Erlang (Programming Language) 1 Erlang (Programming Language)
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Events, Co-Routines, Continuations and Threads OS (And Application)Execution Models System Building
Events, Co-routines, Continuations and Threads OS (and application)Execution Models System Building General purpose systems need to deal with • Many activities – potentially overlapping – may be interdependent • Activities that depend on external phenomena – may requiring waiting for completion (e.g. disk read) – reacting to external triggers (e.g. interrupts) Need a systematic approach to system structuring © Kevin Elphinstone 2 Construction Approaches Events Coroutines Threads Continuations © Kevin Elphinstone 3 Events External entities generate (post) events. • keyboard presses, mouse clicks, system calls Event loop waits for events and calls an appropriate event handler. • common paradigm for GUIs Event handler is a function that runs until completion and returns to the event loop. © Kevin Elphinstone 4 Event Model The event model only requires a single stack Memory • All event handlers must return to the event loop CPU Event – No blocking Loop – No yielding PC Event SP Handler 1 REGS Event No preemption of handlers Handler 2 • Handlers generally short lived Event Handler 3 Data Stack © Kevin Elphinstone 5 What is ‘a’? int a; /* global */ int func() { a = 1; if (a == 1) { a = 2; } No concurrency issues within a return a; handler } © Kevin Elphinstone 6 Event-based kernel on CPU with protection Kernel-only Memory User Memory CPU Event Loop Scheduling? User PC Event Code SP Handler 1 REGS Event Handler 2 User Event Data Handler 3 Huh? How to support Data Stack multiple Stack processes? © Kevin Elphinstone 7 Event-based kernel on CPU with protection Kernel-only Memory User Memory CPU PC Trap SP Dispatcher User REGS Event Code Handler 1 User-level state in PCB Event PCB Handler 2 A User Kernel starts on fresh Timer Event Data stack on each trap (Scheduler) PCB B No interrupts, no blocking Data Current in kernel mode Thead PCB C Stack Stack © Kevin Elphinstone 8 Co-routines Originally described in: • Melvin E. -
Designing an Ultra Low-Overhead Multithreading Runtime for Nim
Designing an ultra low-overhead multithreading runtime for Nim Mamy Ratsimbazafy Weave [email protected] https://github.com/mratsim/weave Hello! I am Mamy Ratsimbazafy During the day blockchain/Ethereum 2 developer (in Nim) During the night, deep learning and numerical computing developer (in Nim) and data scientist (in Python) You can contact me at [email protected] Github: mratsim Twitter: m_ratsim 2 Where did this talk came from? ◇ 3 years ago: started writing a tensor library in Nim. ◇ 2 threading APIs at the time: OpenMP and simple threadpool ◇ 1 year ago: complete refactoring of the internals 3 Agenda ◇ Understanding the design space ◇ Hardware and software multithreading: definitions and use-cases ◇ Parallel APIs ◇ Sources of overhead and runtime design ◇ Minimum viable runtime plan in a weekend 4 Understanding the 1 design space Concurrency vs parallelism, latency vs throughput Cooperative vs preemptive, IO vs CPU 5 Parallelism is not 6 concurrency Kernel threading 7 models 1:1 Threading 1 application thread -> 1 hardware thread N:1 Threading N application threads -> 1 hardware thread M:N Threading M application threads -> N hardware threads The same distinctions can be done at a multithreaded language or multithreading runtime level. 8 The problem How to schedule M tasks on N hardware threads? Latency vs 9 Throughput - Do we want to do all the work in a minimal amount of time? - Numerical computing - Machine learning - ... - Do we want to be fair? - Clients-server - Video decoding - ... Cooperative vs 10 Preemptive Cooperative multithreading: -
Introducing Elixir Getting Started in Functional Programming [St. Laurent & Eisenberg 2014-09-25].Pdf
Introducing Elixir Introducing Elixir Introducing Elixir is an excellent language if you want to learn about functional programming, and with this hands-on introduction, you’ll discover just how powerful and fun Elixir can be. This language combines the robust functional programming of Erlang with a syntax similar to Ruby, and includes powerful features for metaprogramming. This book shows you how to write simple Elixir programs by teaching one skill at a time. Once you pick up pattern matching, process-oriented programming, and other concepts, you’ll understand why Elixir makes it easier to build concurrent and resilient programs that scale up and down with ease. ■ Get comfortable with IEx, Elixir’s command-line interface ■ Discover atoms, pattern matching, and guards: the foundations of your program structure ■ Delve into the heart of Elixir with recursion, strings, lists, and higher-order functions ■ Create processes, send messages among them, and apply pattern matching to incoming messages ■ Store and manipulate structured data with Erlang Term Storage and the Mnesia database ■ Build resilient applications with Erlang’s Open Telecom Platform Introducing ■ Define macros with Elixir’s metaprogramming tools St. Laurent & Eisenberg Laurent St. Simon St. Laurent is a Strategic Content Director at O’Reilly Media, Inc., focusing primarily on web-related topics. He is co-chair of O’Reilly’s Fluent and OSCON conferences. Simon has written or co-written books, including Introducing Erlang, Learning Rails 3, and XML Pocket Reference, Third Edition (all O’Reilly). J. David Eisenberg is a programmer and instructor in San Jose, California, with a talent for teaching and explaining. -
Mnesia Database Management System (MNESIA)
Mnesia Database Management System (MNESIA) version 4.4 Typeset in LATEX from SGML source using the DocBuilder-0.9.8.5 Document System. Contents 1 Mnesia User’s Guide 1 1.1 Introduction.......................................... 1 1.1.1AboutMnesia..................................... 1 1.1.2TheMnesiaDataBaseManagementSystem(DBMS)............... 2 1.2 GettingStartedwithMnesia................................. 4 1.2.1StartingMnesiaforthefirsttime.......................... 4 1.2.2AnIntroductoryExample.............................. 5 1.3 BuildingAMnesiaDatabase................................. 14 1.3.1DefiningaSchema.................................. 15 1.3.2TheDataModel................................... 16 1.3.3StartingMnesia.................................... 16 1.3.4CreatingNewTables................................. 19 1.4 TransactionsandOtherAccessContexts.......................... 21 1.4.1TransactionProperties................................ 22 1.4.2Locking........................................ 23 1.4.3DirtyOperations................................... 27 1.4.4RecordNamesversusTableNames......................... 28 1.4.5ActivityConceptandVariousAccessContexts................... 30 1.4.6Nestedtransactions.................................. 32 1.4.7PatternMatching................................... 33 1.4.8Iteration........................................ 35 1.5 MiscellaneousMnesiaFeatures................................ 36 1.5.1Indexing........................................ 37 1.5.2DistributionandFaultTolerance......................... -
MCP Q4`18 Release Notes Version Q4-18 Mirantis Cloud Platform Release Notes Version Q4`18
MCP Q4`18 Release Notes version q4-18 Mirantis Cloud Platform Release Notes version Q4`18 Copyright notice 2021 Mirantis, Inc. All rights reserved. This product is protected by U.S. and international copyright and intellectual property laws. No part of this publication may be reproduced in any written, electronic, recording, or photocopying form without written permission of Mirantis, Inc. Mirantis, Inc. reserves the right to modify the content of this document at any time without prior notice. Functionality described in the document may not be available at the moment. The document contains the latest information at the time of publication. Mirantis, Inc. and the Mirantis Logo are trademarks of Mirantis, Inc. and/or its affiliates in the United States an other countries. Third party trademarks, service marks, and names mentioned in this document are the properties of their respective owners. ©2021, Mirantis Inc. Page 2 Mirantis Cloud Platform Release Notes version Q4`18 What’s new This section provides the details about the features and enhancements introduced with the latest MCP release version. Note The MCP integration of the community software projects, such as OpenStack, Kubernetes, OpenContrail, and Ceph, includes the integration of the features which the MCP consumers can benefit from. Refer to the MCP Q4`18 Deployment Guide for the software features that can be deployed and managed by MCP DriveTrain. MCP DriveTrain • Encryption of sensitive data in the Reclass model • Galera verification and restoration pipeline • Jenkins version upgrade • Partitioning table for the VCP images Encryption of sensitive data in the Reclass model SECURITY Implemented the GPG encryption to protect sensitive data in the Git repositories of the Reclass model as well as the key management mechanism for secrets encryption and decryption. -
Fujitsu PCI Expansion Unit
