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J. Sens. Actuator Netw. 2018, 7(2), 18; https://doi.org/10.3390/jsan7020018

Performance Analysis of a 3D Wireless Massively Parallel Computer

Department of Computer Science, University of York, York YO10 5DD, UK
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Author to whom correspondence should be addressed.
Received: 28 February 2018 / Revised: 5 April 2018 / Accepted: 10 April 2018 / Published: 19 April 2018
(This article belongs to the Special Issue Energy Efficient Networking)
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Abstract

In previous work, the authors presented a 3D hexagonal wireless direct-interconnect network for a massively parallel computer, with a focus on analysing processor utilisation. In this study, we consider the characteristics of such an architecture in terms of link utilisation and power consumption. We have applied a store-and-forward packet-switching algorithm to both our proposed architecture and a traditional wired 5D direct network (the same as IBM’s Blue Gene). Simulations show that for small and medium-size networks the link utility of the proposed architecture is comparable with (and in some cases even better than) traditional 5D networks. This work demonstrates that there is a potential for wireless processing array concepts to address High-Performance Computing (HPC) challenges whilst alleviating some significant physical construction drawbacks of traditional systems. View Full-Text
Keywords: wireless networks; packet-switching; buffer management; on-chip radio communication; parallel computing; interconnect network; link utility; power consumption wireless networks; packet-switching; buffer management; on-chip radio communication; parallel computing; interconnect network; link utility; power consumption
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kamali Sarvestani, A.M.; Bailey, C.; Austin, J. Performance Analysis of a 3D Wireless Massively Parallel Computer. J. Sens. Actuator Netw. 2018, 7, 18.

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