ELI: a Simple System for Array Programming
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Modern Programming Languages CS508 Virtual University of Pakistan
Modern Programming Languages (CS508) VU Modern Programming Languages CS508 Virtual University of Pakistan Leaders in Education Technology 1 © Copyright Virtual University of Pakistan Modern Programming Languages (CS508) VU TABLE of CONTENTS Course Objectives...........................................................................................................................4 Introduction and Historical Background (Lecture 1-8)..............................................................5 Language Evaluation Criterion.....................................................................................................6 Language Evaluation Criterion...................................................................................................15 An Introduction to SNOBOL (Lecture 9-12).............................................................................32 Ada Programming Language: An Introduction (Lecture 13-17).............................................45 LISP Programming Language: An Introduction (Lecture 18-21)...........................................63 PROLOG - Programming in Logic (Lecture 22-26) .................................................................77 Java Programming Language (Lecture 27-30)..........................................................................92 C# Programming Language (Lecture 31-34) ...........................................................................111 PHP – Personal Home Page PHP: Hypertext Preprocessor (Lecture 35-37)........................129 Modern Programming Languages-JavaScript -
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. -
About ILE C/C++ Compiler Reference
IBM i 7.3 Programming IBM Rational Development Studio for i ILE C/C++ Compiler Reference IBM SC09-4816-07 Note Before using this information and the product it supports, read the information in “Notices” on page 121. This edition applies to IBM® Rational® Development Studio for i (product number 5770-WDS) and to all subsequent releases and modifications until otherwise indicated in new editions. This version does not run on all reduced instruction set computer (RISC) models nor does it run on CISC models. This document may contain references to Licensed Internal Code. Licensed Internal Code is Machine Code and is licensed to you under the terms of the IBM License Agreement for Machine Code. © Copyright International Business Machines Corporation 1993, 2015. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents ILE C/C++ Compiler Reference............................................................................... 1 What is new for IBM i 7.3.............................................................................................................................3 PDF file for ILE C/C++ Compiler Reference.................................................................................................5 About ILE C/C++ Compiler Reference......................................................................................................... 7 Prerequisite and Related Information.................................................................................................. -
R from a Programmer's Perspective Accompanying Manual for an R Course Held by M
DSMZ R programming course R from a programmer's perspective Accompanying manual for an R course held by M. Göker at the DSMZ, 11/05/2012 & 25/05/2012. Slightly improved version, 10/09/2012. This document is distributed under the CC BY 3.0 license. See http://creativecommons.org/licenses/by/3.0 for details. Introduction The purpose of this course is to cover aspects of R programming that are either unlikely to be covered elsewhere or likely to be surprising for programmers who have worked with other languages. The course thus tries not be comprehensive but sort of complementary to other sources of information. Also, the material needed to by compiled in short time and perhaps suffers from important omissions. For the same reason, potential participants should not expect a fully fleshed out presentation but a combination of a text-only document (this one) with example code comprising the solutions of the exercises. The topics covered include R's general features as a programming language, a recapitulation of R's type system, advanced coding of functions, error handling, the use of attributes in R, object-oriented programming in the S3 system, and constructing R packages (in this order). The expected audience comprises R users whose own code largely consists of self-written functions, as well as programmers who are fluent in other languages and have some experience with R. Interactive users of R without programming experience elsewhere are unlikely to benefit from this course because quite a few programming skills cannot be covered here but have to be presupposed. -
Technical Study Desktop Internationalization
Technical Study Desktop Internationalization NIC CH A E L T S T U D Y [This page intentionally left blank] X/Open Technical Study Desktop Internationalisation X/Open Company Ltd. December 1995, X/Open Company Limited All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the copyright owners. X/Open Technical Study Desktop Internationalisation X/Open Document Number: E501 Published by X/Open Company Ltd., U.K. Any comments relating to the material contained in this document may be submitted to X/Open at: X/Open Company Limited Apex Plaza Forbury Road Reading Berkshire, RG1 1AX United Kingdom or by Electronic Mail to: [email protected] ii X/Open Technical Study (1995) Contents Chapter 1 Internationalisation.............................................................................. 1 1.1 Introduction ................................................................................................. 1 1.2 Character Sets and Encodings.................................................................. 2 1.3 The C Programming Language................................................................ 5 1.4 Internationalisation Support in POSIX .................................................. 6 1.5 Internationalisation Support in the X/Open CAE............................... 7 1.5.1 XPG4 Facilities......................................................................................... -
