than interpreted code.
3. The phases of a compiler include a front end that analyzes the source code and produces intermediate code, and a back end that optimizes and generates the target code.
Computer programming involves analyzing a computing problem, developing algorithms to solve it, implementing those algorithms in a programming language, and testing the resulting program. The purpose of programming is to automate tasks and solve problems. Programmers write computer software by performing tasks like coding, debugging, testing, and maintaining programs. Programming languages can be high-level or low-level, with high-level languages being more abstract but easier for humans to read and write.
This document provides an overview of key concepts related to programming languages. It discusses the definition of a programming language and the history and evolution of popular languages from 1951 to present. It covers programming language paradigms like procedural, object-oriented, functional, and logic-based languages. It also discusses factors that influence language design like efficiency, regularity, and issues in language translation. Finally, it summarizes the structure and operation of computers and how different programming models map to underlying computer architectures.
Program control instructions can change the program counter to alter the flow of code execution. Conditional branch instructions and subroutine calls change the program counter based on status bit conditions or function needs. When an interrupt occurs internally or externally, the CPU handles it through fetch, decode and execute operations to switch to supervisor mode and service the interrupt.
Java is a general-purpose computer-programming language that is concurrent, class-based, object-oriented,[15] and specifically designed to have as few implementation dependencies as possible
Topics Covered:
Linker: Types of Linker:
Loaders : Types of loader
Example of Translator, Link and Load Time Address
Object Module
Difference between Static and Dynamic Binding
Translator, Link and Load Time Address
Program Relocatability
C++ is most often used programming language. This slide will help you to gain more knowledge on C++ programming. In this slide you will learn the fundamentals of C++ programming. The slide will also help you to fetch more details on Object Oriented Programming concepts. Each of the concept under Object Oriented Programming is explained in detail and in more smoother way as it will helpful for everyone to understand.
The document discusses the process of writing a computer program. It explains that programming involves breaking a problem down into a logical sequence of steps. There are two main phases: the problem-solving phase where the problem is analyzed and an algorithm is developed, and the implementation phase where the algorithm is translated into a programming language and tested. The process also includes a maintenance phase to modify the program as needed over time.
This document provides an introduction to programming languages. It defines what a programming language and program are, explaining that a programming language allows programmers to write instructions for a computer in a coded language. It classifies languages as high-level or low-level and discusses how computers understand different languages. The document also addresses why we need programming languages, how to select a language for a problem, and gives an overview of the basic steps to write a computer program.
The document discusses the evolution of programming languages from first to third generation languages. First generation languages were machine code using binary, while second generation introduced assembly language with mnemonic codes. Third generation languages brought features like loops and conditionals, making code easier to write but requiring compilers or interpreters to convert to machine code. Some examples of early third generation languages discussed are FORTRAN, BASIC, COBOL, and PASCAL.
This document provides an overview of computer programming, including what programming is, why we program, popular programming paradigms and languages. It discusses that programming involves instructing a computer to carry out tasks and is a creative process. Popular paradigms include procedural, structured, functional and object-oriented programming. Popular languages discussed include C, C++, Java, C#, PHP and Python. The document provides recommendations for learning resources and approaches for learning to program.
Computer languages allow humans to communicate with computers through programming. There are different types of computer languages at different levels of abstraction from machine language up to high-level languages. High-level languages are closer to human language while low-level languages are closer to machine-readable code. Programs written in high-level languages require compilers or interpreters to convert them to machine-readable code that can be executed by computers.
1. The document introduces computer programming and discusses its importance in modern society. It touches on how computers have evolved to become indispensable tools that help solve problems across many domains.
2. It outlines the typical steps involved in programming: problem analysis, algorithm development, coding, testing and debugging. Key aspects like problem definition, input/output determination, and logical processing of data are important parts of problem analysis.
3. The document emphasizes that programming requires both logic and creativity to develop well-designed solutions. Proper documentation is also important for program maintenance and future modifications.
The document discusses the different phases of a compiler: lexical analysis, syntax analysis, semantic analysis, intermediate code generation, code optimization, and code generation. It explains that a compiler takes source code as input and translates it into an equivalent language. The compiler performs analysis and synthesis in multiple phases, with each phase transforming the representation of the source code. Key activities include generating tokens, building a syntax tree, type checking, generating optimized intermediate code, and finally producing target machine code. Symbol tables are also used to store identifier information as the compiler runs.
This document summarizes a class lecture on imperative programming. It discusses list appending vs mutable list appending and how the latter does not create new cons cells. It then compares the performance of list appending vs built-in append for immutable lists. Mutable list reversing is discussed, with an exercise to implement mlist-reverse! efficiently without extra consing. Students are reminded that problem set 5 is due Friday unless they provide feedback on exercises or the online course book, in which case it can be turned in on Monday.
Programming Paradigms Which One Is The Best?Netguru
The document discusses different programming paradigms and which one may be best. It describes object-oriented programming, imperative programming, and declarative programming. For each, it provides examples in code to illustrate the paradigm. It argues that while imperative programming is popular and easy, it can be error-prone and not scale well. Declarative programming is described as simpler, safer, and more scalable by declaring intent rather than implementation. In the end, the document concludes that no single paradigm is best, and that they are often used together in practice.
This document discusses the evolution of programming languages from early machine languages to modern higher-level languages. It begins with an introduction to human and computer languages. It then covers the development of machine languages, assembly languages, and higher-level languages like FORTRAN and COBOL. The document discusses the advantages of each generation of languages and examples of languages from the 1950s to modern times.
This document outlines several criteria for evaluating programming languages:
Readability is affected by a language's simplicity, structure, and syntax and determines how easily programs can be understood. Writability, which influences how easily programs can be created, is impacted by simplicity, structure, and abstraction. Reliability, the ability of a program to perform as specified, depends on type checking, exception handling, aliasing, and readability and writability. Cost is determined by factors like training, development, compilation, and maintenance, and is most dependent on a language's readability and writability.
Compiler vs Interpreter-Compiler design ppt.Md Hossen
This document presents a comparison between compilers and interpreters. It discusses that both compilers and interpreters translate high-level code into machine-readable code, but they differ in their execution process. Compilers translate entire programs at once during compilation, while interpreters translate code line-by-line at runtime. As a result, compiled code generally runs faster but cannot be altered as easily during execution as interpreted code. The document provides examples of compiler and interpreter code and outlines advantages of each approach.
Assembly language is a low-level programming language that corresponds directly to a processor's machine language instructions. It uses symbolic codes that are assembled into machine-readable object code. Assembly languages are commonly used when speed, compact code size, or direct hardware interaction is important. Assemblers translate assembly language into binary machine code that can be directly executed by processors.
The document provides an overview of compilers by discussing:
1. Compilers translate source code into executable target code by going through several phases including lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
2. An interpreter directly executes source code statement by statement while a compiler produces target code as translation. Compiled code generally runs faster than interpreted code.
3. The phases of a compiler include a front end that analyzes the source code and produces intermediate code, and a back end that optimizes and generates the target code.
Computer programming involves analyzing a computing problem, developing algorithms to solve it, implementing those algorithms in a programming language, and testing the resulting program. The purpose of programming is to automate tasks and solve problems. Programmers write computer software by performing tasks like coding, debugging, testing, and maintaining programs. Programming languages can be high-level or low-level, with high-level languages being more abstract but easier for humans to read and write.
This document provides an overview of key concepts related to programming languages. It discusses the definition of a programming language and the history and evolution of popular languages from 1951 to present. It covers programming language paradigms like procedural, object-oriented, functional, and logic-based languages. It also discusses factors that influence language design like efficiency, regularity, and issues in language translation. Finally, it summarizes the structure and operation of computers and how different programming models map to underlying computer architectures.