PCI Expansion Unit The PCI Expansion Unit—an I/O expansion option for Oracle’s Fujitsu SPARC M12 and Fujitsu M10 family of servers—meets demands for applications requiring extensive scalability, mission-critical levels of availability, and Key Features seamless data center integration. The PCI Supports the Fujitsu SPARC M12 and Fujitsu M10 family of servers Expansion Unit takes maximum advantage of Provides 11 PCIe 3.0 slots in a small 2U form factor the high I/O bandwidth of Fujitsu SPARC M12 Powers on and off in synchronization with the power status of Fujitsu and Fujitsu M10 servers. SPARC M12 and Fujitsu M10 servers Enables hot swapping of PCIe cards, power supplies, and fans Monitored and controlled from the XSCF System Monitoring Function of the Fujitsu SPARC M12 and Fujitsu M10 servers Configured with 1 + 1 power supply PRODUCT OVERVIEW redundancy and supports a dual Perfectly suited for Fujitsu SPARC servers, the PCI Expansion Unit offers significant power feed configuration I/O adapter expandability. One or more PCI Expansion Units can be connected to both Fujitsu SPARC M12 and Fujitsu M10 servers, providing connectivity of up to 11 additional PCI Express (PCIe) slots per PCI Expansion Unit in a space-saving rackmount chassis (2U). PCIe generation 3 is supported by the PCI Expansion Unit. Easy Monitoring and Control from Fujitsu SPARC Servers The PCI Expansion Unit can be easily monitored and controlled in conjunction with the XSCF (eXtended System Control Facility) system monitoring function of Fujitsu SPARC servers. PCI Expansion Units power on and off in synchronization with the power status of Fujitsu SPARC servers. -
Implementing Erlang/OTP on Intel Galileo
DEGREE PROJECT, IN INFORMATION TECHNOLOGY, FIRST LEVEL STOCKHOLM, SWEDEN 2015-06-22 Implementing Erlang/OTP on Intel Galileo Paul Coada, Erkut Kaya KTH ROYAL INSTITUTE OF TECHNOLOGY INFORMATION AND COMMUNICATION TECHNOLOGY Abstract The Intel Galileo, inspired by the well-known Arduino board, is a development board with many possibilities because of its strength. The Galileo is has an Intel processor capable of running GNU/Linux and can be connected to the internet, which opens up the possibility to be controlled remotely. The programming language that comes with the Intel Galileo is the same as for the Arduino development boards, and is therefore very limited and does not utilize the Galileo’s entire strength. Our aim with this project is to integrate a more suitable programming language; a language that can make better use of the relatively powerful processor to control the components of the board. The programming language of choice is Erlang, and the reason is obvious. Erlang can be described as a process-oriented programming language based on the functional programming paradigm and its power in concurrency. The result of the project was the successful integration of a complete version of GNU/Linux on the board and the cross-compilation of Erlang/OTP onto the board. Having Erlang running on the system opens up many possibilities for future work, amongst all: creating Erlang programs for the Intel Galileo, integrating an effective API, and measuring the pros and cons of using Erlang on an Intel Galileo. Keywords Embedded, Arduino, multiprogramming, concurrency, Internet of Things, cross-compilation, API Sammanfattning Intel Galileo är ett utvecklingskort som bygger på Arduinos succé. -
System Support for Online Reconfiguration
System Support for Online Reconfiguration ¡ ¡ ¢ Craig A. N. Soules Jonathan Appavoo Kevin Hui Robert W. Wisniewski ¢ ¢ ¡ Dilma Da Silva Gregory R. Ganger Orran Krieger Michael Stumm ¢ ¢ ¢ ¢ Marc Auslander Michal Ostrowski Bryan Rosenburg Jimi Xenidis Abstract Request Response Profiler LRU Online reconfiguration provides a way to extend and re- place active operating system components. This pro- vides administrators, developers, applications, and the (a) After profiler is interposed around the page manager. system itself with a way to update code, adapt to chang- ing workloads, pinpoint performance problems, and per- form a variety of other tasks while the system is running. Request With generic support for interposition and hot-swapping, Response LRU FIFO a system allows active components to be wrapped with additional functionality or replaced with different im- (b) LRU page manager before hot-swap with FIFO. plementations that have the same interfaces. This pa- per describes support for online reconfiguration in the Request K42 operating system and our initial experiences us- Response FIFO ing it. It describes four base capabilities that are com- bined to implement generic support for interposition and (c) After FIFO page manager is fully swapped. hot-swapping. As examples of its utility, the paper de- scribes some performance enhancements that have been Figure 1: Online reconfiguration. This figure shows two online re- achieved with K42’s online reconfiguration mechanisms configuration mechanisms: interposition and hot-swapping. (a) shows including adaptive algorithms, common case optimiza- an LRU page manager and an interposed profiler that can watch the tions, and workload specific specializations. component’s calls/returns to see how it is performing. -
An Ideal Match?
