At-most-once semantics in asynchronous shared memory

Kentros, Sotirios; Kiayias, Aggelos; Nicolaou, Nicolas; Shvartsman, Alexander A.

doi:10.1145/1583991.1584003

Cited by 6 publications

(15 citation statements)

References 5 publications

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“…At-most-once semantics [15] requires m jobs to be executed with the guarantee that no job is performed more than once. A trivial solution simply does not execute any job.…”

Section: Methodsmentioning

confidence: 99%

“…However, the effectiveness of an algorithm for solving the at-most-once problem is the number of completed jobs, and it is desired to find algorithms with the maximal effectiveness possible. Section 6 shows how to use a randomized multi-valued consensus algorithm in order to solve the at-most-once problem while obtaining the optimal effectiveness, thus answering an open question raised in [15].…”

Section: Methodsmentioning

confidence: 99%

“…This requirement was studied in contexts of at-most-once message delivery, and of atmost-once remote procedure calls (RPC), and has been recently addressed in the context of asynchronous shared memory by Kentros et al [15]. In this problem, n processes are required to perform m jobs.…”

Section: Application: the At-most-once Problemmentioning

confidence: 99%

“…Kentros et al [15] present a deterministic solution for the atmost-once problem with effectiveness (k − 1) h , where m = k h and n = 2 h , for wait-free solutions 2 . They also prove a lower bound of m − f on the effectiveness of any such algorithm.…”

Section: Application: the At-most-once Problemmentioning

confidence: 99%

“…Another algorithm for the at-most-once problem can use the tree construction of Kentros et al [15], and add a binary consensus object for every node in the tree, in order to improve the effectiveness of their algorithm. This addition prevents having a location in the array of a node in the tree, which is not addressed by processes of either "side" because of a possibility for a conflict (we refer the reader to the above paper for more details on their algorithm).…”

Section: Application: the At-most-once Problemmentioning

confidence: 99%

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Multi-sided shared coins and randomized set-agreement

Hillel

2010

Proceedings of the Twenty-Second Annual ACM Symposium on Parallelism in Algorithms and Architectures

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This paper presents wait-free randomized algorithms for solving set-agreement in asynchronous shared-memory systems under a strong adversary. First, the definition of a shared-coin algorithm is generalized to a multi-sided shared-coin algorithm, and it is shown how to use any multi-sided shared coin in order to obtain a randomized set-agreement algorithm for agreeing on k values out of k + 1. Then, an implementation is given for a (k + 1)-sided shared coin for n processes with a constant agreement parameter, O(n 2 /k) total step complexity, and O(n/k) individual step complexity. This implementation yields a randomized set-agreement algorithm for agreeing on k values out of k + 1 with a total step complexity of O(n 2 /k + nk) and an individual step complexity of O(n/k + k). Next, other set-agreement algorithms for agreeing on values out of k + 1, where is smaller than k, are presented. This includes the case of multi-valued consensus in which = 1, k > 1. To the best of our knowledge, these are the first wait-free algorithms for set-agreement in the asynchronous shared-memory model under a strong adversary that are not for the specific case of binary consensus, where = k = 1. Finally, an application of asynchronous wait-free multi-valued consensus is presented, in implementing atmost-once semantics with optimal effectiveness.

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“…At-most-once semantics [15] requires m jobs to be executed with the guarantee that no job is performed more than once. A trivial solution simply does not execute any job.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Application: the At-most-once Problemmentioning

confidence: 99%

Section: Application: the At-most-once Problemmentioning

confidence: 99%

Section: Application: the At-most-once Problemmentioning

confidence: 99%

See 3 more Smart Citations

Multi-sided shared coins and randomized set-agreement

Hillel

2010

Proceedings of the Twenty-Second Annual ACM Symposium on Parallelism in Algorithms and Architectures

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Doing-it-All with bounded work and communication

Chlebus

Gąsieniec

Kowalski

et al. 2017

Information and Computation

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We consider the Do-All problem, where p cooperating processors need to complete t similar and independent tasks in an adversarial setting. Here we deal with a synchronous message passing system with processors that are subject to crash failures. Efficiency of algorithms in this setting is measured in terms of work complexity (also known as total available processor steps) and communication complexity (total number of point-to-point messages). When work and communication are considered to be comparable resources, then the overall efficiency is meaningfully expressed in terms of effort defined as work + communication. We develop and analyze a constructive algorithm that has work O(t + p log p ( √ p log p + √ t log t )) and a nonconstructive algorithm that has work O(t + p log 2 p). The latter result is close to the lower bound Ω(t + p log p/ log log p) on work. The effort of each of these algorithms is proportional to its work when the number of crashes is bounded above by c p, for some positive constant c < 1. We also present a nonconstructive algorithm that has effort O(t + p 1.77 ). The results of this paper were announced in a preliminary form in [11] and published in their final version as [10].

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Solving the at-most-once problem with nearly optimal effectiveness

Kentros

Kiayias

2013

Theoretical Computer Science

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We present and analyze a wait-free deterministic algorithm for solving the at-most-once problem: how m shared-memory fail-prone processes perform asynchronously n jobs at most once. Our algorithmic strategy provides for the first time nearly optimal effectiveness, which is a measure that expresses the total number of jobs completed in the worst case. The effectiveness of our algorithm equals n − 2m + 2. This is up to an additive factor of m close to the known effectiveness upper bound n − m + 1 over all possible algorithms and improves on the previously best known deterministic solutions that have effectiveness only n − log m · o(n). We also present an iterative version of our algorithm that for any m = O( 3+ǫ n/ log n) is both effectiveness-optimal and work-optimal, for any constant ǫ > 0. We then employ this algorithm to provide a new algorithmic solution for the Write-All problem which is work optimal for any m = O( 3+ǫ n/ log n). 1The problem becomes very challenging for autonomous processes in a system with concurrent invocations on multiple objects. At-most-once semantics have been thoroughly studied in the context of at-most-once message delivery [8,30,33] and at-most-once process invocation for RPC [6,31,37]. However, finding effective solutions for asynchronous shared-memory multiprocessors, in terms of how many at-most-once invocations can be performed by the cooperating processes, is largely an open problem. Solutions for the at-most-once problem, using only atomic read/write memory, and without specialized hardware support such as conditional writing, provide a useful tool in reasoning about the safety properties of applications developed for a variety of multiprocessor systems, including those not supporting bus-interlocking instructions and multi-core systems. Specifically, in recent years, attention has shifted from increasing clock speed towards chip multiprocessing, in order to increase the performance of systems. Because of the differences in each multi-core system, asynchronous shared memory is becoming an important abstraction for arguing about the safety properties of parallel applications in such systems. In the next years, one can expect chip multiprocessing to appear in a wide range of applications, many of which will have components that need to satisfy at-most-once semantics in order to guarantee safety. Such applications may include autonomous robotic devices, robotic devices for assisted living, automation in production lines or medical facilities. In such applications performing specific jobs at-most-once may be of paramount importance for safety of patients, the workers in a facility, or the devices themselves. Such jobs could be the triggering of a motor in a robotic arm, the activation of the X-ray gun in an X-ray machine, or supplying a dosage of medicine to a patient.Perhaps the most important question in this area is devising algorithms for the at-most-once problem with good effectiveness. The complexity measure of effectiveness [26] describes the number of jobs completed (at-mo...

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At-most-once semantics in asynchronous shared memory

Cited by 6 publications

References 5 publications

Multi-sided shared coins and randomized set-agreement

Multi-sided shared coins and randomized set-agreement

Doing-it-All with bounded work and communication

Solving the at-most-once problem with nearly optimal effectiveness

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