Compendium of Technical White Papers
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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. -
Is Ja Dialect of APL? Reported by Jonathan Barman Eugene Mcdonnell - the Question Is Irrelevant
VICTOR Vol.8 No.2 convert the noun back to verb and as a result various interesting control structures can be created. The verbal nouns thus formed can be put into arrays and some of the benefits that various people have suggested might arise from arrays of functions can be obtained. A design goal is to make compilation possible without limiting the expressive power of J. APL Technology of Computer Simulation, by A Boozin and I Popselov reported by Sylvia Camacho Both authors of this paper are academic mathematicians and their use of APL is just where one might expect it to be: in modelling linear differential equations and stochastic Markovian processes. Questions put showed that they first used APL 15 years ago on terminals to what was described as 'a big old computer'. Currently they use APL*PLUS/PC. They have set up an environment in which they can split the screen and display data graphically with some zoom facilities. This environment can be used in conjunction with raw APL while exploring the results of a model, or can be used in conjunction with data derived from APL or some other source. In fact it is often used by Pascal programmers. Most recently they have been setting up economic models to try to come to terms with the consequences of decentralisation and privatisation. They use both Roman and Cyrillic alphabets. Panel: Is Ja Dialect of APL? reported by Jonathan Barman Eugene McDonnell - The question is irrelevant. Surely proponents of J would not be thrown out of the APL community? Garth Foster - J is quite definitely not APL. -
An APL Subset Interpreter for a New Chip Set / by James Hoskin
An APL Subset Interpreter for a New Chip Set James Hoskin MSc (Physics), University of Calgary A THESIS SUBMI 1 TED IN L'ARTlAl f Ul FILLMENT OF THE REQUIKEMENTS FOR THE DEGREE OF MASTER OF SCIFNCE In the School of Cornput~ngSc~enre " James Hosk~n1987 SIMON FRASER UNIVERSITY May 1987 All rights reserved This thesis may not be reproduced in whole or in part. by photocopy or other means w~thoutthe permission of the author Approval Title of Thesis: An APL Subset Interpreter for a New Chip Set Name. James D Hoskin Degree: Master of ~iience Examining Committee. Chairperson. Dr. W. S. Luk Dr. R. F. Hobson Senior Supervisor Dr J& Weinkam,"I/, Dr. R. D. Cameron External Examiner Dr. Carl McCrosky External Examiner April 28, 1987 Date Approved: PART IAL COPYR l GHT L ICENSE I hereby grant to Simon Fraser University the right to lend my thesis, project or extended essay (the title of which is shown below) to users of the Simon Fraser University Library, and to make partial or single copies only for such users or in response to a request from the library of any other university, or other educational institution, on its own behalf or for one of its users. I further agree that permission for multiple copying of this work for scholarly purposes may be granted by me or the Dean of Graduate Studies. It is understood that copying or publication of this work for financial gain shall not be allowed without my written permission. Title of Thesis/Project/Extended Essay -- - Author: (signature) (date) Abstract The APL language provides a powerful set of functions and operators to handle dynamic array data. -
Kx for Manufacturing Flyer
Kx for At a Glance Powered by the world’s fastest Manufacturing time-series database, Kx enables manufacturers to improve performance at tool, factory and Accelerate Your Evolution Into Industry 4.0 user analytic levels. Kx is able to handle millions of events and Kx is the world’s fastest time-series database and analytics platform for measurements per second, manufacturing. It is a high-performance, cost-effective and low latency solution for gigabytes to petabytes of historical ingesting, processing, and analyzing real-time, streaming and historical data from data, with nanosecond resolution, industrial equipment sensors and factory systems. more efficiently and cost-effectively than any available alternatives. Kx is a single integrated software platform enabling quick, easy implementation and low total cost of ownership. It can be deployed on premises, in the cloud or as a hybrid configuration. The Kx Advantage • Ingest and process over 30 million sensor measurements per second • Supports any sensor measurement, frequency, tags and attributes with nanosecond precision • Stream processing platform with in-built Complex Event Processing • Augment existing systems to support massive increase in sensor data volumes • Ability to capture, store and Performance process 10TBs of data per day Featuring a superior columnar-structured time-series database, kdb+, Kx can scale • Deploy on-premises, in anywhere from thousands to hundreds of millions of sensors, at any measurement the cloud or as a hybrid frequency whilst maintaining extreme levels of performance. configuration • Rich customizable visualization Kx has the ability to ingest and process over 30 million sensor measurements per through integrated dashboards second using just one server. -
