Concurrent Balanced Augmented Trees
Abstract
Augmentation makes search trees tremendously more versatile, allowing them to support efficient aggregation queries, order-statistic queries, and range queries in addition to insertion, deletion, and lookup. In this paper, we present the first lock-free augmented balanced search tree supporting generic augmentation functions. Our algorithmic ideas build upon a recent augmented unbalanced search tree presented by Fatourou and Ruppert [DISC, 2024]. We implement both data structures, solving some memory reclamation challenges in the process, and provide an experimental performance analysis of them. We also present optimized versions of our balanced tree that use delegation to achieve better scalability and performance (by more than 2x in most workloads). Our experiments show that our augmented balanced tree completes updates 2.2 to 30 times faster than the unbalanced augmented tree, and outperforms unaugmented trees by up to several orders of magnitude on 120 threads.
Cite
@article{arxiv.2601.05225,
title = {Concurrent Balanced Augmented Trees},
author = {Evan Wrench and Ajay Singh and Younghun Roh and Panagiota Fatourou and Siddhartha Jayanti and Eric Ruppert and Yuanhao Wei},
journal= {arXiv preprint arXiv:2601.05225},
year = {2026}
}
Comments
Full version of paper appearing in PPoPP 2026