Abstract
The continuous technological advances made energy efficiency a major topic for greener Information Technology systems. Low power Systems-on-Chip (SoC), originally developed in the context of mobile and embedded technologies, are becoming attractive also for scientific and industrial applications given their increasing computing performances, coupled with relatively low cost and power demands. In this work, we investigate the potential of the most representative SoCs for a real life application taken from the field of molecular biology. In particular, we investigate the opportunity of using SoCs for Next-Generation Sequencing (NGS) analysis, considering different applicative scenarios, with different timing and costs requirements. We evaluate the achievable performance together with economical aspects related to the total cost of ownership for a small medium enterprise offering services of NGS sequence alignment, supporting analysis performed in hospitals, research institutes, farms and industries.
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References
Winston, A., Favaloro, G., Healy, T.: Energy strategy for the c-suite. Harvard Bus. Rev. 138–146 (2017)
Rajovic, N., Carpenter, P., Gelado, I., Puzovic, N., Ramirez, A., Valero, M.: Supercomputing with commodity CPUs: are mobile SoCs ready for HPC? In: Proceedings of SC13: International Conference for High Performance Computing, Networking, Storage and Analysis (2013)
Calore, E., Schifano, S.F., Tripiccione, R.: Energy-performance tradeoffs for HPC applications on low power processors. In: Hunold, S., et al. (eds.) Euro-Par 2015. LNCS, vol. 9523, pp. 737–748. Springer, Cham (2015). doi:10.1007/978-3-319-27308-2_59
Hackenberg, D., Ilsche, T., Schone, R., Molka, D., Schmidt, M., Nagel, W.: Power measurement techniques on standard compute nodes: a quantitative comparison. In: 2013 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 194–204 (2013)
Cesini, D., Corni, E., Falabella, A., Ferraro, A., Morganti, L., Calore, E., Schifano, S.F., Michelotto, M., Alfieri, R., De Pietri, R., Boccali, T., Biagioni, A., Lo Cicero, F., Lonardo, A., Martinelli, M., Paolucci, P.S., Pastorelli, E., Vicini, P.: Power efficient computing: the experience of the COSA project
Morganti, L., Cesini, D., Ferraro, A.: Evaluating systems on chip through HPC bioinformatics and astrophysics applications. In: Proceedings of the 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) 2016, pp. 541–544 (2016)
Jain, M., Olsen, H.E., Paten, B., et al.: The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol. 17(1), 239 (2016)
Geist, A., Lucas, R.: Major computer science challenges at exascale. Int. J. High Perform. Comput. Appl. 23(4), 427–436 (2009)
The Mont-Blanc prototype: an alternative approach for HPC systems. In SC16: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 444–455, November 2016. doi:10.1109/SC.2016.37
Marazakis, M., et al.: EUROSERVER: share-anything scale-out microserver design. In: 2016 Design, Automation & Test in Europe Conference & Exhibition, DATE 2016, Dresden, Germany, 14–18 March 2016, pp. 678–683 (2016)
Horak, D., et al.: Energy consumption optimization of the Total-FETI solver and BLAS routines by changing the CPU frequency. In: 2016 International Conference on High Performance Computing Simulation (HPCS), pp. 1031–1032, July 2016. doi:10.1109/HPCSim.2016.7568453
Catalan, S., et al.: Energy balance between voltage-frequency scaling and resilience for linear algebra routines on low-power multicore architectures. Parallel Comput. (2017). doi:10.1016/j.parco.2017.05.004
Furber, S., Temple, S.: Neural systems engineering. J. R. Soc. Interface 4(13), 193–206 (2007). doi:10.1098/rsif.2006.0177
Katevenis, M., et al.: The ExaNeSt project: interconnects, storage and packaging for exascale systems. In: 2016 Euromicro Conference on Digital System Design (DSD), pp. 60–67 (2016). doi:10.1109/DSD.2016.106
Corni, E., Morganti, L., Morigi, M.P., Brancaccio, R., Bettuzzi, M., Levi, G., Peccenini, E., Cesini, D., Ferraro, A.: X-Ray computed tomography applied to objects of cultural heritage: porting and testing the filtered back-projection reconstruction algorithm on low power systems-on-chip. In: 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) 2016, pp. 369–372 (2016)
