A Practical Solution for Scripting Language Compilers
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A Practical Solution for Scripting Language Compilers
A Practical Solution for Scripting Language Compilers Paul Biggar, Edsko de Vries, David Gregg Department of Computer Science, Trinity College Dublin, Dublin 2, Ireland Abstract Although scripting languages are becoming increasingly popular, even mature script- ing language implementations remain interpreted. Several compilers and reimplemen- tations have been attempted, generally focusing on performance. Based on our survey of these reimplementations, we determine that there are three important features of scripting languages that are difficult to compile or reimplement. Since scripting languages are defined primarily through the semantics of their original implementations, they often change semantics between releases. They provide C APIs, used both for foreign-function interfaces and to write third-party extensions. These APIs typically have tight integration with the original implementation, and are used to providelarge standard libraries, which are difficult to re-use, and costly to reimplement. Finally, they support run-time code generation. These features make the important goal of correctness difficult to achieve for compilers and reimplementations. We present a technique to support these features in an ahead-of-time compiler for PHP. Our technique uses the original PHP implementation through the provided C API, both in our compiler, and in our generated code. We support all of these impor- tant scripting language features, particularly focusing on the correctness of compiled programs. Additionally, our approach allows us to automatically support limited fu- ture language changes. We present a discussion and performance evaluation of this technique. Key words: Compiler, Scripting Language 1. Motivation Although scripting languages1 are becoming increasingly popular, most scripting language implementations remain interpreted. Typically, these implementations are slow, between one and two orders of magnitude slower than C. -
Report on Biodiversity and Tropical Forests in Indonesia
Report on Biodiversity and Tropical Forests in Indonesia Submitted in accordance with Foreign Assistance Act Sections 118/119 February 20, 2004 Prepared for USAID/Indonesia Jl. Medan Merdeka Selatan No. 3-5 Jakarta 10110 Indonesia Prepared by Steve Rhee, M.E.Sc. Darrell Kitchener, Ph.D. Tim Brown, Ph.D. Reed Merrill, M.Sc. Russ Dilts, Ph.D. Stacey Tighe, Ph.D. Table of Contents Table of Contents............................................................................................................................. i List of Tables .................................................................................................................................. v List of Figures............................................................................................................................... vii Acronyms....................................................................................................................................... ix Executive Summary.................................................................................................................... xvii 1. Introduction............................................................................................................................1- 1 2. Legislative and Institutional Structure Affecting Biological Resources...............................2 - 1 2.1 Government of Indonesia................................................................................................2 - 2 2.1.1 Legislative Basis for Protection and Management of Biodiversity and -
Proseminar Python - Python Bindings
