AVL Trees with Relaxed Balance

Larsen, Kim S.

doi:10.1006/jcss.2000.1705

Cited by 15 publications

(8 citation statements)

References 23 publications

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“…Given a copy of [23], and this paper, a first year undergraduate student produced our Java implementation of a relaxed-balance AVL tree in less than a week. Its performance was slightly lower than that of Chromatic.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, an amortized constant number of rebalancing steps per Insert or Delete is sufficient to restore balance for any sequence of operations. We have also used our template to implement a non-blocking version of Larsen's leaf-oriented relaxed AVL tree [23]. In such a tree, an amortized logarithmic number of rebalancing steps per Insert or Delete is sufficient to restore balance.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A general technique for non-blocking trees

2014

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“…These implementations improve concurrency by breaking balancing work down into small, local tree transformations that can be performed independently. Analysis in [85] shows that with some modifications, the scheme of [109] guarantees that each update operation causes at most O(log N ) rebalancing operations for an N -node AVL tree. Similar results exist for B-trees [88,109] and red-black trees [20,108].…”

Section: Search Treesmentioning

confidence: 99%

Concurrent Data Structures

Moir¹,

Shavit²

2004

Handbook of Data Structures and Applications

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“…The AVL tree [13] is used for efficiently handling different information relative to the objects and scenarios. This tree has a lower data searching time, which helps to find a particular object for simulation within a shorter time and thus contributes in reduction of ray tracing time.…”

Section: Introductionmentioning

confidence: 99%

Efficient Radio Propagation Prediction Algorithm Including Rough Surface Scattering With Improved Time Complexity

Kausar¹,

Reza²,

Noordin³

et al. 2013

PIER B

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Abstract-Precise modeling of radio propagation is necessary for experiencing the benefits of wireless technology for indoor environments. Among many modeling techniques, the ray tracing based prediction models become popular for indoor wireless radio propagation characterization. Though the ray tracing models are popular, their key deficiency is the slower performance. In this paper, an accelerated technique for three dimensional ray tracing using Adelson-Velski and Landis (AVL) tree data structure is introduced. Here, the AVL tree data structure is coupled with the concepts of quadrant eliminating technique (QET) and nearest neighbor finder (NNF) for optimization and fast characterization of indoor wireless communication. Surface intersection scheme (SIS) is also introduced for optimizing the ray-object intersection time. The AVL tree is used for the effective handling of the objects and environments relative information. The QET technique decreases the ray tracing time by omitting unnecessary object, while NNF decreases the ray-object intersection time by finding the nearest object in an efficient technique. For the validation of the superiority of the proposed technique, a detailed comparison is made with the existing techniques. The comparison shows that the proposed technique has 81.69% lower time consumption than the existing techniques.

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AVL Trees with Relaxed Balance

Cited by 15 publications

References 23 publications

A general technique for non-blocking trees

A general technique for non-blocking trees

Concurrent Data Structures

Efficient Radio Propagation Prediction Algorithm Including Rough Surface Scattering With Improved Time Complexity

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