Concurrent Wait-Free Red Black Trees

Natarajan, Aravind; Savoie, Lee Hilton; Mittal, Neeraj

doi:10.1007/978-3-319-03089-0_4

Cited by 23 publications

(11 citation statements)

References 13 publications

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“…Earlier, Tsay and Li [17] had given a template for waitfree implementations of down-trees, but it has very limited concurrency. A version of this technique was used to implement red-black trees [12]. Natarajan and Mittal [11] recently gave a different non-blocking implementation of leaforiented BSTs that is somewhat simpler than that of Ellen et al Non-blocking implementations of a version of B-trees [15] and B+trees [3] have also appeared.…”

Section: Related Workmentioning

confidence: 98%

“…However, they did not provide a bound on the complexity of their implementation. In the subsequent four years, there have been many novel non-blocking implementations of various types of search trees [3,4,5,9,11,12,14,15]. Like [7], none have complexity analyses; instead, researchers have relied on experiments to evaluate and compare the performance of the search tree implementations.…”

Section: Introductionmentioning

confidence: 98%

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The amortized complexity of non-blocking binary search trees

Ellen

Fatourou

Helga

et al. 2014

Proceedings of the 2014 ACM Symposium on Principles of Distributed Computing

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We improve upon an existing non-blocking implementation of a binary search tree from single-word compare-and-swap instructions. We show that the worst-case amortized step complexity of performing a Find, Insert or Delete operation op on the tree is O(h(op) +ċ(op)) where h(op) is the height of the tree at the beginning of op andċ(op) is the maximum number of operations accessing the tree at any one time during op. This is the first bound on the complexity of a non-blocking implementation of a search tree.

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Section: Related Workmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

The amortized complexity of non-blocking binary search trees

Ellen

Fatourou

Helga

et al. 2014

Proceedings of the 2014 ACM Symposium on Principles of Distributed Computing

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show abstract

“…This is because if a concurrent low-contention adaptation thread were to change the root pointer, it would first need to acquire the lock of base, which it can not. Now we can splice out the parent (lines [21][22][23][24][25][26][27] and unlock the routing node lock(s) that we have taken (lines [28][29].…”

Section: E Low-contention Adaptationmentioning

confidence: 99%

“…Current scalable data structures for concurrent ordered sets and maps either use fine-grained locking [2], [6], [12], [14] or lock-free techniques [7], [10], [15], [16], [20], [26], [27] to enable parallel operations in the tree. For fine-grained synchronization, they all pay a price in sequential efficiency and/or memory usage.…”

Section: Introductionmentioning

confidence: 99%

Contention Adapting Search Trees

Sagonas

Winblad

2015

2015 14th International Symposium on Parallel and Distributed Computing

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With multicores being ubiquitous, concurrent data structures are becoming increasingly important. This paper proposes a novel approach to concurrent data structure design where the data structure collects statistics about contention and adapts dynamically according to this statistics. We use this approach to create a contention adapting binary search tree (CA tree) that can be used to implement concurrent ordered sets and maps. Our experimental evaluation shows that CA trees scale similar to recently proposed algorithms on a big multicore machine on various scenarios with a larger set size, and outperform the same data structures in more contended scenarios and in sequential performance. We also show that CA trees are well suited for optimization with hardware lock elision. In short, we propose a practically useful and easy to implement and show correct concurrent search tree that naturally adapts to the level of contention. 14th International Symposium on Parallel and Distributed Computing 978-1-4673-7148-3/15 $31.00 statLock(base.lock); 11 if (base.valid == false) { 12 statUnlock(base.lock); 13 return doOperation(tree, operation, key); // retry 14 } else { 15 Object result = operation.execute(base.root, key); 16 if (base.lock.statistics > MAX _ CONTENTION) { 17 if (size(base.root) < 2) base.lock.statistics = 0; 18 else highContentionSplit(tree, base, prevNode); 19 } else if (base.lock.statistics < MIN _ CONTENTION) { 20 if (prevNode == null) base.lock.statistics = 0; 21 else lowContentionJoin(tree, base, prevNode);

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“…Some attempts have been made to implement RBTs without using locks. It was observed that the approach of Tsay and Li could be used to implement wait-free RBTs [26] and, furthermore, this could be done so that only updates must copy a path; searches may simply read the path. However, the concurrency of updates is still very limited.…”

Section: Related Workmentioning

confidence: 99%

A general technique for non-blocking trees

2014

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Abstract. We describe a general technique for obtaining provably correct, non-blocking implementations of a large class of tree data structures where pointers are directed from parents to children. Updates are permitted to modify any contiguous portion of the tree atomically. Our non-blocking algorithms make use of the LLX, SCX and VLX primitives, which are multi-word generalizations of the standard LL, SC and VL primitives and have been implemented from single-word CAS [10]. To illustrate our technique, we describe how it can be used in a fairly straightforward way to obtain a non-blocking implementation of a chromatic tree, which is a relaxed variant of a red-black tree. The height of the tree at any time is O(c + log n), where n is the number of keys and c is the number of updates in progress. We provide an experimental performance analysis which demonstrates that our Java implementation of a chromatic tree rivals, and often significantly outperforms, other leading concurrent dictionaries.

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Concurrent Wait-Free Red Black Trees

Cited by 23 publications

References 13 publications

The amortized complexity of non-blocking binary search trees

The amortized complexity of non-blocking binary search trees

Contention Adapting Search Trees

A general technique for non-blocking trees

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