Application and Interpretation
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
An Array-Oriented Language with Static Rank Polymorphism
An array-oriented language with static rank polymorphism Justin Slepak, Olin Shivers, and Panagiotis Manolios Northeastern University fjrslepak,shivers,[email protected] Abstract. The array-computational model pioneered by Iverson's lan- guages APL and J offers a simple and expressive solution to the \von Neumann bottleneck." It includes a form of rank, or dimensional, poly- morphism, which renders much of a program's control structure im- plicit by lifting base operators to higher-dimensional array structures. We present the first formal semantics for this model, along with the first static type system that captures the full power of the core language. The formal dynamic semantics of our core language, Remora, illuminates several of the murkier corners of the model. This allows us to resolve some of the model's ad hoc elements in more general, regular ways. Among these, we can generalise the model from SIMD to MIMD computations, by extending the semantics to permit functions to be lifted to higher- dimensional arrays in the same way as their arguments. Our static semantics, a dependent type system of carefully restricted power, is capable of describing array computations whose dimensions cannot be determined statically. The type-checking problem is decidable and the type system is accompanied by the usual soundness theorems. Our type system's principal contribution is that it serves to extract the implicit control structure that provides so much of the language's expres- sive power, making this structure explicitly apparent at compile time. 1 The Promise of Rank Polymorphism Behind every interesting programming language is an interesting model of com- putation. -
Compiler Error Messages Considered Unhelpful: the Landscape of Text-Based Programming Error Message Research
Working Group Report ITiCSE-WGR ’19, July 15–17, 2019, Aberdeen, Scotland Uk Compiler Error Messages Considered Unhelpful: The Landscape of Text-Based Programming Error Message Research Brett A. Becker∗ Paul Denny∗ Raymond Pettit∗ University College Dublin University of Auckland University of Virginia Dublin, Ireland Auckland, New Zealand Charlottesville, Virginia, USA [email protected] [email protected] [email protected] Durell Bouchard Dennis J. Bouvier Brian Harrington Roanoke College Southern Illinois University Edwardsville University of Toronto Scarborough Roanoke, Virgina, USA Edwardsville, Illinois, USA Scarborough, Ontario, Canada [email protected] [email protected] [email protected] Amir Kamil Amey Karkare Chris McDonald University of Michigan Indian Institute of Technology Kanpur University of Western Australia Ann Arbor, Michigan, USA Kanpur, India Perth, Australia [email protected] [email protected] [email protected] Peter-Michael Osera Janice L. Pearce James Prather Grinnell College Berea College Abilene Christian University Grinnell, Iowa, USA Berea, Kentucky, USA Abilene, Texas, USA [email protected] [email protected] [email protected] ABSTRACT of evidence supporting each one (historical, anecdotal, and empiri- Diagnostic messages generated by compilers and interpreters such cal). This work can serve as a starting point for those who wish to as syntax error messages have been researched for over half of a conduct research on compiler error messages, runtime errors, and century. Unfortunately, these messages which include error, warn- warnings. We also make the bibtex file of our 300+ reference corpus ing, and run-time messages, present substantial difficulty and could publicly available. -
Panel: NSF-Sponsored Innovative Approaches to Undergraduate Computer Science
Panel: NSF-Sponsored Innovative Approaches to Undergraduate Computer Science Stephen Bloch (Adelphi University) Amruth Kumar (Ramapo College) Stanislav Kurkovsky (Central CT State University) Clif Kussmaul (Muhlenberg College) Matt Dickerson (Middlebury College), moderator Project Web site(s) Intervention Delivery Supervision Program http://programbydesign.org curriculum with supporting in class; software normally active, but can be by Design http://picturingprograms.org IDE, libraries, & texts and textbook are done other ways Stephen Bloch http://www.ccs.neu.edu/home/ free downloads matthias/HtDP2e/ or web-based NSF awards 0010064 http://racket-lang.org & 0618543 http://wescheme.org Problets http://www.problets.org in- or after-class problem- applet in none - teacher not needed, Amruth Kumar solving exercises on a browser although some adopters use programming concepts it in active mode too NSF award 0817187 Mobile Game http://www.mgdcs.com/ in-class or take-home PC passive - teacher as Development programming projects facilitator to answer Qs Stan Kurkovsky NSF award DUE-0941348 POGIL http://pogil.org in-class activity paper or web passive - teacher as Clif Kussmaul http://cspogil.org facilitator to answer Qs NSF award TUES 1044679 Project Course(s) Language(s) Focus Program Middle school, Usually Scheme-like teaching problem-solving process, by pre-AP CS in HS, languages leading into Java; particularly test-driven DesignStephen CS0, CS1, CS2 has also been done in Python, development and use of data Bloch in college ML, Java, Scala, ... types to guide coding & testing Problets AP-CS, CS I, CS 2. C, C++, Java, C# code tracing, debugging, Amruth Kumar also as refresher or expression evaluation, to switch languages predicting program state in other courses Mobile Game AP-CS, CS1, CS2 Java core OO programming; DevelopmentSt intro to advanced subjects an Kurkovsky such as AI, networks, security POGILClif CS1, CS2, SE, etc. -