Program control instructions can change the program counter to alter the flow of code execution. Conditional branch instructions and subroutine calls change the program counter based on status bit conditions or function needs. When an interrupt occurs internally or externally, the CPU handles it through fetch, decode and execute operations to switch to supervisor mode and service the interrupt.
Java is a general-purpose computer-programming language that is concurrent, class-based, object-oriented,[15] and specifically designed to have as few implementation dependencies as possible
Topics Covered:
Linker: Types of Linker:
Loaders : Types of loader
Example of Translator, Link and Load Time Address
Object Module
Difference between Static and Dynamic Binding
Translator, Link and Load Time Address
Program Relocatability
C++ is most often used programming language. This slide will help you to gain more knowledge on C++ programming. In this slide you will learn the fundamentals of C++ programming. The slide will also help you to fetch more details on Object Oriented Programming concepts. Each of the concept under Object Oriented Programming is explained in detail and in more smoother way as it will helpful for everyone to understand.
The document discusses the process of writing a computer program. It explains that programming involves breaking a problem down into a logical sequence of steps. There are two main phases: the problem-solving phase where the problem is analyzed and an algorithm is developed, and the implementation phase where the algorithm is translated into a programming language and tested. The process also includes a maintenance phase to modify the program as needed over time.
This document provides an introduction to programming languages. It defines what a programming language and program are, explaining that a programming language allows programmers to write instructions for a computer in a coded language. It classifies languages as high-level or low-level and discusses how computers understand different languages. The document also addresses why we need programming languages, how to select a language for a problem, and gives an overview of the basic steps to write a computer program.
The document discusses the evolution of programming languages from first to third generation languages. First generation languages were machine code using binary, while second generation introduced assembly language with mnemonic codes. Third generation languages brought features like loops and conditionals, making code easier to write but requiring compilers or interpreters to convert to machine code. Some examples of early third generation languages discussed are FORTRAN, BASIC, COBOL, and PASCAL.
This document provides an overview of computer programming, including what programming is, why we program, popular programming paradigms and languages. It discusses that programming involves instructing a computer to carry out tasks and is a creative process. Popular paradigms include procedural, structured, functional and object-oriented programming. Popular languages discussed include C, C++, Java, C#, PHP and Python. The document provides recommendations for learning resources and approaches for learning to program.
Computer languages allow humans to communicate with computers through programming. There are different types of computer languages at different levels of abstraction from machine language up to high-level languages. High-level languages are closer to human language while low-level languages are closer to machine-readable code. Programs written in high-level languages require compilers or interpreters to convert them to machine-readable code that can be executed by computers.
1. The document introduces computer programming and discusses its importance in modern society. It touches on how computers have evolved to become indispensable tools that help solve problems across many domains.
2. It outlines the typical steps involved in programming: problem analysis, algorithm development, coding, testing and debugging. Key aspects like problem definition, input/output determination, and logical processing of data are important parts of problem analysis.
3. The document emphasizes that programming requires both logic and creativity to develop well-designed solutions. Proper documentation is also important for program maintenance and future modifications.
The document discusses the different phases of a compiler: lexical analysis, syntax analysis, semantic analysis, intermediate code generation, code optimization, and code generation. It explains that a compiler takes source code as input and translates it into an equivalent language. The compiler performs analysis and synthesis in multiple phases, with each phase transforming the representation of the source code. Key activities include generating tokens, building a syntax tree, type checking, generating optimized intermediate code, and finally producing target machine code. Symbol tables are also used to store identifier information as the compiler runs.
This document summarizes a class lecture on imperative programming. It discusses list appending vs mutable list appending and how the latter does not create new cons cells. It then compares the performance of list appending vs built-in append for immutable lists. Mutable list reversing is discussed, with an exercise to implement mlist-reverse! efficiently without extra consing. Students are reminded that problem set 5 is due Friday unless they provide feedback on exercises or the online course book, in which case it can be turned in on Monday.
Programming Paradigms Which One Is The Best?Netguru
The document discusses different programming paradigms and which one may be best. It describes object-oriented programming, imperative programming, and declarative programming. For each, it provides examples in code to illustrate the paradigm. It argues that while imperative programming is popular and easy, it can be error-prone and not scale well. Declarative programming is described as simpler, safer, and more scalable by declaring intent rather than implementation. In the end, the document concludes that no single paradigm is best, and that they are often used together in practice.
Functional Programming with Python (RuPy 2008)Adam Byrtek
Functional Programming with Python is a document about functional programming. It discusses how functional programming is a more abstract approach that views a program as evaluations of mathematical functions, focusing more on what to compute rather than how to compute it. The document also notes that functional programming is a more abstract approach than imperative programming, which focuses on algorithms as recipes for how to program.
This document discusses four main programming paradigms: imperative, functional, object-oriented programming, and dynamic programming. It provides examples of each paradigm and lists some common languages that support each style. Imperative programming tells the computer how to do things using commands like loops. Functional programming tells the computer what to do without specifying how. Object-oriented programming models code as objects that represent real-world things. Dynamic programming uses weak or no type checking of data types.
This document describes a TV remote jammer circuit designed using a Timer IC 555. The circuit works by emitting infrared signals at 38kHz, the operating frequency of most TV remotes, which confuses the TV receiver and prevents the remote from changing channels. The circuit uses components like IR LEDs, a diode, IC 555, potentiometer, battery, resistors and capacitors. The IC 555 is used in astable mode to generate a continuous pulse waveform that drives the IR LEDs. The potentiometer allows tuning the circuit frequency to exactly match the remote. The jammer can block remote control of TV volume and channels but must be tuned correctly to 38kHz.
The document contains information about a programming paradigms course for the sixth semester of the computer science and engineering department at A.V.C. College of Engineering. It includes the syllabus, units covered, textbooks, and faculty details. The syllabus covers five units: object-oriented programming fundamentals, inheritance, event-driven programming, generic programming, and concurrent programming. The textbook listed is Core Java: Volume I – Fundamentals by Cay S. Horstmann and Gary Cornell. The faculty name provided is Parvathi.M and their designation is Assistant Professor.
Talks about different programming paradigms, their strengths & weakness, evolution of programming paradigms and some real life application of how companies have gained competitive advantage by using right paradigm to solve their problems.
What is Programming Paradigm
Types of Programming Paradigm
What is web programming
How does it work
What are web programming languages
Module of Web Programming
HTML
CSS
JAVASCRIPT
PHP
ASP .NET
RUBY ON RAILS
JAVA
PYTHON
Course: Programming Languages and Paradigms:
This introduces concepts related to programming languate design: abstraction, a bit of history, the syntax, semantics and pragmatics of programming languages, languages as abstraction, thought shaper, simplifier and law enforcer.program verification, denotational and operational semantics
The document provides an overview of several common programming paradigms, including imperative, functional, logic, object-oriented, and concurrent/distributed programming. For each paradigm, it gives a brief 1-2 sentence description of its characteristics. Imperative programming is described as evolving from machine and assembly language and focusing on non-programming contexts. Functional programming is defined as expression-oriented and close to mathematical specifications. Logic programming is summarized as using logical statements to describe required solution properties. Object-oriented programming views data as objects and has programmers define data types and operations. Concurrent and distributed programming allows for multiple simultaneous operations.
