1. Design Goals the Go Programming Language Was Created in 2007 by Robert Griesmer, Robert Pike and Kenneth Thompson from Google Inc
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Introduction to Concurrent Programming
Introduction to Concurrent Programming Rob Pike Computing Sciences Research Center Bell Labs Lucent Technologies [email protected] February 2, 2000 1 Overview The world runs in parallel, but our usual model of software does not. Programming languages are sequential. This mismatch makes it hard to write systems software that provides the interface between a computer (or user) and the world. Solutions: processes, threads, concurrency, semaphores, spin locks, message-passing. But how do we use these things? Real problem: need an approach to writing concurrent software that guides our design and implementation. We will present our model for designing concurrent software. It’s been used in several languages for over a decade, producing everything from symbolic algebra packages to window systems. This course is not about parallel algorithms or using multiprocessors to run programs faster. It is about using the power of processes and communication to design elegant, responsive, reliable systems. 2 History (Biased towards Systems) Dijkstra: guarded commands, 1976. Hoare: Communicating Sequential Processes (CSP), (paper) 1978. Run multiple communicating guarded command sets in parallel. Hoare: CSP Book, 1985. Addition of channels to the model, rather than directly talking to processes. Cardelli and Pike: Squeak, 1983. Application of CSP model to user interfaces. Pike: Concurrent Window System, (paper) 1988. Application of Squeak approach to systems software. Pike: Newsqueak, 1989. Interpreted language; used to write toy window system. Winterbottom: Alef, 1994. True compiled concurrent language, used to write production systems software. Mullender: Thread library, 1999. Retrofit to C for general usability. 3 Other models exist Our approach is not the only way. -
Safe, Fast and Easy: Towards Scalable Scripting Languages
Safe, Fast and Easy: Towards Scalable Scripting Languages by Pottayil Harisanker Menon A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy. Baltimore, Maryland Feb, 2017 ⃝c Pottayil Harisanker Menon 2017 All rights reserved Abstract Scripting languages are immensely popular in many domains. They are char- acterized by a number of features that make it easy to develop small applications quickly - flexible data structures, simple syntax and intuitive semantics. However they are less attractive at scale: scripting languages are harder to debug, difficult to refactor and suffers performance penalties. Many research projects have tackled the issue of safety and performance for existing scripting languages with mixed results: the considerable flexibility offered by their semantics also makes them significantly harder to analyze and optimize. Previous research from our lab has led to the design of a typed scripting language built specifically to be flexible without losing static analyzability. Inthis dissertation, we present a framework to exploit this analyzability, with the aim of producing a more efficient implementation Our approach centers around the concept of adaptive tags: specialized tags attached to values that represent how it is used in the current program. Our frame- work abstractly tracks the flow of deep structural types in the program, and thuscan ii ABSTRACT efficiently tag them at runtime. Adaptive tags allow us to tackle key issuesatthe heart of performance problems of scripting languages: the framework is capable of performing efficient dispatch in the presence of flexible structures. iii Acknowledgments At the very outset, I would like to express my gratitude and appreciation to my advisor Prof. -
Comparative Studies of Programming Languages; Course Lecture Notes
Comparative Studies of Programming Languages, COMP6411 Lecture Notes, Revision 1.9 Joey Paquet Serguei A. Mokhov (Eds.) August 5, 2010 arXiv:1007.2123v6 [cs.PL] 4 Aug 2010 2 Preface Lecture notes for the Comparative Studies of Programming Languages course, COMP6411, taught at the Department of Computer Science and Software Engineering, Faculty of Engineering and Computer Science, Concordia University, Montreal, QC, Canada. These notes include a compiled book of primarily related articles from the Wikipedia, the Free Encyclopedia [24], as well as Comparative Programming Languages book [7] and other resources, including our own. The original notes were compiled by Dr. Paquet [14] 3 4 Contents 1 Brief History and Genealogy of Programming Languages 7 1.1 Introduction . 7 1.1.1 Subreferences . 7 1.2 History . 7 1.2.1 Pre-computer era . 7 1.2.2 Subreferences . 8 1.2.3 Early computer era . 8 1.2.4 Subreferences . 8 1.2.5 Modern/Structured programming languages . 9 1.3 References . 19 2 Programming Paradigms 21 2.1 Introduction . 21 2.2 History . 21 2.2.1 Low-level: binary, assembly . 21 2.2.2 Procedural programming . 22 2.2.3 Object-oriented programming . 23 2.2.4 Declarative programming . 27 3 Program Evaluation 33 3.1 Program analysis and translation phases . 33 3.1.1 Front end . 33 3.1.2 Back end . 34 3.2 Compilation vs. interpretation . 34 3.2.1 Compilation . 34 3.2.2 Interpretation . 36 3.2.3 Subreferences . 37 3.3 Type System . 38 3.3.1 Type checking . 38 3.4 Memory management . -
