The SprayList: a scalable relaxed priority queue

Alistarh, Dan; Kopinsky, Justin; Li, Jerry; Shavit, Nir

doi:10.1145/2858788.2688523

Cited by 21 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another technique which has proved effective for such data structures is flat combining [25]. A further avenue which has been pursued in recent years in order to reduce the contention for any particular node in such structures is to relax the semantics of the data structure operations [4,12,62]. …”

Section: Experimentationmentioning

confidence: 99%

Concurrent Search Data Structures Can Be Blocking and Practically Wait-Free

David

Guerraoui

2016

Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures

View full text Add to dashboard Cite

We argue that there is virtually no practical situation in which one should seek a "theoretically wait-free" algorithm at the expense of a state-of-the-art blocking algorithm in the case of search data structures: blocking algorithms are simple, fast, and can be made "practically wait-free".We draw this conclusion based on the most exhaustive study of blocking search data structures to date. We consider (a) different search data structures of different sizes, (b) numerous uniform and non-uniform workloads, representative of a wide range of practical scenarios, with different percentages of update operations, (c) with and without delayed threads, (d) on different hardware technologies, including processors providing HTM instructions.We explain our claim that blocking search data structures are practically wait-free through an analogy with the birthday paradox, revealing that, in state-of-the-art algorithms implementing such data structures, the probability of conflicts is extremely small. When conflicts occur as a result of context switches and interrupts, we show that HTM-based locks enable blocking algorithms to cope with them.

show abstract

Section: Experimentationmentioning

confidence: 99%

Concurrent Search Data Structures Can Be Blocking and Practically Wait-Free

David

Guerraoui

2016

Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures

View full text Add to dashboard Cite

show abstract

“…Prior work has developed shared-memory priority queues that scale with the number of cores [2,75], but they do so by relaxing priority order. This restricts them to benchmarks that admit order violations, and loss of order means threads often execute useless work far from the critical path [33,34].…”

Section: Additional Related Workmentioning

confidence: 99%

A scalable architecture for ordered parallelism

Jeffrey¹,

Subramanian²,

Yan³

et al. 2015

Proceedings of the 48th International Symposium on Microarchitecture

View full text Add to dashboard Cite

We present Swarm, a novel architecture that exploits ordered irregular parallelism, which is abundant but hard to mine with current software and hardware techniques. In this architecture, programs consist of short tasks with programmer-specified timestamps. Swarm executes tasks speculatively and out of order, and efficiently speculates thousands of tasks ahead of the earliest active task to uncover ordered parallelism. Swarm builds on prior TLS and HTM schemes, and contributes several new techniques that allow it to scale to large core counts and speculation windows, including a new execution model, speculation-aware hardware task management, selective aborts, and scalable ordered commits.We evaluate Swarm on graph analytics, simulation, and database benchmarks. At 64 cores, Swarm achieves 51-122× speedups over a single-core system, and outperforms software-only parallel algorithms by 3-18×.

show abstract

“…The priority queues are protected by a lock. Lock-free implementations for approximate priority queues [7] can be employed for cases of numerous short-lived tasks where synchronization becomes a bottleneck. This is not the case for our experiments, mainly due to the concurrency hint (of our previous work [28]) that dynamically adjusts the task granularity of analytical operations, including scans, depending on the workload concurrency.…”

Section: Task Scheduler Infrastructurementioning

confidence: 99%

Scaling up concurrent main-memory column-store scans

et al. 2015

View full text Add to dashboard Cite

Main-memory column-stores are called to efficiently use modern non-uniform memory access (NUMA) architectures to service concurrent clients on big data. The efficient usage of NUMA architectures depends on the data placement and scheduling strategy of the column-store. Most column-stores choose a static strategy that involves partitioning all data across the NUMA architecture, and employing a stealingbased task scheduler. In this paper, we implement different strategies for data placement and task scheduling for the case of concurrent scans. We compare these strategies with an extensive sensitivity analysis. Our most significant findings include that unnecessary partitioning can hurt throughput by up to 70%, and that stealing memory-intensive tasks can hurt throughput by up to 58%. Based on our analysis, we envision a design that adapts the data placement and task scheduling strategy to the workload.

show abstract

The SprayList: a scalable relaxed priority queue

Cited by 21 publications

References 29 publications

Concurrent Search Data Structures Can Be Blocking and Practically Wait-Free

Concurrent Search Data Structures Can Be Blocking and Practically Wait-Free

A scalable architecture for ordered parallelism

Scaling up concurrent main-memory column-store scans

Contact Info

Product

Resources

About