Morganti, L., Corni, E., Ferraro, A., Cesini, D., D’Agostino, D., Marelli, I.: Implementing a space-aware stochastic simulator on low-power architectures: a systems biology case study. In: 25th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) 2017 (2017)
Fuller, J.C., Khoueiry, P., Dinkel, H., et al.: Biggest challenges in bioinformatics. EMBO Rep. 14(4), 302–304 (2013)
Church, G.M.: Genomes for all. Sci. Am. 294(1), 46–54 (2006)
de Magalhes, J.P., Finch, C.E., Janssens, G.: Next-generation sequencing in aging research: emerging applications, problems, pitfalls and possible solutions. Ageing Res. Rev. 9(3), 315–323 (2010)
Merelli, I., Calabria, A., Cozzi, P., et al.: SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS. BMC Bioinform. 14(1), S9 (2013)
Merelli, I., Cozzi, P., D’Agostino, D., Clematis, A., Milanesi, L.: Image-based surface matching algorithm oriented to structural biology. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 8(4), 1004–1016 (2011)
Chiappori, F., Merelli, I., Milanesi, L., Marabotti, A.: Static and dynamic interactions between GALK enzyme and known inhibitors: guidelines to design new drugs for galactosemic patients. Eur. J. Med. Chem. 63, 423–434 (2013)
Chiappori, F., D’Ursi, P., Merelli, I., Milanesi, L., Rovida, E.: In silico saturation mutagenesis and docking screening for the analysis of protein-ligand interaction: the Endothelial Protein C Receptor case study. BMC Bioinform. 10(12), S3 (2009)
Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L.: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10(3), R25 (2009)
Li, H., Durbin, R.: Fast and accurate short read alignment with Burrows Wheeler transform. Bioinformatics 25(14), 1754–1760 (2009)
Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., Gingeras, T.R.: STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1), 15–21 (2013)
DNA Sequencing Is Now Improving Faster Than Moore’s Law! https://www.forbes.com/sites/techonomy/2012/01/12/dna-sequencing-is-now-improving-faster-than-moores-law
Applications of Whole Genome Sequencing in food safety management. www.fao.org/3/a-i5619e.pdf
Misale, C., Ferrero, G., Torquati, M., Aldinucci, M.: Sequence alignment tools: one parallel pattern to rule them all? BioMed Res. Int. (2014)
Antal, M., Pop, C., Valea, D., Cioara, T., Anghel, I., Salomie, I.: Optimizing data centres operation to provide ancillary services on-demand. In: Altmann, J., Silaghi, G.C., Rana, O.F. (eds.) GECON 2015. LNCS, vol. 9512, pp. 133–146. Springer, Cham (2016). doi:10.1007/978-3-319-43177-2_9
Galizia, A., Quarati, A.: Job allocation strategies for energy-aware and efficient Grid infrastructures. J. Syst. Softw. 85(7), 1588–1606 (2012)
Quarati, A., Clematis, A., D’Agostino, D.: Delivering cloud services with QoS requirements: business opportunities, architectural solutions and energy-saving aspects. Future Gener. Comput. Syst. 55, 403–427 (2016)
Goiri, I., Katsak, W., Le, K., Nguyen, T.D., Bianchini, R.: Parasol and GreenSwitch: managing datacenters powered by renewable energy. In Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2013), pp. 51–64. ACM, New York (2013)
Acknowledgements
This work has been supported by the Italian Ministry of Education and Research (MIUR) through the Flagship (PB05) InterOmics, the EC-FP7 innovation project MIMOMICS (no. 305280), and the EC- FP7 strep project REPARA (no. 609666), and it was partly funded by the Scientific Commission 5 of the Italian Institute for Nuclear Physics (INFN) through the COSA project.
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D’Agostino, D. et al. (2017). Performance and Economic Evaluations in Adopting Low Power Architectures: A Real Case Analysis. In: Pham, C., Altmann, J., Bañares, J. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2017. Lecture Notes in Computer Science(), vol 10537. Springer, Cham. https://doi.org/10.1007/978-3-319-68066-8_14
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DOI: https://doi.org/10.1007/978-3-319-68066-8_14
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