Proseminar Python - Python Bindings Sven Fischer Student an der TU-Dresden [email protected] Abstract Diese Arbeit beschaftigt¨ sich damit, einen Einblick in die Benut- zung von externen Bibliotheken in Python zu geben und fuhrt¨ den Leser in das Schreiben von eigenen Erweiterungen ein. Daruber¨ hinaus wird darauf eingegangen, wie Projekte in anderen Program- miersprachen Python benutzen konnen.¨ Weiterhin wird kurz auf verschiedene andere Moglichkeiten¨ eingegangen, Python oder Py- thons Benutzung zu erweitern und zu verandern:¨ durch Kompilati- on, Mischen“ mit oder Ubersetzen¨ in anderen Sprachen, oder spe- ” zielle Interpreter. Abbildung 1. Vergleich der Moglichkeiten¨ Python zu erwei- tern (links) und Python einzubetten (rechts). Categories and Subject Descriptors D.3.3 [Programming Lan- guages]: Language Constructs and Features—Modules, packages 1. Daten von C nach Python konvertieren General Terms Languages, Documentation 2. Python Funktion mit konvertierten Werten aufrufen Keywords Python, Extension, Embedding, Library 3. Ergebnis von Python zuruck¨ nach C konvertieren 1. Einfuhrung¨ Diese Konvertierung ist auch das großte¨ Hindernis beim Ver- 1 Python wird mit einer umfangreichen Standartbibliothek ausgelie- knupfen¨ von Python und C , zumindest ist es mit einigem Aufwand fert. Trotzdem gibt es Grunde,¨ warum man Python um verschiedene verbunden. externe Bibliotheken erweitern mochte.¨ Ein Beispiel dafur¨ ware¨ die Es gibt einige Projekte, die sich mit dem Verandern¨ und Erwei- ¨ Geschwindigkeit, die bei Python als interpretierter Sprache nicht tern der Sprache Python beschaftigen.¨ Einen kurzen Uberblick uber¨ immer den Anforderungen entspricht. Weiterhin gibt es genugend¨ einige Moglichkeiten¨ gebe ich in Abschnitt4. Dort gehe ich auf Software von Drittanbietern, welche man in Python nutzbar ma- zwei Python-Interpreter neben dem in der Standard-Distribution chen will - ohne sie in Python zu ubersetzen.¨ Darauf gehe ich in enthaltenen ein und zeige Moglichkeiten¨ auf, Python in andere Abschnitt2 ein. -
Efficient Use of Python on the Clusters
Efficient use of Python on the clusters Ariel Lozano CÉCI training November 21, 2018 Outline I Analyze our code with profiling tools: I cpu: cProfile, line_profiler, kernprof I memory: memory_profiler, mprof I Being a highly abstract dynamically typed language, how to make a more efficient use of hardware internals? I Numpy and Scipy ecosystem (mainly wrappers to C/Fortran compiled code) I binding to compiled code: interfaces between python and compiled modules I compiling: tools to compile python code I parallelism: modules to exploit multicores Sieve of eratostenes Algorithm to find all prime numbers up to any given limit. Ex: Find all the prime numbers less than or equal to 25: I 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Cross out every number displaced by 2 after 2 up to the limit: I 23 45 67 89 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Move to next n non crossed, cross out each non crossed number displaced by n: I 23 45 67 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 I 2 3 45 67 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 The remaining numbers non crossed in the list are all the primes below limit. 2 Trivial optimization: jump directlyp to n to start crossing out. Then, n must loop only up to limit. -
Shed Skin Documentation Release V0.9.4
Shed Skin Documentation Release v0.9.4 Mark Dufour the Shed Skin contributors Mar 16, 2017 Contents 1 An experimental (restricted-Python)-to-C++ compiler1 2 Documentation 3 2.1 Shed Skin documentation........................................3 2.1.1 Introduction...........................................3 2.1.2 Typing restrictions.......................................3 2.1.3 Python subset restrictions....................................4 2.1.4 Library limitations.......................................5 2.1.5 Installation...........................................6 2.1.5.1 Windows........................................6 2.1.5.2 UNIX.........................................6 2.1.5.2.1 Using a package manager..........................6 2.1.5.2.2 Manual installation.............................6 2.1.5.3 OSX..........................................7 2.1.5.3.1 Manual installation.............................7 2.1.6 Compiling a standalone program................................8 2.1.7 Generating an extension module................................8 2.1.7.1 Limitations.......................................9 2.1.7.2 Numpy integration...................................9 2.1.8 Distributing binaries...................................... 10 2.1.8.1 Windows........................................ 10 2.1.8.2 UNIX......................................... 10 2.1.9 Multiprocessing......................................... 10 2.1.10 Calling C/C++ code....................................... 11 2.1.10.1 Standard library................................... -