Knowledge Management Enviroments for High Throughput Biology
Knowledge Management Enviroments for High Throughput Biology Abhey Shah A Thesis submitted for the degree of MPhil Biology Department University of York September 2007 Abstract With the growing complexity and scale of data sets in computational biology and chemoin- formatics, there is a need for novel knowledge processing tools and platforms. This thesis describes a newly developed knowledge processing platform that is different in its emphasis on architecture, flexibility, builtin facilities for datamining and easy cross platform usage. There exist thousands of bioinformatics and chemoinformatics databases, that are stored in many different forms with different access methods, this is a reflection of the range of data structures that make up complex biological and chemical data. Starting from a theoretical ba- sis, FCA (Formal Concept Analysis) an applied branch of lattice theory, is used in this thesis to develop a file system that automatically structures itself by it’s contents. The procedure of extracting concepts from data sets is examined. The system also finds appropriate labels for the discovered concepts by extracting data from ontological databases. A novel method for scaling non-binary data for use with the system is developed. Finally the future of integrative systems biology is discussed in the context of efficiently closed causal systems. Contents 1 Motivations and goals of the thesis 11 1.1 Conceptual frameworks . 11 1.2 Biological foundations . 12 1.2.1 Gene expression data . 13 1.2.2 Ontology . 14 1.3 Knowledge based computational environments . 15 1.3.1 Interfaces . 16 1.3.2 Databases and the character of biological data . -
Static Analyses for Stratego Programs
Static analyses for Stratego programs BSc project report June 30, 2011 Author name Vlad A. Vergu Author student number 1195549 Faculty Technical Informatics - ST track Company Delft Technical University Department Software Engineering Commission Ir. Bernard Sodoyer Dr. Eelco Visser 2 I Preface For the successful completion of their Bachelor of Science, students at the faculty of Computer Science of the Technical University of Delft are required to carry out a software engineering project. The present report is the conclusion of this Bachelor of Science project for the student Vlad A. Vergu. The project has been carried out within the Software Language Design and Engineering Group of the Computer Science faculty, under the direct supervision of Dr. Eelco Visser of the aforementioned department and Ir. Bernard Sodoyer. I would like to thank Dr. Eelco Visser for his past and ongoing involvment and support in my educational process and in particular for the many opportunities for interesting and challenging projects. I further want to thank Ir. Bernard Sodoyer for his support with this project and his patience with my sometimes unconvential way of working. Vlad Vergu Delft; June 30, 2011 II Summary Model driven software development is gaining momentum in the software engi- neering world. One approach to model driven software development is the design and development of domain-specific languages allowing programmers and users to spend more time on their core business and less on addressing non problem- specific issues. Language workbenches and support languages and compilers are necessary for supporting development of these domain-specific languages. One such workbench is the Spoofax Language Workbench. -
Blossom: a Language Built to Grow
Macalester College DigitalCommons@Macalester College Mathematics, Statistics, and Computer Science Honors Projects Mathematics, Statistics, and Computer Science 4-26-2016 Blossom: A Language Built to Grow Jeffrey Lyman Macalester College Follow this and additional works at: https://digitalcommons.macalester.edu/mathcs_honors Part of the Computer Sciences Commons, Mathematics Commons, and the Statistics and Probability Commons Recommended Citation Lyman, Jeffrey, "Blossom: A Language Built to Grow" (2016). Mathematics, Statistics, and Computer Science Honors Projects. 45. https://digitalcommons.macalester.edu/mathcs_honors/45 This Honors Project - Open Access is brought to you for free and open access by the Mathematics, Statistics, and Computer Science at DigitalCommons@Macalester College. It has been accepted for inclusion in Mathematics, Statistics, and Computer Science Honors Projects by an authorized administrator of DigitalCommons@Macalester College. For more information, please contact [email protected]. In Memory of Daniel Schanus Macalester College Department of Mathematics, Statistics, and Computer Science Blossom A Language Built to Grow Jeffrey Lyman April 26, 2016 Advisor Libby Shoop Readers Paul Cantrell, Brett Jackson, Libby Shoop Contents 1 Introduction 4 1.1 Blossom . .4 2 Theoretic Basis 6 2.1 The Potential of Types . .6 2.2 Type basics . .6 2.3 Subtyping . .7 2.4 Duck Typing . .8 2.5 Hindley Milner Typing . .9 2.6 Typeclasses . 10 2.7 Type Level Operators . 11 2.8 Dependent types . 11 2.9 Hoare Types . 12 2.10 Success Types . 13 2.11 Gradual Typing . 14 2.12 Synthesis . 14 3 Language Description 16 3.1 Design goals . 16 3.2 Type System . 17 3.3 Hello World . -