Compendium of Technical White Papers
COMPENDIUM OF TECHNICAL WHITE PAPERS Compendium of Technical White Papers from Kx Technical Whitepaper Contents Machine Learning 1. Machine Learning in kdb+: kNN classification and pattern recognition with q ................................ 2 2. An Introduction to Neural Networks with kdb+ .......................................................................... 16 Development Insight 3. Compression in kdb+ ................................................................................................................. 36 4. Kdb+ and Websockets ............................................................................................................... 52 5. C API for kdb+ ............................................................................................................................ 76 6. Efficient Use of Adverbs ........................................................................................................... 112 Optimization Techniques 7. Multi-threading in kdb+: Performance Optimizations and Use Cases ......................................... 134 8. Kdb+ tick Profiling for Throughput Optimization ....................................................................... 152 9. Columnar Database and Query Optimization ............................................................................ 166 Solutions 10. Multi-Partitioned kdb+ Databases: An Equity Options Case Study ............................................. 192 11. Surveillance Technologies to Effectively Monitor Algo and High Frequency Trading .................. -
The Next 700 Semantics: a Research Challenge Shriram Krishnamurthi Brown University [email protected] Benjamin S
The Next 700 Semantics: A Research Challenge Shriram Krishnamurthi Brown University [email protected] Benjamin S. Lerner Northeastern University [email protected] Liam Elberty Unaffiliated Abstract Modern systems consist of large numbers of languages, frameworks, libraries, APIs, and more. Each has characteristic behavior and data. Capturing these in semantics is valuable not only for understanding them but also essential for formal treatment (such as proofs). Unfortunately, most of these systems are defined primarily through implementations, which means the semantics needs to be learned. We describe the problem of learning a semantics, provide a structuring process that is of potential value, and also outline our failed attempts at achieving this so far. 2012 ACM Subject Classification Software and its engineering → General programming languages; Software and its engineering → Language features; Software and its engineering → Semantics; Software and its engineering → Formal language definitions Keywords and phrases Programming languages, desugaring, semantics, testing Digital Object Identifier 10.4230/LIPIcs.SNAPL.2019.9 Funding This work was partially supported by the US National Science Foundation and Brown University while all authors were at Brown University. Acknowledgements The authors thank Eugene Charniak and Kevin Knight for useful conversations. The reviewers provided useful feedback that improved the presentation. © Shriram Krishnamurthi and Benjamin S. Lerner and Liam Elberty; licensed under Creative Commons License CC-BY 3rd Summit on Advances in Programming Languages (SNAPL 2019). Editors: Benjamin S. Lerner, Rastislav Bodík, and Shriram Krishnamurthi; Article No. 9; pp. 9:1–9:14 Leibniz International Proceedings in Informatics Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany 9:2 The Next 700 Semantics: A Research Challenge 1 Motivation Semantics is central to the trade of programming language researchers and practitioners. -
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 . -
Proceedings of the 8Th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (Ed.) Sponsors
