Programming Concepts I
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Rubyperf.Pdf
Ruby Performance. Tips, Tricks & Hacks Who am I? • Ezra Zygmuntowicz (zig-mun-tuv-itch) • Rubyist for 4 years • Engine Yard Founder and Architect • Blog: http://brainspl.at Ruby is Slow Ruby is Slow?!? Well, yes and no. The Ruby Performance Dichotomy Framework Code VS Application Code Benchmarking: The only way to really know performance characteristics Profiling: Measure don’t guess. ruby-prof What is all this good for in real life? Merb Merb Like most useful code it started as a hack, Merb == Mongrel + Erb • No cgi.rb !! • Clean room implementation of ActionPack • Thread Safe with configurable Mutex Locks • Rails compatible REST routing • No Magic( well less anyway ;) • Did I mention no cgi.rb? • Fast! On average 2-4 times faster than rails Design Goals • Small core framework for the VC in MVC • ORM agnostic, use ActiveRecord, Sequel, DataMapper or roll your own db access. • Prefer simple code over magic code • Keep the stack traces short( I’m looking at you alias_method_chain) • Thread safe, reentrant code Merb Hello World No code is faster then no code • Simplicity and clarity trumps magic every time. • When in doubt leave it out. • Core framework to stay small and simple and easy to extend without gross hacks • Prefer plugins for non core functionality • Plugins can be gems Key Differences • No auto-render. The return value of your controller actions is what gets returned to client • Merb’s render method just returns a string, allowing for multiple renders and more flexibility • PartController’s allow for encapsualted applets without big performance cost Why not work on Rails instead of making a new framework? • Originally I was trying to optimize Rails and make it more thread safe. -
Insert Here Your Thesis' Task
Insert here your thesis' task. Czech Technical University in Prague Faculty of Information Technology Department of Software Engineering Master's thesis New Ruby parser and AST for SmallRuby Bc. Jiˇr´ıFajman Supervisor: Ing. Marcel Hlopko 18th February 2016 Acknowledgements I would like to thank to my supervisor Ing. Marcel Hlopko for perfect coop- eration and valuable advices. I would also like to thank to my family for support. Declaration I hereby declare that the presented thesis is my own work and that I have cited all sources of information in accordance with the Guideline for adhering to ethical principles when elaborating an academic final thesis. I acknowledge that my thesis is subject to the rights and obligations stip- ulated by the Act No. 121/2000 Coll., the Copyright Act, as amended. In accordance with Article 46(6) of the Act, I hereby grant a nonexclusive au- thorization (license) to utilize this thesis, including any and all computer pro- grams incorporated therein or attached thereto and all corresponding docu- mentation (hereinafter collectively referred to as the \Work"), to any and all persons that wish to utilize the Work. Such persons are entitled to use the Work in any way (including for-profit purposes) that does not detract from its value. This authorization is not limited in terms of time, location and quan- tity. However, all persons that makes use of the above license shall be obliged to grant a license at least in the same scope as defined above with respect to each and every work that is created (wholly or in part) based on the Work, by modifying the Work, by combining the Work with another work, by including the Work in a collection of works or by adapting the Work (including trans- lation), and at the same time make available the source code of such work at least in a way and scope that are comparable to the way and scope in which the source code of the Work is made available. -
UNIVERSITY of CALIFORNIA, SAN DIEGO Toward Understanding And
UNIVERSITY OF CALIFORNIA, SAN DIEGO Toward Understanding and Dealing with Failures in Cloud-Scale Systems A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science by Peng Huang Committee in charge: Professor Yuanyuan Zhou, Chair Professor Tara Javidi Professor Ranjit Jhala Professor George Porter Professor Stefan Savage 2016 Copyright Peng Huang, 2016 All rights reserved. The Dissertation of Peng Huang is approved and is acceptable in quality and form for publication on microfilm and electronically: Chair University of California, San Diego 2016 iii DEDICATION To my parents, brother and fiancée for their unconditional love and support. iv EPIGRAPH Quis custodiet ipsos custodes? (But who can watch the watchmen?) Juvenal Anything that can go wrong, will go wrong. Murphy’s law Those who fail to learn from the mistakes are doomed to repeat them. George Santayana In the middle of the night, [...] He would awaken and find himeself wondering if one of the machines had stopped working for some new, unknown reason. Or he would wake up thinking about the latest failure, the one whose cause they’d been looking for a whole week and sitll hadn’t found. The bogeyman—la machine—was there in his bedroom. Tracy Kidder, The Soul of a New Machine v TABLE OF CONTENTS SignaturePage...................................... .................. iii Dedication ......................................... .................. iv Epigraph........................................... .................. v TableofContents -
