Mastering Concurrent Computing Through Sequential Thinking
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ACM SIGACT News Distributed Computing Column 28
ACM SIGACT News Distributed Computing Column 28 Idit Keidar Dept. of Electrical Engineering, Technion Haifa, 32000, Israel [email protected] Sergio Rajsbaum, who edited this column for seven years and established it as a relevant and popular venue, is stepping down. This issue is my first step in the big shoes he vacated. I would like to take this opportunity to thank Sergio for providing us with seven years’ worth of interesting columns. In producing these columns, Sergio has enjoyed the support of the community at-large and obtained material from many authors, who greatly contributed to the column’s success. I hope to enjoy a similar level of support; I warmly welcome your feedback and suggestions for material to include in this column! The main two conferences in the area of principles of distributed computing, PODC and DISC, took place this summer. This issue is centered around these conferences, and more broadly, distributed computing research as reflected therein, ranging from reviews of this year’s instantiations, through influential papers in past instantiations, to examining PODC’s place within the realm of computer science. I begin with a short review of PODC’07, and highlight some “hot” trends that have taken root in PODC, as reflected in this year’s program. Some of the forthcoming columns will be dedicated to these up-and- coming research topics. This is followed by a review of this year’s DISC, by Edward (Eddie) Bortnikov. For some perspective on long-running trends in the field, I next include the announcement of this year’s Edsger W. -
Fast and Correct Load-Link/Store-Conditional Instruction Handling in DBT Systems
Fast and Correct Load-Link/Store-Conditional Instruction Handling in DBT Systems Abstract Atomic Read Memory Dynamic Binary Translation (DBT) requires the implemen- Operation tation of load-link/store-conditional (LL/SC) primitives for guest systems that rely on this form of synchronization. When Equals targeting e.g. x86 host systems, LL/SC guest instructions are YES NO typically emulated using atomic Compare-and-Swap (CAS) expected value? instructions on the host. Whilst this direct mapping is efficient, this approach is problematic due to subtle differences between Write new value LL/SC and CAS semantics. In this paper, we demonstrate that to memory this is a real problem, and we provide code examples that fail to execute correctly on QEMU and a commercial DBT system, Exchanged = true Exchanged = false which both use the CAS approach to LL/SC emulation. We then develop two novel and provably correct LL/SC emula- tion schemes: (1) A purely software based scheme, which uses the DBT system’s page translation cache for correctly se- Figure 1: The compare-and-swap instruction atomically reads lecting between fast, but unsynchronized, and slow, but fully from a memory address, and compares the current value synchronized memory accesses, and (2) a hardware acceler- with an expected value. If these values are equal, then a new ated scheme that leverages hardware transactional memory value is written to memory. The instruction indicates (usually (HTM) provided by the host. We have implemented these two through a return register or flag) whether or not the value was schemes in the Synopsys DesignWare® ARC® nSIM DBT successfully written. -
Edsger Dijkstra: the Man Who Carried Computer Science on His Shoulders
INFERENCE / Vol. 5, No. 3 Edsger Dijkstra The Man Who Carried Computer Science on His Shoulders Krzysztof Apt s it turned out, the train I had taken from dsger dijkstra was born in Rotterdam in 1930. Nijmegen to Eindhoven arrived late. To make He described his father, at one time the president matters worse, I was then unable to find the right of the Dutch Chemical Society, as “an excellent Aoffice in the university building. When I eventually arrived Echemist,” and his mother as “a brilliant mathematician for my appointment, I was more than half an hour behind who had no job.”1 In 1948, Dijkstra achieved remarkable schedule. The professor completely ignored my profuse results when he completed secondary school at the famous apologies and proceeded to take a full hour for the meet- Erasmiaans Gymnasium in Rotterdam. His school diploma ing. It was the first time I met Edsger Wybe Dijkstra. shows that he earned the highest possible grade in no less At the time of our meeting in 1975, Dijkstra was 45 than six out of thirteen subjects. He then enrolled at the years old. The most prestigious award in computer sci- University of Leiden to study physics. ence, the ACM Turing Award, had been conferred on In September 1951, Dijkstra’s father suggested he attend him three years earlier. Almost twenty years his junior, I a three-week course on programming in Cambridge. It knew very little about the field—I had only learned what turned out to be an idea with far-reaching consequences. a flowchart was a couple of weeks earlier. -
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. -
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. -
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. -
A Separation Logic for Refining Concurrent Objects
