Algorithms for the Certified Write-All Problem

Anderson, Richard; Woll, Heather

doi:10.1137/s0097539794319126

Cited by 47 publications

(102 citation statements)

References 6 publications

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“…In [1] is particularly good. However, this bound can only be achieved assuming that a set of permutations of 1 ... P with a specific property is given, which requires exponential time to calculate.…”

Section: In [8] An Overview Is Given Of the Algorithms And Pram Simulmentioning

confidence: 93%

“…As log" log q' goes to 0 when q goes to infinity, algorithm AWT has superior complexity. However, the constant amount of work that must be done in the preprocessing phase (which is independent of Nand P) is exponential in q (see [1]). In order to outperform algorithm X' for any II' and' P, it must be the case that E < log(~).…”

Section: In [8] An Overview Is Given Of the Algorithms And Pram Simulmentioning

confidence: 99%

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An algorithm for the asynchronous Write-All problem based on process collision

Groote¹,

Hesselink²,

Mauw³

et al. 2001

Distrib Comput

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The problem of using P processes to write a given value to all positions of a shared array of size N is called the Write-All problem. We present and analyze an algorithm with work load O(N· plog(~»), where x = Nl/log(P), OUf algorithm is a generalization of the naive twoprocessor algorithm where the two processes each start at one side of the array and walk towards each other until they collide.

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Section: In [8] An Overview Is Given Of the Algorithms And Pram Simulmentioning

confidence: 93%

Section: In [8] An Overview Is Given Of the Algorithms And Pram Simulmentioning

confidence: 99%

An algorithm for the asynchronous Write-All problem based on process collision

Groote¹,

Hesselink²,

Mauw³

et al. 2001

Distrib Comput

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show abstract

“…. , g + − 1, represented by a status-unit with name g are old at v. Progress trees were introduced for processes to keep track of their collective progress performing a collection of tasks [4,7].…”

Section: Status-unitsmentioning

confidence: 99%

Efficient Fetch-and-Increment

Ellen

Ramachandran

Woelfel

2012

Lecture Notes in Computer Science

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Abstract.A Fetch&Inc object stores a non-negative integer and supports a single operation, fi, that returns the value of the object and increments it. Such objects are used in many asynchronous shared memory algorithms, such as renaming, mutual exclusion, and barrier synchronization. We present an efficient implementation of a wait-free Fetch&Inc object from registers and load-linked/store-conditional (ll/sc) objects. In a system with p processes, every fi operation finishes in O(log 2 p) steps, and only a polynomial number of registers and O(log p)-bit ll/sc objects are needed. The maximum number of fi operations that can be supported is limited only by the maximum integer that can be stored in a shared register. This is the first wait-free implementation of a Fetch&Inc object that achieves both poly-logarithmic step complexity and polynomial space complexity, but does not require unrealistically large ll/sc objects or registers.

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“…[4,5,[11][12][13]16,19,[22][23][24], has focused on algorithms and lower bounds for the asynchronous version of task allocation, also known as do-all [17], or write-all [19], where processes move at arbitrary speeds and are crash-prone. Task allocation is closely connected to many other fundamental distributed problems, such as mutual exclusion [10], distributed clocks [8], and shared-memory collect [3].…”

mentioning

confidence: 99%

“…Previous work. Task allocation is also known as do-all [17]; the shared memory variant is also called write-all [5,19]. The recent book by Georgiou and Shvartsman [17] gives a detailed overview of work on the problem.…”

mentioning

confidence: 99%

Dynamic Task Allocation in Asynchronous Shared Memory

Alistarh¹,

Aspnes²,

Bender³

et al. 2013

Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms

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Task allocation is a classic distributed problem in which a set of p potentially faulty processes must cooperate to perform a set of tasks. This paper considers a new dynamic version of the problem, in which tasks are injected adversarially during an asynchronous execution. We give the first asynchronous shared-memory algorithm for dynamic task allocation, and we prove that our solution is optimal within logarithmic factors. The main algorithmic idea is a randomized concurrent data structure called a dynamic to-do tree, which allows processes to pick new tasks to perform at random from the set of available tasks, and to insert tasks at random empty locations in the data structure. Our analysis shows that these properties avoid duplicating work unnecessarily. On the other hand, since the adversary controls the input as well the scheduling, it can induce executions where lots of processes contend for a few available tasks, which is inefficient. However, we prove that every algorithm has the same problem: given an arbitrary input, if OPT is the worst-case complexity of the optimal algorithm on that input, then the expected work complexity of our algorithm on the same input is O(OPT log 3 m), where m is an upper bound on the number of tasks that are present in the system at any given time.

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Algorithms for the Certified Write-All Problem

Cited by 47 publications

References 6 publications

An algorithm for the asynchronous Write-All problem based on process collision

An algorithm for the asynchronous Write-All problem based on process collision

Efficient Fetch-and-Increment

Dynamic Task Allocation in Asynchronous Shared Memory

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