Embedding Concurrency: a Lua Case Study
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Ironpython in Action
IronPytho IN ACTION Michael J. Foord Christian Muirhead FOREWORD BY JIM HUGUNIN MANNING IronPython in Action Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> IronPython in Action MICHAEL J. FOORD CHRISTIAN MUIRHEAD MANNING Greenwich (74° w. long.) Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> For online information and ordering of this and other Manning books, please visit www.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact Special Sales Department Manning Publications Co. Sound View Court 3B fax: (609) 877-8256 Greenwich, CT 06830 email: [email protected] ©2009 by Manning Publications Co. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15% recycled and processed without the use of elemental chlorine. -
Incorparating the Actor Model Into SCIVE on an Abstract Semantic Level
Incorparating the Actor Model into SCIVE on an Abstract Semantic Level Christian Frohlich¨ ∗ Marc E. Latoschik† AI and VR Group, Bielefeld University AI and VR Group, Bielefeld University. ABSTRACT Since visual perception is very important for generating immer- sive environments, most VR applications focus on the graphical This paper illustrates the temporal synchronization and process flow simulation output. Tools like OpenGL Performer, Open Inven- facilities of a real-time simulation system which is based on the ac- tor [12], Open Scene Graph, OpenSG [11] or the VRML successor tor model. The requirements of such systems are compared against X3D [8] are based upon a hierarchical scene graph structure, which the actor model’s basic features and structures. The paper describes allows for complex rendering tasks. Extension mechanisms like how a modular architecture benefits from the actor model on the field routing or rapid prototyping via scipting interfaces allow for module level and points out how the actor model enhances paral- new customized node types and interconnected application graphs. lelism and concurrency even down to the entity level. The actor idea is incorporated into a novel simulation core for intelligent vir- Purpose-built VR application frameworks adopt and extend these mechanisms, by adding in- and output customization, see e.g. tual environments (SCIVE). SCIVE supports well-known and es- TM tablished Virtual Reality paradigms like the scene graph metaphor, Lightning [4], Avango [13], VR Juggler [3] or the CAVELib . field routing concepts etc. In addition, SCIVE provides an explicit Also network distribution paradigms are often integrated to enable semantic representation of its internals as well as of the specific distributed rendering on a cluster architecture, as can be seen in virtual environments’ contents. -
Actor-Based Concurrency by Srinivas Panchapakesan
Concurrency in Java and Actor- based concurrency using Scala By, Srinivas Panchapakesan Concurrency Concurrent computing is a form of computing in which programs are designed as collections of interacting computational processes that may be executed in parallel. Concurrent programs can be executed sequentially on a single processor by interleaving the execution steps of each computational process, or executed in parallel by assigning each computational process to one of a set of processors that may be close or distributed across a network. The main challenges in designing concurrent programs are ensuring the correct sequencing of the interactions or communications between different computational processes, and coordinating access to resources that are shared among processes. Advantages of Concurrency Almost every computer nowadays has several CPU's or several cores within one CPU. The ability to leverage theses multi-cores can be the key for a successful high-volume application. Increased application throughput - parallel execution of a concurrent program allows the number of tasks completed in certain time period to increase. High responsiveness for input/output-intensive applications mostly wait for input or output operations to complete. Concurrent programming allows the time that would be spent waiting to be used for another task. More appropriate program structure - some problems and problem domains are well-suited to representation as concurrent tasks or processes. Process vs Threads Process: A process runs independently and isolated of other processes. It cannot directly access shared data in other processes. The resources of the process are allocated to it via the operating system, e.g. memory and CPU time. Threads: Threads are so called lightweight processes which have their own call stack but an access shared data. -
CIS 192: Lecture 12 Deploying Apps and Concurrency
