A Region Inference Algorithm
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Four Lectures on Standard ML
Mads Tofte March Lab oratory for Foundations of Computer Science Department of Computer Science Edinburgh University Four Lectures on Standard ML The following notes give an overview of Standard ML with emphasis placed on the Mo dules part of the language The notes are to the b est of my knowledge faithful to The Denition of Standard ML Version as regards syntax semantics and terminology They have b een written so as to b e indep endent of any particular implemen tation The exercises in the rst lectures can b e tackled without the use of a machine although having access to an implementation will no doubt b e b enecial The pro ject in Lecture presupp oses access to an implementation of the full language including mo dules At present the Edinburgh compiler do es not fall into this category the author used the New Jersey Standard ML compiler Lecture gives an introduction to ML aimed at the reader who is familiar with some programming language but do es not know ML Both the Core Language and the Mo dules are covered by way of example Lecture discusses the use of ML mo dules in the development of large programs A useful metho dology for programming with functors signatures and structures is presented Lecture gives a fairly detailed account of the static semantics of ML mo dules for those who really want to understand the crucial notions of sharing and signature matching Lecture presents a one day pro ject intended to give the student an opp ortunity of mo difying a nontrivial piece of software using functors signatures and structures -
Understanding and Evolving the ML Module System
Understanding and Evolving the ML Module System Derek Dreyer May 2005 CMU-CS-05-131 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Robert Harper (co-chair) Karl Crary (co-chair) Peter Lee David MacQueen (University of Chicago) Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Copyright c 2005 Derek Dreyer This research was sponsored in part by the National Science Foundation under grant CCR-0121633 and EIA- 9706572, and the US Air Force under grant F19628-95-C-0050 and a generous fellowship. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: ML, module systems, type systems, functors, abstract data types, lambda calculus, recursive modules, singleton kinds Abstract The ML module system stands as a high-water mark of programming language support for data abstraction. Nevertheless, it is not in a fully evolved state. One prominent weakness is that module interdependencies in ML are restricted to be acyclic, which means that mutually recursive functions and data types must be written in the same module even if they belong conceptually in different modules. Existing efforts to remedy this limitation either involve drastic changes to the notion of what a module is, or fail to allow mutually recursive modules to hide type information from one another. Another issue is that there are several dialects of ML, and the module systems of these dialects differ in subtle yet semantically significant ways that have been difficult to account for in any rigorous way. -
Lecture Notes in Computer Science 2566 Edited by G
Lecture Notes in Computer Science 2566 Edited by G. Goos, J. Hartmanis, and J. van Leeuwen 3 Berlin Heidelberg New York Barcelona Hong Kong London Milan Paris Tokyo Torben Æ. Mogensen David A. Schmidt I. Hal Sudborough (Eds.) The Essence of Computation Complexity, Analysis, Transformation Essays Dedicated to Neil D. Jones 13 Series Editors Gerhard Goos, Karlsruhe University, Germany Juris Hartmanis, Cornell University, NY, USA Jan van Leeuwen, Utrecht University, The Netherlands Volume Editors Torben Æ. Mogensen University of Copenhagen, DIKU Universitetsparken 1, 2100 Copenhagen, Denmark E-mail: [email protected] David A. Schmidt Kansas State University, Department of Computing and Information Sciences 234 Nichols Hall, Manhattan, KS 66506 USA E-mail: [email protected] I. Hal Sudborough University of Texas at Dallas, Department of Computer Science P.O. Box 830688, Richardson, TX 75083 USA E-mail: [email protected] The illustration appearing on the cover of this book is the work of Daniel Rozenberg (DADARA). Cataloging-in-Publication Data applied for A catalog record for this book is available from the Library of Congress. Bibliographic information published by Die Deutsche Bibliothek. Die Deutsche Bibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the Internet at <http://dnb.ddb.de>. CR Subject Classification (1998): F.3, F.1, D.2 ISSN 0302-9743 ISBN 3-540-00326-6 Springer-Verlag Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. -
A Simpli Ed Account of Polymorphic References 1 Introduction 2 a Language with Mutable Data Structures
A Simpli ed AccountofPolymorphic References Rob ert Harp er Scho ol of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 Abstract A pro of of the soundness of Tofte's imp erativetyp e discipli ne with resp ect to a structured op erational semantics is given. The presentation is based on a semantic formalism that combines the b ene ts of the approaches considered byWright and Felleisen, and byTofte, leading to a particularly simple pro of of soundness of Tofte's typ e disciplin e. Keywords: formal semantics, functional programming, programming languages, typ e theory, refer- ences and assignment. 1 Intro duction The extension of Damas and Milner's p olymorphic typ e system for pure functional programs [2] to accomo- date mutable cells has proved to b e problematic. The nave extension of the pure language with op erations to allo cate a cell, and to retrieve and mo dify its contents is unsound [11]. The problem has received consid- erable attention, notably by Damas [3], Tofte [10,11], and LeroyandWeiss [7]. Tofte's solution is based on a greatest xed p oint construction to de ne the semantic typing relation [11] (see also [8]). This metho d has b een subsequently used by Leroy and Weiss [7]andTalpin and Jouvelot [9]. It was subsequently noted by Wright and Felleisen [13] that the pro of of soundness can b e substantially simpli ed if the argument is made by induction on the length of an execution sequence, rather than on the structure of the typing derivation. Using this metho d they establish the soundness of a restriction of the language to require that let-b ound expressions b e values. -
The Definition of Standard ML, Revised