Parallel Functional Programming with APL
Parallel Functional Programming, APL Lecture 1 Parallel Functional Programming with APL Martin Elsman Department of Computer Science University of Copenhagen DIKU December 19, 2016 Martin Elsman (DIKU) Parallel Functional Programming, APL Lecture 1 December 19, 2016 1 / 18 Outline 1 Outline Course Outline 2 Introduction to APL What is APL APL Implementations and Material APL Scalar Operations APL (1-Dimensional) Vector Computations Declaring APL Functions (dfns) APL Multi-Dimensional Arrays Iterations Declaring APL Operators Function Trains Examples Reading Martin Elsman (DIKU) Parallel Functional Programming, APL Lecture 1 December 19, 2016 2 / 18 Outline Course Outline Teachers Martin Elsman (ME), Ken Friis Larsen (KFL), Andrzej Filinski (AF), and Troels Henriksen (TH) Location Lectures in Small Aud, Universitetsparken 1 (UP1); Labs in Old Library, UP1 Course Description See http://kurser.ku.dk/course/ndak14009u/2016-2017 Course Outline Week 47 48 49 50 51 1–3 Mon 13–15 Intro, Futhark Parallel SNESL APL (ME) Project Futhark (ME) Haskell (AF) (ME) (KFL) Mon 15–17 Lab Lab Lab Lab Project Wed 13–15 Futhark Parallel SNESL Invited APL Project (ME) Haskell (AF) Lecture (ME) / (KFL) (John Projects Reppy) Martin Elsman (DIKU) Parallel Functional Programming, APL Lecture 1 December 19, 2016 3 / 18 Introduction to APL What is APL APL—An Ancient Array Programming Language—But Still Used! Pioneered by Ken E. Iverson in the 1960’s. E. Dijkstra: “APL is a mistake, carried through to perfection.” There are quite a few APL programmers around (e.g., HIPERFIT partners). Very concise notation for expressing array operations. Has a large set of functional, essentially parallel, multi- dimensional, second-order array combinators. -
Unix Standardization and Implementation
Contents 1. Preface/Introduction 2. Standardization and Implementation 3. File I/O 4. Standard I/O Library 5. Files and Directories 6. System Data Files and Information 7. Environment of a Unix Process 8. Process Control 9. Signals 10.Inter-process Communication * All rights reserved, Tei-Wei Kuo, National Taiwan University, 2003. Standardization and Implementation Why Standardization? Proliferation of UNIX versions What should be done? The specifications of limits that each implementation must define! * All rights reserved, Tei-Wei Kuo, National Taiwan University, 2003. 1 UNIX Standardization ANSI C American National Standards Institute ISO/IEC 9899:1990 International Organization for Standardization (ISO) Syntax/Semantics of C, a standard library Purpose: Provide portability of conforming C programs to a wide variety of OS’s. 15 areas: Fig 2.1 – Page 27 * All rights reserved, Tei-Wei Kuo, National Taiwan University, 2003. UNIX Standardization ANSIC C <assert.h> - verify program assertion <ctype.h> - char types <errno.h> - error codes <float.h> - float point constants <limits.h> - implementation constants <locale.h> - locale catalogs <math.h> - mathematical constants <setjmp.h> - nonlocal goto <signal.h> - signals <stdarg.h> - variable argument lists <stddef.h> - standard definitions <stdio.h> - standard library <stdlib.h> - utilities functions <string.h> - string operations <time.h> - time and date * All rights reserved, Tei-Wei Kuo, National Taiwan University, 2003. 2 UNIX Standardization POSIX.1 (Portable Operating System Interface) developed by IEEE Not restricted for Unix-like systems and no distinction for system calls and library functions Originally IEEE Standard 1003.1-1988 1003.2: shells and utilities, 1003.7: system administrator, > 15 other communities Published as IEEE std 1003.1-1990, ISO/IEC9945-1:1990 New: the inclusion of symbolic links No superuser notion * All rights reserved, Tei-Wei Kuo, National Taiwan University, 2003. -
Programming Language
Programming language A programming language is a formal language, which comprises a set of instructions that produce various kinds of output. Programming languages are used in computer programming to implement algorithms. Most programming languages consist of instructions for computers. There are programmable machines that use a set of specific instructions, rather than general programming languages. Early ones preceded the invention of the digital computer, the first probably being the automatic flute player described in the 9th century by the brothers Musa in Baghdad, during the Islamic Golden Age.[1] Since the early 1800s, programs have been used to direct the behavior of machines such as Jacquard looms, music boxes and player pianos.[2] The programs for these The source code for a simple computer program written in theC machines (such as a player piano's scrolls) did not programming language. When compiled and run, it will give the output "Hello, world!". produce different behavior in response to different inputs or conditions. Thousands of different programming languages have been created, and more are being created every year. Many programming languages are written in an imperative form (i.e., as a sequence of operations to perform) while other languages use the declarative form (i.e. the desired result is specified, not how to achieve it). The description of a programming language is usually split into the two components ofsyntax (form) and semantics (meaning). Some languages are defined by a specification document (for example, theC programming language is specified by an ISO Standard) while other languages (such as Perl) have a dominant implementation that is treated as a reference. -