KDB Kernel Debugger and Kdb Command
AIX Version 7.2 KDB kernel debugger and kdb command IBM Note Before using this information and the product it supports, read the information in “Notices” on page 323. This edition applies to AIX Version 7.2 and to all subsequent releases and modifications until otherwise indicated in new editions. © Copyright International Business Machines Corporation 2015. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents About this document.............................................................................................ix Highlighting..................................................................................................................................................ix Case-sensitivity in AIX................................................................................................................................ ix ISO 9000......................................................................................................................................................ix KDB kernel debugger and kdb command................................................................ 1 KDB kernel debugger................................................................................................................................... 1 Invoking the KDB kernel debugger........................................................................................................ 2 The kdb command..................................................................................................................................3 -
To the Members of First Derivatives Plc
First Derivatives plc First Derivatives plc Annual report and accounts 2019 Annual report and accounts 2019 AT THE CORE OF DATA SCIENCE FD’s world-leading analytics technology and data science expertise are disrupting industries, helping our clients to generate more revenue and increase their operational efficiency See more online at: firstderivatives.com and kx.com STRATEGIC REPORT CORPORATE GOVERNANCE FINANCIAL STATEMENTS 01 Highlights 24 Board of Directors 41 Independent auditor’s report 02 At a glance 26 Chairman’s governance 47 Consolidated statement 04 Chairman’s review statement of comprehensive income 06 Business model 27 Governance framework 49 Consolidated balance sheet 08 Business review 29 Report of the Audit Committee 50 Company balance sheet 14 Strategy 32 Report of the Nomination 51 Consolidated statement 15 Financial review Committee of changes in equity 20 Principal risks and uncertainties 34 Report of the Remuneration 53 Company statement of changes 22 People strategy Committee in equity 38 Directors’ report 55 Consolidated cash flow 40 Statement of Directors’ statement responsibilities 56 Company cash flow statement 57 Notes 116 Directors and advisers IBC Global directory Highlights FINANCIAL HIGHLIGHTS Report Strategic Revenue £m Operating profit £m £217.4m £18.7m 18.7 217.4 14.7 186.0 151.7 Corporate Governance 12.2 2017 2018 2019 2017 2018 2019 Adjusted diluted EPS p Net debt £m 83.2p £16.5m Financial Statements Financial 16.5 16.2 83.2 72.2 13.5 61.3 2017 2018 2019 2017 2018 2019 OPERATIONAL HIGHLIGHTS • FinTech -
APL Literature Review
APL Literature Review Roy E. Lowrance February 22, 2009 Contents 1 Falkoff and Iverson-1968: APL1 3 2 IBM-1994: APL2 8 2.1 APL2 Highlights . 8 2.2 APL2 Primitive Functions . 12 2.3 APL2 Operators . 18 2.4 APL2 Concurrency Features . 19 3 Dyalog-2008: Dyalog APL 20 4 Iverson-1987: Iverson's Dictionary for APL 21 5 APL Implementations 21 5.1 Saal and Weiss-1975: Static Analysis of APL1 Programs . 21 5.2 Breed and Lathwell-1968: Implementation of the Original APLn360 25 5.3 Falkoff and Orth-1979: A BNF Grammar for APL1 . 26 5.4 Girardo and Rollin-1987: Parsing APL with Yacc . 27 5.5 Tavera and other-1987, 1998: IL . 27 5.6 Brown-1995: Rationale for APL2 Syntax . 29 5.7 Abrams-1970: An APL Machine . 30 5.8 Guibas and Wyatt-1978: Optimizing Interpretation . 31 5.9 Weiss and Saal-1981: APL Syntax Analysis . 32 5.10 Weigang-1985: STSC's APL Compiler . 33 5.11 Ching-1986: APL/370 Compiler . 33 5.12 Ching and other-1989: APL370 Prototype Compiler . 34 5.13 Driscoll and Orth-1986: Compile APL2 to FORTRAN . 35 5.14 Grelck-1999: Compile APL to Single Assignment C . 36 6 Imbed APL in Other Languages 36 6.1 Burchfield and Lipovaca-2002: APL Arrays in Java . 36 7 Extensions to APL 37 7.1 Brown and Others-2000: Object-Oriented APL . 37 1 8 APL on Parallel Computers 38 8.1 Willhoft-1991: Most APL2 Primitives Can Be Parallelized . 38 8.2 Bernecky-1993: APL Can Be Parallelized . -
A Lambda Calculus for Transfinite Arrays
