Actor-Based Concurrency by Srinivas Panchapakesan
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Multithreading (Concurrency) in Java “Some People, When Confronted
Multithreading (Concurrency) in Java “Some people, when confronted with a problem, think, "I know, I’ll use threads," and then two they hav erpoblesms.” — Ned Batchelder Multi-tasking In the beginning, computers ran in batch mode: a single program with total access to all resources. Later, a multi-tasking operating system enabled multiple (independent) jobs to run (more or less) simultaneously, even though there was only a single CPU. The jobs take turns running some of their code at one time. This type of interleaving of jobs is called concurrency. Multitasking is performing two or more tasks (or jobs) at roughly the same time. Nearly all operating systems are capable of multitasking by using one of two multitasking techniques: process-based multitasking and thread-based multitasking. Each running job was (and sometimes still is) called a process, which has state (a collection of data and handles to system resources such as files and devices) and a single sequence of instructions. It turns out that many tasks are easier to write, debug, and manage if organized as multiple copies of a process (e.g., a web server). Processes are sometimes called tasks, threads, or jobs. (Although there are some distinctions between these concepts, we will ignore them for now.) Time slice, (context) switching, and scheduling are operating system (“OS”) terms related to multi-tasking. The amount of time a task is allowed to use the CPU exclusively is called a time slice, typically 10ms (a hundredth of a second) but can vary depending on your OS and other factors from 1ms to more than 120ms. -
Edsger W. Dijkstra: a Commemoration
Edsger W. Dijkstra: a Commemoration Krzysztof R. Apt1 and Tony Hoare2 (editors) 1 CWI, Amsterdam, The Netherlands and MIMUW, University of Warsaw, Poland 2 Department of Computer Science and Technology, University of Cambridge and Microsoft Research Ltd, Cambridge, UK Abstract This article is a multiauthored portrait of Edsger Wybe Dijkstra that consists of testimo- nials written by several friends, colleagues, and students of his. It provides unique insights into his personality, working style and habits, and his influence on other computer scientists, as a researcher, teacher, and mentor. Contents Preface 3 Tony Hoare 4 Donald Knuth 9 Christian Lengauer 11 K. Mani Chandy 13 Eric C.R. Hehner 15 Mark Scheevel 17 Krzysztof R. Apt 18 arXiv:2104.03392v1 [cs.GL] 7 Apr 2021 Niklaus Wirth 20 Lex Bijlsma 23 Manfred Broy 24 David Gries 26 Ted Herman 28 Alain J. Martin 29 J Strother Moore 31 Vladimir Lifschitz 33 Wim H. Hesselink 34 1 Hamilton Richards 36 Ken Calvert 38 David Naumann 40 David Turner 42 J.R. Rao 44 Jayadev Misra 47 Rajeev Joshi 50 Maarten van Emden 52 Two Tuesday Afternoon Clubs 54 2 Preface Edsger Dijkstra was perhaps the best known, and certainly the most discussed, computer scientist of the seventies and eighties. We both knew Dijkstra |though each of us in different ways| and we both were aware that his influence on computer science was not limited to his pioneering software projects and research articles. He interacted with his colleagues by way of numerous discussions, extensive letter correspondence, and hundreds of so-called EWD reports that he used to send to a select group of researchers. -
Glossary of Technical and Scientific Terms (French / English
Glossary of Technical and Scientific Terms (French / English) Version 11 (Alphabetical sorting) Jean-Luc JOULIN October 12, 2015 Foreword Aim of this handbook This glossary is made to help for translation of technical words english-french. This document is not made to be exhaustive but aim to understand and remember the translation of some words used in various technical domains. Words are sorted alphabetically. This glossary is distributed under the licence Creative Common (BY NC ND) and can be freely redistributed. For further informations about the licence Creative Com- mon (BY NC ND), browse the site: http://creativecommons.org If you are interested by updates of this glossary, send me an email to be on my diffusion list : • [email protected] • [email protected] Warning Translations given in this glossary are for information only and their use is under the responsability of the reader. If you see a mistake in this document, thank in advance to report it to me so i can correct it in further versions of this document. Notes Sorting of words Words are sorted by alphabetical order and are associated to their main category. Difference of terms Some words are different between the "British" and the "American" english. This glossary show the most used terms and are generally coming from the "British" english Words coming from the "American" english are indicated with the suffix : US . Difference of spelling There are some difference of spelling in accordance to the country, companies, universities, ... Words finishing by -ise, -isation and -yse can also be written with -ize, Jean-Luc JOULIN1 Glossary of Technical and Scientific Terms -ization and -yze. -
