Performance analysis of single board computer clusters

By Bram Steenwinckel, Dieter De Paepe, Sander Vanden Hautte, Pieter Heyvaert, M. Bentefrit, Pieter Moens, Anastasia Dimou, B. V. D. Bossche, Filip De Turck, Sofie Van Hoecke and 1 more. Future generations computer systems, 2020

363 citations recorded by the source corpus.

Abstract The past few years have seen significant developments in Single Board Computer (SBC) hardware capabilities. These advances in SBCs translate directly into improvements in SBC clusters. In 2018 an individual SBC has more than four times the performance of a 64-node SBC cluster from 2013. This increase in performance has been accompanied by increases in energy efficiency (GFLOPS/W) and value for money (GFLOPS/$). We present systematic analysis of these metrics for three different SBC clusters composed of Raspberry Pi 3 Model B, Raspberry Pi 3 Model B+ and Odroid C2 nodes respectively. A 16-node SBC cluster can achieve up to 60 GFLOPS, running at 80 W. We believe that these improvements open new computational opportunities, whether this derives from a decrease in the physical volume required to provide a fixed amount of computation power for a portable cluster; or the amount of compute power that can be installed given a fixed budget in expendable compute scenarios. We also present a new SBC cluster construction form factor named Pi Stack; this has been designed to support edge compute applications rather than the educational use-cases favoured by previous methods. The improvements in SBC cluster performance and construction techniques mean that these SBC clusters are realising their potential as valuable developmental edge compute devices rather than just educational curiosities.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/3380fc2b4113c6e4c5a8c968edb42cf88d988752

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Citations in the corpus

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PyOD: A Python Toolbox for Scalable Outlier Detection (2019, background): “Industry applications include fraud detection in finance (Ahmed et al., 2016), fault diagnosis in mechanics (Shin et al.”
Resource Management in Fog/Edge Computing (2018, methodology): “Single-board Computers: Single-board computers (SBC) such as Raspberry Pi are often used as fog/edge nodes [7, 14, 83].”
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