Development of Benchmarking Suite for Various Ruby Implementations
MASARYKOVA UNIVERZITA FAKULTA}w¡¢£¤¥¦§¨ INFORMATIKY !"#$%&'()+,-./012345<yA| Development of benchmarking suite for various Ruby implementations BACHELOR THESIS Richard Ludvigh Brno, Spring 2015 Declaration Hereby I declare, that this paper is my original authorial work, which I have worked out by my own. All sources, references and literature used or excerpted during elaboration of this work are properly cited and listed in complete reference to the due source. Richard Ludvigh Advisor: RNDr. Adam Rambousek ii Acknowledgement I would like to thank my supervisor RNDr. Adam Rambousek who has supported me throughout my thesis. I would also like to thank my advisor Ing. Václav Tunka for advice and consultation regarding technical aspects of work as well as guidance and feedback through- out my thesis. Access to the CERIT-SC computing and storage facilities provided under the programme Center CERIT Scientific Cloud, part of the Op- erational Program Research and Development for Innovations, reg. no. CZ. 1.05/3.2.00/08.0144, is greatly appreciated. iii Abstract Ruby is a modern, dynamic, pure object-oriented programming lan- guage. It has multiple implementations which provide different per- formance. The aim of this bachelor thesis is to provide a container- based tool to benchmark and monitor those performance differences inside an isolated environment. iv Keywords Ruby, JRuby, Rubinius, MRI, Docker, benchmarking, performance v Contents 1 Introduction ............................3 2 State of the art ...........................4 3 Ruby performance ........................6 3.1 Introduction to Ruby ....................6 3.2 Ruby implementations ...................7 3.2.1 MRI or CRuby . .7 3.2.2 JRuby . .7 3.2.3 Rubinius . -
Optimizing Ruby on Rails for Performance and Scalability
DEGREE PROJECT IN COMPUTER SCIENCE 120 CREDITS, SECOND CYCLE STOCKHOLM, SWEDEN 2016 Optimizing Ruby on Rails for performance and scalability KIM PERSSON KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF COMPUTER SCIENCE AND COMMUNICATION Optimizing Ruby on Rails for performance and scalability Optimering av Ruby on Rails för prestanda och skalbarhet KIM PERSSON [email protected] Degree project in Computer Science Master’s Programme Computer Science Supervisor: Stefano Markidis Examiner: Jens Lagergren Employer: Slagkryssaren AB February 2016 Abstract Web applications are becoming more and more popular as the bound- aries of what can be done in a browser are pushed forward. Ruby on Rails is a very popular web application framework for the Ruby pro- gramming language. Ruby on Rails allows for rapid prototyping and development of web applications but it suffers from performance prob- lems with large scale applications. This thesis focuses on optimization of a Ruby on Rails application to improve its performance. We performed three optimizations to a benchmark application that was developed for this project. First, we removed unnecessary modules from Ruby on Rails and optimized the database interaction with Active Record which is the default object re- lational mapping (ORM) in Ruby on Rails. It allows us to fetch and store models in the database without having to write database specific query code. These optimizations resulted in up to 36% decrease in ap- plication response time. Second, we implemented a caching mechanism for JavaScript Object Notation (JSON) representation of models in the optimized application. This second optimization resulted in a total of 96% response time decrease for one of the benchmarks. -
Rubinius Rubini Us Rubini.Us Rubini.Us Rubini.Us Rubinius History and Design Goals
Rubinius Rubini us Rubini.us rubini.us http:// rubini.us Rubinius http://godfat.org/slide/2008-12-21-rubinius.pdf History and Design Goals Architecture and Object Model History and Design Goals Architecture and Object Model Evan Phoenix February of 2006 RubySpec MSpec Engine Yard C VM Shotgun C VM Shotgun C++ VM CxxTest LLVM History and Design Goals Architecture and Object Model Reliable, Rock Solid Code Reliable, Rock Solid Code Full Test Coverage 健康 Clean, Readable Code Clean, Readable Code Little Lines in Each File Clean, Readable Code Macro, Code Generator, Rake Task Clean, Readable Code CMake Clean, Readable Code CMake Clean, Readable Code C++ Object to Ruby Object 1 to 1 Mapping 清新 健康 清新 Modern Techniques Modern Techniques Pluggable Garbage