Coroutines: Language and Implementation Impact
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The Early History of Smalltalk
The Early History of Smalltalk http://www.accesscom.com/~darius/EarlyHistoryS... The Early History of Smalltalk Alan C. Kay Apple Computer [email protected]# Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. HOPL-II/4/93/MA, USA © 1993 ACM 0-89791-571-2/93/0004/0069...$1.50 Abstract Most ideas come from previous ideas. The sixties, particularly in the ARPA community, gave rise to a host of notions about "human-computer symbiosis" through interactive time-shared computers, graphics screens and pointing devices. Advanced computer languages were invented to simulate complex systems such as oil refineries and semi-intelligent behavior. The soon-to- follow paradigm shift of modern personal computing, overlapping window interfaces, and object-oriented design came from seeing the work of the sixties as something more than a "better old thing." This is, more than a better way: to do mainframe computing; for end-users to invoke functionality; to make data structures more abstract. Instead the promise of exponential growth in computing/$/volume demanded that the sixties be regarded as "almost a new thing" and to find out what the actual "new things" might be. For example, one would compute with a handheld "Dynabook" in a way that would not be possible on a shared mainframe; millions of potential users meant that the user interface would have to become a learning environment along the lines of Montessori and Bruner; and needs for large scope, reduction in complexity, and end-user literacy would require that data and control structures be done away with in favor of a more biological scheme of protected universal cells interacting only through messages that could mimic any desired behavior. -
Bringing GNU Emacs to Native Code
Bringing GNU Emacs to Native Code Andrea Corallo Luca Nassi Nicola Manca [email protected] [email protected] [email protected] CNR-SPIN Genoa, Italy ABSTRACT such a long-standing project. Although this makes it didactic, some Emacs Lisp (Elisp) is the Lisp dialect used by the Emacs text editor limitations prevent the current implementation of Emacs Lisp to family. GNU Emacs can currently execute Elisp code either inter- be appealing for broader use. In this context, performance issues preted or byte-interpreted after it has been compiled to byte-code. represent the main bottleneck, which can be broken down in three In this work we discuss the implementation of an optimizing com- main sub-problems: piler approach for Elisp targeting native code. The native compiler • lack of true multi-threading support, employs the byte-compiler’s internal representation as input and • garbage collection speed, exploits libgccjit to achieve code generation using the GNU Com- • code execution speed. piler Collection (GCC) infrastructure. Generated executables are From now on we will focus on the last of these issues, which con- stored as binary files and can be loaded and unloaded dynamically. stitutes the topic of this work. Most of the functionality of the compiler is written in Elisp itself, The current implementation traditionally approaches the prob- including several optimization passes, paired with a C back-end lem of code execution speed in two ways: to interface with the GNU Emacs core and libgccjit. Though still a work in progress, our implementation is able to bootstrap a func- • Implementing a large number of performance-sensitive prim- tional Emacs and compile all lexically scoped Elisp files, including itive functions (also known as subr) in C. -
Object-Oriented Programming in the Beta Programming Language
OBJECT-ORIENTED PROGRAMMING IN THE BETA PROGRAMMING LANGUAGE OLE LEHRMANN MADSEN Aarhus University BIRGER MØLLER-PEDERSEN Ericsson Research, Applied Research Center, Oslo KRISTEN NYGAARD University of Oslo Copyright c 1993 by Ole Lehrmann Madsen, Birger Møller-Pedersen, and Kristen Nygaard. All rights reserved. No part of this book may be copied or distributed without the prior written permission of the authors Preface This is a book on object-oriented programming and the BETA programming language. Object-oriented programming originated with the Simula languages developed at the Norwegian Computing Center, Oslo, in the 1960s. The first Simula language, Simula I, was intended for writing simulation programs. Si- mula I was later used as a basis for defining a general purpose programming language, Simula 67. In addition to being a programming language, Simula1 was also designed as a language for describing and communicating about sys- tems in general. Simula has been used by a relatively small community for many years, although it has had a major impact on research in computer sci- ence. The real breakthrough for object-oriented programming came with the development of Smalltalk. Since then, a large number of programming lan- guages based on Simula concepts have appeared. C++ is the language that has had the greatest influence on the use of object-oriented programming in industry. Object-oriented programming has also been the subject of intensive research, resulting in a large number of important contributions. The authors of this book, together with Bent Bruun Kristensen, have been involved in the BETA project since 1975, the aim of which is to develop con- cepts, constructs and tools for programming. -
