Concurrent Balanced Augmented Trees

TL;DR

This paper introduces a lock-free, augmented balanced search tree supporting various queries, with optimized implementations that significantly outperform previous unbalanced and unaugmented trees in scalability and speed.

Contribution

It presents the first lock-free augmented balanced search tree with generic augmentation support, building on recent unbalanced tree work and improving performance through delegation techniques.

Findings

Augmented balanced tree completes updates 2.2 to 30 times faster than unbalanced versions.

Our data structures outperform unaugmented trees by several orders of magnitude on 120 threads.

Optimizations using delegation improve scalability and performance by more than 2x in most workloads.

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.