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Optimal Worksharing of DNA Sequence Analysis on Accelerated Platforms

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Resource Management for Big Data Platforms

Part of the book series: Computer Communications and Networks ((CCN))

Abstract

In this chapter, we describe an optimized approach for DNA sequence analysis on a heterogeneous platform that is accelerated with the Intel Xeon Phi. Such platforms commonly comprise one or two general purpose CPUs and one (or more) Xeon Phi coprocessors. Our parallel DNA sequence analysis algorithm is based on Finite Automata and finds patterns in large-scale DNA sequences. To determine the optimal worksharing (that is, DNA sequence fractions for the host and accelerating device) we propose a solution that combines combinatorial optimization and machine learning. The objective function that we aim to minimize is the execution time of the DNA sequence analysis. We use combinatorial optimization to efficiently explore the system configuration space and determine with machine learning the near-optimal system configuration for execution of the DNA sequence analysis. We evaluate our approach empirically using real-world DNA segments of various organisms. For experimentation, we use an accelerated platform that comprises two 12-core Intel Xeon E5 CPUs and an Intel Xeon Phi 7120P accelerator with 61 cores.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Linnaeus University, 351 95, Vaxjo, Sweden

    Suejb Memeti & Sabri Pllana

  2. Cracow University of Technology, 31 155, Cracow, Poland

    Joanna Kołodziej

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  1. Suejb Memeti
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  2. Sabri Pllana
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  3. Joanna Kołodziej
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Correspondence to Suejb Memeti .

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Editors and Affiliations

  1. University Politehnica of Bucharest, Bucharest, Romania

    Florin Pop

  2. Cracow University of Technology, Cracow, Poland

    Joanna Kołodziej

  3. Second University of Naples, Naples, Caserta, Italy

    Beniamino Di Martino

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Memeti, S., Pllana, S., Kołodziej, J. (2016). Optimal Worksharing of DNA Sequence Analysis on Accelerated Platforms. In: Pop, F., Kołodziej, J., Di Martino, B. (eds) Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-44881-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-44881-7_14

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