Efficient Concurrent Range Queries in B+-trees using RCU-HTM

Siakavaras, Dimitrios; Billis, Panagiotis; Nikas, Konstantinos; Goumas, Georgios; Koziris, Nectarios

doi:10.1145/3350755.3400237

Cited by 5 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lock‐based STs can be implemented with various granularities. Coarse‐grained locking, that is, a single lock protecting the entire ST, is the simplest and most straightforward implementation; however, its performance is poor since all operations are serialized 1 . Fine‐grained approaches either lock specific parts of the tree in an adaptive way 2 or each node individually 3‐8 .…”

Section: Introductionmentioning

confidence: 99%

“…The most straightforward coarse‐grained HTM‐based ST, where each operation is enclosed in a transaction exhibits large transactions both in terms of memory footprint and duration. This approach can be easily applied to any ST, however, in today's HTM systems its large transactions lead to low performance due to the large number of capacity aborts 1,21 . Avni and Kuszmaul 22 present a fine‐grained HTM‐based tree that uses smaller transactions by excluding the search path from them.…”

Section: Introductionmentioning

confidence: 99%

“…Although cg‐htm is very easy to implement and performs very well under certain workloads, 1 it has a severe drawback. Enclosing the whole operation in a single transaction increases its size both in terms of read/write‐set sizes and time duration.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

RCU‐HTM: A generic synchronization technique for highly efficient concurrent search trees

Siakavaras

Nikas

Goumas

et al. 2021

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Concurrent search trees (STs) are among the most widely used data structures to store and retrieve data in contemporary multithreaded applications. Despite the high amount of prior work, it still remains challenging to implement highly efficient concurrent STs. This is mainly due to the fact that both traditional synchronization methods (i.e., locks and atomic operations) and more novel ones (i.e., read‐copy‐update and transactional memory) fail to provide solutions that are generic and at the same time able to attain high performance under diverse workloads and contention levels. In this work, we present RCU‐HTM, a technique that combines read‐copy‐update (RCU) and hardware transactional memory (HTM), and: (a) supports the implementation of a concurrent version of any type of search tree, and (b) achieves high performance across all execution scenarios. We also incorporate a low‐overhead, epoch‐based memory reclamation scheme to make our RCU‐HTM trees practical for large‐scale long‐running applications. To showcase the capabilities of our technique, we implement and evaluate multiple RCU‐HTM trees and compare their performance with several state‐of‐the‐art competitors. More specifically, we apply RCU‐HTM to 12 different types of binary, B+ and (a,b)‐trees and compare against 18 state‐of‐the‐art implementations that use four different synchronization mechanisms, namely, locks, atomic operations, RCU, and HTM. We evaluate the trees under different levels of contention by varying the size of the tree, the operations mix, and the number of threads, for a total of 210 execution scenarios for each implementation. Our evaluation shows that in the majority of executions, RCU‐HTM trees outperform their state‐of‐the‐art alternatives, and even in the cases where they do not, their performance is very close to that of the best implementation.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

RCU‐HTM: A generic synchronization technique for highly efficient concurrent search trees

Siakavaras

Nikas

Goumas

et al. 2021

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

Brief Announcement: LIT: Lookup Interlocked Table for Range Queries

Miller,

Hassan,

Palmieri

2024

Proceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures

View full text Add to dashboard Cite

SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

Ge,

Zhang,

Shi

et al. 2023

Proc. ACM Manag. Data

View full text Add to dashboard Cite

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is the learned index, which uses a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04x compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+.

show abstract

Efficient Concurrent Range Queries in B+-trees using RCU-HTM

Cited by 5 publications

References 4 publications

RCU‐HTM: A generic synchronization technique for highly efficient concurrent search trees

RCU‐HTM: A generic synchronization technique for highly efficient concurrent search trees

Brief Announcement: LIT: Lookup Interlocked Table for Range Queries

SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

Contact Info

Product

Resources

About