This document discusses different programming paradigms and languages. It describes batch programs which run without user interaction and event-driven programs which respond to user events. It lists many popular programming languages from Machine Language to Java and C#, and describes low-level languages that are close to machine code and high-level languages that are more human-readable. It also discusses the different types of language translators like compilers, interpreters, and assemblers and how they convert code between languages. Finally, it covers testing, debugging, and different types of errors in programming.
The document discusses programming paradigms and introduces imperative programming. It defines imperative programming as a paradigm that describes computation in terms of statements that change a program's state. Imperative programming uses commands to update variables in storage and defines sequences of commands for the computer to perform. The document contrasts structured and unstructured programming and discusses concepts like l-values, r-values, iteration, and goto statements as they relate to imperative programming.
This document discusses parameter passing mechanisms in programming languages. It explains different parameter passing techniques like call by value, call by reference, call by name. It also discusses formal and actual parameters, how they are associated during a subprogram call, and how their values are copied or linked during the subprogram entry and exit. Implementation of formal parameters involves storage in the activation record and handling input/output types by copying or using pointers.
This presentation will introduce you to programming languages that support different programming paradigms and to give you the knowledge of basic concepts and techniques that will allow them to differentiate between various programming paradigms.
This document discusses object-oriented programming paradigms and characteristics. It begins by stating that most will work in IT and knowing OO paradigms can help careers. It then discusses the key characteristics of OO paradigms - abstraction, encapsulation, inheritance, and polymorphism. Examples are given to explain each characteristic from programming and modeling perspectives. The document concludes that a strong OO model reinforces the weak model using these four key characteristics.
This document discusses four main programming paradigms: object-oriented, imperative, functional, and logic. It defines what a programming paradigm is and how it influences language design. For each paradigm it provides a key characteristic, example use cases, and what problems it is best suited for. The object-oriented paradigm views everything as objects, the imperative views problems as sequences of steps, functional views problems as expressions and functions, and logic views problems through predicate calculus. The document emphasizes that different paradigms are suited for different problems and learning multiple allows programmers to choose the best approach. It provides an overview to introduce the basic concepts of major programming paradigms.
SM Lecture Six : Corporate Strategy and DiversificationStratMgt Advisor
This document provides an overview of strategic management concepts related to strategic choices and corporate strategy. It discusses various strategic options companies can pursue, including market penetration, product development, market development, and diversification. It defines related and unrelated diversification strategies and explains potential advantages and disadvantages. The document also covers corporate strategy and the development of corporate portfolios, including through international expansion and divestment of unrelated businesses. Key frameworks discussed include the Ansoff matrix and evaluating potential for synergy through diversification.
The objectives of the seminar are to shed a light on the premises of FP and give you a basic understanding of the pillars of FP so that you would feel enlightened at the end of the session. When you walk away from the seminar you should feel an inner light about the new way of programming and an urge & motivation to code like you never before did!
Functional programming should not be confused with imperative (or procedural) programming. Neither it is like object oriented programming. It is something different. Not radically so, since the concepts that we will be exploring are familiar programming concepts, just expressed in a different way. The philosophy behind how these concepts are applied to solving problems are also a little different. We shall learn and talk about essentially the fundamental elements of Functional Programming.
A programming language is a set of rules that allows humans to tell computers what operations to perform. Programming languages provide tools for developing executable models for problem domains and exist at various levels from high-level languages that are closer to human language to low-level machine code. Some of the principal programming paradigms include imperative, object-oriented, logic/declarative, and functional programming. Popular high-level languages include FORTRAN, COBOL, BASIC, C, C++, Java, and markup languages like HTML and XML.
Konrad Zuse developed Plankalkül in 1941 as the first high-level programming language for his Z3 computer. John Mauchly designed Short Code, the first higher-level language used for a computer, in 1949. Grace Hopper created the A-0 system in 1951, one of the first compiler systems. FORTRAN was developed in the 1950s to simplify scientific programming. COBOL was created in 1959 to standardize business programming. ALGOL influenced many languages with its focus on algorithm description. Simula 67 introduced important object-oriented concepts like classes and inheritance.
The document provides a history of programming languages from the 1940s to the projected year 2100. It discusses early pioneers and the first programming language as well as the evolution of programming languages throughout each decade. Popular modern languages discussed include Python, Java, R, Julia, Lisp, JavaScript, C++, and Mojo in the context of artificial intelligence and machine learning. The document takes a high-level view of the evolution of programming over eight decades from the 1940s to 2000s and looks ahead to future trends.
The document provides a detailed history of programming languages from the 1940s to the projected future. It discusses the evolution of early machine codes and pseudocodes in the 1940s-1950s that paved the way for higher-level languages. Major milestones included the introduction of FORTRAN in 1957, which was one of the first high-level languages, followed by an explosion of new languages in the 1960s. The 1970s saw a focus on simplicity and abstraction with languages like Pascal. Object-oriented programming emerged in the 1980s with C++ and Smalltalk. The rise of the internet in the 1990s drove increased use of scripting languages and the creation of Java. Looking ahead, the future remains uncertain but programming continues
A History of Computer Programming Languages.pdfSohaib Roomi
- The document provides a history of computer programming languages from Charles Babbage's difference engine in 1822 to modern languages like C. It describes how early languages involved physically rewiring computers for each program and how John Von Neumann developed the concept of stored programs in 1945, allowing computers to be reprogrammed more easily. It then summarizes the development of many important early languages like FORTRAN, COBOL, LISP, ALGOL, and Pascal through the 1970s and how C became widely popular in the 1970s as it was developed alongside Unix.
This document provides information about a programming languages concepts course, including details about the course code, title, lecturer, and description. The course aims to describe the evolution of programming languages and understand different computation models or paradigms like imperative, functional, logic and object-oriented programming. It will cover topics like syntax, semantics, data types, expressions and control structures over 13 weeks. Students will complete an assignment on MATLAB/Octave and two term exams. The course objectives are listed as understanding different programming language paradigms and concepts to select the proper language to solve problems.
This document provides a history of early programming languages, beginning with Charles Babbage's Analytical Engine in 1837 and Ada Lovelace's work on the first computer program. It discusses several influential early languages including Plankalkül (1943), Short Code (1949), A-0 (1951), FORTRAN (1954), COBOL (1959), and LISP (1958). FORTRAN helped popularize higher-level languages for science and engineering. COBOL was designed for business applications. LISP pioneered many concepts in functional programming and AI. The document provides brief descriptions and the impact of each language.
Oplægget blev holdt ved et seminar i InfinIT-interessegruppen Højniveausprog til Indlejrede Systemer den 2. oktober 2013. Læs mere om interessegruppen her: http://infinit.dk/dk/interessegrupper/hoejniveau_sprog_til_indlejrede_systemer/hoejniveau_sprog_til_indlejrede_systemer.htm
The document provides an overview of using Python for bioinformatics, discussing what Python is, why it is useful for bioinformatics, how to set up Python in integrated development environments like Eclipse with PyDev, how to share code using Git and GitHub, and includes examples of Hello World and bioinformatics programs in Python. It introduces Python and argues it is well-suited for bioinformatics due to its extensive standard libraries, ease of use, and wide adoption in science. The document demonstrates how to install Python, set up an IDE, create and run simple Python programs, and use version control with Git and GitHub to collaborate on projects.
Can programming be liberated from the von neumman styleshady_10
John Backus.
Can programming be liberated from the von neumman style.
Conventional programming languages are growing ever more enormous, but not stronger. Inherent defects at the most basic level cause them to be both fat and weak: their primitive word-at-a-time style of programming inherited from their common ancestor --the von Neumann computer, their close coupling of semantics to state transitions, their division of programming into a world of expressions and a world of statements, their inability to effectively use powerful combining forms for building new programs from existing ones, and their lack of useful mathematical properties for reasoning about programs.
