Computing the LZ-End parsing: Easy to implement and practically efficient

TL;DR

This paper presents a simplified, easy-to-implement algorithm for computing the LZ-End parsing that is both theoretically efficient and practically faster than existing methods, improving compression and random access capabilities.

Contribution

The authors provide a detailed, practical algorithm for LZ-End parsing with simplified implementation and improved efficiency, validated by benchmarking.

Findings

Faster parsing than previous algorithms in practice

Simplified implementation with fewer resources

Maintains competitive compression and random access features

Abstract

The LZ-End parsing [Kreft & Navarro, 2011] of an input string yields compression competitive with the popular Lempel-Ziv 77 scheme, but also allows for efficient random access. Kempa and Kosolobov showed that the parsing can be computed in time and space linear in the input length [Kempa & Kosolobov, 2017], however, the corresponding algorithm is hardly practical. We put the spotlight on their suboptimal algorithm that computes the parsing in time $\mathcal{O}(n \lg\lg n)$. It requires a comparatively small toolset and is therefore easy to implement, but at the same time very efficient in practice. We give a detailed and simplified description with a full listing that incorporates undocumented tricks from the original implementation, but also uses lazy evaluation to reduce the workload in practice and requires less working memory by removing a level of indirection. We legitimize our algorithm in a brief benchmark, obtaining the parsing faster than the state of the art.