Pfc6168.Pdf (438.8Kb)
ESCUELA TÉCNICA SUPERIOR DE INGENIERÍA DE TELECOMUNICACIÓN UNIVERSIDAD POLITÉCNICA DE CARTAGENA Proyecto Fin de Carrera TÍTULO: Iphone Bookshelf AUTOR: David Zamora Gutiérrez DIRECTOR: Francesc Burrull i Mestres Febrero / 2015 INDEX IPhone application………………………………………………………………... o Tools……………………………………………………………………… . Iphone…………………………………………………………….. Objective-C……………………………………………………….. o Code………………………………………………………………………. Web site…………………………………………………………………………... o Tools……………………………………………………………………… . Codeigniter……………………………………………………….. Php………………………………………………………………... Http……………………………………………………………….. Html………………………………………………………………. Mysql……………………………………………………………... Apache……………………………………………………………. CSS……………………………………………………………….. E-books…………………………………………………………… o Code………………………………………………………………………. References……………………………………………………………………....... IPHONE APPLICATION TOOLS IPHONE The iPhone is a line of Internet- and multimedia-enabled smartphones designed and marketed by Apple Inc. The first iPhone was unveiled by Apple CEO Steve Jobs on January 9, 2007, and released on June 29, 2007. An iPhone can function as a video camera (video recording was not a standard feature until the iPhone 3GS was released), a camera phone, can send texts and receive visual voicemail, a portable media player, and an Internet client with email and web browsing capabilities, and both Wi-Fi and 3G connectivity. The user interface is built around the device's multi-touch screen, including a virtual keyboard rather than a physical one. Third-party as well as Apple application software is available from the App Store, which launched in mid-2008 and now has over 350,000 "apps" approved by Apple. These apps have diverse functionalities, including games, reference, GPS navigation, social networking, e-books... To create applications for this device it’s use the APPLE SDK. APPLE SDK The SDK basically consists of a set of tools that Apple provides to build, debug and test our developments. It contains the following programs: - XCODE: Xcode is a suite of tools, developed by Apple, for developing software for Mac OS X and iOS. -
Phalanger Compiling PHP for .NET and Silverlight
Phalanger Compiling PHP for .NET and Silverlight http://php-compiler.net Tomáš Petříček, C# MVP ([email protected]) Formerly by: Tomáš Matoušek, Ladislav Prošek, et al. Agenda Phalanger Introduction What is Phalanger? And what is it good for? Phalanger for Silverlight How (and why) to write Silverlight client apps in PHP .NET and PHP interoperability Calling C# code from PHP Calling PHP code from C# Interesting Compilation Problems Conditional type binding Calling overloaded methods DEMO Let’s look at some cool demo first :-)! http://tomasp.net/files/copter/default.html Introduction . Phalanger: PHP Language Compiler for .NET . Initially targeting .NET Framework 1.1 . Now using .NET 2.0, Mono and Silverlight 2.0 . We’re planning to use (parts of) DLR in the future . What can you do with Phalanger? . Compile various open-source PHP applications for .NET . Use PHP functions & objects from (other) .NET languages . Use .NET classes from PHP programs . Write Silverlight applications in PHP Agenda Phalanger Introduction What is Phalanger? And what is it good for? Phalanger for Silverlight How (and why) to write Silverlight client apps in PHP .NET and PHP interoperability Calling C# code from PHP Calling PHP code from C# Interesting Compilation Problems Conditional type binding Calling overloaded methods Phalanger: PHP for Silverlight . Similar as other Silverlight dynamic languages . XAML file is loaded by Silverlight plugin in the browser . PHP source code referenced using XAML element: <php:PhalangerLoader Source="script.phpx" /> . Downloaded, compiled and executed on the client . “Loader” element, compiler and runtime in one DLL file <Canvas xmlns="http://schemas.microsoft.com/client/2007" xmlns:php="clr-namespace:PHP.Silverlight; assembly=Bin/PhpNetCore.dll" Width="800" Height="600" Background="White"> <php:PhalangerLoader Source="simpledemo_php.phpx" /> </Canvas> Phalanger: PHP for Silverlight . -
