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Fast Genomic Data Compression on Multicore Machines

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Cloud Computing, Big Data and Emerging Topics (JCC-BD&ET 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2189))

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Abstract

Nowadays, Genomics has gained relevance since it allows preventing, diagnosing and treating diseases in a personalized way. The reduction in sequencing time and cost has increased the demand and, thus, the amount of genomic data that must be stored or transferred. Consequently, it becomes necessary to develop genome compression algorithms that help to reduce storage usage without consuming too much time. This is now possible thanks to modern multicore machines. This paper improves MtHRCM, a multi-threaded compression algorithm for large collections of genomes, by reducing its sequential component in order to enhance performance and scalability. Experimental results show that our optimized version is faster than MtHRCM and achieves the same compression ratio. Also, they reveal that this new version scales well when increasing the number of threads/cores for smaller test collections, while the high amount of simultaneous I/O requests to disk limits the scalability for larger test collections.

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Authors and Affiliations

  1. III-LIDI, School of Computer Sciences, National University of La Plata, La Plata, Argentina

    Victoria Sanz, Adrián Pousa, Marcelo Naiouf & Armando De Giusti

  2. CIC, Buenos Aires, Argentina

    Victoria Sanz

  3. CONICET, Buenos Aires, Argentina

    Armando De Giusti

Authors
  1. Victoria Sanz
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  2. Adrián Pousa
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  3. Marcelo Naiouf
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  4. Armando De Giusti
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Corresponding author

Correspondence to Victoria Sanz .

Editor information

Editors and Affiliations

  1. III-LIDI, National University of La Plata, La Plata, Argentina

    Marcelo Naiouf

  2. III-LIDI, National University of La Plata, La Plata, Argentina

    Laura De Giusti

  3. III-LIDI, National University of La Plata, La Plata, Argentina

    Franco Chichizola

  4. III-LIDI, National University of La Plata, La Plata, Argentina

    Leandro Libutti

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Sanz, V., Pousa, A., Naiouf, M., De Giusti, A. (2025). Fast Genomic Data Compression on Multicore Machines. In: Naiouf, M., De Giusti, L., Chichizola, F., Libutti, L. (eds) Cloud Computing, Big Data and Emerging Topics. JCC-BD&ET 2024. Communications in Computer and Information Science, vol 2189. Springer, Cham. https://doi.org/10.1007/978-3-031-70807-7_1

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  • DOI: https://doi.org/10.1007/978-3-031-70807-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-70806-0

  • Online ISBN: 978-3-031-70807-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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