This document provides an overview of the CS4200 Compiler Construction course at TU Delft. It discusses the course organization, structure, and assessment. The course is split into two parts - CS4200-A which covers concepts and techniques through lectures, papers, and homework assignments, and CS4200-B which involves building a compiler for a subset of Java as a semester-long project. Students will use the Spoofax language workbench to implement their compiler and will submit assignments through a private GitLab repository.
The major programming languages have evolved over several decades, beginning in the 1940s-1950s with assembly languages and languages like Fortran for scientific computing. Key developments included ALGOL in the late 1950s which introduced block structures, LISP for AI in the late 1950s, COBOL for business in the late 1950s, and BASIC for education in the 1960s. Object-oriented concepts emerged in the 1960s with Simula and became widespread in the 1980s and 1990s with languages like C++, Smalltalk, and Java. Functional programming concepts gained prominence with LISP and languages like ML and Haskell. Modern scripting languages aided system administration tasks. Each new generation of languages incorporated ideas from its
The document provides an overview of the history and evolution of various programming languages. It discusses early languages like FORTRAN, LISP, PASCAL, C, and Java. It also covers scripting languages and their uses. The document explains what Python is as a programming language - that it is interpreted, object-oriented, and high-level. It was named after Monty Python and was created by Guido van Rossum. The document then gives examples of using Python to program Minecraft by importing protein data from PDB files and using coordinates to place blocks to visualize proteins in the game.
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Can Programming Be Liberated from the von Neumann Style? by John Backusmustafa sarac
John Backus was awarded the 1977 ACM Turing Award for his profound contributions to programming languages and systems. He is recognized for his work developing Fortran, one of the first high-level programming languages, and developing the Backus-Naur Form (BNF) for formally specifying programming language syntax. The award citation praised Backus' work on Fortran, which was adopted as a national standard and remained widely used, as well as his contributions to developing Algol and formal methods for specifying programming languages that are still used today.
John Backus was awarded the 1977 ACM Turing Award for his profound contributions to programming languages and systems. He led the team that developed Fortran, one of the first high-level programming languages, in the 1950s. He also served on committees that developed Algol 58 and Algol 60 and was the first to employ the formal notation known as Backus-Naur Form to specify programming language syntax. In his Turing Award lecture, Backus criticized conventional programming languages for being overly complex and inefficient due to being based on the von Neumann model of computing. He proposed an alternative "functional programming" approach based on combining simple programs into more complex ones using algebraic rules.
Want to know how programming works? how it helps the human being with their everyday work? well you can easily find the answers to those questions that are in your minds. Programming, well it is a kind of software that can make games, applications, movies and a lot more. For a start, programming can help us students with our home works and such stuffs. and now, we can learn more about the different languages used in programming, program life cycle, rules and symbols used and its level. Let us discover how programming works!
This course introduces students to fundamental constructs of programming using generic program logic design tools like flowcharting and pseudo coding to formulate algorithmic solutions to basic computing problems.
Introduction to computing and Programming (ppt)arumemartin7
This document discusses programming paradigms. It defines programming paradigm as a programming style, model, or pattern rather than the process of programming itself. The main types of programming paradigms covered are imperative, functional, logical, object-oriented, event-driven, and parallel. Examples of programming languages that correspond to each paradigm are also listed. The key outcomes of the lesson are to define programming paradigm, explain the different types, and discuss related programming languages.
Unit 4 Assignment 1 Comparative Study Of Programming...Carmen Sanborn
- The goal is to design a new programming language by combining common qualities from two existing languages.
- When designing a new language, it is important to consider aspects like syntax, semantics, data types, control structures, modularity, and libraries/frameworks.
- The language design should aim to take useful features from other languages while avoiding their shortcomings to create a language that is efficient, readable, and meets modern programming needs.
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domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology
is a platform independent model of the data and operational structures. The ontology, as used by the service,
has three distinct purposes: documentation, data structure definition and operational definitions. One goal of
the ontology is to place as much of the design and domain specific structures in the ontology rather than in
the application code. The application code interprets the ontology in the backend. The primary purpose of
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those calculations. The calculation itself is highly dependent on the varied types of molecular data found in
the database The complete service is a system with three interacting components, a user interface using
Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the
Google Firestore noSQL document database and Firebase storage. The service uses these three components
to make calculations for thermodynamic quantities based on molecular species structure. These different
platforms are united through the ontology.
JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research
domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology
is a platform independent model of the data and operational structures. The ontology, as used by the service,
has three distinct purposes: documentation, data structure definition and operational definitions. One goal of
the ontology is to place as much of the design and domain specific structures in the ontology rather than in
the application code. The application code interprets the ontology in the backend. The primary purpose of
the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make
those calculations. The calculation itself is highly dependent on the varied types of molecular data found in
the database The complete service is a system with three interacting components, a user interface using
Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the
Google Firestore noSQL document database and Firebase storage. The service uses these three components
to make calculations for thermodynamic quantities based on molecular species structure. These different
platforms are united through the ontology.
JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research
domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology
is a platform independent model of the data and operational structures. The ontology, as used by the service,
has three distinct purposes: documentation, data structure definition and operational definitions. One goal of
the ontology is to place as much of the design and domain specific structures in the ontology rather than in
the application code. The application code interprets the ontology in the backend. The primary purpose of
the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make
those calculations. The calculation itself is highly dependent on the varied types of molecular data found in
the database The complete service is a system with three interacting components, a user interface using
Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the
Google Firestore noSQL document database and Firebase storage. The service uses these three components
to make calculations for thermodynamic quantities based on molecular species structure. These different
platforms are united through the ontology.
ChemConnect: Poster for European Combustion Meeting 2017Edward Blurock
ChemConnect is a freely available combustion database that organizes experimental and modeling chemical data into a searchable network of interconnected concepts. It goes beyond traditional repositories by parsing data sets, extracting fine-grained information, and building relationships between data using semantic techniques. This enriches the data and allows connections to be made between otherwise independent sources. The goal is to provide a collaborative platform for researchers to disseminate and exchange both published and preliminary combustion data.
EU COST Action CM1404: WG€ - Efficient Data ExchangeEdward Blurock
This talk discusses the topic of data exchange within the combustion community. This is a summary of a task force on data exchange within the WG4 working group, Standard definition for data collection and mining toward a virtual chemistry of Smart Energy Carriers, within the SMARTCATS EU COST Action CM1404
ChemConnect: Characterizing CombusAon KineAc Data with ontologies and meta-‐...Edward Blurock
ChemConnect is a database that interconnects fine-grained information extracted from chemical kinetic and thermodynamic sources such as
CHEMKIN mechanism files, NASA polynomial files, and even the information behind automatic generation files.
The key to the interconnection is the Resource Description Framework (RDF) from Semantic Web technologies. The RDF is a triplet where an object item (first) is associated through a descriptor (second) to a subject item.
In this way the information of the object is connected (through the descriptor) to the subject.
In ChemConnect the object is word (text) and the subject can be text or a database item. The search mechanism within ChemConnect uses the object and subject text as search strings.
The presentation also contains an brief introduction to cloud computing.