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. -
Lecture 10: Fault Tolerance Fault Tolerant Concurrent Computing
02/12/2014 Lecture 10: Fault Tolerance Fault Tolerant Concurrent Computing • The main principles of fault tolerant programming are: – Fault Detection - Knowing that a fault exists – Fault Recovery - having atomic instructions that can be rolled back in the event of a failure being detected. • System’s viewpoint it is quite possible that the fault is in the program that is attempting the recovery. • Attempting to recover from a non-existent fault can be as disastrous as a fault occurring. CA463 Lecture Notes (Martin Crane 2014) 26 1 02/12/2014 Fault Tolerant Concurrent Computing (cont’d) • Have seen replication used for tasks to allow a program to recover from a fault causing a task to abruptly terminate. • The same principle is also used at the system level to build fault tolerant systems. • Critical systems are replicated, and system action is based on a majority vote of the replicated sub systems. • This redundancy allows the system to successfully continue operating when several sub systems develop faults. CA463 Lecture Notes (Martin Crane 2014) 27 Types of Failures in Concurrent Systems • Initially dead processes ( benign fault ) – A subset of the processes never even start • Crash model ( benign fault ) – Process executes as per its local algorithm until a certain point where it stops indefinitely – Never restarts • Byzantine behaviour (malign fault ) – Algorithm may execute any possible local algorithm – May arbitrarily send/receive messages CA463 Lecture Notes (Martin Crane 2014) 28 2 02/12/2014 A Hierarchy of Failure Types • Dead process – This is a special case of crashed process – Case when the crashed process crashes before it starts executing • Crashed process – This is a special case of Byzantine process – Case when the Byzantine process crashes, and then keeps staying in that state for all future transitions CA463 Lecture Notes (Martin Crane 2014) 29 Types of Fault Tolerance Algorithms • Robust algorithms – Correct processes should continue behaving thus, despite failures. -
Shared Objects & Mutual Exclusion
Chapter 4 Shared Objects & Mutual Exclusion Shared Objects & Concepts: process interference. Mutual Exclusion mutual exclusion and locks. Models: model checking for interference modelling mutual exclusion Practice: thread interference in shared Java objects mutual exclusion in Java (synchronized objects/methods). 1 2 2015 Concurrency: shared objects & mutual exclusion ©Magee/Kramer 2nd Edition 2015 Concurrency: shared objects & mutual exclusion ©Magee/Kramer 2nd Edition A Concert Hall Booking System Concert Hall Booking System const False = 0 A central computer connected to remote terminals via communication links const True = 1 is used to automate seat reservations for a concert hall. range Bool = False..True To book a seat, a client chooses a free seat and the clerk enters the number SEAT = SEAT[False], of the chosen seat at the terminal and issues a ticket, if it is free. SEAT[reserved:Bool] = ( when (!reserved) reserve -> SEAT[True] A system is required which avoids double bookings of the same seat whilst | query[reserved] -> SEAT[reserved] allowing clients free choice of the available seats. | when (reserved) reserve -> ERROR //error of reserved twice Construct an abstract model of the system and demonstrate that your model does ). not permit double bookings. Like STOP, ERROR range Seats = 1..2 is a predefined FSP ||SEATS = (seat[Seats]:SEAT). local process (state), numbered -1 in the 3 equivalent LTS. 4 2015 Concurrency: shared objects & mutual exclusion ©Magee/Kramer 2nd Edition 2015 Concurrency: shared objects & mutual exclusion ©Magee/Kramer 2nd Edition Concert Hall Booking System Concert Hall Booking System – no interference? LOCK = (acquire -> release -> LOCK). TERMINAL = (choose[s:Seats] //lock for the booking system -> seat[s].query[reserved:Bool] -> if (!reserved) then TERMINAL = (choose[s:Seats] -> acquire (seat[s].reserve -> TERMINAL) -> seat[s].query[reserved:Bool] else -> if (!reserved) then TERMINAL (seat[s].reserve -> release-> TERMINAL) ). -