Squinting at Power Series
Squinting at Power Series M. Douglas McIlroy AT&T Bell Laboratories Murray Hill, New Jersey 07974 ABSTRACT Data streams are an ideal vehicle for handling power series. Stream implementations can be read off directly from simple recursive equations that de®ne operations such as multiplication, substitution, exponentiation, and reversion of series. The bookkeeping that bedevils these algorithms when they are expressed in traditional languages is completely hidden when they are expressed in stream terms. Communicating processes are the key to the simplicity of the algorithms. Working versions are presented in newsqueak, the language of Pike's ``squint'' system; their effectiveness depends critically on the stream protocol. Series and streams Power series are natural objects for stream processing. Pertinent computations are neatly describable by recursive equations. CSP (communicating sequential process) languages1, 2 are good vehicles for implementation. This paper develops the algorithmic ideas and reduces them to practice in a working CSP language,3 not coincidentally illustrating the utility of concurrent programming notions in untangling the logic of some intricate computations on sequences. This elegant approach to power series computation, ®rst demonstrated but never published by Kahn and MacQueen as an application of their data stream system, is still little known.4 Power series are represented as streams of coef®cients, the exponents being given implicitly by ordinal position. (This is an in®nite analogue of the familiar representation of a polynomial as an array of coef®cients.) Thus the power series for the exponential function ∞ 1 1 1 e x = Σ x n / n! = 1 + x + __ x 2 + __ x 3 + ___ x 4 + . -
Go Web App Example
Go Web App Example Titaniferous and nonacademic Marcio smoodges his thetas attuned directs decreasingly. Fustiest Lennie seethe, his Pan-Americanism ballasts flitted gramophonically. Flavourless Elwyn dematerializing her reprobates so forbiddingly that Fonsie witness very sartorially. Ide support for web applications possible through gvm is go app and psych and unlock new subcommand go library in one configuration with embedded interface, take in a similar Basic Role-Based HTTP Authorization in fare with Casbin. Tools and web framework for everything there is big goals. Fully managed environment is go app, i is a serverless: verifying user when i personally use the example, decentralized file called marshalling which are both of. Simple Web Application with light Medium. Go apps into go library for example of examples. Go-bootstrap Generates a gait and allowance Go web project. In go apps have a value of. As of December 1st 2019 Buffalo with all related packages require Go Modules and. Authentication in Golang In building web and mobile. Go web examples or go is made against threats to run the example applying the data from the set the search. Why should be restarted for go app. Worth the go because you know that endpoint is welcome page then we created in addition to get started right of. To go apps and examples with fmt library to ensure a very different cloud network algorithms and go such as simple. This example will set users to map support the apps should be capable of examples covers both directories from the performance and application a form and array using firestore implementation. -
BEGINNING OBJECT-ORIENTED PROGRAMMING with JAVASCRIPT Build up Your Javascript Skills and Embrace PRODUCT Object-Oriented Development for the Modern Web INFORMATION