This was presented at the COST Action 1404 SMARTCATS workshop on Databases and Systems Use Cases (http//http://www.smartcats.eu/wg4ws1dp/)
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The goal of the Very Open Data Project is to provide a software-technical foundation for this exchange of data, more specifically to provide an open database platform for data from the raw data coming from experimental measurements or models through intermediate manipulations to finally published results. The sheer expanse of the amount data involved creates some unique software-technical challenges. One of these challenges is addressed in the part of the study presented here, namely to characterize scientific data (with the initial focus being detailed chemistry data from the combustion kinetic community), so that efficient searches can be made. A formalization of this characterization comes in the form of schemas of descriptions of tags and keywords describing data and ontologies describing the relationship between data types and the relationship between the characterizations themselves. These will be translated to meta-data tags connected to the data points within a non-relational data of data for the community.
The focus of the initial work will be on data and its accessibility. As the project progresses, the emphasis will shift on not only having available data accessible for the community, but that the community itself will be able to, with emphasis on minimal effort, will be able contribute their own data. This will involve, for example, the concepts of the ‘electronic lab notebook’ and the existence and availability of extensive concept extraction tools, primarily from the chemical informatics field.
This describes a tabulation method based on computing, retaining and accessing a large, on the order of millions, number of individual kinetic time step calculations and approximations. It is essentially an extension of Pope’s In Situ Adaptive Tabulation
(ISAT) method. The primary differences lie in that not all configurations need be stored in memory and that a polynomial approximation is only calculated when enough points have accumulated within a localized area to be able to calculate the
polynomial approximation. The latter increases efficiency because no extra points are evaluated to form an approximation (as is done in ISAT). The speed up is expected to be that of ISAT.
Characterization Ignition Behavior through Morphing to Generic Ignition CurvesEdward Blurock
Presented at the International Conference of Chemical Kinetics, Ghent, Belgium, July, 2015
The qualitative notion that ignition processes have similar behavior, even over an extensive range of starting conditions, is quantitatively demonstrated through the production of a single ’generic’ ignition curve. The key to the production of the generic curve is the recognition that the basic shapes of the species and temperature profiles occurring in the ignition process differ only in their ’timing’. By ’morphing’ the time scale, the profile shapes can be made to align. From the aligned profile shapes, a generic or ’average’ profile can be derived. Synchronizing chemical events modifies the ignition progress times. In addition to fixing the ignition time to have the progress value of one, intermediate ignition events (such as selected profile maxima or inflection points) that occur before ignition are also aligned to have specific ’normalized’ times. With this additional synchronization, a single generic curve, derived from the average of the morphed curves, can be derived. This generic curve represents a kinetic modelers intuitive notion of the mechanism of the process.
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another version of abstraction
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A very general overview of computer science from machine, operating systems, networks, applications...
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A overview of computability and complexity (for non-mathematicians). definition of algorithm, turing machines, lambda, calculus and concepts of complexity
This document discusses different sorting techniques used in data structures. It describes sorting as segregating items into groups according to specified criteria. It then explains various sorting algorithms like bubble sort, selection sort, insertion sort, merge sort, and quick sort. For bubble sort, it provides an example to demonstrate how it works by exchanging elements to push larger elements to the end of the list over multiple passes.
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In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
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Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
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3. Wikipedia
Lecture: Programming
Paradigm
Definitions Edward Blruock
In computer science,
imperative programming is a programming paradigm
that uses statements that change a program's state.
In much the same way that
the imperative mood in natural languages expresses
commands, an imperative program consists of
commands for the computer to perform.
Imperative programming focuses
on describing how a program operates..
https://en.wikipedia.org/wiki/Imperative_programming
4. Blog discussion
Lecture: Programming
Paradigm
Definitions Edward Blruock
Imperative Programming is what most professional programmers use in their
day-to-day jobs. It's the name given to languages like C, C++, Java, COBOL, etc. In
imperative programming, you tell the computer what to do. "Computer, add x
and y," or "Computer, slap a dialog box onto the screen." And (usually) the
computer goes and does it. This is where most of us spend our lives, in looping
structures and if-then-else statements and the like.
http://stackoverflow.com/questions/602444/what-is-functional-declarative-and-imperative-programming
The focus is on what steps the computer should take rather than
what the computer will do (ex. C, C++, Java).
An imperative language specfies a series of instructions that the
computer executes in sequence (do this, then do that).
expressions describe sequence of actions to perform (associative)
5. Imperative Programming Course
Lecture: Programming
Paradigm
CIS: Edward Blruock
• Translate basic functional idioms into imperative ones.
• Design simple loops, using invariants to explain why they work correctly.
• Use subroutines and modules to structure more complex programs.
• Specify a module as an abstract datatype, and formalise the relationship
between that specification and an implementation.
• Design simple data structures.
• Understand the imperative implementation of some common algorithms.
• Basic imperative programming constructs: assignments, conditionals, procedures and loops.
Comparison of imperative and functional programming. Examples.
• Method of invariants: correctness rules for while loops; proof of termination. Examples
including summing an array, slow and fast exponentiation. Unit testing; debugging.
• Examples: string comparison, printing numbers in decimal.
• Binary search.
• Quicksort.
• Programming with abstract datatypes.
• Objects and classes as modules; specification; data abstraction.
• Reference-linked data structures: linked lists, binary trees.
6. Caml Language Chapter
Lecture: Programming
Paradigm
Definitions CIS: Edward Blruock
This style of programming is directly inspired by assembly
programming.
You find it in the earliest general-purpose programming languages
(Fortran, C, Pascal, etc.). In Objective Caml the following elements of
the language fit into this model:
• modifiable data structures, such as arrays, or records with mutable
fields;
• input-output operations;
• control structures such as loops and exceptions.
http://caml.inria.fr/pub/docs/oreilly-book/pdf/chap3.pdf
7. Comment
Lecture: Programming
Paradigm
Definitions CIS: Edward Blruock
the choice between several programming styles
offers the greatest flexibility for writing algorithms,
which is the principal objective of any programming language.
Besides, a program written in a style
which is close to the algorithm used
will be simpler,
and hence will have a better chance
of being correct (or at least, rapidly correctable).
http://caml.inria.fr/pub/docs/oreilly-book/pdf/chap3.pdf
9. Von Neumann Machines and
Imperative Programming
• Commands in an imperative language are
similar to the native machine instructions of
traditional computer hardware – the von
Neumann-Eckley model.
• John von Neumann: first person to document the
basic concepts of stored program computers.
• Von Neumann was a famous Hungarian
mathematician; came to US in 1930s & became
interested in computers while participating in the
development of the hydrogen bomb.
10. The “von Neumann” Computer
• A memory unit: able to store both data and instructions
– Random access
– Internally, data and instructions are stored in the same address space
& and are indistinguishable
• A calculating unit (the ALU)
• A control unit, (the CPU)
Stored program → an instruction set
• Duality of instructions and data → programs can be self modifying
• Von Neumann outlined this structure in a document known as the “First
Draft of a Report on the EDVAC”
June, 1945
11. The von Neumann Computer –
Historical Background
• Earlier computers had fixed programs: they
were hardwired to do one thing.
• Sometimes external programs were
implemented with paper tape or by setting
switches.
• Eckert and Mauchly considered stored
program computers as early as 1944
• During WW II they designed & built the ENIAC
(although for simplicity the stored program
concept was not included at first)
12. The von Neumann Computer –
Historical Background
• Later (with von Neumann), they worked on
the EDVAC
• First stored program electronic computer:
the Manchester ESSM (Baby)
– Victoria University of Manchester
– Executed its first program June 21, 1948
• A number of other stored program
machines were under development around
this time
13. History of Imperative Languages
• First imperative languages: assembly
languages
• 1954-1955: Fortran (FORmula TRANslator)
John Backus developed for IBM 704
• Late 1950’s: Algol (ALGOrithmic Language)
• 1958: Cobol (COmmon Business Oriented
Language) Developed by a government
committee; Grace Hopper very influential.