Sec9on 6: Mutual Exclusion
Sec$on 6: Mutual Exclusion Michelle Ku5el [email protected] Toward sharing resources (memory) Have been studying parallel algorithms using fork- join – Lower span via parallel tasks Algorithms all had a very simple structure to avoid race condi$ons – Each thread had memory “only it accessed” • Example: array sub-range – On fork, “loaned” some of its memory to “forkee” and did not access that memory again un$l aer join on the “forkee” slide adapted from: Sophomoric Parallelism & 2 Concurrency, Lecture 4 Toward sharing resources (memory) Strategy won’t work well when: – Memory accessed by threads is overlapping or unpredictable – Threads are doing independent tasks needing access to same resources (rather than implemen$ng the same algorithm) slide adapted from: Sophomoric Parallelism & 3 Concurrency, Lecture 4 Race Condi$ons A race condi*on is a bug in a program where the output and/or result of the process is unexpectedly and cri$cally dependent on the relave sequence or $ming of other events. The idea is that the events race each other to influence the output first. Examples Mul$ple threads: 1. Processing different bank-account operaons – What if 2 threads change the same account at the same $me? 2. Using a shared cache (e.g., hashtable) of recent files – What if 2 threads insert the same file at the same $me? 3. Creang a pipeline (think assembly line) with a queue for handing work to next thread in sequence? – What if enqueuer and dequeuer adjust a circular array queue at the same $me? Sophomoric Parallelism & 5 Concurrency, Lecture 4 Concurrent -
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. -
Mastering Concurrent Computing Through Sequential Thinking
review articles DOI:10.1145/3363823 we do not have good tools to build ef- A 50-year history of concurrency. ficient, scalable, and reliable concur- rent systems. BY SERGIO RAJSBAUM AND MICHEL RAYNAL Concurrency was once a specialized discipline for experts, but today the chal- lenge is for the entire information tech- nology community because of two dis- ruptive phenomena: the development of Mastering networking communications, and the end of the ability to increase processors speed at an exponential rate. Increases in performance come through concur- Concurrent rency, as in multicore architectures. Concurrency is also critical to achieve fault-tolerant, distributed services, as in global databases, cloud computing, and Computing blockchain applications. Concurrent computing through sequen- tial thinking. Right from the start in the 1960s, the main way of dealing with con- through currency has been by reduction to se- quential reasoning. Transforming problems in the concurrent domain into simpler problems in the sequential Sequential domain, yields benefits for specifying, implementing, and verifying concur- rent programs. It is a two-sided strategy, together with a bridge connecting the Thinking two sides. First, a sequential specificationof an object (or service) that can be ac- key insights ˽ A main way of dealing with the enormous challenges of building concurrent systems is by reduction to sequential I must appeal to the patience of the wondering readers, thinking. Over more than 50 years, more sophisticated techniques have been suffering as I am from the sequential nature of human developed to build complex systems in communication. this way. 12 ˽ The strategy starts by designing —E.W. -
Java Concurrency Framework by Sidartha Gracias
Java Concurrency Framework Sidartha Gracias Executive Summary • This is a beginners introduction to the java concurrency framework • Some familiarity with concurrent programs is assumed – However the presentation does go through a quick background on concurrency – So readers unfamiliar with concurrent programming should still get something out of this • The structure of the presentation is as follows – A brief history into concurrent programming is provided – Issues in concurrent programming are explored – The framework structure with all its major components are covered in some detail – Examples are provided for each major section to reinforce some of the major ideas Concurrency in Java - Overview • Java like most other languages supports concurrency through thread – The JVM creates the Initial