A Concurrent Blink-Tree Algorithm Using a Cooperative Locking Protocol

Lim, Sungchae; Ahn, Joung Real; Kim, Myoung Ho

doi:10.1007/3-540-45073-4_20

Cited by 2 publications

(2 citation statements)

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“…In the context of nonblocking algorithms, researchers [3,8,13] describe algorithms where threads cooperate to complete an operation when they would otherwise block for synchronization. In the area of databases, Lim, Ahn, and Kim [10] describe a concurrent B link tree algorithm which uses cooperative locking to handle nodes with concurrent underflow.…”

Section: Resultsmentioning

confidence: 99%

Helper locks for fork-join parallel programming

Agrawal

Leiserson

Sukha

2010

Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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Helper locks allow programs with large parallel critical sections, called parallel regions, to execute more efficiently by enlisting processors that might otherwise be waiting on the helper lock to aid in the execution of the parallel region. Suppose that a processor p is executing a parallel region A after having acquired the lock L protecting A. If another processor p ′ tries to acquire L, then instead of blocking and waiting for p to complete A, processor p ′ joins p to help it complete A. Additional processors not blocked on L may also help to execute A.The HELPER runtime system can execute fork-join computations augmented with helper locks and parallel regions. HELPER supports the unbounded nesting of parallel regions. We provide theoretical completion-time and space-usage bounds for a design of HELPER based on work stealing. Specifically, let V be the number of parallel regions in a computation, let T 1 be its work, and let e T ∞ be its "aggregate span" -the sum of the spans (critical-path lengths) of all its parallel regions. We prove that HELPER completes the computation in expected time O(T 1 /P + e T ∞ + PV ) on P processors. This bound indicates that programs with a small number of highly parallel critical sections can attain linear speedup. For the space bound, we prove that HELPER completes a program using only O(P e S 1 ) stack space, where e S 1 is the sum, over all regions, of the stack space used by each region in a serial execution. Finally, we describe a prototype of HELPER implemented by modifying the Cilk multithreaded runtime system. We used this prototype to implement a concurrent hash table with a resize operation protected by a helper lock.

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Section: Resultsmentioning

confidence: 99%

Helper locks for fork-join parallel programming

Agrawal

Leiserson

Sukha

2010

Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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“…FAST [18] exploits the usage of SIMD instructions to further optimize searches within index nodes for cache performance. Meanwhile, Blink-Tree [28] provides a concurrent tree restructuring mechanism for handling underflow nodes as well as overflow nodes. Except the series of B+Tree, the choices of index for databases also have many candidates for specific purposes, such as T-trees [25], red-black trees [7], LSM tree [38], and more recently, HOT [6].…”

Section: Related Workmentioning

confidence: 99%

Updatable learned index with precise positions

Yong

Chen

et al. 2021

Proc. VLDB Endow.

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Index plays an essential role in modern database engines to accelerate the query processing. The new paradigm of "learned index" has significantly changed the way of designing index structures in DBMS. The key insight is that indexes could be regarded as learned models that predict the position of a lookup key in the dataset. While such studies show promising results in both lookup time and index size, they cannot efficiently support update operations. Although recent studies have proposed some preliminary approaches to support update, they are at the cost of scarifying the lookup performance as they suffer from the overheads brought by imprecise predictions in the leaf nodes. In this paper, we propose LIPP, a brand new framework of learned index to address such issues. Similar with state-of-the-art learned index structures, LIPP is able to support all kinds of index operations, namely lookup query, range query, insert, delete, update and bulkload. Meanwhile, we overcome the limitations of previous studies by properly extending the tree structure when dealing with update operations so as to eliminate the deviation of location predicted by the models in the leaf nodes. Moreover, we further propose a dynamic adjustment strategy to ensure that the height of the tree index is tightly bounded and provide comprehensive theoretical analysis to illustrate it. We conduct an extensive set of experiments on several real-life and synthetic datasets. The results demonstrate that our method consistently outperforms state-of-the-art solutions, achieving by up to 4X for a broader class of workloads with different index operations.

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