14. Turing Completeness
• A language is Turing complete if it can be
used to implement any algorithm.
• Central to the study of computability
• Alan Turing: A British mathematician,
logician, and eventually computer
scientist.
15. Imperative Programming
• Imperative languages are Turing complete if
they support integers, basic arithmetic
operators, assignment, sequencing, looping and
branching.
• Modern imperative languages generally also
include features such as
– Expressions and assignment
– Control structures (loops, decisions)
– I/O commands
– Procedures and functions
– Error and exception handling
– Library support for data structures
17. FORTRAN
• `The IBM Mathematical FORmula TRANslating system'.
• Designed circa 1955 (by, amongst others, Backus).
• First successful attempt to improve on "assembly
languages".
• Still widely used for numerical applications.
• Has been revised to take account of ideas on structured
programming etc., however is decreasing in popularity
18. ALGOL 60
• `ALGOrithmic Language 1960'.
• Developed in 1950s through a joint European-
American committee.
• First block-structured language.
• First language whose syntax was defined using
BNF.
• Direct ancestor of most modern imperative
languages.
19. BNF: Backus–Naur Form
BNF slide 19
a formal mathematical way to describe a language,
was developed by John Backus
(and possibly Peter Naur as well)
to describe the syntax of the Algol 60 programming language.
21. COBOL
• `COmmon Business Oriented Language'
• Developed in 1950s through a committee
consisting mainly of US computer manufacturers.
• Designed to process large data files and is
therefore the most extensively used language for
data processing.
• Has been revised several times, but revisions
have been unable to take into account
programming concepts such as structured
programming and modularity.
23. • BASIC - Beginner's All-purpose Symbolic Instruction
Code. Teaching language made popular by the advent of
the microcomputer in the mid 70s.
• PL/1 - Intended to be an all purpose programming
language to support both scientific and data processing
applications. Did not live up to its aspirations.
• Algol'68 - Successor to ALGOL'60. Proved less popular
than its predecessor.
• Pascal - A popular block structured teaching language
devised by Niklaus Wirth.
• Simula'67 - Simulation language, significance of which
is that it introduced the "class" concept.
26. • Modula-2 - Pascal extended with modules.
• Ada - Pascal based language sponsored by the US
Department of Defence.
• BCPL - Systems programming languages, significance of
which is that it is a precursor to C.
• C - Systems programming language used in the
development of the UNIX system. Is at a lower level than
most imperative languages. Standardised in 1988 to
ANSI C.
• C++ - Object-oriented extension to C.
• Java
27. Variables: Locations and Values
• When a variable is declared, it is bound to
some memory location and becomes its
identifier
– Location could be in global, heap, or stack storage
• l-value: memory location (address)
• r-value: value stored at the memory location
identified by l-value
• Assignment: A (target) = B (expression)
– Destructive update: overwrites the memory
location identified by A with a value of expression B
• What if a variable appears on both sides of assignment?
28. Variables and Assignment
• On the RHS of an assignment, use the
variable’s r-value; on the LHS, use its l-
value
– Example: x = x+1
– Meaning: “get r-value of x, add 1, store the
result into the l-value of x”
• An expression that does not have an l-value
cannot appear on the LHS of an assignment
– What expressions don’t have l-values?
• Example: 1=x+1
29. slide 29
l-Values and r-Values (1)
• Any expression or assignment statement in
an imperative language can be understood in
terms of l-values and r-values of variables
involved
– In C, also helps with complex pointer
dereferencing and pointer arithmetic
• Literal constants
– Have r-values, but not l-values
• Variables
– Have both r-values and l-values
– Example: x=x*y means “compute rval(x)*rval(y)
and store it in lval(x)”
30. slide 30
l-Values and r-Values (2)
• Pointer variables
– Their r-values are l-values of another variable
• Intuition: the value of a pointer is an address
• Overriding r-value and l-value computation in
C
– &x always returns l-value of x
– *p always return r-value of p
• If p is a pointer, this is an l-value of another variable
What are the values of
p and x at this point?
31. slide 31
l-Values and r-Values (3)
• Declared functions and procedures
– Have l-values, but no r-values
32. Modifiable Data Structures
Lecture: Programming
Paradigm
Data structures CIS: Edward Blruock
variable bound to a value
keeps this value to the end of its lifetime.
You can only modify this binding with a redefinition
in which case we are not really talking about the “same” variable;
rather, a new variable of the same name
now masks the old one,
which is no longer directly accessible,
but which remains unchanged.
With modifiable values, you can change the value associated with
a variable without having to redeclare the latter.
You have access to the value of a variable
for writing as well as for reading.
http://caml.inria.fr/pub/docs/oreilly-book/pdf/chap3.pdf
33. Order of Evaluation of Arguments
Lecture: Programming
Paradigm
Data Structures CIS: Edward Blruock
Order of evaluation of the operands of almost all C++ operators
(including the order of evaluation of function arguments in a
function-call expression and the order of evaluation of the
subexpressions within any expression)
is unspecified.
The compiler can evaluate operands in any order,
and may choose another order
when the same expression is evaluated again.
http://en.cppreference.com/w/cpp/language/eval_order
34. Order of Evaluation of Arguments
Lecture: Programming
Paradigm
Data Structures CIS: Edward Blruock
…there is no concept of left-to-right or right-to-left evaluation in C++.
This is not to be confused
with left-to-right and right-to-left associativity of operators:
the expression
f1() + f2() + f3()
is parsed as
( f1() + f2() ) + f3()
due to left-to-right associativity of operator+,
but the function call to f3 may be evaluated
first, last, or between f1() or f2() at run time.
http://en.cppreference.com/w/cpp/language/eval_order
35. Order of Evaluation of Arguments
Lecture: Programming
Paradigm
Data Structures
CIS: Edward Blruock
Undefined behavior
1) If a side effect on a scalar object is unsequenced relative to another side effect
on the same scalar object, the behavior is undefined.
i = ++i + i++; // undefined behavior
i = i++ + 1; // undefined behavior
(but i = ++i + 1; is well-defined)
f(++i, ++i); // undefined behavior
f(i = -1, i = -1); // undefined behavior
2) If a side effect on a scalar object is unsequenced relative to a value
computation using the value of the same scalar object, the behavior is undefined.
cout << i << i++; // undefined behavior
a[i] = i++; // undefined behavior
http://en.cppreference.com/w/cpp/language/eval_order
36. The Downfall of Imperative
Programming
Lecture: Programming
Paradigm
Data Structures
CIS: Edward Blruock
There is no doubt in my mind, and most experts agree,
that concurrency and parallelism are the future of programming.
https://www.fpcomplete.com/blog/2012/04/the-downfall-of-imperative-programming
They are all failing because of one problem -- data races.
Imperative programs
will always be vulnerable
to data races
because they contain
mutable variables.
http://wiki.ccs.tulane.edu/index.php5/Data-Race_Condition
37. Data Race
Lecture: Programming
Paradigm
Data Structures CIS: Edward Blruock
A data race occurs when:
1. two or more threads in a single process access the same
memory location concurrently, and
2. at least one of the accesses is for writing, and the threads are
not using any exclusive locks
3. to control their accesses to that memory.
