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

Patrick Dinklage
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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.
计算 LZ 端解析:易于实现,实用高效
对输入字符串进行 LZ-End 解析 [Kreft & Navarro, 2011],不仅能获得与流行的 Lempel-Ziv 77 方案相媲美的压缩效果,还能实现高效的随机访问。Kempa 和 Kosolobov 的研究表明,解析可以在输入长度线性的时间和空间内完成[Kempa & Kosolobov,2017],但相应的算法几乎不实用。我们将焦点放在他们的次优算法上,该算法可以在$mathcal{O}(n \lg\lg n)$的时间内计算解析。我们给出了一个详细的简化描述,并列出了一个完整的清单,其中包含了原始实现中未被记录的技巧,但也包含了懒惰评估以减少实际工作量,并通过移除一级间接来减少所需的工作内存。我们在一个简短的基准测试中证明了我们算法的合理性,其解析速度快于目前的技术水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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