Macros in C/C++
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A New Macro-Programming Paradigm in Data Collection Sensor Networks
SpaceTime Oriented Programming: A New Macro-Programming Paradigm in Data Collection Sensor Networks Hiroshi Wada and Junichi Suzuki Department of Computer Science University of Massachusetts, Boston Program Insert This paper proposes a new programming paradigm for wireless sensor networks (WSNs). It is designed to significantly reduce the complexity of WSN programming by providing a new high- level programming abstraction to specify spatio-temporal data collections in a WSN from a global viewpoint as a whole rather than sensor nodes as individuals. Abstract SpaceTime Oriented Programming (STOP) is new macro-programming paradigm for wireless sensor networks (WSNs) in that it supports both spatial and temporal aspects of sensor data and it provides a high-level abstraction to express data collections from a global viewpoint for WSNs as a whole rather than sensor nodes as individuals. STOP treats space and time as first-class citizens and combines them as spacetime continuum. A spacetime is a three dimensional object that consists of a two special dimensions and a time playing the role of the third dimension. STOP allows application developers to program data collections to spacetime, and abstracts away the details in WSNs, such as how many nodes are deployed in a WSN, how nodes are connected with each other, and how to route data queries in a WSN. Moreover, STOP provides a uniform means to specify data collections for the past and future. Using the STOP application programming interfaces (APIs), application programs (STOP macro programs) can obtain a “snapshot” space at a given time in a given spatial resolutions (e.g., a space containing data on at least 60% of nodes in a give spacetime at 30 minutes ago.) and a discrete set of spaces that meet specified spatial and temporal resolutions. -
Scribble As Preprocessor
Scribble as Preprocessor Version 8.2.0.8 Matthew Flatt and Eli Barzilay September 25, 2021 The scribble/text and scribble/html languages act as “preprocessor” languages for generating text or HTML. These preprocessor languages use the same @ syntax as the main Scribble tool (see Scribble: The Racket Documentation Tool), but instead of working in terms of a document abstraction that can be rendered to text and HTML (and other formats), the preprocessor languages work in a way that is more specific to the target formats. 1 Contents 1 Text Generation 3 1.1 Writing Text Files . .3 1.2 Defining Functions and More . .7 1.3 Using Printouts . .9 1.4 Indentation in Preprocessed output . 11 1.5 Using External Files . 16 1.6 Text Generation Functions . 19 2 HTML Generation 23 2.1 Generating HTML Strings . 23 2.1.1 Other HTML elements . 30 2.2 Generating XML Strings . 32 2.3 HTML Resources . 36 Index 39 Index 39 2 1 Text Generation #lang scribble/text package: scribble-text-lib The scribble/text language provides everything from racket/base, racket/promise, racket/list, and racket/string, but with additions and a changed treatment of the module top level to make it suitable as for text generation or a preprocessor language: • The language uses read-syntax-inside to read the body of the module, similar to §6.7 “Document Reader”. This means that by default, all text is read in as Racket strings; and @-forms can be used to use Racket functions and expression escapes. • Values of expressions are printed with a custom output function. -
SQL Processing with SAS® Tip Sheet
SQL Processing with SAS® Tip Sheet This tip sheet is associated with the SAS® Certified Professional Prep Guide Advanced Programming Using SAS® 9.4. For more information, visit www.sas.com/certify Basic Queries ModifyingBasic Queries Columns PROC SQL <options>; SELECT col-name SELECT column-1 <, ...column-n> LABEL= LABEL=’column label’ FROM input-table <WHERE expression> SELECT col-name <GROUP BY col-name> FORMAT= FORMAT=format. <HAVING expression> <ORDER BY col-name> <DESC> <,...col-name>; Creating a SELECT col-name AS SQL Query Order of Execution: new column new-col-name Filtering Clause Description WHERE CALCULATED new columns new-col-name SELECT Retrieve data from a table FROM Choose and join tables Modifying Rows WHERE