1 A Lambda Calculus for Transfinite Arrays Unifying Arrays and Streams ARTJOMS ŠINKAROVS, Heriot-Watt University SVEN-BODO SCHOLZ, Heriot-Watt University Array programming languages allow for concise and generic formulations of numerical algorithms, thereby providing a huge potential for program optimisation such as fusion, parallelisation, etc. One of the restric- tions that these languages typically have is that the number of elements in every array has to be finite. This means that implementing streaming algorithms in such languages requires new types of data structures, with operations that are not immediately compatible with existing array operations or compiler optimisations. In this paper, we propose a design for a functional language that natively supports infinite arrays. We use ordinal numbers to introduce the notion of infinity in shapes and indices. By doing so, we obtain a calculus that naturally extends existing array calculi and, at the same time, allows for recursive specifications as they are found in stream- and list-based settings. Furthermore, the main language construct that can be thought of as an n-fold cons operator gives rise to expressing transfinite recursion in data, something that lists or streams usually do not support. This makes it possible to treat the proposed calculus as a unifying theory of arrays, lists and streams. We give an operational semantics of the proposed language, discuss design choices that we have made, and demonstrate its expressibility with several examples. We also demonstrate that the proposed formalism preserves a number of well-known universal equalities from array/list/stream theories, and discuss implementation-related challenges. -
Application and Interpretation
Programming Languages: Application and Interpretation Shriram Krishnamurthi Brown University Copyright c 2003, Shriram Krishnamurthi This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License. If you create a derivative work, please include the version information below in your attribution. This book is available free-of-cost from the author’s Web site. This version was generated on 2007-04-26. ii Preface The book is the textbook for the programming languages course at Brown University, which is taken pri- marily by third and fourth year undergraduates and beginning graduate (both MS and PhD) students. It seems very accessible to smart second year students too, and indeed those are some of my most successful students. The book has been used at over a dozen other universities as a primary or secondary text. The book’s material is worth one undergraduate course worth of credit. This book is the fruit of a vision for teaching programming languages by integrating the “two cultures” that have evolved in its pedagogy. One culture is based on interpreters, while the other emphasizes a survey of languages. Each approach has significant advantages but also huge drawbacks. The interpreter method writes programs to learn concepts, and has its heart the fundamental belief that by teaching the computer to execute a concept we more thoroughly learn it ourselves. While this reasoning is internally consistent, it fails to recognize that understanding definitions does not imply we understand consequences of those definitions. For instance, the difference between strict and lazy evaluation, or between static and dynamic scope, is only a few lines of interpreter code, but the consequences of these choices is enormous. -
Kx for Sensors
KxKx forfor SensorsSensors AtAt aa GlanceGlance KxKx forfor SensorsSensors isis anan integratedintegrated VEEVEE Enable, Combine, Integrate and Action your data Enable,Enable, Combine,Combine, IntegrateIntegrate andand ActionAction youryour datadata andand AnalyticsAnalytics solutionsolution forfor ingesting,ingesting, validating,validating, estimatingestimating andand analyzinganalyzing The Industrial Internet of Things (IIoT) is the concept of connected objects and TheThe IndustrialIndustrial InternetInternet ofof ThingsThings (IIoT)(IIoT) isis thethe conceptconcept ofof connectedconnected objectsobjects andand massivemassive amountsamounts ofof streamingstreaming devices, everything from manufacturing equipment, automobiles, jet engines to smart devices,devices, everythingeverything fromfrom manufacturingmanufacturing equipment,equipment, automobiles,automobiles, jetjet enginesengines toto smartsmart (real-time)(real-time) andand historicalhistorical datadata fromfrom meters. Many analysts predict that there will be over 20 billion connected devices by meters.meters. ManyMany analystsanalysts predictpredict thatthat therethere willwill bebe overover 2020 billionbillion connectedconnected devicesdevices byby sensors,sensors, devices,devices, andand otherother datadata 2020. 2020.2020. sources.sources. TheseThese devicesdevices areare equippedequipped withwith sensorssensors thatthat capturecapture informationinformation aboutabout thethe device,device, service,service, weather,weather, location,location, vibration,vibration, motion,motion, -
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.