Py++ Project Report
Py++ Project Report COMS-E6998-3 Advanced Programming Languages and Compilers Fall 2012 Team Members:- Abhas Bodas ([email protected]) Jared Pochtar ([email protected]) Submitted By:- Abhas Bodas ([email protected]) Project Guide:- Prof. Alfred Aho ([email protected]) Page 1 Contents 1. Overview …...................…...................…...................…...................…....................... 3 2. Background Work …...................…...................…...................…..............…............... 4 3. Architecture …...................…...................…...................…...................…..............….. 5 4. Implementation …...................…...................…...................…...................…................ 6 5. Areas of focus / Optimizations …...................…...................…...................…............... 9 6. Results / Conclusion …...................…...................…...................…...................…....... 10 7. Appendix …...................…...................…...................…...................….................…..... 11 Page 2 1. Overview Our project, Py++ paves the way for a compiled Python with speed as the primary focus. Py++ aims to generate fast C/C++ from Python code. We have used the CPython C-API for most of the built in types, objects, and functions. CPython has extensive standard libraries and builtin object support. These objects are quite efficient -- a set, dictionary, or list implementation written in pure C shouldn’t care whether it’s called in an -
Seq: a High-Performance Language for Bioinformatics
1 1 Seq: A High-Performance Language for Bioinformatics 2 3 ANONYMOUS AUTHOR(S)∗ 4 5 The scope and scale of biological data is increasing at an exponential rate, as technologies like 6 next-generation sequencing are becoming radically cheaper and more prevalent. Over the last two 7 decades, the cost of sequencing a genome has dropped from $100 million to nearly $100—a factor of 6 8 over 10 —and the amount of data to be analyzed has increased proportionally. Yet, as Moore’s Law continues to slow, computational biologists can no longer rely on computing hardware to 9 compensate for the ever-increasing size of biological datasets. In a field where many researchers are 10 primarily focused on biological analysis over computational optimization, the unfortunate solution 11 to this problem is often to simply buy larger and faster machines. 12 Here, we introduce Seq, the first language tailored specifically to bioinformatics, which marries 13 the ease and productivity of Python with C-like performance. Seq is a subset of Python—and in 14 many cases a drop-in replacement—yet also incorporates novel bioinformatics- and computational 15 genomics-oriented data types, language constructs and optimizations. Seq enables users to write 16 high-level, Pythonic code without having to worry about low-level or domain-specific optimizations, 17 and allows for seamless expression of the algorithms, idioms and patterns found in many genomics or 18 bioinformatics applications. On equivalent CPython code, Seq attains a performance improvement of 19 up to two orders of magnitude, and a 175× improvement once domain-specific language features and optimizations are used. -
Python in a Hacker's Toolbox