Filter the data GROUP BY Aggregate the data INSERT INTO table SET column-name=value HAVING Filter the aggregate data <, ...column-name=value>; ORDER BY Sort the final data Inserting rows INSERT INTO table <(column-list)> into tables VALUES (value<,...value>); INSERT INTO table <(column-list)> Managing Tables SELECT column-1<,...column-n> FROM input-table; CREATE TABLE table-name Eliminating SELECT DISTINCT CREATE TABLE (column-specification-1<, duplicate rows col-name<,...col-name> ...column-specification-n>); WHERE col-name IN DESCRIBE TABLE table-name-1 DESCRIBE TABLE (value1, value2, ...) <,...table-name-n>; WHERE col-name LIKE “_string%” DROP TABLE table-name-1 DROP TABLE WHERE col-name BETWEEN <,...table-name-n>; Filtering value AND value rows WHERE col-name IS NULL WHERE date-value Managing Views “<01JAN2019>”d WHERE time-value “<14:45:35>”t CREATE VIEW CREATE VIEW table-name AS query; WHERE datetime-value “<01JAN201914:45:35>”dt DESCRIBE VIEW view-name-1 DESCRIBE VIEW <,...view-name-n>; Remerging Summary Statistics DROP VIEW DROP VIEW view-name-1 <,...view-name-n>; SELECT col-name, summary function(argument) FROM input table; Copyright © 2019 SAS Institute Inc. -
Section “Common Predefined Macros” in the C Preprocessor
The C Preprocessor For gcc version 12.0.0 (pre-release) (GCC) Richard M. Stallman, Zachary Weinberg Copyright c 1987-2021 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation. A copy of the license is included in the section entitled \GNU Free Documentation License". This manual contains no Invariant Sections. The Front-Cover Texts are (a) (see below), and the Back-Cover Texts are (b) (see below). (a) The FSF's Front-Cover Text is: A GNU Manual (b) The FSF's Back-Cover Text is: You have freedom to copy and modify this GNU Manual, like GNU software. Copies published by the Free Software Foundation raise funds for GNU development. i Table of Contents 1 Overview :::::::::::::::::::::::::::::::::::::::: 1 1.1 Character sets:::::::::::::::::::::::::::::::::::::::::::::::::: 1 1.2 Initial processing ::::::::::::::::::::::::::::::::::::::::::::::: 2 1.3 Tokenization ::::::::::::::::::::::::::::::::::::::::::::::::::: 4 1.4 The preprocessing language :::::::::::::::::::::::::::::::::::: 6 2 Header Files::::::::::::::::::::::::::::::::::::: 7 2.1 Include Syntax ::::::::::::::::::::::::::::::::::::::::::::::::: 7 2.2 Include Operation :::::::::::::::::::::::::::::::::::::::::::::: 8 2.3 Search Path :::::::::::::::::::::::::::::::::::::::::::::::::::: 9 2.4 Once-Only Headers::::::::::::::::::::::::::::::::::::::::::::: 9 2.5 Alternatives to Wrapper #ifndef :::::::::::::::::::::::::::::: -
Julia: a Fresh Approach to Numerical Computing
RSDG @ UCL Julia: A Fresh Approach to Numerical Computing Mosè Giordano @giordano [email protected] Knowledge Quarter Codes Tech Social October 16, 2019 Julia’s Facts v1.0.0 released in 2018 at UCL • Development started in 2009 at MIT, first • public release in 2012 Julia co-creators won the 2019 James H. • Wilkinson Prize for Numerical Software Julia adoption is growing rapidly in • numerical optimisation, differential equations, machine learning, differentiable programming It is used and taught in several universities • (https://julialang.org/teaching/) Mosè Giordano (RSDG @ UCL) Julia: A Fresh Approach to Numerical Computing October 16, 2019 2 / 29 Julia on Nature Nature 572, 141-142 (2019). doi: 10.1038/d41586-019-02310-3 Mosè Giordano (RSDG @ UCL) Julia: A Fresh Approach to Numerical Computing October 16, 2019 3 / 29 Solving the Two-Language Problem: Julia Multiple dispatch • Dynamic type system • Good performance, approaching that of statically-compiled languages • JIT-compiled scripts • User-defined types are as fast and compact as built-ins • Lisp-like macros and other metaprogramming facilities • No need to vectorise: for loops are fast • Garbage collection: no manual memory management • Interactive shell (REPL) for exploratory work • Call C and Fortran functions directly: no wrappers or special APIs • Call Python functions: use the PyCall package • Designed for parallelism and distributed computation • Mosè Giordano (RSDG @ UCL) Julia: A Fresh Approach to Numerical Computing October 16, 2019 4 / 29 Multiple Dispatch using DifferentialEquations -
Rexx to the Rescue!
