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Evaluation of Clusters based on Systems on a Chip for High-Performance Computing: A Review

Melissa Johanna Aldana | Bio
Universidad de Quindío
Jaime Alberto Buitrago | Bio
Universidad del Quindío
Julián Esteban Gutiérrez | Bio
Universidad del Quindío
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Abstract

High-performance computing systems are the maximum expression in the field of processing for large amounts of data. However, their energy consumption is an aspect of great importance, which was not considered decades ago. Hence, software developers and hardware providers are obligated to approach new challenges to address energy consumption, and costs. Constructing a computational cluster with a large amount of systems on a chip can result in a powerful, ecologic platform, with the capacity to offer sufficient performance for different applications, as long as low costs and minimum energy consumption can be maintained. As a result, energy efficient hardware has an opportunity to impact upon the area of high-performance computing. This article presents a systematic review of the evaluations conducted on clusters of  ystems on a Chip for High-Performance computing in the research setting.

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References

  1. [1] Schadt, E., Linderman, M., Sorenson, J. et al. “Computational solutions to large-scale data management and analysis,” Nat Rev Genet, no. 11, pp. 647–657, 2010. DOI: https://doi.org/10.1038/nrg2857
  2. [2] N. Rajovic, A. Rico, N. Puzovic, C. Adeniyi-Jones, and A. Ramírez, “Tibidabo: Making the case for an ARM-based HPC system,” Future Generation Computer Systems, no. 36, pp.322–334, 2014. DOI: https://doi.org/10.1016/j.future.2013.07.013
  3. [3] N. Balakrishnan, Building and benchmarking a low power ARM cluster, M.S. Thesis, EPCC Edinburgh Parallel Computing Center, The University of Edinburgh, 2012. Available: http://static.epcc.ed.ac.uk/dissertations/hpc-msc/2011-2012/Submission-1126390.pdf
  4. [4] J. W. Weloli, S. Bilavarn, S. Derradji, C. Belleudy and S. Lesmanne, “Efficiency Modeling and Analysis of 64-bit ARM Clusters for HPC,” 2016 Euromicro Conference on Digital System Design (DSD), Limassol, pp. 342-347, 2016. DOI: https://doi.org/10.1109/DSD.2016.74
  5. [5] M. Görtz, R. Kühn, O. Zietek, R. Bernhard, M. Bulinski, D. Duman, B. Freisen, U. Jentsch, T. Klöppner, D. Popovic, and L. Xu, “Energy Efficiency of a Low Power Hardware Cluster for High Performance Computing,” Eibl,M. & Gaedke, M. (Hrsg.), INFORMATIK 2017. Gesellschaft für Informatik, Bonn, pp. 2537-2548, 2017. DOI: https://doi.org/10.18420/in2017_256
  6. [6] J. Saffran et al., “A Low-Cost Energy-Efficient Raspberry Pi Cluster for Data Mining Algorithms,” in Desprez F. et al. (eds) Euro-Par 2016: Parallel Processing Workshops. Euro-Par 2016. Lecture Notes in Computer Science, vol 10104. Springer, Cham. 2017. DOI:https://doi.org/10.1007/978-3-319-58943-5_63
  7. [7] M. Cloutier, C. Paradis, and V. Weaver, “A Raspberry Pi Cluster Instrumented for Fine-Grained Power Measurement,” Electronics, vol. 5, no. 4, p. 61, 2016. DOI: https://doi.org/10.3390/electronics5040061
  8. [8] L. Morganti, D. Cesini, and A. Ferraro, “Evaluating Systems on Chip through HPC Bioinformatic and Astrophysic Applications,” in 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), Heraklion, pp. 541-544, 2016. DOI: https://doi.org/10.1109/PDP.2016.82
  9. [9] J. Maqbool, S. Oh, and G. C. Fox, “Evaluating ARM HPC clusters for scientific workloads,”Concurrency Computation, vol. 27, no. 17, pp. 5390–5410, 2015. DOI: https://doi.org/10.1002/cpe.3602
  10. [10] R. Manchado Garabito, S. Tamames Gómez, M. López González, L. Mohedano Macías, M. D’Agostino, and J. Veiga de Cabo, “Revisiones Sistemáticas Exploratorias,” Medicina y Seguridad del Trabajo, vol. 55, no. 215, pp. 28–51, 2009.
  11. [11] G. Urrútia, and X. Bonfill, “Declaración PRISMA: una propuesta para mejorar la publicación de revisiones sistemáticas y metaanálisis,” Med. Clin. (Barc), vol. 135, no. 11, pp. 507–511, 2010. DOI: https://doi.org/10.1016/j.medcli.2010.01.015
