Quadboost: A Scalable Concurrent Quadtree

TL;DR

Quadboost introduces a scalable, non-blocking concurrent quadtree supporting efficient insert, remove, move, and search operations, significantly improving performance and scalability on multi-core systems for two-dimensional spatial data.

Contribution

It presents a novel decoupling approach and continuous find mechanism that enhance concurrency and search efficiency in a non-blocking quadtree.

Findings

Scales well on multi-core systems with 32 threads.

Outperforms existing concurrent trees by up to 109% in retrieval tasks.

Move operation outperforms previous algorithms by up to 47%.

Abstract

Building concurrent spatial trees is more complicated than binary search trees since a space hierarchy should be preserved during modifications. We present a non-blocking quadtree-quadboost-that supports concurrent insert, remove, move, and contain operations. To increase its concurrency, we propose a decoupling approach that separates physical adjustment from logical removal within the remove operation. In addition, we design a continuous find mechanism to reduce its search cost. The move operation combines the searches for different keys together and modifies different positions with atomicity. The experimental results show that quadboost scales well on a multi-core system with 32 hardware threads. More than that, it outperforms existing concurrent trees in retrieving two-dimensional keys with up to 109% improvement when the number of threads is large. The move operation proved to perform better than the best-known algorithm, with up to 47%.