Proceedings of the 8th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (ed.) Sponsors We gratefully acknowledge the support given to the 8th European Lisp Symposium by the following sponsors: WWWLISPWORKSCOM i Organization Programme Committee Julian Padget – University of Bath, UK (chair) Giuseppe Attardi — University of Pisa, Italy Sacha Chua — Toronto, Canada Stephen Eglen — University of Cambridge, UK Marc Feeley — University of Montreal, Canada Matthew Flatt — University of Utah, USA Rainer Joswig — Hamburg, Germany Nick Levine — RavenPack, Spain Henry Lieberman — MIT, USA Christian Queinnec — University Pierre et Marie Curie, Paris 6, France Robert Strandh — University of Bordeaux, France Edmund Weitz — University of Applied Sciences, Hamburg, Germany Local Organization Christophe Rhodes – Goldsmiths, University of London, UK (chair) Richard Lewis – Goldsmiths, University of London, UK Shivi Hotwani – Goldsmiths, University of London, UK Didier Verna – EPITA Research and Development Laboratory, France ii Contents Acknowledgments i Messages from the chairs v Invited contributions Quicklisp: On Beyond Beta 2 Zach Beane µKanren: Running the Little Things Backwards 3 Bodil Stokke Escaping the Heap 4 Ahmon Dancy Unwanted Memory Retention 5 Martin Cracauer Peer-reviewed papers Efficient Applicative Programming Environments for Computer Vision Applications 7 Benjamin Seppke and Leonie Dreschler-Fischer Keyboard? How quaint. Visual Dataflow Implemented in Lisp 15 Donald Fisk P2R: Implementation of -
Formalizing the Expertise of the Assembly Language Programmer
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY Working Paper 203 September 1980 Formalizing tte Expertise of the Assembly Language Programmer Roger DuWayne Duffey II Abstract: A novel compiler strategy for generating high quality code is described. The quality of the code results from reimplementing the program in the target language using knowledge of the program's behavior. The research is a first step towards formalizing the expertise of the assembly language programmer. The ultimate goal is to formalize code generation and implementation techniques it, the same way that parsing and code generation techniques have been formalized. An experimental code generator based on the reimplementation strategy will be constructed. The code generator will provide a framework for analyzing the costs, applicability, and effectiveness of various implementation techniques. Several common code generation problems will be studied. Code written by experienced programmers and code generated by a conventional optimizing compiler will provide standards of comparison. A.I. Laboratory Working Papers are produced for internal circulation, and may contain information that is, for 4.xample, too preliminary or too detailed for formal publication. It is not intended that they should be considered papers to which reference can be made in the literature. 0 MASSACHUSETTS INSTITUTE OF TECHNOLOGY 1980 I. Introduction This research proposes a novel compiler strategy for generating high quality code. The strategy evolved .from studying the differences between the code produced by experienced programmers and the code produced by conventional optimization techniques. The strategy divides compilation into four serial stages: analyzing the source implementation, undoing source implementation decisions, reimplementing the program in the target language, and assembling the object modules. -
Handout 16: J Dictionary
J Dictionary Roger K.W. Hui Kenneth E. Iverson Copyright © 1991-2002 Jsoftware Inc. All Rights Reserved. Last updated: 2002-09-10 www.jsoftware.com . Table of Contents 1 Introduction 2 Mnemonics 3 Ambivalence 4 Verbs and Adverbs 5 Punctuation 6 Forks 7 Programs 8 Bond Conjunction 9 Atop Conjunction 10 Vocabulary 11 Housekeeping 12 Power and Inverse 13 Reading and Writing 14 Format 15 Partitions 16 Defined Adverbs 17 Word Formation 18 Names and Displays 19 Explicit Definition 20 Tacit Equivalents 21 Rank 22 Gerund and Agenda 23 Recursion 24 Iteration 25 Trains 26 Permutations 27 Linear Functions 28 Obverse and Under 29 Identity Functions and Neutrals 30 Secondaries 31 Sample Topics 32 Spelling 33 Alphabet and Numbers 34 Grammar 35 Function Tables 36 Bordering a Table 37 Tables (Letter Frequency) 38 Tables 39 Classification 40 Disjoint Classification (Graphs) 41 Classification I 42 Classification II 43 Sorting 44 Compositions I 45 Compositions II 46 Junctions 47 Partitions I 48 Partitions II 49 Geometry 50 Symbolic Functions 51 Directed Graphs 52 Closure 53 Distance 54 Polynomials 55 Polynomials (Continued) 56 Polynomials in Terms of Roots 57 Polynomial Roots I 58 Polynomial Roots II 59 Polynomials: Stopes 60 Dictionary 61 I. Alphabet and Words 62 II. Grammar 63 A. Nouns 64 B. Verbs 65 C. Adverbs and Conjunctions 66 D. Comparatives 67 E. Parsing and Execution 68 F. Trains 69 G. Extended and Rational Arithmeti 70 H. Frets and Scripts 71 I. Locatives 72 J. Errors and Suspensions 73 III. Definitions 74 Vocabulary 75 = Self-Classify - Equal 76 =. Is (Local) 77 < Box - Less Than 78 <. -
Chapter 1 Basic Principles of Programming Languages