CIS 192: Lecture 12 Deploying Apps and Concurrency Lili Dworkin University of Pennsylvania Good Question from Way Back I All HTTP requests have 1) URL, 2) headers, 3) body I GET requests: parameters sent in URL I POST requests: parameters sent in body Can GET requests have a body? StackOverflow's response: \Yes, you can send a request body with GET but it should not have any meaning. If you give it meaning by parsing it on the server and changing your response based on its contents you're violating the HTTP/1.1 spec." Good Question from Last Week What is the difference between jsonify and json.dumps? def jsonify(*args, **kwargs): if __debug__: _assert_have_json() return current_app.response_class(json.dumps(dict(* args, **kwargs), indent=None if request.is_xhr else 2), mimetype='application/json') I jsonify returns a Response object I jsonify automatically sets content-type header I jsonify also sets the indentation Review Find a partner! Deploying Apps I We've been running Flask apps locally on a builtin development server I When you're ready to go public, you need to deploy to a production server I Easiest option: use one hosted by someone else! I We'll use Heroku, a platform as a service (PaaS) that makes it easy to deploy apps in a variety of languages Heroku Prerequisites: I Virtualenv (creates standalone Python environments) I Heroku toolbox I Heroku command-line client I Git (for version control and pushing to Heroku) Virtualenv I Allows us to create a virtual Python environment I Unique, isolated environment for each project I Use case: different versions of packages for different projects Virtualenv How to use it? prompt$ pip install virtualenv Now navigate to your project directory: prompt$ virtualenv --no-site-packages venv prompt$ source venv/bin/activate (<name>)prompt$ pip install Flask gunicorn (<name>)prompt$ deactivate prompt% Heroku Toolbox Once you make a Heroku account, install the Heroku toolbox. -
Liam: an Actor Based Programming Model for Hdls
Liam: An Actor Based Programming Model for HDLs Haven Skinner Rafael Trapani Possignolo Jose Renau Dept. of Computer Engineering Dept. of Computer Engineering Dept. of Computer Engineering [email protected] [email protected] [email protected] ABSTRACT frequency being locked down early in the development process, The traditional model for developing synthesizable digital architec- and adds a high cost in effort and man-hours, to make high-level tures is a clock-synchronous pipeline. Latency-insensitive systems changes to the system. Although these challenges can be managed, are an alternative, where communication between blocks is viewed they represent fundamental obstacles to developing large-scale as message passing. In hardware this is generally managed by con- systems. trol bits such as valid/stop signals or token credit mechanisms. An alternative is a latency-insensitive design, where the system Although prior work has shown that there are numerous benefits is abstractly seen as being comprised of nodes, which communicate to building latency-insensitive digital hardware, a major factor dis- via messages. Synchronous hardware systems can be automatically couraging its use is the difficulty of managing the additional logic transformed to latency-insensitive designs, which we refer to as and infrastructure required to implement it. elastic systems [1–3]. Alternately, the designer can manually con- We propose a new programming paradigm for Hardware Descrip- trol the latency-insensitive backend [4, 5], we call such systems tion Languages, built on the Actor Model, to implicitly implement Fluid Pipelines. The advantage of exposing the elasticity to the latency-insensitive hardware designs. We call this model Liam, for designer is an increase in performance and flexibility in transforma- Latency-Insensitive Actor-based programming Model. -
Threading and GUI Issues for R
Threading and GUI Issues for R Luke Tierney School of Statistics University of Minnesota March 5, 2001 Contents 1 Introduction 2 2 Concurrency and Parallelism 2 3 Concurrency and Dynamic State 3 3.1 Options Settings . 3 3.2 User Defined Options . 5 3.3 Devices and Par Settings . 5 3.4 Standard Connections . 6 3.5 The Context Stack . 6 3.5.1 Synchronization . 6 4 GUI Events And Blocking IO 6 4.1 UNIX Issues . 7 4.2 Win32 Issues . 7 4.3 Classic MacOS Issues . 8 4.4 Implementations To Consider . 8 4.5 A Note On Java . 8 4.6 A Strategy for GUI/IO Management . 9 4.7 A Sample Implementation . 9 5 Threads and GUI’s 10 6 Threading Design Space 11 6.1 Parallelism Through HL Threads: The MXM Options . 12 6.2 Light-Weight Threads: The XMX Options . 12 6.3 Multiple OS Threads Running One At A Time: MSS . 14 6.4 Variations on OS Threads . 14 6.5 SMS or MXS: Which To Choose? . 14 7 Light-Weight Thread Implementation 14 1 March 5, 2001 2 8 Other Issues 15 8.1 High-Level GUI Interfaces . 16 8.2 High-Level Thread Interfaces . 16 8.3 High-Level Streams Interfaces . 16 8.4 Completely Random Stuff . 16 1 Introduction This document collects some random thoughts on runtime issues relating to concurrency, threads, GUI’s and the like. Some of this is extracted from recent R-core email threads. I’ve tried to provide lots of references that might be of use. -
Massachusetts Institute of Technology Artificial Intelligence Laboratory