When these three conditions hold,
the order of accesses is non-deterministic,
and the computation may give different results
from run to run depending on that order.
http://docs.oracle.com/cd/E19205-01/820-0619/geojs/index.html
38. Data Race Failure: Life Critical Systems
Lecture: Programming
Paradigm
Data Structures CIS: Edward Blruock
The accidents occurred when the high-power electron beam
was activated instead of the intended low power beam,
…Previous models had hardware interlocks in place to prevent this,
but Therac-25 had removed them,
depending instead on software interlocks for safety.
The software interlock could fail due to a race condition.
The defect was as follows:
a one-byte counter in a testing routine frequently overflowed;
if an operator provided manual input to the machine at the precise
moment that this counter overflowed, the interlock would fail.
39. SCOPE
The value of a variable
Lecture: Programming
Paradigm
Edward Blruock
40. Variable Scope
• The scope of a variable is the range of statements in
a program over which it’s visible
• Typical cases:
• Explicitly declared => local variables
• Explicitly passed to a subprogram => parameters
• The nonlocal variables of a program unit are those
that are visible but not declared.
• Global variables => visible everywhere.
• The scope rules of a language determine how
references to names are associated with variables.
• The two major schemes are static scoping and
dynamic scoping
41. Static Scope
• Also known as “lexical scope”
• Based on program text and can be determined
prior to execution (e.g., at compile time)
• To connect a name reference to a variable, you (or
the compiler) must find the declaration
• Search process: search declarations, first locally,
then in increasingly larger enclosing scopes, until
one is found for the given name
• Enclosing static scopes (to a specific scope) are
called its static ancestors; the nearest static
ancestor is called a static parent
42. Blocks
• A block is a section of code in which local
variables are allocated/deallocated at the
start/end of the block.
• Provides a method of creating static scopes
inside program units
• Introduced by ALGOL 60 and found in
most PLs.
• Variables can be hidden from a unit by
having a "closer" variable with same name
C++ and Ada allow access to these "hidden"
variables
43. Examples of Blocks
C and C++:
for (...) {
int index;
...
}
Ada:
declare LCL :
FLOAT;
begin
...
end
Common Lisp:
(let ((a 1)
(b foo)
(c))
(setq a (* a a))
(bar a b c))
44. Static scoping example
MAIN MAIN
A B A B
C D E C D E
MAIN
A
C
D
B
E
MAIN
A B
C D E
MAIN calls A and B
A calls C and D
B calls A and E
45. Evaluation of Static Scoping
Suppose the spec is changed so that D must now
access some data in B
Solutions:
1. Put D in B (but then C can no longer call it and D cannot
access A's variables)
2. Move the data from B that D needs to MAIN (but then all
procedures can access them)
Same problem for procedure access!
Overall: static scoping often encourages many globals
46. Dynamic Scope
• Based on calling sequences of program units, not
their textual layout (temporal versus spatial)
• References to variables are connected to declarations by searching
back through the chain of subprogram calls that forced execution to
this point
• Used in APL, Snobol and LISP
– implemented as interpreters rather than compilers.
• Consensus is that PLs with dynamic scoping leads to programs
which are difficult to read and maintain.
– Lisp switch to using static scoping as it’s default circa 1980, though
dynamic scoping is still possible as an option.
47. Dynamic Scope
• Bindings between names and objects depend on
the flow of control at run time
– The current binding is the one found most recently
during execution
• Example
– If the scoping is static, the
output of the program is 1
– If the scoping is dynamic,
output is 1 or 2 depending on
the value read at line 8 (>0
or <=0 respectively)
48. Accessing Variables with Dynamic
Scope
• Two approaches:
–Keep a stack (*association list*) of all active
variables.
• When you need to find a variable,
–hunt down from top of stack.
• This is equivalent to searching the activation records on
the dynamic chain.
– Keep a central table with one slot for every variable
name.
• If names cannot be created at run time, the table layout (and
the location of every slot) can be fixed at compile time.
Otherwise, you'll need a hash function or something to do
lookup.
• Every subroutine changes the table entries for its locals at
entry and exit.
49. Referencing Environments
• The referencing environment of a statement is
the collection of all names that are visible in
the statement
• In a static scoped language, that is the local variables plus
all of the visible variables in all of the enclosing scopes.
• A subprogram is active if its execution has begun
but has not yet terminated
• In a dynamic-scoped language, the referencing
environment is the local variables plus all visible
variables in all active subprograms. See book example (p.
185)
50. CALL BY VALUE OR REFERENCE
Variables in function calls
Function calls
51. Call-by-Value (CBV)
• In call-by-value (eager evaluation), arguments
to functions are fully evaluated before the
function is invoked
– This is the standard evaluation order that we're
used to from C, C++, and Java
52. Call-by-Value in Imperative Languages
– What does this program print?
– Prints 0
void f(int x) {
x = 3;
}
int main() {
int x = 0;
f(x);
printf("%dn", x);
}
53. Call-by-Value in Imperative Languages, con't.
• Actual parameter is copied to stack location of
formal parameter
• Modification of formal parameter not reflected
in actual parameter!
int main() {
int x = 0;
f(x);
printf("%dn", x);
}
x 0void f(int x) {
x = 3;
}
x 03
54. Call-by-Reference (CBR)
• Alternative idea: Implicitly pass a pointer or
reference to the actual parameter
– If the function writes to it the actual parameter is
modified
int main() {
int x = 0;
f(x);
printf("%dn", x);
}
void f(int x) {
x = 3;
}
x 0
x
3
55. CMSC 330 55
Call-by-Reference (cont’d)
• Advantages
– The entire argument doesn't have to be copied to the
called function
• It's more efficient if you’re passing a large (multi-word)
argument
• Can do this without explicit pointer manipulation
– Allows easy multiple return values
• Disadvantages
– Can you pass a non-variable (e.g., constant, function
result) by reference?
– It may be hard to tell if a function modifies an
argument
– What if you have aliasing?
56. 56
Aliasing
• We say that two names are aliased if they
refer to the same object in memory
– C examples (this is what makes optimizing C hard)
int x;
int *p, *q; /*Note that C uses pointers to
simulate call by reference */
p = &x; /* *p and x are aliased */
q = p; /* *q, *p, and x are aliased */
struct list { int x; struct list *next; }
struct list *p, *q;
...
q = p; /* *q and *p are aliased */
/* so are p->x and q->x */
/* and p->next->x and q->next->x... */
57. 57
Call-by-Reference (cont’d)
• Call-by-reference is still around (e.g.,
C++/Java)
– Older languages (e.g., Fortran, Ada, C with
pointers) still use it
– Possible efficiency gains not worth the confusion
– “The hardware” is basically call-by-value
58. 58
Evaluation Order Discussion
• Call-by-value is the standard for languages
with side effects
– When we have side effects, we need to know the
order in which things are evaluated, otherwise
programs have unpredictable behavior
– Call-by-reference can sometimes give different
results
– Call-by-value specifies the order at function calls
• But there are alternatives to call by value and
call by reference ...
