Sapling: accelerating suffix array queries with learned data models | Zendy

Melanie Kirsche | Zendy; Arun Das | Zendy; Michael C. Schatz | Zendy

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Sapling: accelerating suffix array queries with learned data models

Author(s) -

Melanie Kirsche,

Arun Das,

Michael C. Schatz

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa911

Subject(s) - computer science , suffix , suffix array , memory footprint , source code , data structure , binary number , compressed suffix array , binary code , speedup , suffix tree , data mining , theoretical computer science , parallel computing , programming language , philosophy , linguistics , arithmetic , mathematics

As genomic data becomes more abundant, efficient algorithms and data structures for sequence alignment become increasingly important. The suffix array is a widely used data structure to accelerate alignment, but the binary search algorithm used to query, it requires widespread memory accesses, causing a large number of cache misses on large datasets.

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