Lock-Free Augmented Trees

TL;DR

This paper introduces a general lock-free technique to augment concurrent trees with additional information, enabling efficient order-statistic and range queries without compromising update performance.

Contribution

The authors present a novel, general method for augmenting lock-free trees, supporting a wide range of classical augmented data structures in concurrent settings.

Findings

Developed a lock-free trie supporting order-statistic queries in O(log N) steps.

Created a lock-free binary search tree with order-statistic queries in O(h) steps.

Enhanced augmentations to achieve O(log |S|) query steps with minimal update overhead.

Abstract

Augmenting an existing sequential data structure with extra information to support greater functionality is a widely used technique. For example, search trees are augmented to build sequential data structures like order-statistic trees, interval trees, tango trees, link/cut trees and many others. We study how to design concurrent augmented tree data structures. We present a new, general technique that can augment a lock-free tree to add any new fields to each tree node, provided the new fields' values can be computed from information in the node and its children. This enables the design of lock-free, linearizable analogues of a wide variety of classical augmented data structures. As a first example, we give a wait-free trie that stores a set $S$ of elements drawn from $\{1,\ldots,N\}$ and supports linearizable order-statistic queries such as finding the $k$th smallest element of $S$. Updates and queries take $O(\log N)$ steps. We also apply our technique to a lock-free binary search tree (BST), where changes to the structure of the tree make the linearization argument more challenging. Our augmented BST supports order statistic queries in $O(h)$ steps on a tree of height $h$. The augmentation does not affect the asymptotic running time of the updates. For both our trie and BST, we give an alternative augmentation to improve searches and order-statistic queries to run in $O(\log |S|)$ steps (with a small increase in step complexity of updates). As an added bonus, our technique supports arbitrary multi-point queries (such as range queries) with the same time complexity as they would have in the corresponding sequential data structure.