Collectors Modern Techniques Pluggable Garbage Collectors • Stop-and-Copy Modern Techniques Pluggable Garbage Collectors • Stop-and-Copy • Mark-and-Sweep Modern Techniques Optimizers Modern Techniques Git, Rake, LLVM Squeak the Smalltalk-80 Implementation Squeak Slang Squeak • Alan Kay • Dan Ingalls • Adele Goldberg Smalltalk Xerox PARC Smalltalk Object-Oriented (differ from Simula and C++) Smalltalk GUI Smalltalk MVC History and Design Goals Architecture and Object Model Real Machine C++ Virtual Machine Real Machine kernel/bootstrap C++ Virtual Machine Real Machine kernel/platform kernel/bootstrap C++ Virtual Machine Real Machine kernel/common kernel/platform kernel/bootstrap C++ Virtual Machine Real Machine kernel/delta kernel/common kernel/platform kernel/bootstrap C++ Virtual Machine Real -
Prioritizing Pull Requests
Prioritizing pull requests Version of June 17, 2015 Erik van der Veen Prioritizing pull requests THESIS submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in COMPUTER SCIENCE by Erik van der Veen born in Voorburg, the Netherlands Software Engineering Research Group Q42 Department of Software Technology Waldorpstraat 17F Faculty EEMCS, Delft University of Technology 2521 CA Delft, the Netherlands The Hague, the Netherlands www.ewi.tudelft.nl www.q42.com c 2014 Erik van der Veen. Cover picture: Finding the pull request that needs the most attention. Prioritizing pull requests Author: Erik van der Veen Student id: 1509381 Email: [email protected] Abstract Previous work showed that in the pull-based development model integrators face challenges with regard to prioritizing work in the face of multiple concurrent pull requests. We identified the manual prioritization heuristics applied by integrators and ex- tracted features from these heuristics. The features are used to train a machine learning model, which is capable of predicting a pull request’s importance. The importance is then used to create a prioritized order of the pull requests. Our main contribution is the design and initial implementation of a prototype service, called PRioritizer, which automatically prioritizes pull requests. The service works like a priority inbox for pull requests, recommending the top pull requests the project owner should focus on. It keeps the pull request list up-to-date when pull requests are merged or closed. In addition, the service provides functionality that GitHub is currently lacking. We implemented pairwise pull request conflict detection and several new filter and sorting options e.g. -
Debugging at Full Speed
Debugging at Full Speed Chris Seaton Michael L. Van De Vanter Michael Haupt Oracle Labs Oracle Labs Oracle Labs University of Manchester michael.van.de.vanter [email protected] [email protected] @oracle.com ABSTRACT Ruby; D.3.4 [Programming Languages]: Processors| Debugging support for highly optimized execution environ- run-time environments, interpreters ments is notoriously difficult to implement. The Truffle/- Graal platform for implementing dynamic languages offers General Terms an opportunity to resolve the apparent trade-off between Design, Performance, Languages debugging and high performance. Truffle/Graal-implemented languages are expressed as ab- Keywords stract syntax tree (AST) interpreters. They enjoy competi- tive performance through platform support for type special- Truffle, deoptimization, virtual machines ization, partial evaluation, and dynamic optimization/deop- timization. A prototype debugger for Ruby, implemented 1. INTRODUCTION on this platform, demonstrates that basic debugging services Although debugging and code optimization are both es- can be implemented with modest effort and without signifi- sential to software development, their underlying technolo- cant impact on program performance. Prototyped function- gies typically conflict. Deploying them together usually de- ality includes breakpoints, both simple and conditional, at mands compromise in one or more of the following areas: lines and at local variable assignments. The debugger interacts with running programs by insert- • Performance: Static compilers -
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 -
Maglev: a Fast and Reliable Software Network Load Balancer