  12. [12] C. Kaewkasi, and W. Srisuruk, “A study of big data processing constraints on a low-power Hadoop cluster,” 2014 International Computer Science and Engineering Conference (ICSEC), Khon Kaen, pp. 267-272, 2014. DOI: https://doi.org/10.1109/ICSEC.2014.6978206
  13. [13] E. L. Padoin, D, P. Velho, and P. O. A. Navaux, “Evaluating Performance and Energy on ARM-based Clusters for High Performance Computing,” in 41st International Conference on Parallel Processing Workshops, Pittsburgh, 2012. DOI: https://doi.org/10.1109/ICPPW.2012.21
  14. [14] A. Selinger, K. Rupp, and S. Selberherr, “Evaluation of Mobile ARM-Based SoCs for High Performance Computing,” in Proceedings of the 24th High Performance Computing Symposium (HPC ’16). Society for Computer Simulation International, pp. 1–7, 2016. DOI: https://doi.org/10.22360/SpringSim.2016.HPC.022
  15. [15] C. Salazar, “Medidas de rendimiento y comparación entre el Clúster Cruz I y el Clúster Cruz II,” Revista de la Facultad de Ciencias de la UNI, Revciuni, vol. 17, no. 1, pp. 9–16, 2014.
  16. [16] C. Kaewkasi and W. Srisuruk, “Optimizing performance and power consumption for an ARM-based big data cluster,”TENCON 2014 - IEEE Region 10 Conference, Bangkok, pp. 1-6, 2014. DOI: https://doi.org/10.1109/TENCON.2014.7022399
  17. [18] I. Stamelos, D. Soudris, and C. Kachris, “Performance and energy evaluation of spark applications on low-power SoCs,”in 2016 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS), Agios Konstantinos, pp. 300-305, 2016. DOI: https://doi.org/10.1109/SAMOS.2016.7818362
  18. [19] A. Mappuji, N. Effendy, M. Mustaghfirin, F. Sondok, R. P. Yuniar and S. P. Pangesti, “Study of Raspberry Pi 2 quad-core Cortex-A7 CPU cluster as a mini supercomputer,” in 8th International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, 2016, pp. 1-4, 2016. DOI: https://doi.org/10.1109/ICITEED.2016.7863250
  19. [20] N. Rajovic, P. M. Carpenter, I. Gelado, N. Puzovic, A. Ramirez and M. Valero, “Supercomputing with commodity CPUs: Are mobile SoCs ready for HPC?,” in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, pp. 1-12, 2013. DOI: https://doi.org/10.1145/2503210.2503281
  20. [21] J. Zhang, S. You and L. Gruenwald, “Tiny GPU Cluster for Big Spatial Data: A Preliminary Performance Evaluation,” in IEEE 35th International Conference on Distributed Computing Systems Workshops, pp. 142-147, 2015. DOI: https://doi.org/10.1109/ICDCSW.2015.33
  21. [22] Z. Krpić, G. Horvat, D. Žagar and G. Martinović, “Towards an energy efficient SoC computing cluster,” 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, pp. 178-182, 2014. DOI: https://doi.org/10.1109/MIPRO.2014.6859556
  22. [23] L. O. Salvador, Building a low consumption cluster using SBC technology, B.Sc. Thesis, Ingeniería Informática, Universidad de Cantabria, 2016. Available: http://hdl.handle.net/10902/9383
  23. [24] M. Tsuji, W. T. C. Kramer and M. Sato, “A Performance Projection of Mini-Applications onto Benchmarks Toward the Performance Projection of Real-Applications,” 2017 IEEE International Conference on Cluster Computing (Cluster), Honolulu, HI, pp. 826-833, 2017. DOI: https://doi.org/10.1109/CLUSTER.2017.123
  24. [25] M. Sayeed, H. Bae, Y. Zheng, B. Armstrong, R. Eigenmann and F. Saied, “Measuring High-Performance Computing with Real Applications,” Computing in Science & Engineering, vol. 10, no. 4, pp. 60-70, 2008. DOI: https://doi.org/10.1109/MCSE.2008.98
  25. [26] A. Remy. Solving dense linear systems on accelerated multicore architectures, PhD thesis, Hardware Architecture, Université Paris Sud - Paris XI, 2015. Available: https://tel.archivesouvertes.fr/tel-01225745/document
  26. [27] Top 500.org, “Top 500 The list,” 2018. [Online]. Available: https://www.top500.org/ [Accessed: 28-Jan-2018].
How to Cite
Aldana, M. J., Buitrago, J. A., & Gutiérrez, J. E. (2019). Evaluation of Clusters based on Systems on a Chip for High-Performance Computing: A Review. Revista Ingenierías Universidad De Medellín, 19(37), 75-92. https://doi.org/10.22395/rium.v19n37a4

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