Cache-Friendly Search Trees; or, In Which Everything Beats std::set

TL;DR

This paper explores cache-oblivious search trees, demonstrating their practical advantages over std::set and B-trees in modern hardware by emphasizing cache efficiency for ordered dictionaries.

Contribution

It provides a comprehensive overview of cache-oblivious data structures and presents empirical results showing their superior cache performance compared to traditional structures.

Findings

Cache-oblivious search trees outperform std::set in cache efficiency.

Empirical results show improved performance of cache-oblivious structures on modern hardware.

Cache-oblivious data structures are practical and beneficial for real-world applications.

Abstract

While a lot of work in theoretical computer science has gone into optimizing the runtime and space usage of data structures, such work very often neglects a very important component of modern computers: the cache. In doing so, very often, data structures are developed that achieve theoretically-good runtimes but are slow in practice due to a large number of cache misses. In 1999, Frigo et al. introduced the notion of a cache-oblivious algorithm: an algorithm that uses the cache to its advantage, regardless of the size or structure of said cache. Since then, various authors have designed cache-oblivious algorithms and data structures for problems from matrix multiplication to array sorting. We focus in this work on cache-oblivious search trees; i.e. implementing an ordered dictionary in a cache-friendly manner. We will start by presenting an overview of cache-oblivious data structures, especially cache-oblivious search trees. We then give practical results using these cache-oblivious structures on modern-day machinery, comparing them to the standard std::set and other cache-friendly dictionaries such as B-trees.