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

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.

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Notes

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    http://www.calxeda.com/.

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

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    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.

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

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    http://www.raspbian.org/.

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

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

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

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Correspondence to Simon J. Cox.

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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

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Keywords

  • Low-power cluster
  • MPI
  • ARM
  • Low cost
  • Education
  • Hadoop
  • HDFS
  • HPL