Scalable Flat-Combining Based Synchronous Queues

Hendler, Danny; Incze, Itai; Shavit, Nir; Tzafrir, Moran

doi:10.1007/978-3-642-15763-9_8

Cited by 22 publications

(18 citation statements)

References 10 publications

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“…In follow-on work, Hendler et al [2010a] demonstrate that additional speedup can be obtained by dividing the work of combining among several parallel worker threads. When contention is high, however, large numbers of requests are completed by a single thread, which then benefits from three important effects.…”

Section: Combining As a General Technique 85mentioning

confidence: 95%

Shared-Memory Synchronization

Scott¹

2013

Synthesis Lectures on Computer Architecture

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Synthesis Lectures on Computer Architecture publishes 50-to 100-page publications on topics pertaining to the science and art of designing, analyzing, selecting and interconnecting hardware components to create computers that meet functional, performance and cost goals. e scope will largely follow the purview of premier computer architecture conferences, such as ISCA, HPCA, MICRO, and ASPLOS. ABSTRACTSince the advent of time sharing in the 1960s, designers of concurrent and parallel systems have needed to synchronize the activities of threads of control that share data structures in memory. In recent years, the study of synchronization has gained new urgency with the proliferation of multicore processors, on which even relatively simple user-level programs must frequently run in parallel.is lecture offers a comprehensive survey of shared-memory synchronization, with an emphasis on "systems-level" issues. It includes sufficient coverage of architectural details to understand correctness and performance on modern multicore machines, and sufficient coverage of higher-level issues to understand how synchronization is embedded in modern programming languages.e primary intended audience is "systems programmers"-the authors of operating systems, library packages, language run-time systems, concurrent data structures, and server and utility programs. Much of the discussion should also be of interest to application programmers who want to make good use of the synchronization mechanisms available to them, and to computer architects who want to understand the ramifications of their design decisions on systems-level code.

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Section: Combining As a General Technique 85mentioning

confidence: 95%

Shared-Memory Synchronization

Scott¹

2013

Synthesis Lectures on Computer Architecture

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“…S&S Dual Queue: the dual queue of Scherer & Scott [10]. FC Dual Queue: a flat-combining dual queue inspired by the work of Hendler et al [3,4].…”

Section: Resultsmentioning

confidence: 99%

“…The Java library also includes a dual Exchanger class, in which operations of a single type "match up." Other synchronous queues include the flat combining version of Hendler et al [3] and the elimination-diffraction trees of Afek et al [1]. Recently [4], we have developed two fast dual queues based on the LCRQ algorithm of Morrison and Afek [8].…”

Section: Introductionmentioning

confidence: 99%

Brief announcement

Izraelevitz

Scott

2014

Proceedings of the 2014 ACM Symposium on Principles of Distributed Computing

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A dual container has the property that when it is empty, the remove method will insert an explicit reservation ("antidata") into the container, rather than returning an error flag. This convention gives the container explicit control over the order in which pending requests will be satisfied once data becomes available. The dual pattern also allows the method's caller to spin on a thread-local flag, avoiding memory contention. In this paper we introduce a new nonblocking construction that allows any nonblocking container for data to be paired with almost any nonblocking container for antidata. This construction provides a composite ordering discipline-e.g., it can satisfy pending pops from a stack in FIFO order, or satisfy pending dequeues in order of thread priority.

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“…Here the idea would be an extension of the style of our queue implementation: have a shared data structure but reduce overhead in bottleneck spots using flat combining. We have recently been able to show how this parallel flat-combining approach can benefit the design of unfair synchronous queues [5].…”

Section: Discussionmentioning

confidence: 99%

Flat combining and the synchronization-parallelism tradeoff

Hendler

Incze

Shavit

et al. 2010

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

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261

258

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Traditional data structure designs, whether lock-based or lock-free, provide parallelism via fine grained synchronization among threads.We introduce a new synchronization paradigm based on coarse locking, which we call flat combining. The cost of synchronization in flat combining is so low, that having a single thread holding a lock perform the combined access requests of all others, delivers, up to a certain non-negligible concurrency level, better performance than the most effective parallel finely synchronized implementations. We use flat-combining to devise, among other structures, new linearizable stack, queue, and priority queue algorithms that greatly outperform all prior algorithms.

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Scalable Flat-Combining Based Synchronous Queues

Cited by 22 publications

References 10 publications

Shared-Memory Synchronization

Shared-Memory Synchronization

Brief announcement

Flat combining and the synchronization-parallelism tradeoff

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