Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Iridis-pi: a low-cost, compact demonstration cluster

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In this paper, we report on our “Iridis-Pi” cluster, which consists of 64 Raspberry Pi Model B nodes each equipped with a 700 MHz ARM processor, 256 Mbit of RAM and a 16 GiB SD card for local storage. The cluster has a number of advantages which are not shared with conventional data-centre based cluster, including its low total power consumption, easy portability due to its small size and weight, affordability, and passive, ambient cooling. We propose that these attributes make Iridis-Pi ideally suited to educational applications, where it provides a low-cost starting point to inspire and enable students to understand and apply high-performance computing and data handling to tackle complex engineering and scientific challenges. We present the results of benchmarking both the computational power and network performance of the “Iridis-Pi.” We also argue that such systems should be considered in some additional specialist application areas where these unique attributes may prove advantageous. We believe that the choice of an ARM CPU foreshadows a trend towards the increasing adoption of low-power, non-PC-compatible architectures in high performance clusters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. http://www.calxeda.com/.

  2. See http://www.kickstarter.com/projects/adapteva/parallella-a-supercomputer-for-everyone, accessed 10 Dec 2012.

  3. Also shown in a video available online, https://www.youtube.com/watch?v=Jq5nrHz9I94, assembled according to the guide at http://www.southampton.ac.uk/~sjc/raspberrypi/pi_supercomputer_southampton_web.pdf.

  4. See http://www.raspberrypi.org/.

  5. http://www.raspbian.org/.

  6. http://www.netlib.org/benchmark/linpackc.new.

  7. http://www.amd.com/us/press-releases/Pages/press-release-2012Oct29.aspx accessed 10 Dec 2012.

  8. See http://www.kickstarter.com/projects/adapteva/parallella-a-supercomputer-for-everyone, accessed 10 Dec 2012.

References

  1. Open source ARM userland. http://www.raspberrypi.org/archives/2221

  2. Petitet, A., Whaley, R.C., Dongarra, J., Cleary, A.: HPL—a portable implementation of the high-performance linpack benchmark for distributed-memory computers. http://www.netlib.org/benchmark/hpl/. Accessed Jan 2013

  3. Andersen, D.G., Franklin, J., Kaminsky, M., Phanishayee, A., Tan, L., Vasudevan, V.: Fawn: a fast array of wimpy nodes. Commun. ACM 54(7), 101–109 (2011). doi:10.1145/1965724.1965747

    Article  Google Scholar 

  4. Argonne National Laboratory: MPICH2. http://www-unix.mcs.anl.gov/mpi/mpich2/

  5. Asanovic, K., Bodik, R., Catanzaro, B.C., Gebis, J.J., Husbands, P., Keutzer, K., Patterson, D.A., Plishker, W.L., Shalf, J., Williams, S.W., Yelick, K.A.: The landscape of parallel computing research: a view from Berkeley. Tech. Rep. UCB/EECS-2006-183, EECS Department, University of California, Berkeley. http://www.eecs.berkeley.edu/Pubs/TechRpts/2006/EECS-2006-183.html (2006)

  6. Dongarra, J.J., Luszczek, P., Petitet, A.: The linpack benchmark: past, present and future. Concurr. Comput., Pract. Exp. 15(9), 803–820 (2003). doi:10.1002/cpe.728

    Article  Google Scholar 

  7. Gates, M., Tirumala, A., Ferguson, J., Dugan, J., Qin, F., Gibbs, K., Estabrook, J.: Iperf—the TCP/UDP bandwidth measurement tool. http://sourceforge.net/projects/iperf/ [Online]

  8. Hamilton, J.: Cooperative expendable micro-slice servers (cems): low cost, low power servers for internet-scale services. www.mvdirona.com/jrh/talksandpapers/jameshamilton_cems.pdf (2009)

  9. Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2, 164–168 (1944)

    MATH  MathSciNet  Google Scholar 

  10. Lim, K., Ranganathan, P., Chang, J., Patel, C., Mudge, T., Reinhardt, S.: Understanding and designing new server architectures for emerging warehouse-computing environments. Comput. Archit. News 36(3), 315–326 (2008). doi:10.1145/1394608.1382148

    Article  Google Scholar 

  11. Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11(2), 431–441 (1963). doi:10.1137/0111030

    Article  MATH  MathSciNet  Google Scholar 

  12. Raspberry Pi Foundation: Raspberry Pi FAQs. http://www.raspberrypi.org/faqs. Accessed 2012

  13. Snell, Q.O., Mikler, A.R., Gustafson, J.L.: Netpipe: a network protocol independent performance evaluator. In: IASTED International Conference on Intelligent Information Management and Systems (1996)

    Google Scholar 

  14. Szalay, A.S., Bell, G.C., Huang, H.H., Terzis, A., White, A.: Low-power amdahl-balanced blades for data intensive computing. Oper. Syst. Rev. 44(1), 71–75 (2010). doi:10.1145/1740390.1740407

    Article  Google Scholar 

  15. Whaley, R.C., Petitet, A.: Minimizing development and maintenance costs in supporting persistently optimized BLAS. Softw. Pract. Exp. 35(2), 101–121 (2005). http://www.cs.utsa.edu/~whaley/papers/spercw04.ps

    Article  Google Scholar 

  16. Ghemawat, S., Gobioff, H., Leung, S.-T.: The Google file system. In: ACM Symposium on Operating Systems Principles, pp. 29–43 (2003)

    Google Scholar 

  17. Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: Symposium on Mass Storage Systems (2010)

    Google Scholar 

  18. Dean, J., Ghemawat, S.: MapReduce: simplied data processing on large clusters. In: Operating Systems Design and Implementation, pp. 137–150 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering and the Environment, University of Southampton, Southampton, UK

    Simon J. Cox, James T. Cox, Richard P. Boardman, Steven J. Johnston, Mark Scott & Neil S. O’Brien

Authors
  1. Simon J. Cox
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James T. Cox
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard P. Boardman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Steven J. Johnston
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mark Scott
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Neil S. O’Brien
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simon J. Cox.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cox, S.J., Cox, J.T., Boardman, R.P. et al. Iridis-pi: a low-cost, compact demonstration cluster. Cluster Comput 17, 349–358 (2014). https://doi.org/10.1007/s10586-013-0282-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-013-0282-7

Keywords

Search

Navigation