Session: G9 Rexx to the Rescue! Damon Anderson Anixter May 21, 2008 • 9:45 a.m. – 10:45 a.m. Platform: DB2 for z/OS Rexx is a powerful tool that can be used in your daily life as an z/OS DB2 Professional. This presentation will cover setup and execution for the novice. It will include Edit macro coding examples used by DBA staff to solve everyday tasks. DB2 data access techniques will be covered as will an example of calling a stored procedure. Examples of several homemade DBA tools built with Rexx will be provided. Damon Anderson is a Senior Technical DBA and Technical Architect at Anixter Inc. He has extensive experience in DB2 and IMS, data replication, ebusiness, Disaster Recovery, REXX, ISPF Dialog Manager, and related third-party tools and technologies. He is a certified DB2 Database Administrator. He has written articles for the IDUG Solutions Journal and presented at IDUG and regional user groups. Damon can be reached at [email protected] 1 Rexx to the Rescue! 5 Key Bullet Points: • Setting up and executing your first Rexx exec • Rexx execs versus edit macros • Edit macro capabilities and examples • Using Rexx with DB2 data • Examples to clone data, compare databases, generate utilities and more. 2 Agenda: • Overview of Anixter’s business, systems environment • The Rexx “Setup Problem” Knowing about Rexx but not knowing how to start using it. • Rexx execs versus Edit macros, including coding your first “script” macro. • The setup and syntax for accessing DB2 • Discuss examples for the purpose of providing tips for your Rexx execs • A few random tips along the way. -
The Semantics of Syntax Applying Denotational Semantics to Hygienic Macro Systems
The Semantics of Syntax Applying Denotational Semantics to Hygienic Macro Systems Neelakantan R. Krishnaswami University of Birmingham <[email protected]> 1. Introduction body of a macro definition do not interfere with names oc- Typically, when semanticists hear the words “Scheme” or curring in the macro’s arguments. Consider this definition of and “Lisp”, what comes to mind is “untyped lambda calculus a short-circuiting operator: plus higher-order state and first-class control”. Given our (define-syntax and typical concerns, this seems to be the essence of Scheme: it (syntax-rules () is a dynamically typed applied lambda calculus that sup- ((and e1 e2) (let ((tmp e1)) ports mutable data and exposes first-class continuations to (if tmp the programmer. These features expose a complete com- e2 putational substrate to programmers so elegant that it can tmp))))) even be characterized mathematically; every monadically- representable effect can be implemented with state and first- In this definition, even if the variable tmp occurs freely class control [4]. in e2, it will not be in the scope of the variable definition However, these days even mundane languages like Java in the body of the and macro. As a result, it is important to support higher-order functions and state. So from the point interpret the body of the macro not merely as a piece of raw of view of a working programmer, the most distinctive fea- syntax, but as an alpha-equivalence class. ture of Scheme is something quite different – its support for 2.2 Open Recursion macros. The intuitive explanation is that a macro is a way of defining rewrites on abstract syntax trees. -
Concatenative Programming
