A Lazy Concurrent List-Based Set Algorithm

Heller, Steve; Herlihy, Maurice; Luchangco, Victor; Moir, Mark; Scherer, William N.; Shavit, Nir

doi:10.1007/11795490_3

Cited by 161 publications

(192 citation statements)

References 8 publications

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“…This means that for each addition or deletion of a node in the list, a modification lock has to be obtained, whereas simple comparison operations do not require explicit locks. This idea of modification locks can be implemented using a novel concurrent data structure called the Lazy List [20]. We adjusted the basic structure for the use in skyline computation and show the resulting code in Figure 5.…”

Section: Lazy List Parallel Bnlmentioning

confidence: 99%

Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval

Selke

Lofi

Balke

2010

Database Systems for Advanced Applications

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Abstract. Until recently algorithms continuously gained free performance improvements due to ever increasing processor speeds. Unfortunately, this development has reached its limit. Nowadays, new generations of CPUs focus on increasing the number of processing cores instead of simply increasing the performance of a single core. Thus, sequential algorithms will be excluded from future technological advances. Instead, highly scalable parallel algorithms are needed to fully tap new hardware potentials. In this paper we establish a design space for parallel algorithms in the domain of personalized database retrieval, taking skyline algorithms as a representative example. We will investigate the spectrum of base operations of different retrieval algorithms and various parallelization techniques to develop a set of highly scalable and high-performing skyline algorithms for different retrieval scenarios. Finally, we extensively evaluate these algorithms to showcase their superior characteristics.

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Section: Lazy List Parallel Bnlmentioning

confidence: 99%

Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval

Selke

Lofi

Balke

2010

Database Systems for Advanced Applications

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“…If this latter optik trylock version fails, the predecessor's OPTIK lock is reverted, instead of unlocked, in order to avoid false conflicts with other concurrent operations. Notice that due to OPTIK, (i) no deleted flag is required (as in [22]), and (ii) the OPTIK lock of the deleted node is never released, which prohibits updates from reusing this node. Essentially, the linearization point of a deletion is the actual write on the pred->next pointer in line 24.…”

Section: Optik-based Linked Listmentioning

confidence: 99%

Optimistic concurrency with OPTIK

GuerraouiRachid

TrigonakisVasileios

2016

SIGPLAN Not.

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We introduce OPTIK, a new practical design pattern for designing and implementing fast and scalable concurrent data structures. OPTIK relies on the commonly-used technique of version numbers for detecting conflicting concurrent operations. We show how to implement the OPTIK pattern using the novel concept of OPTIK locks. These locks enable the use of version numbers for implementing very efficient optimistic concurrent data structures. Existing state-of-the-art lock-based data structures acquire the lock and then check for conflicts. In contrast, with OPTIK locks, we merge the lock acquisition with the detection of conflicting concurrency in a single atomic step, similarly to lock-free algorithms. We illustrate the power of our OPTIK pattern and its implementation by introducing four new algorithms and by optimizing four state-of-the-art algorithms for linked lists, skip lists, hash tables, and queues. Our results show that concurrent data structures built using OPTIK are more scalable than the state of the art.

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“…A vast amount of work has been dedicated to the development of correct and scalable CSDS algorithms [3][4][5][7][8][9][10]14,17].…”

Section: Introductionmentioning

confidence: 99%

“…Our work is based on the vast amount of prior practical work that points to a single direction for achieving scalability: Strip down synchronization (i.e., every construct that induces coordination of concurrent threads), which is a major impediment to scalability. To achieve minimal synchronization, all existing patterns for designing concurrent data structures do, directly or indirectly, promote concurrent designs that are close to their sequential counterparts: concrete CSDS algorithms [10,13], RCU [17], RLU [16], OPTIK [8], ASCY [4], etc.…”

Section: Introductionmentioning

confidence: 99%

Sequential Proximity

Antoniadis¹,

Guerraoui²,

Stainer³

et al. 2017

Networked Systems

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Abstract. Establishing the scalability of a concurrent algorithm a priori, before implementing and evaluating it on a concrete multi-core platform, seems difficult, if not impossible. In the context of search data structures however, according to all practical work of the past decade, algorithms that scale share a common characteristic: They all resemble standard sequential implementations for their respective data structure type and strive to minimize the number of synchronization operations.In this paper, we present sequential proximity, a theoretical framework to determine whether a concurrent search algorithm is close to its sequential counterpart. With sequential proximity we take the first step towards a theory of scalability for concurrent search algorithms.

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A Lazy Concurrent List-Based Set Algorithm

Cited by 161 publications

References 8 publications

Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval

Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval

Optimistic concurrency with OPTIK

Sequential Proximity

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