Title :

Fast and scalable minimal perfect hashing for massive key sets

Author(s) :

Limasset, Antoine [Auteur]
Scalable, Optimized and Parallel Algorithms for Genomics [GenScale]
Rizk, Guillaume [Auteur]
Scalable, Optimized and Parallel Algorithms for Genomics [GenScale]
Chikhi, Rayan [Auteur]
Bioinformatics and Sequence Analysis [BONSAI]
Peterlongo, Pierre [Auteur]
Scalable, Optimized and Parallel Algorithms for Genomics [GenScale]

Conference title :

16th International Symposium on Experimental Algorithms

City :

London

Country :

Royaume-Uni

Start date of the conference :

2017-06-21

English keyword(s) :

and phrases Minimal Perfect Hash Functions
Algorithms
Data Structures
Big Data

HAL domain(s) :

Informatique [cs]/Bio-informatique [q-bio.QM]
Informatique [cs]/Algorithme et structure de données [cs.DS]

English abstract : [en]

Minimal perfect hash functions provide space-efficient and collision-free hashing on static sets. Existing algorithms and implementations that build such functions have practical limitations on the number of input elements ...
Show more >Minimal perfect hash functions provide space-efficient and collision-free hashing on static sets. Existing algorithms and implementations that build such functions have practical limitations on the number of input elements they can process, due to high construction time, RAM or external memory usage. We revisit a simple algorithm and show that it is highly competitive with the state of the art, especially in terms of construction time and memory usage. We provide a parallel C++ implementation called BBhash. It is capable of creating a minimal perfect hash function of 10^10 elements in less than 7 minutes using 8 threads and 5 GB of memory, and the resulting function uses 3.7 bits/element. To the best of our knowledge, this is also the first implementation that has been successfully tested on an input of cardinality 10^12. Source code: https://github.com/rizkg/BBHashShow less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Méthodes d'extraction d'information biologique dans les données HTS non assemblées

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL