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Using SIMD Instructions to Accelerate Sequence Similarity Searches Inside a Database System

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Databases Theory and Applications (ADC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10837))

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Abstract

Database systems are optimised for managing large data sets, but they face difficulties making an impact to life sciences where the typical use cases involve much more complex analytical algorithms than found in traditional OLTP or OLAP scenarios. Although many database management systems (DBMS) are extensible via stored procedures to implement transactions or complex algorithms, these stored procedures are usually unable to leverage the inbuilt optimizations provided by the query engine, so other optimization avenues must be explored.

In this paper, we investigate how sequence alignment algorithms, one of the most common operations carried out on a bioinformatics or genomics database, can be efficiently implemented close to the data within an extensible database system. We investigate the use of single instruction, multiple data (SIMD) extensions to accelerate logic inside an DBMS. We also compare it to implementations of the same logic outside the DBMS.

Our implementation of an SIMD-accelerated Smith Waterman sequence-alignment algorithm shows an order of magnitude improvement on a non-accelerated version while running inside a DBMS. Our SIMD accelerated version also performs with little to no overhead inside the DBMS compared to the same logic running outside the DBMS.

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Authors and Affiliations

  1. The University of Sydney, Sydney, NSW, 2006, Australia

    Sidath Randeni Kadupitige & Uwe Röhm

Authors
  1. Sidath Randeni Kadupitige
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  2. Uwe Röhm
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Corresponding author

Correspondence to Sidath Randeni Kadupitige .

Editor information

Editors and Affiliations

  1. ICT, Griffith University, Southport, Queensland, Australia

    Junhu Wang

  2. Nanyang Technological University, Singapore, Singapore

    Gao Cong

  3. Faculty of Information and Communication Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia

    Jinjun Chen

  4. The University of Melbourne, Melbourne, Victoria, Australia

    Jianzhong Qi

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Randeni Kadupitige, S., Röhm, U. (2018). Using SIMD Instructions to Accelerate Sequence Similarity Searches Inside a Database System. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds) Databases Theory and Applications. ADC 2018. Lecture Notes in Computer Science(), vol 10837. Springer, Cham. https://doi.org/10.1007/978-3-319-92013-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-92013-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92012-2

  • Online ISBN: 978-3-319-92013-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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