Python in a hacker's toolbox v. 2016 Gynvael Coldwind Security PWNing Conference, Warszawa 2016 Keynote? Raczej prelekcja techniczna. O prelegencie All opinions expressed during this presentations are mine and mine alone. They are not opinions of my lawyer, barber and especially not my employer. Menu Sandboxing Język i VM Bezpieczeństwo RE Ta prezentacja zawiera fragmenty ● Data, data, data... ● "On the battlefield with the dragons" (+ Mateusz Jurczyk) ● "Ataki na systemy i sieci komputerowe" ● "Pwning (sometimes) with style - Dragons' notes on CTFs" (+ Mateusz Jurczyk) ● "Python in a hacker's toolbox" Język i VM Python 2.6, 2.7, 3, CPython?, IronPython??, Jython???, oh my... Python Python 2.6, 2.7, 3, CPython?, IronPython??, Jython???, oh my... Język rozwijany przez Python Software Foundation Python Python 2.6, 2.7, 3, CPython?, IronPython??, Jython???, oh my... Język rozwijany przez Python Software Foundation Python Python Enhancement Proposal (w skrócie: PEP) Python 2.6, 2.7, 3, CPython?, IronPython??, Jython???, oh my... Język rozwijany przez Python Software Foundation Python Python Enhancement Proposal (w skrócie: PEP) Python 2.6, 2.7, 3, CPython?, IronPython??, Jython???, oh my... Python Implementacje https://wiki.python.org/moin/PythonImplementations Python 2.6, 2.7, 3, CPython?, IronPython??, Jython???, oh my... CPython Brython CLPython Jython HotPy pyjs Python Implementacje PyMite PyPy pyvm SNAPpy RapydScript IronPython tinypy https://wiki.python.org/moin/PythonImplementations Python 2.6, 2.7, 3, CPython?, IronPython??, Jython???, oh my... Jython Python Implementacje https://wiki.python.org/moin/PythonImplementations Python 2.6, 2.7, 3, CPython?, IronPython??, Jython???, oh my... Brython Python Implementacje https://wiki.python.org/moin/PythonImplementations Python 2.6, 2.7, 3, CPython?, IronPython??, Jython???, oh my.. -
Les Possibilités De Dispersion Et Éléments D'habitat-Refuge Dans Un
Les possibilités de dispersion et éléments d’habitat-refuge dans un paysage d’agriculture intensive fragmenté par un réseau routier dense : le cas de la petite faune dans la plaine du Bas-Rhin Jonathan Jumeau To cite this version: Jonathan Jumeau. Les possibilités de dispersion et éléments d’habitat-refuge dans un paysage d’agriculture intensive fragmenté par un réseau routier dense : le cas de la petite faune dans la plaine du Bas-Rhin. Ecosystèmes. Université de Strasbourg, 2017. Français. NNT : 2017STRAJ120. tel-01768001 HAL Id: tel-01768001 https://tel.archives-ouvertes.fr/tel-01768001 Submitted on 16 Apr 2018 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. UNIVERSITÉ DE STRASBOURG ÉCOLE DOCTORALE 414 UMR7178 – UMR6553 THÈSE présentée par : Jonathan JUMEAU soutenue le : 16 octobre 2017 pour obtenir le grade de : Docteur de l’université de Strasbourg Discipline/ Spécialité : Biologie de la conservation Les possibilités de dispersion et éléments d’habitat-refuge dans un paysage d'agriculture intensive fragmenté par un réseau routier dense : le cas de la petite faune dans la plaine du Bas-Rhin THÈSE dirigée par : Dr. HANDRICH Yves Chargé de recherches, Université de Strasbourg Dr. -
High Performance Python (From Training at Europython 2011) Release 0.2
High Performance Python (from Training at EuroPython 2011) Release 0.2 Ian Ozsvald (@ianozsvald) July 24, 2011 CONTENTS 1 Testimonials from EuroPython 2011 2 2 Motivation 4 2.1 Changelog ................................................ 4 2.2 Credits .................................................. 4 2.3 Other talks ................................................ 5 3 The Mandelbrot problem 6 4 Goal 8 4.1 MacBook Core2Duo 2.0GHz ...................................... 8 4.2 2.9GHz i3 desktop with GTX 480 GPU ................................. 11 5 Using this as a tutorial 13 6 Versions and dependencies 14 7 Pure Python (CPython) implementation 15 8 Profiling with cProfile and line_profiler 18 9 Bytecode analysis 21 10 A (slightly) faster CPython implementation 22 11 PyPy 24 11.1 numpy .................................................. 24 12 Psyco 26 13 Cython 27 13.1 Compiler directives ............................................ 30 13.2 prange .................................................. 31 14 Cython with numpy arrays 32 15 ShedSkin 33 15.1 Profiling ................................................. 34 15.2 Faster code ................................................ 34 16 numpy vectors 35 i 17 numpy vectors and cache considerations 37 18 NumExpr on numpy vectors 39 19 pyCUDA 41 19.1 numpy-like interface ........................................... 42 19.2 ElementWise ............................................... 43 19.3 SourceModule .............................................. 44 20 multiprocessing 46 21 ParallelPython 48 22 Other