PHOBIC: Perfect Hashing with Optimized Bucket Sizes and Interleaved Coding

A minimal perfect hash function (or MPHF) maps a set of \(n\) keys to \([n]:=\{1,\ldots,n\}\) without collisions. Such functions find widespread application e.g. in bioinformatics and databases. In this paper we revisit PTHash – a construction technique particularly designed for fast queries. PTHash distributes the input keys into small buckets and, for each bucket, it searches for a hash function seed that places its keys in the output domain without collisions. The collection of all seeds is then stored in a compressed way. Since the first buckets are easier to place, buckets are considered in non-increasing order of size. Additionally, PTHash heuristically produces an imbalanced distribution of bucket sizes by distributing 60% of the keys into 30% of the buckets. Our main contribution is to characterize, up to lower order terms, an optimal choice for the expected bucket sizes, improving construction throughput for space efficient configurations both in theory and practice. Further contributions include a new encoding scheme for seeds that works across partitions of the data structure and a GPU parallelization. We call our technique PHOBIC – Perfect Hashing with Optimized Bucket sizes and Interleaved Coding. Compared to PTHash, PHOBIC is 0.17 bits/key more space efficient for same query time and construction throughput. For a configuration with fast queries, our GPU implementation can construct an MPHF at 2.17 bits/key in 28 ns/key, which can be queried in 37 ns on the CPU.

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Publication Details

Journal
ACM Transactions on Algorithms
Published
2026-09-24
DOI
https://doi.org/10.1145/3849725
Citations
2
Primary Topic
Algorithms and Data Compression
Type
article
Field-Weighted Citation Impact
0.00

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article

PHOBIC: Perfect Hashing with Optimized Bucket Sizes and Interleaved Coding

Giulio Ermanno Pibiri, Stefan Hermann, Stefan Walzer, Peter W. Sanders et al.
2 citations
ACM Transactions on Algorithms
Algorithms and Data Compression
article

PHOBIC: Perfect Hashing with Optimized Bucket Sizes and Interleaved Coding

Giulio Ermanno Pibiri, Stefan Hermann, Stefan Walzer, Peter W. Sanders, Hans‐Peter Lehmann
article en
2 citations

Abstract

A minimal perfect hash function (or MPHF) maps a set of \(n\) keys to \([n]:=\{1,\ldots,n\}\) without collisions. Such functions find widespread application e.g. in bioinformatics and databases. In this paper we revisit PTHash – a construction technique particularly designed for fast queries. PTHash distributes the input keys into small buckets and, for each bucket, it searches for a hash function seed that places its keys in the output domain without collisions. The collection of all seeds is then stored in a compressed way. Since the first buckets are easier to place, buckets are considered in non-increasing order of size. Additionally, PTHash heuristically produces an imbalanced distribution of bucket sizes by distributing 60% of the keys into 30% of the buckets. Our main contribution is to characterize, up to lower order terms, an optimal choice for the expected bucket sizes, improving construction throughput for space efficient configurations both in theory and practice. Further contributions include a new encoding scheme for seeds that works across partitions of the data structure and a GPU parallelization. We call our technique PHOBIC – Perfect Hashing with Optimized Bucket sizes and Interleaved Coding. Compared to PTHash, PHOBIC is 0.17 bits/key more space efficient for same query time and construction throughput. For a configuration with fast queries, our GPU implementation can construct an MPHF at 2.17 bits/key in 28 ns/key, which can be queried in 37 ns on the CPU.

ACM Transactions on Algorithms
Karlsruhe Institute of Technology (DE), Ca' Foscari University of Venice (IT)
European Commission, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 100%
Algorithms and Data Compression
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PHOBIC: Perfect Hashing with Optimized Bucket Sizes and Interleaved Coding — Giulio Ermanno Pibiri, Stefan Hermann, et al. · ACM Transactions on Algorithms (2026) | TGRS Research Map | TGRS