RAL-TR-1998-060 Co-Array Fortran for Parallel Programming
RAL-TR-1998-060 Co-Array Fortran for parallel programming 1 by R. W. Numrich23 and J. K. Reid Abstract Co-Array Fortran, formerly known as F−− , is a small extension of Fortran 95 for parallel processing. A Co-Array Fortran program is interpreted as if it were replicated a number of times and all copies were executed asynchronously. Each copy has its own set of data objects and is termed an image. The array syntax of Fortran 95 is extended with additional trailing subscripts in square brackets to give a clear and straightforward representation of any access to data that is spread across images. References without square brackets are to local data, so code that can run independently is uncluttered. Only where there are square brackets, or where there is a procedure call and the procedure contains square brackets, is communication between images involved. There are intrinsic procedures to synchronize images, return the number of images, and return the index of the current image. We introduce the extension; give examples to illustrate how clear, powerful, and flexible it can be; and provide a technical definition. Categories and subject descriptors: D.3 [PROGRAMMING LANGUAGES]. General Terms: Parallel programming. Additional Key Words and Phrases: Fortran. Department for Computation and Information, Rutherford Appleton Laboratory, Oxon OX11 0QX, UK August 1998. 1 Available by anonymous ftp from matisa.cc.rl.ac.uk in directory pub/reports in the file nrRAL98060.ps.gz 2 Silicon Graphics, Inc., 655 Lone Oak Drive, Eagan, MN 55121, USA. Email: -
Programming the Capabilities of the PC Have Changed Greatly Since the Introduction of Electronic Computers
1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in INTRODUCTION TO PROGRAMMING LANGUAGE Topic Objective: At the end of this topic the student will be able to understand: History of Computer Programming C++ Definition/Overview: Overview: A personal computer (PC) is any general-purpose computer whose original sales price, size, and capabilities make it useful for individuals, and which is intended to be operated directly by an end user, with no intervening computer operator. Today a PC may be a desktop computer, a laptop computer or a tablet computer. The most common operating systems are Microsoft Windows, Mac OS X and Linux, while the most common microprocessors are x86-compatible CPUs, ARM architecture CPUs and PowerPC CPUs. Software applications for personal computers include word processing, spreadsheets, databases, games, and myriad of personal productivity and special-purpose software. Modern personal computers often have high-speed or dial-up connections to the Internet, allowing access to the World Wide Web and a wide range of other resources. Key Points: 1. History of ComputeWWW.BSSVE.INr Programming The capabilities of the PC have changed greatly since the introduction of electronic computers. By the early 1970s, people in academic or research institutions had the opportunity for single-person use of a computer system in interactive mode for extended durations, although these systems would still have been too expensive to be owned by a single person. The introduction of the microprocessor, a single chip with all the circuitry that formerly occupied large cabinets, led to the proliferation of personal computers after about 1975. Early personal computers - generally called microcomputers - were sold often in Electronic kit form and in limited volumes, and were of interest mostly to hobbyists and technicians. -
Tangent: Automatic Differentiation Using Source-Code Transformation for Dynamically Typed Array Programming
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming Bart van Merriënboer Dan Moldovan Alexander B Wiltschko MILA, Google Brain Google Brain Google Brain [email protected] [email protected] [email protected] Abstract The need to efficiently calculate first- and higher-order derivatives of increasingly complex models expressed in Python has stressed or exceeded the capabilities of available tools. In this work, we explore techniques from the field of automatic differentiation (AD) that can give researchers expressive power, performance and strong usability. These include source-code transformation (SCT), flexible gradient surgery, efficient in-place array operations, and higher-order derivatives. We implement and demonstrate these ideas in the Tangent software library for Python, the first AD framework for a dynamic language that uses SCT. 1 Introduction Many applications in machine learning rely on gradient-based optimization, or at least the efficient calculation of derivatives of models expressed as computer programs. Researchers have a wide variety of tools from which they can choose, particularly if they are using the Python language [21, 16, 24, 2, 1]. These tools can generally be characterized as trading off research or production use cases, and can be divided along these lines by whether they implement automatic differentiation using operator overloading (OO) or SCT. SCT affords more opportunities for whole-program optimization, while OO makes it easier to support convenient syntax in Python, like data-dependent control flow, or advanced features such as custom partial derivatives. We show here that it is possible to offer the programming flexibility usually thought to be exclusive to OO-based tools in an SCT framework. -
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 ..................