Concurrent Data Structures

Moir, Mark; Shavit, Nir

doi:10.1201/9781420035179.ch47

Cited by 51 publications

(43 citation statements)

References 101 publications

(194 reference statements)

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“…The vector is split into chunks which are processed in parallel. The overhead of splitting can be offset by using cooperative tasks [14,21], but, in the case of RB-Vector the cost of splitting is much smaller compared to the cost of combining (assembling) the partial results returned by parallel execution contexts. This is where the RRB trees make a difference: by allowing efficient structural changes, it enables the concatenation to occur in effectively constant time, much better than the previous O(n) for the Scala Vector.…”

Section: Related Workmentioning

confidence: 99%

RRB vector: a practical general purpose immutable sequence

StuckiNicolas¹,

RompfTiark

UrecheVlad³

et al. 2015

SIGPLAN Not.

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State-of-the-art immutable collections have wildly differing performance characteristics across their operations, often forcing programmers to choose different collection implementations for each task. Thus, changes to the program can invalidate the choice of collections, making code evolution costly. It would be desirable to have a collection that performs well for a broad range of operations.To this end, we present the RRB-Vector, an immutable sequence collection that offers good performance across a large number of sequential and parallel operations. The underlying innovations are: (1) the Relaxed-Radix-Balanced (RRB) tree structure, which allows efficient structural reorganization, and (2) an optimization that exploits spatio-temporal locality on the RRB data structure in order to offset the cost of traversing the tree.In our benchmarks, the RRB-Vector speedup for parallel operations is lower bounded by 7× when executing on 4 CPUs of 8 cores each. The performance for discrete operations, such as appending on either end, or updating and removing elements, is consistently good and compares favorably to the most important immutable sequence collections in the literature and in use today. The memory footprint of RRB-Vector is on par with arrays and an order of magnitude less than competing collections.

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Section: Related Workmentioning

confidence: 99%

RRB vector: a practical general purpose immutable sequence

StuckiNicolas¹,

RompfTiark

UrecheVlad³

et al. 2015

SIGPLAN Not.

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“…Atomic collection objects including stacks and queues are among the most heavily investigated concurrent objects [19]. We demonstrate that our approximation is effective in uncovering refinement violations for these objects, in the sense that most violations can be uncovered with coarse approximations, i.e., k ≤ 3, depending on the data structure.…”

Section: Atomic Collectionsmentioning

confidence: 97%

Tractable Refinement Checking for Concurrent Objects

Bouajjani

Emmi

Enea

et al. 2015

Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

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Efficient implementations of concurrent objects such as semaphores, locks, and atomic collections are essential to modern computing. Yet programming such objects is error prone: in minimizing the synchronization overhead between concurrent object invocations, one risks the conformance to reference implementations -or in formal terms, one risks violating observational refinement. Testing this refinement even within a single execution is intractable, limiting existing approaches to executions with very few object invocations.We develop a polynomial-time (per execution) approximation to refinement checking. The approximation is parameterized by an accuracy k ∈ N representing the degree to which refinement violations are visible. In principle, more violations are detectable as k increases, and in the limit, all are detectable. Our insight for this approximation arises from foundational properties on the partial orders characterizing the happens-before relations between object invocations: they are interval orders, with a well defined measure of complexity, i.e., their length. Approximating the happens-before relation with a possibly-weaker interval order of bounded length can be efficiently implemented by maintaining a bounded number of integer counters. In practice, we find that refinement violations can be detected with very small values of k, and that our approach scales far beyond existing refinement-checking approaches.

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“…A detailed overview of concurrent data structures is given by Moir and Shavit [18]. To date, concurrent data structures remain an active area of research-we restrict this summary to those relevant to this work.…”

Section: Related Workmentioning

confidence: 99%

FlowPools: A Lock-Free Deterministic Concurrent Dataflow Abstraction

Prokopec

Miller

Schlatter

et al. 2013

Languages and Compilers for Parallel Computing

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Abstract. Implementing correct and deterministic parallel programs is challenging. Even though concurrency constructs exist in popular programming languages to facilitate the task of deterministic parallel programming, they are often too low level, or do not compose well due to underlying blocking mechanisms. In this paper, we present the design and implementation of a fundamental data structure for composable deterministic parallel dataflow computation through the use of functional programming abstractions. Additionally, we provide a correctness proof, showing that the implementation is linearizable, lock-free, and deterministic. Finally, we show experimental results which compare our FlowPool against corresponding operations on other concurrent data structures, and show that in addition to offering new capabilities, FlowPools reduce insertion time by 49 − 54% on a 4-core i7 machine with respect to comparable concurrent queue data structures in the Java standard library.

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Concurrent Data Structures

Cited by 51 publications

References 101 publications

RRB vector: a practical general purpose immutable sequence

RRB vector: a practical general purpose immutable sequence

Tractable Refinement Checking for Concurrent Objects

FlowPools: A Lock-Free Deterministic Concurrent Dataflow Abstraction

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