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY Working Paper 190 June 1979 A FAIR POWER DOMAIN FOR ACTOR COMPUTATIONS Will Clinger AI Laboratory Working Papers are produced for internal circulation, and may contain information that is, for example, too preliminary or too detailed for formal publication. Although some will be given a limited external distribution, it is not intended that they should be considered papers to which reference can be made in the literature. This report describes research conducted at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for this research was provided in part by the Office of Naval Research of the Department of Defense under Contract N00014-75-C-0522. O MASSAnuTsms INSTTUTE OF TECMGoOry FM DRAFT 1 June 1979 -1- A fair power domain A FAIR POWER DOMAIN FOR ACTOR COMPUTATIONS Will Clingerl 1. Abstract Actor-based languages feature extreme concurrency, allow side effects, and specify a form of fairness which permits unbounded nondeterminism. This makes it difficult to provide a satisfactory mathematical foundation for their semantics. Due to the high degree of parallelism, an oracle semantics would be intractable. A weakest precondition semantics is out of the question because of the possibility of unbounded nondeterminism. The most attractive approach, fixed point semantics using power domains, has not been helpful because the available power domain constructions, although very general, seemed to deal inadequately with fairness. By taking advantage of the relatively complex structure of the actor computation domain C, however, a power domain P(C) can be defined which is similar to Smyth's weak power domain but richer. -
Actor Model of Computation
Published in ArXiv http://arxiv.org/abs/1008.1459 Actor Model of Computation Carl Hewitt http://carlhewitt.info This paper is dedicated to Alonzo Church and Dana Scott. The Actor model is a mathematical theory that treats “Actors” as the universal primitives of concurrent digital computation. The model has been used both as a framework for a theoretical understanding of concurrency, and as the theoretical basis for several practical implementations of concurrent systems. Unlike previous models of computation, the Actor model was inspired by physical laws. It was also influenced by the programming languages Lisp, Simula 67 and Smalltalk-72, as well as ideas for Petri Nets, capability-based systems and packet switching. The advent of massive concurrency through client- cloud computing and many-core computer architectures has galvanized interest in the Actor model. An Actor is a computational entity that, in response to a message it receives, can concurrently: send a finite number of messages to other Actors; create a finite number of new Actors; designate the behavior to be used for the next message it receives. There is no assumed order to the above actions and they could be carried out concurrently. In addition two messages sent concurrently can arrive in either order. Decoupling the sender from communications sent was a fundamental advance of the Actor model enabling asynchronous communication and control structures as patterns of passing messages. November 7, 2010 Page 1 of 25 Contents Introduction ............................................................ 3 Fundamental concepts ............................................ 3 Illustrations ............................................................ 3 Modularity thru Direct communication and asynchrony ............................................................. 3 Indeterminacy and Quasi-commutativity ............... 4 Locality and Security ............................................ -
Due to Global Interpreter Lock (GIL), Python Threads Do Not Provide Efficient Multi-Core Execution, Unlike Other Languages Such As Golang
Due to global interpreter lock (GIL), python threads do not provide efficient multi-core execution, unlike other languages such as golang. Asynchronous programming in python is focusing on single core execution. Happily, Nexedi python code base already supports high performance multi-core execution either through multiprocessing with distributed shared memory or by relying on components (NumPy, MariaDB) that already support multiple core execution. The impatient reader will find bellow a table that summarises current options to achieve high performance multi-core performance in python. High Performance multi-core Python Cheat Sheet Use python whenever you can rely on any of... Use golang whenever you need at the same time... low latency multiprocessing high concurrency cython's nogil option multi-core execution within single userspace shared multi-core execution within wendelin.core distributed memory shared memory something else than cython's parallel module or cython's parallel module nogil option Here are a set of rules to achieve high performance on a multi-core system in the context of Nexedi stack. use ERP5's CMFActivity by default, since it can solve 99% of your concurrency and performance problems on a cluster or multi-core system; use parallel module in cython if you need to use all cores with high performance within a single transaction (ex. HTTP request); use threads in python together with nogil option in cython in order to use all cores on a multithreaded python process (ex. Zope); use cython to achieve C/C++ performance without mastering C/C++ as long as you do not need multi-core asynchronous programming; rewrite some python in cython to get performance boost on selected portions of python libraries; use numba to accelerate selection portions of python code with JIT that may also work in a dynamic context (ex. -
Computer Science (COM S) 1
