Fingerprinting-based minimal perfect hashing revisited

Q2 Mathematics Journal of Experimental Algorithmics Pub Date : 2023-05-13 DOI:10.1145/3596453
Piotr Beling
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引用次数: 1

Abstract

In the paper we study a fingerprint-based minimal perfect hash function (FMPH for short). While FMPH is not as space-efficient as some other minimal perfect hash functions (for example RecSplit, CHD, or PTHash), it has a number of practical advantages that make it worthy of consideration. FMPH is simple and quite fast to evaluate. Its construction requires very little auxiliary memory, takes a short time and, in addition, can be parallelized or carried out without holding keys in memory. In this paper, we propose an effective method (called FMPHGO) that reduces the size of FMPH, as well as a number of implementation improvements. In addition, we experimentally study FMPHGO performance and find the best values for its parameters. Our benchmarks show that with our method and an efficient structure to support the rank queries on a bit vector, the FMPH size can be reduced to about 2.1 bits/key, which is close to the size achieved by state-of-the-art methods and noticeably larger only compared to RecSplit. FMPHGO preserves most of the FMPH advantages mentioned above, but significantly reduces its construction speed. However, FMPHGO’s construction speed is still competitive with methods of similar space efficiency (like CHD or PTHash), and seems to be good enough for practical applications.
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基于指纹的最小完美哈希重新审视
本文研究了一种基于指纹的最小完美散列函数(简称FMPH)。虽然FMPH不像其他一些最小完美哈希函数(例如RecSplit、CHD或PTHash)那样具有空间效率,但它具有许多实用优势,值得考虑。FMPH是一种简单且快速的评估方法。它的构造只需要很少的辅助内存,耗时很短,此外,可以并行化或在不占用内存的情况下执行。在本文中,我们提出了一种有效的方法(称为FMPHGO),它可以减少FMPH的大小,并对实现进行了一些改进。此外,我们还对FMPHGO的性能进行了实验研究,并找到了其参数的最佳值。我们的基准测试表明,通过我们的方法和有效的结构来支持比特向量上的秩查询,FMPH大小可以减少到大约2.1比特/密钥,这与最先进的方法实现的大小接近,并且仅与RecSplit相比明显更大。FMPHGO保留了上述FMPH的大部分优点,但显著降低了其构建速度。然而,FMPHGO的构建速度与类似空间效率的方法(如CHD或PTHash)相比仍然具有竞争力,并且对于实际应用来说似乎足够好。
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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来源期刊
Journal of Experimental Algorithmics
Journal of Experimental Algorithmics Mathematics-Theoretical Computer Science
CiteScore
3.10
自引率
0.00%
发文量
29
期刊介绍: The ACM JEA is a high-quality, refereed, archival journal devoted to the study of discrete algorithms and data structures through a combination of experimentation and classical analysis and design techniques. It focuses on the following areas in algorithms and data structures: ■combinatorial optimization ■computational biology ■computational geometry ■graph manipulation ■graphics ■heuristics ■network design ■parallel processing ■routing and scheduling ■searching and sorting ■VLSI design
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