Chapter 1 Basic Principles of Programming Languages Although there exist many programming languages, the differences among them are insignificant compared to the differences among natural languages. In this chapter, we discuss the common aspects shared among different programming languages. These aspects include: programming paradigms that define how computation is expressed; the main features of programming languages and their impact on the performance of programs written in the languages; a brief review of the history and development of programming languages; the lexical, syntactic, and semantic structures of programming languages, data and data types, program processing and preprocessing, and the life cycles of program development. At the end of the chapter, you should have learned: what programming paradigms are; an overview of different programming languages and the background knowledge of these languages; the structures of programming languages and how programming languages are defined at the syntactic level; data types, strong versus weak checking; the relationship between language features and their performances; the processing and preprocessing of programming languages, compilation versus interpretation, and different execution models of macros, procedures, and inline procedures; the steps used for program development: requirement, specification, design, implementation, testing, and the correctness proof of programs. The chapter is organized as follows. Section 1.1 introduces the programming paradigms, performance, features, and the development of programming languages. Section 1.2 outlines the structures and design issues of programming languages. Section 1.3 discusses the typing systems, including types of variables, type equivalence, type conversion, and type checking during the compilation. Section 1.4 presents the preprocessing and processing of programming languages, including macro processing, interpretation, and compilation. -
A Modern Reversible Programming Language April 10, 2015
Arrow: A Modern Reversible Programming Language Author: Advisor: Eli Rose Bob Geitz Abstract Reversible programming languages are those whose programs can be run backwards as well as forwards. This condition impacts even the most basic constructs, such as =, if and while. I discuss Janus, the first im- perative reversible programming language, and its limitations. I then introduce Arrow, a reversible language with modern features, including functions. Example programs are provided. April 10, 2015 Introduction: Many processes in the world have the property of reversibility. To start washing your hands, you turn the knob to the right, and the water starts to flow; the process can be undone, and the water turned off, by turning the knob to the left. To turn your computer on, you press the power button; this too can be undone, by again pressing the power button. In each situation, we had a process (turning the knob, pressing the power button) and a rule that told us how to \undo" that process (turning the knob the other way, and pressing the power button again). Call the second two the inverses of the first two. By a reversible process, I mean a process that has an inverse. Consider two billiard balls, with certain positions and velocities such that they are about to collide. The collision is produced by moving the balls accord- ing to the laws of physics for a few seconds. Take that as our process. It turns out that we can find an inverse for this process { a set of rules to follow which will undo the collision and restore the balls to their original states1. -
APL-The Language Debugging Capabilities, Entirely in APL Terms (No Core Symbol Denotes an APL Function Named' 'Compress," Dumps Or Other Machine-Related Details)
DANIEL BROCKLEBANK APL - THE LANGUAGE Computer programming languages, once the specialized tools of a few technically trained peo p.le, are now fundamental to the education and activities of millions of people in many profes SIons, trades, and arts. The most widely known programming languages (Basic, Fortran, Pascal, etc.) have a strong commonality of concepts and symbols; as a collection, they determine our soci ety's general understanding of what programming languages are like. There are, however, several ~anguages of g~eat interest and quality that are strikingly different. One such language, which shares ItS acronym WIth the Applied Physics Laboratory, is APL (A Programming Language). A SHORT HISTORY OF APL it struggled through the 1970s. Its international con Over 20 years ago, Kenneth E. Iverson published tingent of enthusiasts was continuously hampered by a text with the rather unprepossessing title, A inefficient machine use, poor availability of suitable terminal hardware, and, as always, strong resistance Programming Language. I Dr. Iverson was of the opinion that neither conventional mathematical nota to a highly unconventional language. tions nor the emerging Fortran-like programming lan At the Applied Physics Laboratory, the APL lan guages were conducive to the fluent expression, guage and its practical use have been ongoing concerns publication, and discussion of algorithms-the many of the F. T. McClure Computing Center, whose staff alternative ideas and techniques for carrying out com has long been convinced of its value. Addressing the putation. His text presented a solution to this nota inefficiency problems, the Computing Center devel tion dilemma: a new formal language for writing clear, oped special APL systems software for the IBM concise computer programs.