Hop Client-Side Compilation
Chapter 1 Hop Client-Side Compilation Florian Loitsch1, Manuel Serrano1 Abstract: Hop is a new language for programming interactive Web applications. It aims to replace HTML, JavaScript, and server-side scripting languages (such as PHP, JSP) with a unique language that is used for client-side interactions and server-side computations. A Hop execution platform is made of two compilers: one that compiles the code executed by the server, and one that compiles the code executed by the client. This paper presents the latter. In order to ensure compatibility of Hop graphical user interfaces with popular plain Web browsers, the client-side Hop compiler has to generate regular HTML and JavaScript code. The generated code runs roughly at the same speed as hand- written code. Since the Hop language is built on top of the Scheme program- ming language, compiling Hop to JavaScript is nearly equivalent to compiling Scheme to JavaScript. SCM2JS, the compiler we have designed, supports the whole Scheme core language. In particular, it features proper tail recursion. How- ever complete proper tail recursion may slow down the generated code. Despite an optimization which eliminates up to 40% of instrumentation for tail call in- tensive benchmarks, worst case programs were more than two times slower. As a result Hop only uses a weaker form of tail-call optimization which simplifies recursive tail-calls to while-loops. The techniques presented in this paper can be applied to most strict functional languages such as ML and Lisp. SCM2JS can be downloaded at http://www-sop.inria.fr/mimosa/person- nel/Florian.Loitsch/scheme2js/. -
SI 413, Unit 3: Advanced Scheme
SI 413, Unit 3: Advanced Scheme Daniel S. Roche ([email protected]) Fall 2018 1 Pure Functional Programming Readings for this section: PLP, Sections 10.7 and 10.8 Remember there are two important characteristics of a “pure” functional programming language: • Referential Transparency. This fancy term just means that, for any expression in our program, the result of evaluating it will always be the same. In fact, any referentially transparent expression could be replaced with its value (that is, the result of evaluating it) without changing the program whatsoever. Notice that imperative programming is about as far away from this as possible. For example, consider the C++ for loop: for ( int i = 0; i < 10;++i) { /∗ some s t u f f ∗/ } What can we say about the “stuff” in the body of the loop? Well, it had better not be referentially transparent. If it is, then there’s no point in running over it 10 times! • Functions are First Class. Another way of saying this is that functions are data, just like any number or list. Functions are values, in fact! The specific privileges that a function earns by virtue of being first class include: 1) Functions can be given names. This is not a big deal; we can name functions in pretty much every programming language. In Scheme this just means we can do (define (f x) (∗ x 3 ) ) 2) Functions can be arguments to other functions. This is what you started to get into at the end of Lab 2. For starters, there’s the basic predicate procedure?: (procedure? +) ; #t (procedure? 10) ; #f (procedure? procedure?) ; #t 1 And then there are “higher-order functions” like map and apply: (apply max (list 5 3 10 4)) ; 10 (map sqrt (list 16 9 64)) ; '(4 3 8) What makes the functions “higher-order” is that one of their arguments is itself another function. -
CPS323 Lecture: Concurrency Materials: 1. Coroutine Projectables
CPS323 Lecture: Concurrency Materials: 1. Coroutine Projectables 2. Handout for Ada Concurrency + Executable form (simple_tasking.adb, producer_and_consumer.adb) I. Introduction - ------------ A. Most of our attention throughout the CS curricum has been focussed on _sequential_ programming, in which a program does exactly one thing at a time, following a predictable order of steps. In particular, we expect a given program will always follow the exact same sequence of steps when run on a given set of data. B. An alternative is to think in terms of _concurrent_ programming, in which a "program" (at least in principle) does multiple things at the same time, following an order of steps that is not predictable and may differ between runs even if the same program is run on exactly the same data. C. Concurrency has long been an interest in CS, but has become of particular interest as we move into a situation in which raw hardware speeds have basically plateaued and multicore processors are coming to dominate the hardware scene. There are at least three reasons why this topic is of great interest. 1. Achieving maximum performance using technologies such as multicore processors. 2. Systems that inherently require multiple processors working together cooperatively on a single task - e.g. distributed or embedded systems. 3. Some problems are more naturally addressed by thinking in terms of multiple concurrrent components, rather than a single monolithic program. D. There are two broad approaches that might be taken to concurrency: 1. Make use of system libraries, without directly supporting parallelism in the language. (The approach taken by most languages - e.g. -
