A low-cost energy-efficient raspberry Pi cluster for data mining algorithms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 10104 LNCS, Issue , 2017, Pages 788-799

  Saffran, João ^a   Garcia, Gabriel ^a   Souza, Matheus A ^a   Penna, Pedro H ^b   Castro, Márcio ^b   Góes, Luís F W ^a   Freitas, Henrique C ^a  

^a PONTIFICAL CATHOLIC UNIVERSITY OF MINAS GERAIS   (Brazil)

^b FEDERAL UNIVERSITY OF SANTA CATARINA   (Brazil)

Author keywords

Apriori; Intel Xeon Phi; K means; Raspberry Pi cluster

Indexed keywords

BIG DATA; CLUSTERING ALGORITHMS; DISTRIBUTED COMPUTER SYSTEMS; ENERGY EFFICIENCY;

APRIORI; DATA MINING ALGORITHM; EXTRACT INFORMATIONS; HIGH PERFORMANCE COMPUTING (HPC); INTEL XEON PHI; K-MEANS; RASPBERRY PI CLUSTER; RELIABLE RESULTS;

DATA MINING;

EID: 85020404174     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-58943-5_63     Document Type: Conference Paper

References (19)

1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499, VLDB 1994. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1994)
2. Aroca, R.V., Gonçalves, L.M.G.: Towards green data centers: a comparison of x86 and ARM architectures power efficiency. J. Parallel Distrib. Comput. 72(12), 1770–1780 (2012)
3. Ashby, S., Beckman, P., Chen, J., Colella, P., et al.: The opportunities and challenges of exascale computing. Technical report, Summary report of the advanced scientific computing advisory committee (ASCAC) subcommittee - Office of Science, U.S. Department of Energy Fall (2010)
4. Cox, S.J., Cox, J.T., Boardman, R.P., et al.: Iridis-Pi: a low-cost, compact demonstration cluster. Cluster Comput. 17(2), 349–358 (2013)
5. d’Amore, M., Baggio, R., Valdani, E.: A practical approach to big data in tourism: a low cost Raspberry Pi cluster. In: Tussyadiah, I., Inversini, A. (eds.) Information and Communication Technologies in Tourism 2015, pp. 169–181. Springer, Cham (2015). doi:10.1007/978-3-319-14343-9 13
6. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall Inc., Upper Saddle River (1988)
7. Kanungo, T., Mount, D., Netanyahu, N., et al.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
8. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)
9. Kruger, M.J.: Building a Parallella board cluster. Bachelor of science honours thesis, Rhodes University, Grahamstown, South Africa (2015)
10. Lawson, G., Sosonkina, M., Shen, Y.: Energy evaluation for applications with different thread affinities on the Intel Xeon Phi. In: Workshop on Applications for Multi-Core Architectures (WAMCA), pp. 54–59. IEEE Computer Society (2014)
11. Lim, D.J., Anderson, T.R., Shott, T.: Technological forecasting of supercomputer development: the march to exascale computing. Omega 51, 128–135 (2015)
12. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1: Statistics, pp. 281–297. University of California Press, Berkeley (1967)
13. Pfalzgraf, A.M., Driscoll, J.A.: A low-cost computer cluster for high-performance computing education. In: IEEE International Conference on Electro/Information Technology, pp. 362–366, June 2014
14. Rajovic, N., Carpenter, P.M., Gelado, I., Puzovic, N., Ramirez, A., Valero, M.: Supercomputing with commodity CPUS: are mobile SoCs ready for HPC? In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2013, NY, USA, pp. 40:1–40:12 (2013). http://doi.acm.org/10.1145/2503210.2503281
15. Simon, H.D.: Barriers to exascale computing. In: Daydé, M., Marques, O., Nakajima, K. (eds.) VECPAR 2012. LNCS, vol. 7851, pp. 1–3. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38718-0 1
16. Trefethen, A.E., Thiyagalingam, J.: Energy-aware software: challenges, opportunities and strategies. J. Comput. Sci. 4(6), 444–449 (2013). Scalable Algorithms for Large-Scale Systems Workshop (ScalA2011), Supercomputing 2011
17. Tso, F.P., White, D.R., Jouet, S., Singer, J., Pezaros, D.P.: The glasgow Raspberry Pi cloud: a scale model for cloud computing infrastructures. In: 33rd International Conference on Distributed Computing Systems Workshops, pp. 108–112. IEEE, July 2013
18. Villa, O., Johnson, D.R., O’Connor, M., et al.: Scaling the power wall: a path to exascale. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 830–841. IEEE Press (2014)
19. Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)