BEGINNING OBJECT-ORIENTED PROGRAMMING WITH JAVASCRIPT Build up your JavaScript skills and embrace PRODUCT object-oriented development for the modern web INFORMATION FORMAT DURATION PUBLISHED ON Instructor-Led Training 3 Days 22nd November, 2017 DESCRIPTION OUTLINE JavaScript has now become a universal development Diving into Objects and OOP Principles language. Whilst offering great benefits, the complexity Creating and Managing Object Literals can be overwhelming. Defining Object Constructors Using Object Prototypes and Classes In this course we show attendees how they can write Checking Abstraction and Modeling Support robust and efficient code with JavaScript, in order to Analyzing OOP Principles in JavaScript create scalable and maintainable web applications that help developers and businesses stay competitive. Working with Encapsulation and Information Hiding Setting up Strategies for Encapsulation LEARNING OUTCOMES Using the Meta-Closure Approach By completing this course, you will: Using Property Descriptors Implementing Information Hiding in Classes • Cover the new object-oriented features introduced as a part of ECMAScript 2015 Inheriting and Creating Mixins • Build web applications that promote scalability, Implementing Objects, Inheritance, and Prototypes maintainability and usability Using and Controlling Class Inheritance • Learn about key principles like object inheritance and Implementing Multiple Inheritance JavaScript mixins Creating and Using Mixins • Discover how to skilfully develop asynchronous code Defining Contracts with Duck Typing within larger JS applications Managing Dynamic Typing • Complete a variety of hands-on activities to build Defining Contracts and Interfaces up your experience of real-world challenges and Implementing Duck Typing problems Comparing Duck Typing and Polymorphism WHO SHOULD ATTEND Advanced Object Creation Mastering Patterns, Object Creation, and Singletons This course is for existing developers who are new to Implementing an Object Factory object-oriented programming in the JavaScript language. -
A Descent Into Limbo Brian W
A Descent into Limbo Brian W. Kernighan [email protected] Revised April 2005 by Vita Nuova ABSTRACT ‘‘If, reader, you are slow now to believe What I shall tell, that is no cause for wonder, For I who saw it hardly can accept it.’’ Dante Alighieri, Inferno, Canto XXV. Limbo is a new programming language, designed by Sean Dorward, Phil Winter- bottom, and Rob Pike. Limbo borrows from, among other things, C (expression syntax and control flow), Pascal (declarations), Winterbottom’s Alef (abstract data types and channels), and Hoare’s CSP and Pike’s Newsqueak (processes). Limbo is strongly typed, provides automatic garbage collection, supports only very restricted pointers, and compiles into machine-independent byte code for execution on a virtual machine. This paper is a brief introduction to Limbo. Since Limbo is an integral part of the Inferno system, the examples here illustrate not only the language but also a cer- tain amount about how to write programs to run within Inferno. 1. Introduction This document is a quick look at the basics of Limbo; it is not a replacement for the reference manual. The first section is a short overview of concepts and constructs; subsequent sections illustrate the language with examples. Although Limbo is intended to be used in Inferno, which emphasizes networking and graphical interfaces, the discussion here begins with standard text- manipulation examples, since they require less background to understand. Modules: A Limbo program is a set of modules that cooperate to perform a task. In source form, a module consists of a module declaration that specifies the public interface ߝ the functions, abstract data types, and constants that the module makes visible to other modules ߝ and an implementation that provides the actual code. -
4500/6500 Programming Languages What Are Types? Different Definitions
What are Types? ! Denotational: Collection of values from domain CSCI: 4500/6500 Programming » integers, doubles, characters ! Abstraction: Collection of operations that can be Languages applied to entities of that type ! Structural: Internal structure of a bunch of data, described down to the level of a small set of Types fundamental types ! Implementers: Equivalence classes 1 Maria Hybinette, UGA Maria Hybinette, UGA 2 Different Definitions: Types versus Typeless What are types good for? ! Documentation: Type for specific purposes documents 1. "Typed" or not depends on how the interpretation of the program data representations is determined » Example: Person, BankAccount, Window, Dictionary » When an operator is applied to data objects, the ! Safety: Values are used consistent with their types. interpretation of their values could be determined either by the operator or by the data objects themselves » Limit valid set of operation » Languages in which the interpretation of a data object is » Type Checking: Prevents meaningless operations (or determined by the operator applied to it are called catch enough information to be useful) typeless languages; ! Efficiency: Exploit additional information » those in which the interpretation is determined by the » Example: a type may dictate alignment at at a multiple data object itself are called typed languages. of 4, the compiler may be able to use more efficient 2. Sometimes it just means that the object does not need machine code instructions to be explicitly declared (Visual Basic, Jscript -
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.