24 November 2020 An ideal match? Investigating how well-suited Concurrent ML is to implementing Belief Propagation for Stereo Matching James Cooper [email protected] OutlineI 1 Stereo Matching Generic Stereo Matching Belief Propagation 2 Concurrent ML Overview Investigation of Alternatives Comparative Benchmarks 3 Concurrent ML and Belief Propagation 4 Conclusion Recapitulation Prognostication 5 References Outline 1 Stereo Matching Generic Stereo Matching Belief Propagation 2 Concurrent ML Overview Investigation of Alternatives Comparative Benchmarks 3 Concurrent ML and Belief Propagation 4 Conclusion Recapitulation Prognostication 5 References Outline 1 Stereo Matching Generic Stereo Matching Belief Propagation 2 Concurrent ML Overview Investigation of Alternatives Comparative Benchmarks 3 Concurrent ML and Belief Propagation 4 Conclusion Recapitulation Prognostication 5 References Stereo Matching Generally SM is finding correspondences between stereo images Images are of the same scene Captured simultaneously Correspondences (`disparity') are used to estimate depth SM is an ill-posed problem { can only make best guess Impossible to perform `perfectly' in general case Stereo Matching ExampleI (a) Left camera's image (b) Right camera's image Figure 1: The popular 'Tsukuba' example stereo matching images, so called because they were created by researchers at the University of Tsukuba, Japan. They are probably the most widely-used benchmark images in stereo matching. Stereo Matching ExampleII (a) Ground truth disparity map (b) Disparity map generated using a simple Belief Propagation Stereo Matching implementation Figure 2: The ground truth disparity map for the Tsukuba images, and an example of a possible real disparity map produced by using Belief Propagation Stereo Matching. The ground truth represents what would be expected if stereo matching could be carried out `perfectly'. -
LIST of NOSQL DATABASES [Currently 150]
Your Ultimate Guide to the Non - Relational Universe! [the best selected nosql link Archive in the web] ...never miss a conceptual article again... News Feed covering all changes here! NoSQL DEFINITION: Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above. [based on 7 sources, 14 constructive feedback emails (thanks!) and 1 disliking comment . Agree / Disagree? Tell me so! By the way: this is a strong definition and it is out there here since 2009!] LIST OF NOSQL DATABASES [currently 150] Core NoSQL Systems: [Mostly originated out of a Web 2.0 need] Wide Column Store / Column Families Hadoop / HBase API: Java / any writer, Protocol: any write call, Query Method: MapReduce Java / any exec, Replication: HDFS Replication, Written in: Java, Concurrency: ?, Misc: Links: 3 Books [1, 2, 3] Cassandra massively scalable, partitioned row store, masterless architecture, linear scale performance, no single points of failure, read/write support across multiple data centers & cloud availability zones. API / Query Method: CQL and Thrift, replication: peer-to-peer, written in: Java, Concurrency: tunable consistency, Misc: built-in data compression, MapReduce support, primary/secondary indexes, security features. -
Tcl and Java Performance
Tcl and Java Performance http://ptolemy.eecs.berkeley.edu/~cxh/java/tclblend/scriptperf/scriptperf.html Tcl and Java Performance by H. John Reekie, University of California at Berkeley Christopher Hylands, University of California at Berkeley Edward A. Lee, University of California at Berkeley Abstract Combining scripting languages such as Tcl with lower−level programming languages such as Java offers new opportunities for flexible and rapid software development. In this paper, we benchmark various combinations of Tcl and Java against the two languages alone. We also provide some comparisons with JavaScript. Performance can vary by well over two orders of magnitude. We also uncovered some interesting threading issues that affect performance on the Solaris platform. "There are lies, damn lies and statistics" This paper is a work in progress, we used the information here to give our group some generalizations on the performance tradeoffs between various scripting languages. Updating the timing results to include JDK1.2 with a Just In Time (JIT) compiler would be useful. Introduction There is a growing trend towards integration of multiple languages through scripting. In a famously controversial white paper (Ousterhout 97), John Ousterhout, now of Scriptics Corporation, argues that scripting −− the use of a high−level, untyped, interpreted language to "glue" together components written in a lower−level language −− provides greater reuse benefits that other reuse technologies. Although traditionally a language such as C or C++ has been the lower−level language, more recent efforts have focused on using Java. Recently, Sun Microsystems laboratories announced two products aimed at fulfilling this goal with the Tcl and Java programming languages. -
Nexto Series
Technical Characteristics Nexto Series Doc Code: CE114000 Revision: I Nexto Series Programmable Logic Controller Nexto Series is a powerful and complete Programmable Logic Controllers (PLCs) series with exclusive and innovative features, targeted for covering control systems requirements from medium to large applications or high performance industrial machines. Nexto Series architecture provides a wide range of input and output modules. These modules, combined with a powerful 32-bit processor and a high-speed-Ethernet-based backplane, cover many user applications, such as high-speed machinery control, complex distributed and redundant process applications or even large I/O systems for building automation. Besides, it delivers modules for motion control, communication and interfaces to the most popular fieldbuses, among other features. Nexto Series architecture uses a state-of-the-art high-speed Ethernet backplane bus technology, which allows input, output and processed information to be shared among all modules within the system. The I/O modules can be easily distributed in the field. It can be used local as well as remote I/Os without any loss in performance. In addition, Nexto Series brings a complete tool for programming, configuration, simulation and debugging user application: MasterTool IEC XE. MasterTool IEC XE is flexible and easy to use software that provides the six programming languages defined by IEC 61131-3 standard: Structured Text (ST), Sequential Function Chart (SFC), Function Block Diagram (FBD), Ladder Diagram (LD), Instruction List (IL) and Continuous Function Chart (CFC). MasteTool IEC XE allows the use of different languages on the same application, providing to the user a powerful way to organize the application and to reuse codes used in previous applications.