Concatenative Programming From Ivory to Metal Jon Purdy ● Why Concatenative Programming Matters (2012) ● Spaceport (2012–2013) Compiler engineering ● Facebook (2013–2014) Site integrity infrastructure (Haxl) ● There Is No Fork: An Abstraction for Efficient, Concurrent, and Concise Data Access (ICFP 2014) ● Xamarin/Microsoft (2014–2017) Mono runtime (performance, GC) What I Want in a ● Prioritize reading & modifying code over writing it Programming ● Be expressive—syntax closely Language mirroring high-level semantics ● Encourage “good” code (reusable, refactorable, testable, &c.) ● “Make me do what I want anyway” ● Have an “obvious” efficient mapping to real hardware (C) ● Be small—easy to understand & implement tools for ● Be a good citizen—FFI, embedding ● Don’t “assume you’re the world” ● Forth (1970) Notable Chuck Moore Concatenative ● PostScript (1982) Warnock, Geschke, & Paxton Programming ● Joy (2001) Languages Manfred von Thun ● Factor (2003) Slava Pestov &al. ● Cat (2006) Christopher Diggins ● Kitten (2011) Jon Purdy ● Popr (2012) Dustin DeWeese ● … History Three ● Lambda Calculus (1930s) Alonzo Church Formal Systems of ● Turing Machine (1930s) Computation Alan Turing ● Recursive Functions (1930s) Kurt Gödel Church’s Lambdas e ::= x Variables λx.x ≅ λy.y | λx. e Functions λx.(λy.x) ≅ λy.(λz.y) | e1 e2 Applications λx.M[x] ⇒ λy.M[y] α-conversion (λx.λy.λz.xz(yz))(λx.λy.x)(λx.λy.x) ≅ (λy.λz.(λx.λy.x)z(yz))(λx.λy.x) (λx.M)E ⇒ M[E/x] β-reduction ≅ λz.(λx.λy.x)z((λx.λy.x)z) ≅ λz.(λx.λy.x)z((λx.λy.x)z) ≅ λz.z Turing’s Machines ⟨ ⟩ M -
Metaprogramming with Julia
Metaprogramming with Julia https://szufel.pl Programmers effort vs execution speed Octave R Python Matlab time, time, log scale - C JavaScript Java Go Julia C rozmiar kodu Sourcewego w KB Source: http://www.oceanographerschoice.com/2016/03/the-julia-language-is-the-way-of-the-future/ 2 Metaprogramming „Metaprogramming is a programming technique in which computer programs have the ability to treat other programs as their data. It means that a program can be designed to read, generate, analyze or transform other programs, and even modify itself while running.” (source: Wikipedia) julia> code = Meta.parse("x=5") :(x = 5) julia> dump(code) Expr head: Symbol = args: Array{Any}((2,)) 1: Symbol x 2: Int64 5 3 Metaprogramming (cont.) julia> code = Meta.parse("x=5") :(x = 5) julia> dump(code) Expr head: Symbol = args: Array{Any}((2,)) 1: Symbol x 2: Int64 5 julia> eval(code) 5 julia> x 5 4 Julia Compiler system not quite accurate picture... Source: https://www.researchgate.net/ publication/301876510_High- 5 level_GPU_programming_in_Julia Example 1. Select a field from an object function getValueOfA(x) return x.a end function getValueOf(x, name::String) return getproperty(x, Symbol(name)) end function getValueOf2(name::String) field = Symbol(name) code = quote (obj) -> obj.$field end return eval(code) end function getValueOf3(name::String) return eval(Meta.parse("obj -> obj.$name")) end 6 Let’s test using BenchmarkTools struct MyStruct a b end x = MyStruct(5,6) @btime getValueOfA($x) @btime getValueOf($x,"a") const getVal2 = getValueOf2("a") @btime -
Dynamic Economics Quantitative Methods and Applications to Macro and Micro
Dynamic Economics Quantitative Methods and Applications to Macro and Micro J¶er^ome Adda and Nicola Pavoni MACT1 2003-2004. I Overview Dynamic Programming Theory ² { Contraction mapping theorem. { Euler equation Numerical Methods ² Econometric Methods ² Applications ² MACT1 2003-2004. I Numerical Methods Examples: Cake Eating Deterministic Cake eating: ² V (K) = max u(c) + ¯V (K c) c ¡ with { K: size of cake. K 0 ¸ { c: amount of cake consumed. c 0 ¸ Stochastic Cake eating: ² V (K; y) = max u(c) + ¯Ey0 V (K0; y0) c K0 = K c + y ¡ Discrete Cake eating: ² V (K; ") = max[u(K; "); ¯E 0 V (½K; "0)] ½ [0; 1] " 2 MACT1 2003-2004. I How do we Solve These Models? Not necessarily a closed form solution for V (:). ² Numerical approximations. ² MACT1 2003-2004. I Solution Methods Value function iterations. (Contraction Mapping Th.) ² Policy function iterations. (Contraction Mapping Th.) ² Projection methods. (Euler equation) ² MACT1 2003-2004. I Value Function Iterations Value Function Iterations Vn(S) = max u(action; S) + ¯EVn 1(S0) action ¡ Vn(:) = T Vn 1(:) ¡ Take advantage of the Contraction Mapping Theorem. If T is ² the contraction operator, we use the fact that d(Vn; Vn 1) ¯d(Vn 1; Vn 2) ¡ · ¡ ¡ n Vn(:) = T V0(:) This guarantee that: ² 1. successive iterations will converge to the (unique) ¯xed point. 