59. 59
Call-by-Name (CBN)
• Call-by-name (lazy evaluation)
– First described in description of Algol (1960)
– Generalization of Lambda expressions (to be
discussed later)
– Idea simple: In a function:
Let add x y = x+y
add (a*b) (c*d)
Then each use of x and y in the function definition is
just a literal substitution of the actual arguments,
(a*b) and (c*d), respectively
– But implementation: Highly complex, inefficient, and
provides little improvement over other mechanisms,
as later slides demonstrate
Example:
add (a*b) (c*d) =
(a*b) + (c*d) executed function
60. 60
Call-by-Name (cont’d)
• In call-by-name, arguments to functions are
evaluated at the last possible moment, just
before they're needed
let add x y = x + y
let z = add (add 3 1) (add 4 1)
OCaml; cbv; arguments
evaluated here
add x y = x + y
z = add (add 3 1) (add 4 1)
Haskell; cbn; arguments
evaluated here
61. Call-by-Name (cont’d)
• What would be an example where this
difference matters?
let cond p x y = if p then x else y
let rec loop n = loop n
let z = cond true 42 (loop 0)
cond p x y = if p then x else y
loop n = loop n
z = cond True 42 (loop 0)
OCaml; eager; infinite recursion
at call
Haskell; lazy; never evaluated
because parameter is never used
62. CMSC 330 62
Cool Application of Lazy Evaluation
• Build control structures with functions
• Build “infinite” data structures
let cond p x y = if p then x else y
let rec integers n = n::(integers (n+1))
let rec take n i = match i with
h::t -> if n > 0 then h::(take (n-1) t)
else []
take 10 (integers 0) (* infinite loop in cbv *)
64. Control Flow
• Basic paradigms for control flow:
–Sequencing
–Selection
–Iteration
– Procedural Abstraction
– Recursion
– Concurrency
– Exception Handling and Speculation
– Nondeterminacy
65. • Sequencing
– specifies a linear ordering on statements
• one statement follows another
– very imperative, Von-Neuman
• In assembly, the only way to “jump” around
is to use branch statements.
• Early programming languages mimicked this,
such as Fortran (and even Basic and C).
Sequencing
66. • In 1968, Edsger Dijkstra wrote an article condemning
the goto statement.
• While hotly debated after this, gotos have essentially
disappeared from modern programming language.
• This is the advent of “structured programming”, a
model which took off in the 1970’s. Emphasizes:
– Top down design
– Modularization of code
– Structured types
– Descriptive variables
– Iteration
The end of goto
68. • Getting rid of goto was actually fairly easy,
since it was usually used in certain ways.
– Goto to jump to end of current subroutine: use
return instead
– Goto to escape from the middle of a loop: use
exit or break
– Goto to repeat sections of code: loops
Alternatives to goto
69. • Several settings are very useful for gotos,
however.
– Want to end a procedure/loop early (for example, if
target value is found).
• Solution: break or continue
– Problem: What about “bookkeeping”? We’re
breaking out of code which might end a scope - need
to call desctructors, deallocate variables, etc.
– Adds overhead to stack control - must be support for
“unwinding the stack”
Biggest need for goto
70. Breaks
Programming practice
Lecture: Programming
Paradigm
CIS: Edward Blruock
// Simple loop with an early exit
for (;;) {
int ch;
ch = read();
if (ch == EOF)
break; // With a loop escape
parse(ch);
}
// Simple loop with no loop escape mechanism
bool incomplete = true;
while (incomplete) {
int ch;
ch = read();
if (ch == EOF)
incomplete = false; // Without a loop escape
else
parse(ch);
}
71. • Another example: exceptions
• Goto was generally used as error handling, to
exit a section of code without continuing
• Modern languages generally throw and catch
exceptions, instead.
– Adds overhead
– But allows more graceful recovery if a section of
code is unable to fulfill its contract.
Biggest need for goto
72. • Blocks of code are executed in a sequence.
• Block are generally indicated by { … } or similar construct.
• Interesting note: without side effects (as in Agol 68), blocks
are essentially useless - the value is just the last return
• In other languages, such as Euclid and Turing, functions
which return a value are not allowed to have a side effect at
all.
– Main advantage: these are idempotent - any function call will
have the same value, no matter when it occurs
• Clearly, that is not always desirable, of course. (Think of the
rand function, which should definitely not return the same
thing every time!)
Sequencing
73. • Selection: introduced in Algol 60
– sequential if statements
if ... then ... else
if ... then ... elsif ... else
– Lisp variant:
(cond
(C1) (E1)
(C2) (E2)
...
(Cn) (En)
(T) (Et)
)
Selection
74. • Jump is especially useful in the presence of short-
circuiting
(minimal evaluation of boolean expressions)
the second argument is executed or evaluated only if the first argument
does not suffice to determine the value of the expression
if ((A > B) and (C > D)) or (E <> F) then
then_clause
else
else_clause
Selection
75. • Code generated w/o short-circuiting (Pascal)
r1 := A -- load
r2 := B
r1 := r1 > r2
r2 := C
r3 := D
r2 := r2 > r3
r1 := r1 & r2
r2 := E
r3 := F
r2 := r2 $<>$ r3
r1 := r1 $|$ r2
if r1 = 0 goto L2
L1: then_clause -- label not actually used
goto L3
L2: else_clause
L3:
Selection
76. • Code generated w/ short-circuiting (C)
r1 := A
r2 := B
if r1 <= r2 goto L4
r1 := C
r2 := D
if r1 > r2 goto L1
L4: r1 := E
r2 := F
if r1 = r2 goto L2
L1: then_clause
goto L3
L2: else_clause
L3:
Selection
77. • The case/switch statement was introduced in Algol W to
simplify certain if-else situations.
• Useful when comparing the same integer to a large
variety of possibilities:
• i := (complex expression)
if i == 1: …
elsif i in 2,7: …
• Case (complex expression)
1: …
2-7: …
Selection: Case/switch
78. • While it looks nicer, principle reason is code
optimization.
• Instead of complex branching, just loads
possible destinations into simple array.
• Additional implementations:
• If set of labels is large and sparse (e.g. 1, 2-7,
8-100, 101, 102-105, …) then can make it more
space efficient using hash tables or some other
data structure.
Selection: Case/switch
79. • Ability to perform some set of operations
repeatedly.
– Loops
– Recursion
• Can think of iteration as the only way a function
won’t run in linear time.
• In a real sense, this is the most powerful
component of programming.
• In general, loops are more common in imperative
languages, while recursion is more common in
functional languages.
Iteration
80. • Enumeration-controlled: originated in Fortran
– Pascal or Fortran-style for loops
do i = 1, 10, 2
…
enddo
– Changed to standard for loops later, eg Modula-2
FOR i := first TO last BY step DO
…
END
Iteration
81. • Can control enter or leave the loop other than through enumeration mechanism?
– Usually not a big deal - break, continue, etc. (NOT goto.)
• What happens if the loop body alters variables used to compute end-of-loop
condition?
– Some languages only compute this once. (Not C.)
• What happens if the loop modifies the index variable itself?
– Most languages prohibit this entirely, although some leave it up to the programmer.
• Can the program read the index after the loop has been completed, and if so, what
is its value?
– Ties into issue of scope, and is very language dependent.
Iteration: Some issues
82. • The for loop in C is called a combination loop - it allows one to use
more complex structures in the for loop.
• Essentially, for loops are almost another variant of while loops, with
more complex updates and true/false evaluations each time.
• Operator overloading (such as operator++) combined with iterators
actually allow highly non-enumerative for loops.
• Example:
for (list<int>::iterator it = mylist.begin(); it !=
mylist.end(); it++) {
…
}
Iteration: Loops in C
83. • Other languages (Ruby, Python, C# etc.) require any
container to provide an iterator that enumerates items in
that class.
• This is extremely high level, and relatively new.
• Example:
for item in mylist:
#code to look at items
Iteration: iterator based loops
84. • While loops are different than the standard, Fortran-style
for loops, since no set number of enumerations is
predefined.
• These are inherently strong - closer to if statements, in
some ways, but with repetition built in also.
• Down side: Much more difficult to code properly, and
more difficult to debug.
• Code optimization is also (in some sense) harder - none of
the for loop tricks will work.
Iteration: logically controlled loops