A Separation Logic for Refining Concurrent Objects Aaron Turon ∗ Mitchell Wand Northeastern University {turon, wand}@ccs.neu.edu Abstract do { tmp = *C; } until CAS(C, tmp, tmp+1); Fine-grained concurrent data structures are crucial for gaining per- implements inc using an optimistic approach: it takes a snapshot formance from multiprocessing, but their design is a subtle art. Re- of the counter without acquiring a lock, computes the new value cent literature has made large strides in verifying these data struc- of the counter, and uses compare-and-set (CAS) to safely install the tures, using either atomicity refinement or separation logic with new value. The key is that CAS compares *C with the value of tmp, rely-guarantee reasoning. In this paper we show how the owner- atomically updating *C with tmp + 1 and returning true if they ship discipline of separation logic can be used to enable atomicity are the same, and just returning false otherwise. refinement, and we develop a new rely-guarantee method that is lo- Even for this simple data structure, the fine-grained imple- calized to the definition of a data structure. We present the first se- mentation significantly outperforms the lock-based implementa- mantics of separation logic that is sensitive to atomicity, and show tion [17]. Likewise, even for this simple example, we would prefer how to control this sensitivity through ownership. The result is a to think of the counter in a more abstract way when reasoning about logic that enables compositional reasoning about atomicity and in- its clients, giving it the following specification: terference, even for programs that use fine-grained synchronization and dynamic memory allocation. -
Lecture Notes in Computer Science 2584 Edited by G
Lecture Notes in Computer Science 2584 Edited by G. Goos, J. Hartmanis, and J. van Leeuwen 3 Berlin Heidelberg New York Barcelona Hong Kong London Milan Paris Tokyo André Schiper Alex A. Shvartsman Hakim Weatherspoon Ben Y. Zhao (Eds.) Future Directions in Distributed Computing Research and Position Papers 13 Series Editors Gerhard Goos, Karlsruhe University, Germany Juris Hartmanis, Cornell University, NY, USA Jan van Leeuwen, Utrecht University, The Netherlands Volume Editors André Schiper École Polytechnique Fédérale de Lausanne, Faculté Informatique et Communication IN-Ecublens, 1015 Lausanne, Switzerland E-mail: andre.schiper@epfl.ch Alex A. Shvartsman University of Connecticut, Computer Science and Engineering Unit 3155, Storrs, CT 06269, USA E-mail: [email protected] and [email protected] Hakim Weatherspoon Ben Y. Zhao University of California at Berkeley, Computer Science Division 447/443 Soda Hall, Berkeley, CA 94704-1776, USA E-mail: {hweather, ravenben}@cs.berkeley.edu Cataloging-in-Publication Data applied for A catalog record for this book is available from the Library of Congress Bibliographic information published by Die Deutsche Bibliothek Die Deutsche Bibliothek lists this publication in the Deutsche Nationalbibliographie; detailed bibliographic data is available in the Internet at <http://dnb.ddb.de>. CR Subject Classification (1998): C.2.4, D.1.3, D.2.12, D.4.3-4, F.1.2 ISSN 0302-9743 ISBN 3-540-00912-4 Springer-Verlag Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. -
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 -
Lock-Free Programming
Lock-Free Programming Geoff Langdale L31_Lockfree 1 Desynchronization ● This is an interesting topic ● This will (may?) become even more relevant with near ubiquitous multi-processing ● Still: please don’t rewrite any Project 3s! L31_Lockfree 2 Synchronization ● We received notification via the web form that one group has passed the P3/P4 test suite. Congratulations! ● We will be releasing a version of the fork-wait bomb which doesn't make as many assumptions about task id's. – Please look for it today and let us know right away if it causes any trouble for you. ● Personal and group disk quotas have been grown in order to reduce the number of people running out over the weekend – if you try hard enough you'll still be able to do it. L31_Lockfree 3 Outline ● Problems with locking ● Definition of Lock-free programming ● Examples of Lock-free programming ● Linux OS uses of Lock-free data structures ● Miscellanea (higher-level constructs, ‘wait-freedom’) ● Conclusion L31_Lockfree 4 Problems with Locking ● This list is more or less contentious, not equally relevant to all locking situations: – Deadlock – Priority Inversion – Convoying – “Async-signal-safety” – Kill-tolerant availability – Pre-emption tolerance – Overall performance L31_Lockfree 5 Problems with Locking 2 ● Deadlock – Processes that cannot proceed because they are waiting for resources that are held by processes that are waiting for… ● Priority inversion – Low-priority processes hold a lock required by a higher- priority process – Priority inheritance a possible solution L31_Lockfree -
Download Distributed Systems Free Ebook
DISTRIBUTED SYSTEMS DOWNLOAD FREE BOOK Maarten van Steen, Andrew S Tanenbaum | 596 pages | 01 Feb 2017 | Createspace Independent Publishing Platform | 9781543057386 | English | United States Distributed Systems - The Complete Guide The hope is that together, the system can maximize resources and information while preventing failures, as if one system fails, it won't affect the availability of the service. Banker's algorithm Dijkstra's algorithm DJP algorithm Prim's algorithm Dijkstra-Scholten algorithm Dekker's algorithm generalization Smoothsort Shunting-yard algorithm Distributed Systems marking algorithm Concurrent algorithms Distributed Systems algorithms Deadlock prevention algorithms Mutual exclusion algorithms Self-stabilizing Distributed Systems. Learn to code for free. For the first time computers would be able to send messages to other systems with a local IP address. The messages passed between machines contain forms of data that the systems want to share like databases, objects, and Distributed Systems. Also known as distributed computing and distributed databases, a distributed system is a collection of independent components located on different machines that share messages with each other in order to achieve common goals. To prevent infinite loops, running the code requires some amount of Ether. As mentioned in many places, one of which this great articleyou cannot have consistency and availability without partition tolerance. Because it works in batches jobs a problem arises where if your job fails — Distributed Systems need to restart the whole thing. While in a voting system an attacker need only add nodes to the network which is Distributed Systems, as free access to the network is a design targetin a CPU power based scheme an attacker faces a physical limitation: getting access to more and more powerful hardware.