The Contention Avoiding Concurrent Priority Queue

Sagonas, Konstantinos; Winblad, Kjell

doi:10.1007/978-3-319-52709-3_23

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…We believe that further practical improvements could be possible by better avoiding conflicting queue accesses of the sticky variant even when few queues are used. It would also be interesting to make MultiQueues contention aware to achieve higher quality and more graceful degradation than the simple binary switch between local and global access used in the CAPQ [21].…”

Section: Discussionmentioning

confidence: 99%

“…We compare the MultiQueue to the following state-of-the-art concurrent priority queues. CAPQ [21] dynamically detects contention to switch from using a shared skip list based priority queue to thread-local buffers. We used the implementation found in the Github repository of the k-LSM 3 for all of the above priority queues.…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

“…The contention avoiding priority queue (CAPQ) [21,22] is based on a centralized skip-list S but switches to thread-local insertion and deletion buffers when it detects contention on S. To maintain some global view, operations will still occasionally use S. This combines high accuracy in uncontended situations with high throughput under contention. However, the quality penalty for switching to local buffers is fairly high.…”

Section: Related Workmentioning

confidence: 99%

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MultiQueues: Simple Relaxed Concurrent Priority Queues

Rihani

Sanders

Dementiev

2015

Proceedings of the 27th ACM Symposium on Parallelism in Algorithms and Architectures

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Priority queues with parallel access are an attractive data structure for applications like prioritized online scheduling, discrete event simulation, or greedy algorithms. However, a classical priority queue constitutes a severe bottleneck in this context, leading to very small throughput. Hence, there has been significant interest in concurrent priority queues with relaxed semantics. We investigate the complementary quality criteria rank error (how close are deleted elements to the global minimum) and delay (for each element x, how many elements with lower priority are deleted before x). In this paper, we introduce MultiQueues as a natural approach to relaxed priority queues based on multiple sequential priority queues. Their naturally high theoretical scalability is further enhanced by using three orthogonal ways of batching operations on the sequential queues. Experiments indicate that MultiQueues present a very good performance-quality tradeoff and considerably outperform competing approaches in at least one of these aspects.We employ a seemingly paradoxical technique of "wait-free locking" that might be of more general interest to convert sequential data structures to relaxed concurrent data structures. ACM Subject Classification Computing methodologies → Concurrent computing methodologiesKeywords and phrases concurrent data structure, priority queues, randomized algorithms, wait-free locking Related Version This paper continues a previous paper that only appeared as a preprint and SPAA brief announcement listed below. New contributions compared to that are measures for better locality, more analysis of element delays, and more realistic experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison With Other Approachesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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MultiQueues: Simple Relaxed Concurrent Priority Queues

Rihani

Sanders

Dementiev

2015

Proceedings of the 27th ACM Symposium on Parallelism in Algorithms and Architectures

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“…A parallel research thread has been on providing efficient relaxed schedulers with guarantees on the maximum amount of priority rank relaxation, e.g. [6,23,26,24]. Of these, arguably the most popular design is the Multi-Queue [23], which works roughly as follows: given n threads, we instantiate m ≥ n concurrent priority queues, which will store tasks.…”