Maglev: A Fast and Reliable Software Network Load Balancer Daniel E. Eisenbud, Cheng Yi, Carlo Contavalli, Cody Smith, Roman Kononov, Eric Mann-Hielscher, Ardas Cilingiroglu, Bin Cheyney, Wentao Shang†* and Jinnah Dylan Hosein‡* Google Inc. †UCLA ‡SpaceX [email protected] Abstract Maglev is Google’s network load balancer. It is a large distributed software system that runs on commodity Linux servers. Unlike traditional hardware network load balancers, it does not require a specialized physical rack deployment, and its capacity can be easily adjusted by adding or removing servers. Network routers distribute packets evenly to the Maglev machines via Equal Cost Multipath (ECMP); each Maglev machine then matches Figure 1: Hardware load balancer and Maglev. the packets to their corresponding services and spreads them evenly to the service endpoints. To accommodate high and ever-increasing traffic, Maglev is specifically balancers form a critical component of Google’s produc- optimized for packet processing performance. A single tion network infrastructure. Maglev machine is able to saturate a 10Gbps link with A network load balancer is typically composed of small packets. Maglev is also equipped with consistent multiple devices logically located between routers and hashing and connection tracking features, to minimize service endpoints (generally TCP or UDP servers), as the negative impact of unexpected faults and failures on shown in Figure 1. The load balancer is responsible for connection-oriented protocols. Maglev has been serving matching each packet to its corresponding service and Google’s traffic since 2008. It has sustained the rapid forwarding it to one of that service’s endpoints. global growth of Google services, and it also provides Network load balancers have traditionally been imple- network load balancing for Google Cloud Platform. -
Inter-Language Collaboration in an Object-Oriented Virtual Machine Bachelor’S Thesis
Inter-language Collaboration in an Object-oriented Virtual Machine Bachelor’s Thesis MATTHIAS SPRINGER, Hasso Plattner Institute, University of Potsdam TIM FELGENTREFF, Hasso Plattner Institute, University of Potsdam (Supervisor) TOBIAS PAPE, Hasso Plattner Institute, University of Potsdam (Supervisor) ROBERT HIRSCHFELD, Hasso Plattner Institute, University of Potsdam (Supervisor) Multi-language virtual machines have a number of advantages. They allow software developers to use soft- ware libraries that were written for different programming languages. Furthermore, language implementors do not have to bother with low-level VM functionality and their implementation can benefit from optimiza- tions in existing virtual machines. MagLev is an implementation of the Ruby programming language on top of the GemStone/S virtual machine for the Smalltalk programming language. In this work, we present how software components written in both languages can interact. We show how MagLev unifies the Smalltalk and the Ruby object model, taking into account Smalltalk meta classes, Ruby singleton classes, and Ruby modules. Besides, we show how we can call Ruby methods from Smalltalk and vice versa. We also present MagLev’s concept of bridge methods for implementing Ruby method calling conventions. Finally, we compare our solution to other language implementations and virtual machines. Additional Key Words and Phrases: Virtual Machines, Ruby, Smalltalk, MagLev, Language Implementation 1. MULTI-LANGUAGE VIRTUAL MACHINES Multi-language virtual machines are virtual machines that support the execution of source code written in multiple languages. According to Vraný [Vraný 2010], we have to distinguish multi-lanuguage virtual machines from interpreters and native code execution. Languages inside multi-language virtual machines are usually compiled to byte code and share the same object space. -
Building Blocks of a Scalable Web Crawler
Building blocks of a scalable web crawler Marc Seeger Computer Science and Media Stuttgart Media University September 15, 2010 A Thesis Submitted in Fulfilment of the Requirements for a Degree of Master of Science in Computer Science and Media Primary thesis advisor: Prof. Walter Kriha Secondary thesis advisor: Dr. Dries Buytaert I I Abstract The purpose of this thesis was the investigation and implementation of a good architecture for collecting, analysing and managing website data on a scale of millions of domains. The final project is able to automatically collect data about websites and analyse the content management system they are using. To be able to do this efficiently, different possible storage back-ends were examined and a system was implemented that is able to gather and store data at a fast pace while still keeping it searchable. This thesis is a collection of the lessons learned while working on the project combined with the necessary knowledge that went into architectural decisions. It presents an overview of the different infrastructure possibilities and general approaches and as well as explaining the choices that have been made for the implemented system. II Acknowledgements I would like to thank Acquia and Dries Buytaert for allowing me to experience life in the USA while working on a great project. I would also like to thank Chris Brookins for showing me what agile project management is all about. Working at Acquia combined a great infrastructure and atmosphere with a pool of knowledgeable people. Both these things helped me immensely when trying to find and evaluate a matching architecture to this project.