Concurrent Balanced Augmented Trees

2026-01-08

ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming

AI Summary

This work addresses the challenge of achieving efficient aggregation, order statistics, and range queries under high concurrency by proposing the first lock-free concurrent balanced augmented search tree. Building upon the Fatourou–Ruppert unbalanced augmented tree, the design incorporates a balancing mechanism alongside efficient memory reclamation and delegation-based optimization strategies, substantially improving scalability and performance. Experimental results demonstrate that, under 120 threads, the proposed structure achieves a 2.2× to 30× speedup over its unbalanced counterpart and outperforms non-augmented trees by several orders of magnitude. This study presents the first search tree design that successfully integrates balancing, functional augmentation, and lock-free concurrency.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Multiagent Systems: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• concurrent
• balanced
• augmented
• search tree
• lock-free

Innovation

• lock-free
• augmented balanced tree
• concurrent data structures
• delegation
• memory reclamation