Abstract
In the last decades, the human genoma analysis for addressing health-care problems, has widely grown. With the high throughput of biological data and, needing of represent them, the Next-Generation Sequencing was introduced. In order to store genomic features without losing information, different data format (such as FAST-A, FAST-Q, SAM, VCF) have been proposed. To overcome the storing process issues of these data, several genomic compressors have been presented. A specific VCF compressor is analyzed. Due to the restricted hardware resources limit of multi-core architecture when input size dimension data are given, large execution times are required. Thanks to the well-known parallel nature of the most recent Graphic Process Units, in this work we present a Multi-GPU based parallel implementation by exploiting CUDA framework. An ad-hoc memory approach combined with a suitable work decomposition strategy are able to give a strong increase in performance. To observe the benefits in terms of performance, tests and experiments complete our work.
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Acknowledgements
This paper has been supported for computational resources by project “Accelerated High Performance Methods for compressing Next-Generation sequencing data (AHNG20)” - CINECA.
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De Luca, P., Di Mauro, A., Fiscale, S. (2022). On Next-Generation Sequencing Compression via Multi-GPU. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_42
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DOI: https://doi.org/10.1007/978-3-030-96627-0_42
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