Non-blocking binary search trees

Ellen, Faith; Fatourou, Panagiota; Ruppert, Eric; Breugel, Franck van

doi:10.1145/1835698.1835736

Cited by 165 publications

(201 citation statements)

References 19 publications

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“…With this in mind, we opt for an external BST representation [7,9] to design the LFkD-tree. In this design, only leaf-nodes contain the data-points and internal-nodes route a traversal, see g. 1 (b).…”

Section: A High-level Summary Of the Workmentioning

confidence: 99%

Concurrent Linearizable Nearest Neighbour Search in LockFree-kD-tree

Chatterjee

Walulya

Tsigas

2018

Proceedings of the 19th International Conference on Distributed Computing and Networking

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The Nearest neighbour search (NNS) is an important problem in a large number of application domains dealing with multidimensional data. In concurrent settings, where dynamic modications are allowed, a linearizable implementation of NNS is highly desirable to discover the latest nearest neighbour of a given target data-point.In this paper, we introduce the LockFree-kD-tree (LFkD-tree): a lock-free concurrent kD-tree, which implements an abstract data type (ADT) that provides the operations Add, Remove, Contains, and NNS. Our implementation is linearizable. The operations in the LFkDtree use single-word read and compare-and-swap (CAS) atomic primitives, which are readily supported on commonly available multi-core processors. We experimentally evaluate the LFkD-tree using several benchmarks comprising real-world and synthetic datasets. The experiments show that the presented design is scalable and achieves signicant speed-up compared to the implementations of an existing sequential kD-tree and a recently proposed multidimensional indexing structure, PH-tree.

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Section: A High-level Summary Of the Workmentioning

confidence: 99%

Concurrent Linearizable Nearest Neighbour Search in LockFree-kD-tree

Chatterjee

Walulya

Tsigas

2018

Proceedings of the 19th International Conference on Distributed Computing and Networking

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“…Figure 2a). The first two levels with three nodes (1, 2, 3) fit within a single block transfer while the next two levels need to be loaded in two separate block transfers that contain nodes (6,7,8) 1 and nodes (13,14,15), respectively. Generally, the number of data block transfers for a BFS tree of size N is (log 2 N − log 2 B) = log 2 N/B ∼ log 2 N for N B.…”

Section: Design Overviewmentioning

confidence: 99%

“…Concurrent B-trees (e.g., B-link tree [28]) only perform well if their B size is optimal. Highly concurrent search trees such as non-blocking concurrent search trees [14,30] and Software Transactional Memory (STM)-based search trees [1,11], however, do not take into account fine-grained data locality.…”

Section: Introductionmentioning

confidence: 99%

GreenBST: Energy-Efficient Concurrent Search Tree

Umar

Anshus

2016

Euro-Par 2016: Parallel Processing

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Abstract. Like other fundamental abstractions for energy-efficient computing, search trees need to support both high concurrency and finegrained data locality. However, existing locality-aware search trees such as ones based on the van Emde Boas layout (vEB-based trees), poorly support concurrent (update) operations while existing highly-concurrent search trees such as the non-blocking binary search trees do not consider data locality. We present GreenBST, a practical energy-efficient concurrent search tree that supports fine-grained data locality as vEB-based trees do, but unlike vEB-based trees, GreenBST supports high concurrency. GreenBST is a k-ary leaf-oriented tree of GNodes where each GNode is a fixed size tree-container with the van Emde Boas layout. As a result, GreenBST minimizes data transfer between memory levels while supporting highly concurrent (update) operations. Our experimental evaluation using the recent implementation of non-blocking binary search trees, highly concurrent B-trees, conventional vEB trees, as well as the portably scalable concurrent trees shows that GreenBST is efficient: its energy efficiency (in operations/Joule) and throughput (in operations/second) are up to 65% and 69% higher, respectively, than the other trees on a high performance computing (HPC) platform (Intel Xeon), an embedded platform (ARM), and an accelerator platform (Intel Xeon Phi). The results also provide insights into how to develop energy-efficient data structures in general.

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“…Bronson et al presented a scalable concurrent implementation of an AVL tree based on transactional memory mechanisms that require a xed number of locks to perform deletions [20]. Recently, the rst non-blocking implementation of a binary search tree was proposed [21].…”

Section: Related Workmentioning

confidence: 99%

Lock-Free Resizeable Concurrent Tries

Prokopec

Bagwell

Odersky

2013

Languages and Compilers for Parallel Computing

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Abstract. This paper describes an implementation of a non-blocking concurrent hash trie based on single-word compare-and-swap instructions in a shared-memory system. Insert, lookup and remove operations modifying dierent parts of the hash trie can be run completely independently. Remove operations ensure that the unneeded memory is freed and that the trie is kept compact. A pseudocode for these operations is presented and a proof of correctness is given we show that the implementation is linearizable and lock-free. Finally, benchmarks are presented that compare concurrent hash trie operations against the corresponding operations on other concurrent data structures.

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Non-blocking binary search trees

Cited by 165 publications

References 19 publications

Concurrent Linearizable Nearest Neighbour Search in LockFree-kD-tree

Concurrent Linearizable Nearest Neighbour Search in LockFree-kD-tree

GreenBST: Energy-Efficient Concurrent Search Tree

Lock-Free Resizeable Concurrent Tries

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