Computer Science (COM S) 1 COM S 105B: Short Course in Computer Programming: MATLAB COMPUTER SCIENCE (COM S) (2-0) Cr. 2. Any experimental courses offered by COM S can be found at: Prereq: Com S 104 registrar.iastate.edu/faculty-staff/courses/explistings/ (http:// 8-week course in programming using MATLAB. www.registrar.iastate.edu/faculty-staff/courses/explistings/) COM S 106: Introduction to Web Programming Courses primarily for undergraduates: (3-0) Cr. 3. F.S. Introduction to web programming basics. Fundamentals of developing COM S 101: Orientation web pages using a comprehensive web development life cycle. Learn Cr. R. F.S. to design and code practical real-world homepage programs and earn Introduction to the computer science discipline and code of ethics, Com adequate experience with current web design techniques such as HTML5 S courses, research and networking opportunities, procedures, policies, and cascading style sheets. Students also learn additional programming help and computing resources, extra-curricular activities offered by the languages including JavaScript, jQuery, PHP, SQL, and MySQL. Strategies Department of Computer Science and Iowa State University. Discussion for accessibility, usability and search engine optimization. No prior of issues relevant to student adjustment to college life. Offered on a computer programming experience necessary. satisfactory-fail basis only. Offered on a satisfactory-fail basis only. COM S 107: Windows Application Programming COM S 103: Computer Literacy and Applications (3-0) Cr. 3. F.S. Cr. 4. F.S.SS. Introduction to computer programming for non-majors using a language Introduction to computer literacy and applications. Literacy: Impact of such as the Visual Basic language. -
Specialising Dynamic Techniques for Implementing the Ruby Programming Language
SPECIALISING DYNAMIC TECHNIQUES FOR IMPLEMENTING THE RUBY PROGRAMMING LANGUAGE A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Engineering and Physical Sciences 2015 By Chris Seaton School of Computer Science This published copy of the thesis contains a couple of minor typographical corrections from the version deposited in the University of Manchester Library. [email protected] chrisseaton.com/phd 2 Contents List of Listings7 List of Tables9 List of Figures 11 Abstract 15 Declaration 17 Copyright 19 Acknowledgements 21 1 Introduction 23 1.1 Dynamic Programming Languages.................. 23 1.2 Idiomatic Ruby............................ 25 1.3 Research Questions.......................... 27 1.4 Implementation Work......................... 27 1.5 Contributions............................. 28 1.6 Publications.............................. 29 1.7 Thesis Structure............................ 31 2 Characteristics of Dynamic Languages 35 2.1 Ruby.................................. 35 2.2 Ruby on Rails............................. 36 2.3 Case Study: Idiomatic Ruby..................... 37 2.4 Summary............................... 49 3 3 Implementation of Dynamic Languages 51 3.1 Foundational Techniques....................... 51 3.2 Applied Techniques.......................... 59 3.3 Implementations of Ruby....................... 65 3.4 Parallelism and Concurrency..................... 72 3.5 Summary............................... 73 4 Evaluation Methodology 75 4.1 Evaluation Philosophy -
A Model Based Realisation of Actor Model to Conceptualise an Aid for Complex Dynamic Decision-Making
A Model based Realisation of Actor Model to Conceptualise an Aid for Complex Dynamic Decision-making Souvik Barat1, Vinay Kulkarni1, Tony Clark2 and Balbir Barn3 1Tata Consultancy Services Research, Pune, India 2Sheffield Hallam University, Sheffield, U.K. 3Middlesex University, London, U.K. {souvik.barat, vinay.vkulkarni}@tcs.com, [email protected], [email protected] Keywords: Organisational Decision Making, Simulation, Actor Model of Computation, Actor based Simulation. Abstract: Effective decision-making of modern organisation requires deep understanding of various aspects of organisation such as its goals, structure, business-as-usual operational processes etc. The large size and complex structure of organisations, socio-technical characteristics, and fast business dynamics make this decision-making a challenging endeavour. The state-of-practice of decision-making that relies heavily on human experts is often reported as ineffective, imprecise and lacking in agility. This paper evaluates a set of candidate technologies and makes a case for using actor based simulation techniques as an aid for complex dynamic decision-making. The approach is justified by enumeration of basic requirements of complex dynamic decision-making and the conducting a suitability of analysis of state-of-the-art enterprise modelling techniques. The research contributes a conceptual meta-model that represents necessary aspects of organisation for complex dynamic decision-making together with a realisation in terms of a meta model that extends Actor model of computation. The proposed approach is illustrated using a real life case study from business process outsourcing industry. 1 INTRODUCTION reports from leading consulting organisations such as McKinsey and Harvard Business Review (Kahneman Modern organisations constantly attempt to meet et al., 2011, Meissner et al.