thread, which begins execution from main – The main method can then spawn additional threads Thread Basics • All modern OS support the idea of processes – independently running programs that are isolated from each other • Thread can be thought of as light weight processes – Like processes they have independent program counters, call stacks etc – Unlike Processes they share main memory, file pointers and other process state – This means thread are easier for the OS to maintain and switch between – This also means we need to synchronize threads for access to shared resources Threads Continued… • So why use threads ? – Multi CPU systems: Most modern systems host multiple CPU’s, by splitting execution between them we can greatly speed up execution – Handling Asynchronous Events: Servers handle multiple clients. Processing each client is best done through a separate thread, because the Server blocks until a new message is received – UI or event driven Processing: event handlers that respond to user input are best handled through separate threads, this makes code easier to write and understand Synchronization Primitives in Java • How does the java language handle synchronization – Concurrent execution is supported through the Thread class. -
Java Concurrency in Practice
Java Concurrency in practice Chapters: 1,2, 3 & 4 Bjørn Christian Sebak ([email protected]) Karianne Berg ([email protected]) INF329 – Spring 2007 Chapter 1 - Introduction Brief history of concurrency Before OS, a computer executed a single program from start to finnish But running a single program at a time is an inefficient use of computer hardware Therefore all modern OS run multiple programs (in seperate processes) Brief history of concurrency (2) Factors for running multiple processes: Resource utilization: While one program waits for I/O, why not let another program run and avoid wasting CPU cycles? Fairness: Multiple users/programs might have equal claim of the computers resources. Avoid having single large programs „hog“ the machine. Convenience: Often desirable to create smaller programs that perform a single task (and coordinate them), than to have one large program that do ALL the tasks What is a thread? A „lightweight process“ - each process can have many threads Threads allow multiple streams of program flow to coexits in a single process. While a thread share process-wide resources like memory and files with other threads, they all have their own program counter, stack and local variables Benefits of threads 1) Exploiting multiple processors 2) Simplicity of modeling 3) Simplified handling of asynchronous events 4) More responsive user interfaces Benefits of threads (2) Exploiting multiple processors The processor industry is currently focusing on increasing number of cores on a single CPU rather than increasing clock speed. Well-designed programs with multiple threads can execute simultaneously on multiple processors, increasing resource utilization. -
Concurrent Computing
Concurrent Computing CSCI 201 Principles of Software Development Jeffrey Miller, Ph.D. [email protected] Outline • Threads • Multi-Threaded Code • CPU Scheduling • Program USC CSCI 201L Thread Overview ▪ Looking at the Windows taskbar, you will notice more than one process running concurrently ▪ Looking at the Windows task manager, you’ll see a number of additional processes running ▪ But how many processes are actually running at any one time? • Multiple processors, multiple cores, and hyper-threading will determine the actual number of processes running • Threads USC CSCI 201L 3/22 Redundant Hardware Multiple Processors Single-Core CPU ALU Cache Registers Multiple Cores ALU 1 Registers 1 Cache Hyper-threading has firmware logic that appears to the OS ALU 2 to contain more than one core when in fact only one physical core exists Registers 2 4/22 Programs vs Processes vs Threads ▪ Programs › An executable file residing in memory, typically in secondary storage ▪ Processes › An executing instance of a program › Sometimes this is referred to as a Task ▪ Threads › Any section of code within a process that can execute at what appears to be simultaneously › Shares the same resources allotted to the process by the OS kernel USC CSCI 201L 5/22 Concurrent Programming ▪ Multi-Threaded Programming › Multiple sections of a process or multiple processes executing at what appears to be simultaneously in a single core › The purpose of multi-threaded programming is to add functionality to your program ▪ Parallel Programming › Multiple sections of