A Low-Cost Implementation of Coroutines for C
University of Wollongong Research Online Department of Computing Science Working Faculty of Engineering and Information Paper Series Sciences 1983 A low-cost implementation of coroutines for C Paul A. Bailes University of Wollongong Follow this and additional works at: https://ro.uow.edu.au/compsciwp Recommended Citation Bailes, Paul A., A low-cost implementation of coroutines for C, Department of Computing Science, University of Wollongong, Working Paper 83-9, 1983, 24p. https://ro.uow.edu.au/compsciwp/76 Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: [email protected] A LOW-COST IMPLEMENTATION OF COROUTINES FOR C Paul A. Bailes Department of Computing Science University of Wollongong Preprint No. 83-9 November 16, 1983 P.O. Box 1144, WOLLONGONG N.S.W. 2500, AUSTRALIA tel (042)-282-981 telex AA29022 A Low-Cost Implementation of Coroutines for C Paul A. Bailes Department of Computing Science University of Wollongong Wollongong N.S.W. 2500 Australia ABSTRACT We identify a set of primitive operations supporting coroutines, and demonstrate their usefulness. We then address their implementation in C accord ing to a set of criteria aimed at maintaining simplicity, and achieve a satisfactory compromise between it and effectiveness. Our package for the PDP-II under UNIXt allows users of coroutines in C programs to gain access to the primitives via an included definitions file and an object library; no penalty is imposed upon non-coroutine users. October 6, 1983 tUNIX is a Trademark of Bell Laboratories. A Low-Cost Implementation of Coroutines for C Paul A. -
Supporting Return-Value Optimisation in Coroutines
Document P1663R0 Reply To Lewis Baker <[email protected]> Date 2019-06-18 Audience Evolution Supporting return-value optimisation in coroutines Abstract 2 Background 3 Motivation 4 RVO for coroutines and co_await expressions 6 Return-Value Optimisation for normal functions 6 Return-Value Optimisation for co_await expressions 8 Status Quo 8 Passing a handle to the return-value slot 8 Resuming with an exception 10 Multiple Resumption Paths and Symmetric Transfer 10 Simplifying the Awaiter concept 11 Specifying the return-type of an awaitable 12 Calculating the address of the return-value 12 Benefits and Implications of RVO-style awaitables 14 Generalising set_exception() to set_error<E>() 17 Avoiding copies when returning a temporary 17 A library solution to avoiding construction of temporaries 19 Language support for avoiding construction of temporaries 21 Avoiding copies for nested calls 22 Adding support for this incrementally 23 Conclusion 24 Acknowledgements 24 Appendix - Examples 25 Abstract Note that this paper is intended to motivate the adoption of changes described in P1745R0 for C++20 to allow adding support for async RVO in a future version of C++. The changes described in this paper can be added incrementally post-C++20. The current design of the coroutines language feature defines the lowering of a co_await expression such that the result of the expression is produced by a call to the await_resume() method on the awaiter object. This design means that a producer that asynchronously produces a value needs to store the value somewhere before calling .resume() so that the await_resume() method can then return that value. -
Kotlin Coroutines Deep Dive Into Bytecode #Whoami
Kotlin Coroutines Deep Dive into Bytecode #whoami ● Kotlin compiler engineer @ JetBrains ● Mostly working on JVM back-end ● Responsible for (almost) all bugs in coroutines code Agenda I will talk about ● State machines ● Continuations ● Suspend and resume I won’t talk about ● Structured concurrency and cancellation ● async vs launch vs withContext ● and other library related stuff Agenda I will talk about Beware, there will be code. A lot of code. ● State machines ● Continuations ● Suspend and resume I won’t talk about ● Structured concurrency and cancellation ● async vs launch vs withContext ● and other library related stuff Why coroutines ● No dependency on a particular implementation of Futures or other such rich library; ● Cover equally the "async/await" use case and "generator blocks"; ● Make it possible to utilize Kotlin coroutines as wrappers for different existing asynchronous APIs (such as Java NIO, different implementations of Futures, etc). via coroutines KEEP Getting Lazy With Kotlin Pythagorean Triples fun printPythagoreanTriples() { for (i in 1 until 100) { for (j in 1 until i) { for (k in i until 100) { if (i * i + j * j < k * k) { break } if (i * i + j * j == k * k) { println("$i^2 + $j^2 == $k^2") } } } } } Pythagorean Triples fun printPythagoreanTriples() { for (i in 1 until 100) { for (j in 1 until i) { for (k in i until 100) { if (i * i + j * j < k * k) { break } if (i * i + j * j == k * k) { println("$i^2 + $j^2 == $k^2") } } } } } Pythagorean Triples fun printPythagoreanTriples() { for (i in 1 until 100) { for (j -
Csc 520 Principles of Programming Languages Coroutines Coroutines