2. starting guess for V0 can be arbitrary. Successive iterations: ² { Start with a given V0(:). Usually V0(:) = 0. { Compute V1(:) = T V0(:) { Iterate Vn(:) = T Vn 1(:) ¡ { Stop when d(Vn; Vn 1) < ". ¡ MACT1 2003-2004. I Value Function Iterations: Deterministic Cake Eating Model: ² V (K) = max u(c) + ¯V (K c) c ¡ Can be rewritten as: ² V (K) = max u(K K0) + ¯V (K0) K0 ¡ The iterations will be on ² Vn(K) = max u(K K0) + ¯Vn 1(K0) K0 ¡ ¡ example: take u(c) = ln(c). -
Top 10 Uses of Macro %Varlist - in Proc SQL, Data Step and Elsewhere
PhUSE 2016 Paper CS09 Top 10 uses of macro %varlist - in proc SQL, Data Step and elsewhere Jean-Michel Bodart, Business & Decision Life Sciences, Brussels, Belgium ABSTRACT The SAS® macro-function %VARLIST(), a generic utility function that can retrieve lists of variables from one or more datasets, and process them in various ways in order to generate code fragments, was presented at the PhUSE 2015 Annual Conference in Vienna. That first paper focused on generating code for SQL joins of arbitrary complexity. However the macro was also meant to be useful in the Data Step as well as in other procedure calls, in global statements and as a building block in other macros. The full code of macro %VARLIST is freely available from the PhUSE Wiki (http://www.phusewiki.org/wiki/index.php?title=SAS_macro-function_%25VARLIST). The current paper reviews and provides examples of the top ten uses of %VARLIST() according to a survey of real-life SAS programs developed to create Analysis Datasets, Summary Tables and Figures in post-hoc and exploratory analyses of clinical trials data. It is meant to provide users with additional information about how and where to use it best. INTRODUCTION SAS Macro Language has been available for a long time. It provides users with the tools to control program flow execution, execute repeatedly and/or conditionally single or multiple SAS Data steps, Procedure steps and/or Global statements that are aggregated, included in user-written macro definitions. However, the macro statements that implement the repeating and conditional loops cannot be submitted on their own, as “open code”, but only as part of calls to the full macro definition. -
(Dynamic (Programming Paradigms)) Performance and Expressivity Didier Verna
(Dynamic (programming paradigms)) Performance and expressivity Didier Verna To cite this version: Didier Verna. (Dynamic (programming paradigms)) Performance and expressivity. Software Engineer- ing [cs.SE]. LRDE - Laboratoire de Recherche et de Développement de l’EPITA, 2020. hal-02988180 HAL Id: hal-02988180 https://hal.archives-ouvertes.fr/hal-02988180 Submitted on 4 Nov 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE D’HABILITATION À DIRIGER LES RECHERCHES Sorbonne Université Spécialité Sciences de l’Ingénieur (DYNAMIC (PROGRAMMING PARADIGMS)) PERFORMANCE AND EXPRESSIVITY Didier Verna [email protected] Laboratoire de Recherche et Développement de l’EPITA (LRDE) 14-16 rue Voltaire 94276 Le Kremlin-Bicêtre CEDEX Soutenue le 10 Juillet 2020 Rapporteurs: Robert Strandh Université de Bordeaux, France Nicolas Neuß FAU, Erlangen-Nürnberg, Allemagne Manuel Serrano INRIA, Sophia Antipolis, France Examinateurs: Marco Antoniotti Université de Milan-Bicocca, Italie Ralf Möller Université de Lübeck, Allemagne Gérard Assayag IRCAM, Paris, France DOI 10.5281/zenodo.4244393 Résumé (French Abstract) Ce rapport d’habilitation traite de travaux de recherche fondamentale et appliquée en informatique, effectués depuis 2006 au Laboratoire de Recherche et Développement de l’EPITA (LRDE). Ces travaux se situent dans le domaine des langages de pro- grammation dynamiques, et plus particulièrement autour de leur expressivité et de leur performance.