The Definition of Standard ML The Definition of Standard ML (Revised) Robin Milner, Mads Tofte, Robert Harper and David MacQueen The MIT Press Cambridge, Massachusetts London, England c 1997 Robin Milner All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. Printed and bound in the United States of America. Library of Congress Cataloging-in-Publication Data The definition of standard ML: revised / Robin Milner ::: et al. p. cm. Includes bibliographical references and index. ISBN 0-262-63181-4 (alk. paper) 1. ML (Computer program language) I. Milner, R. (Robin), 1934- QA76.73.M6D44 1997 005:1303|dc21 97-59 CIP Contents 1 Introduction 1 2 Syntax of the Core 3 2.1 Reserved Words . 3 2.2 Special constants . 3 2.3 Comments . 4 2.4 Identifiers . 5 2.5 Lexical analysis . 6 2.6 Infixed operators . 6 2.7 Derived Forms . 7 2.8 Grammar . 7 2.9 Syntactic Restrictions . 9 3 Syntax of Modules 12 3.1 Reserved Words . 12 3.2 Identifiers . 12 3.3 Infixed operators . 12 3.4 Grammar for Modules . 13 3.5 Syntactic Restrictions . 13 4 Static Semantics for the Core 16 4.1 Simple Objects . 16 4.2 Compound Objects . 17 4.3 Projection, Injection and Modification . 17 4.4 Types and Type functions . 19 4.5 Type Schemes . 19 4.6 Scope of Explicit Type Variables . 20 4.7 Non-expansive Expressions . 21 4.8 Closure . -
Implementation of the Typed Call-By-Value Λ-Calculus Using a Stack of Regions
Implementation of the Typed Call-by-Value λ-calculus using a Stack of Regions Mads Tofte, University of Copenhagen∗ Jean-Pierre Talpin, European Computer-Industry Research Center † Abstract from the source program. Heap-based schemes are less eager to reuse memory. Generational garbage collec- We present a translation scheme for the polymorphi- tion collects young objects when the allocation space cally typed call-by-value λ-calculus. All runtime val- is used up. Hayes[11] discusses how to reclaim large, ues, including function closures, are put into regions. old objects. The store consists of a stack of regions. Region in- Garbage collection can be very fast. Indeed, there ference and effect inference are used to infer where is a much quoted argument that the amortized cost of regions can be allocated and de-allocated. Recursive copying garbage collection tends to zero, as memory functions are handled using a limited form of polymor- tends to infinity[2, page 206]. Novice functional pro- phic recursion. The translation is proved correct with grammers often report that on their machines, mem- respect to a store semantics, which models a region- ory is a constant, not a variable, and that this constant based run-time system. Experimental results suggest has to be uncomfortably large for their programs to that regions tend to be small, that region allocation is run well. The practical ambition of our work is to frequent and that overall memory demands are usually reduce the required size of this constant significantly. modest, even without garbage collection. We shall present measurements that indicate that our translation scheme holds some promise in this respect. -
Tips for Computer Scientists Standard ML (Revised)
Tips for Computer Scientists on Standard ML (Revised) Mads Tofte August 30, 2009 This document and all examples in it are available from http://www.itu.dk/people/tofte Preface Contents This note is inspired by a brilliant piece of writ- 1 Numbers 1 ing, entitled Tips for Danes on Punctuation in 2 Overloaded Arithmetic Operators 1 English, by John Dienhart, Department of En- 3 Strings 1 glish, Odense University (1980). In a mere 11 4 Lists 1 pages, Dienhart’s lucid writing gives the reader 5 Expressions 1 the impression that punctuation in English is 6 Declarations 1 pretty easy and that any Dane can get it right 7 Function Values 2 in an afternoon or so. 8 Constructed Values 3 In the same spirit, this note is written for col- 9 Patterns 4 leagues and mature students who would like to 10 Pattern Matching 5 get to know Standard ML without spending too 11 Function-value Bindings (revisited) 6 much time on it. It is intended to be a relaxed 12 Function Application 6 stroll through the structure of Standard ML, with 13 Type Expressions 7 plenty of small examples, without falling into 14 Type Abbreviations 7 the trap of being just a phrase book. 15 Datatype Declarations 7 I present enough of the grammar that the 16 Exceptions 8 reader can start programming in Standard ML, 17 References 9 should the urge arise. 18 Procedures 10 The full grammar and a formal definition of 19 Input and Output 10 the semantics can be found in the 1997 language 20 The top-level loop 11 definition[2]. -
A Retrospective on Region-Based Memory Management
Higher-Order and Symbolic Computation, 17, 245–265, 2004 c 2004 Kluwer Academic Publishers. Manufactured in The Netherlands. A Retrospective on Region-Based Memory Management MADS TOFTE [email protected] LARS BIRKEDAL∗ [email protected] MARTIN ELSMAN [email protected] NIELS HALLENBERG [email protected] The IT University of Copenhagen, Denmark Abstract. We report on our experience with designing, implementing, proving correct, and evaluating a region- based memory management system. Keywords: dynamic storage management, regions, Standard ML 1. Introduction Originally, Region-based Memory Management was conceived as a purely theoretical idea intended to solve a practical problem in the context of Standard ML, namely inventing a dynamic memory management discipline that is less space demanding and more predictable than generic garbage collection techniques, such as generational garbage collection. Over the subsequent nine years, considerable effort has been devoted to refining the idea, proving it correct, and investigating whether it worked in practice. The practical experiments revealed weaknesses, which led to new program analyses, new theory, and yet more experimentation. In short, we have sought to work on memory management as an experimental science: the work should be scientific in the sense that it should rest on a solid mathematical foundation and it should be experimental in the sense that it should be tested against competing state-of-the-art implementation technology. The purpose of this paper is to step back and consider the process as a whole. We first describe the main technical developments, with an emphasis on what motivated the developments. We then summarise what we think has gone well and what has not gone so well. -