Coroutines Coroutines are supported by Simula and Modula-2. CSc 520 They are similar to Java’s threads, except the programmer has to explicitly transfer control from one Principles of Programming execution context to another. Languages Thus, like threads several coroutines can exist simultaneously but unlike threads there is no central 36: Procedures — Coroutines scheduler that decides which coroutine should run next. Christian Collberg A coroutine is represented by a closure. [email protected] A special operation transfer(C) shifts control to the coroutine C, at the location where C last left off. Department of Computer Science University of Arizona Copyright c 2005 Christian Collberg 520—Spring 2005—36 [1] 520—Spring 2005—36 [2] Coroutines. Coroutines. The next slide shows an example from Scott where two var us, cfs: coroutine; coroutines execute “concurrently”, by explicitly transferring control between each other. coroutine update_screen() { ... In the example one coroutine displays a moving detach screen-saver, the other walks the file-system to check loop { for corrupt files. ... transfer(cfs) ... } } coroutine check_file_system() { ... } main () { ... } 520—Spring 2005—36 [3] 520—Spring 2005—36 [4] Coroutines. Coroutines in Modula-2 coroutine check_file_system() { Modula-2's system module provides two functions to create and transfer ... between coroutines: detach for all files do { PROCEDURE NEWPROCESS( proc: PROC; (* The procedure *) ... transfer(cfs) addr: ADDRESS; (* The stack *) ... transfer(cfs) size: CARDINAL; (* The stack size *) ... transfer(cfs) ... VAR new: ADDRESS); (* The coroutine *) } PROCEDURE TRANSFER( } VAR source: ADDRESS; (* Current coroutine *) VAR destination: ADDRESS); (* New coroutine *) main () { us := new update_screen(); The ®rst time TRANSFER is called source will be instantiated to cfs := new check_file_system(); the main (outermost) coroutine. -
Practical Perl Tools Parallel Asynchronicity, Part 2
COLUMNS Practical Perl Tools Parallel Asynchronicity, Part 2 DAVID N. BLANK-EDELMAN David Blank-Edelman is elcome back for Part 2 of this mini-series on modern methods the Technical Evangelist at for doing multiple things at once in Perl. In the first part (which I Apcera (the comments/ highly recommend that you read first before reading this sequel), views here are David’s alone W and do not represent Apcera/ we talked about some of the simpler methods for multi-tasking like the use Ericsson) . He has spent close to thirty years of fork() and Parallel::ForkManager. We had just begun to explore using the in the systems administration/DevOps/SRE Coro module as the basis for using threads in Perl (since the native stuff is so field in large multiplatform environments ucky) when we ran out of time. Let’s pick up the story roughly where we left it. including Brandeis University, Cambridge Technology Group, MIT Media Laboratory, Coro and Northeastern University. He is the author As a quick reminder, Coro offers an implementation of coroutines that we are going to use as of the O’Reilly Otter book Automating System just a pleasant implementation of cooperative threading (see the previous column for more Administration with Perl and is a frequent invited picayune details around the definition of a coroutine). By cooperative, we mean that each speaker/organizer for conferences in the field. thread gets queued up to run, but can only do so after another thread has explicitly signaled David is honored to serve on the USENIX that it is ready to cede its time in the interpreter. -
A History of Clojure
A History of Clojure RICH HICKEY, Cognitect, Inc., USA Shepherd: Mira Mezini, Technische Universität Darmstadt, Germany Clojure was designed to be a general-purpose, practical functional language, suitable for use by professionals wherever its host language, e.g., Java, would be. Initially designed in 2005 and released in 2007, Clojure is a dialect of Lisp, but is not a direct descendant of any prior Lisp. It complements programming with pure functions of immutable data with concurrency-safe state management constructs that support writing correct multithreaded programs without the complexity of mutex locks. Clojure is intentionally hosted, in that it compiles to and runs on the runtime of another language, such as the JVM. This is more than an implementation strategy; numerous features ensure that programs written in Clojure can leverage and interoperate with the libraries of the host language directly and efficiently. In spite of combining two (at the time) rather unpopular ideas, functional programming and Lisp, Clojure has since seen adoption in industries as diverse as finance, climate science, retail, databases, analytics, publishing, healthcare, advertising and genomics, and by consultancies and startups worldwide, much to the career-altering surprise of its author. Most of the ideas in Clojure were not novel, but their combination puts Clojure in a unique spot in language design (functional, hosted, Lisp). This paper recounts the motivation behind the initial development of Clojure and the rationale for various design decisions and language constructs. It then covers its evolution subsequent to release and adoption. CCS Concepts: • Software and its engineering ! General programming languages; • Social and pro- fessional topics ! History of programming languages.