BioSEAL: In-Memory Biological Sequence Alignment Accelerator for Large-Scale Genomic Data

Roman Kaplan, Leonid Yavits, Ran Ginosasr

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

Genome sequences contain hundreds of millions of DNA base pairs. Finding the degree of similarity between two genomes requires executing a compute-intensive dynamic programming algorithm, such as Smith-Waterman. Traditional von Neumann architectures have limited parallelism and cannot provide an efficient solution for large-scale genomic data. Approximate heuristic methods (e.g. BLAST) are commonly used. However, they are suboptimal and still compute-intensive. In this work, we present BioSEAL, a biological sequence alignment accelerator. BioSEAL is a massively parallel non-von Neumann processing-in-memory architecture for large-scale DNA and protein sequence alignment. BioSEAL is based on resistive content addressable memory, capable of energy-efficient and highperformance associative processing. We present an associative processing algorithm for entire database sequence alignment on BioSEAL and compare its performance and power consumption with state-of-art solutions. We show that BioSEAL can achieve up to 57× speedup and 156× better energy efficiency, compared with existing solutions for genome sequence alignment and protein sequence database search.

Original languageEnglish
Title of host publicationSYSTOR 2020 - Proceedings of the 13th ACM International Systems and Storage Conference
PublisherAssociation for Computing Machinery
Pages36-48
Number of pages13
ISBN (Electronic)9781450375887
DOIs
StatePublished - 30 May 2020
Externally publishedYes
Event13th ACM International Systems and Storage Conference, SYSTOR 2020 - Haifa, Israel
Duration: 13 Oct 202015 Oct 2020

Publication series

NameSYSTOR 2020 - Proceedings of the 13th ACM International Systems and Storage Conference

Conference

Conference13th ACM International Systems and Storage Conference, SYSTOR 2020
Country/TerritoryIsrael
CityHaifa
Period13/10/2015/10/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

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