Section: Introductionmentioning

confidence: 99%

Multi-Queues Can Be State-of-the-Art Priority Schedulers

Postnikova¹,

Koval²,

Nadiradze³

et al. 2021

Preprint

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Designing and implementing efficient parallel priority schedulers is an active research area. An intriguing proposed design is the Multi-Queue: given n threads and m ≥ n distinct priority queues, task insertions are performed uniformly at random, while, to delete, a thread picks two queues uniformly at random, and removes the observed task of higher priority. This approach scales well, and has probabilistic rank guarantees: roughly, the rank of each task removed, relative to remaining tasks in all other queues, is O(m) in expectation. Yet, the performance of this pattern is below that of well-engineered schedulers, which eschew theoretical guarantees for practical efficiency. We investigate whether it is possible to design and implement a Multi-Queue-based task scheduler that is both highly-efficient and has analytical guarantees. We propose a new variant called the Stealing Multi-Queue (SMQ), a cache-efficient variant of the Multi-Queue, which leverages both queue affinity-each thread has a local queue, from which tasks are usually removed; but, with some probability, threads also attempt to steal higher-priority tasks from the other queues-and task batching, that is, the processing of several tasks in a single insert / remove step. These ideas are well-known for task scheduling without priorities; our theoretical contribution is showing that, despite relaxations, this design can still provide rank guarantees, which in turn implies bounds on total work performed. We provide a general SMQ implementation which can surpass state-of-the-art schedulers such as Galois and PMOD in terms of performance on popular graph-processing benchmarks. Notably, the performance improvement comes mainly from the superior rank guarantees provided by our scheduler, confirming that analytically-reasoned approaches can still provide performance improvements for priority task scheduling.

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“…A standard example is parallelizing Dijkstra's single-source shortest paths (SSSP) algorithm, e.g. [3,22,20]: the scheduler can retrieve vertices in relaxed order without breaking correctness, as the distance at each vertex is guaranteed to eventually converge to the minimum. The trade-off is between the performance gains arising from using simpler, more scalable schedulers, and the loss of determinism and the wasted work due to relaxed priority order.…”

mentioning

confidence: 99%

Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms

Alistarh

Brown

Kopinsky

et al. 2018

Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing

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There has been significant progress in understanding the parallelism inherent to iterative sequential algorithms: for many classic algorithms, the depth of the dependence structure is now well understood, and scheduling techniques have been developed to exploit this shallow dependence structure for efficient parallel implementations. A related, applied research strand has studied methods by which certain iterative task-based algorithms can be efficiently parallelized via relaxed concurrent priority schedulers. These allow for high concurrency when inserting and removing tasks, at the cost of executing superfluous work due to the relaxed semantics of the scheduler.In this work, we take a step towards unifying these two research directions, by showing that there exists a family of relaxed priority schedulers that can efficiently and deterministically execute classic iterative algorithms such as greedy maximal independent set (MIS) and matching. Our primary result shows that, given a randomized scheduler with an expected relaxation factor of k in terms of the maximum allowed priority inversions on a task, and any graph on n vertices, the scheduler is able to execute greedy MIS with only an additive factor of poly(k) expected additional iterations compared to an exact (but not scalable) scheduler. This counter-intuitive result demonstrates that the overhead of relaxation when computing MIS is not dependent on the input size or structure of the input graph. Experimental results show that this overhead can be clearly offset by the gain in performance due to the highly scalable scheduler. In sum, we present an efficient method to deterministically parallelize iterative sequential algorithms, with provable runtime guarantees in terms of the number of executed tasks to completion. arXiv:1808.04155v1 [cs.DS] 13 Aug 2018 may only depend on a small subset of other nodes, namely its neighbors which are higher priority in the random order. Blelloch, Fineman and Shun [7] performed an in-depth study of the asymptotic properties of this dependence structure, proving that, for any graph, the maximal depth of a chain of dependences is in fact O(log 2 n) with high probability, where n is the number of nodes in the graph. Recently, an impressive analytic result by Fischer and Noever [13] provided tight Θ(log n) bounds on the maximal dependency depth for greedy sequential MIS, effectively closing this problem for MIS. Beyond greedy MIS, there has been significant progress in analyzing the dependency structure of other fundamental sequential algorithms, such as algorithms for matching [7], list contraction [25], Knuth shuffle [25], linear programming [5], and graph connectivity [5].An alternative approach has been to employ relaxed data structures to schedule task-based programs. Starting with Karp and Zhang [19], the general idea is that, in some applications, the scheduler can relax the strict order induced by following the sequential algorithm, and allow tasks to be processed speculatively ahead of their dependencies, without loss o...

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The Contention Avoiding Concurrent Priority Queue

Cited by 6 publications

References 17 publications

MultiQueues: Simple Relaxed Concurrent Priority Queues

MultiQueues: Simple Relaxed Concurrent Priority Queues

Multi-Queues Can Be State-of-the-Art Priority Schedulers

Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms

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