University of Oulu

J. Haavisto and J. Riekki, "Interoperable GPU Kernels as Latency Improver for MEC," 2020 2nd 6G Wireless Summit (6G SUMMIT), Levi, Finland, 2020, pp. 1-5, doi: 10.1109/6GSUMMIT49458.2020.9083751

Interoperable GPU kernels as latency improver for MEC

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Author: Haavisto, Juuso1; Riekki, Jukka1
Organizations: 1Center for Ubiquitous Computing, University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-05-07


Mixed reality (MR) applications are expected to become common when 5G goes mainstream. However, the latency requirements are challenging to meet due to the resources required by video-based remoting of graphics, that is, decoding video codecs. We propose an approach towards tackling this challenge: a client-server implementation for transacting intermediate representation (IR) between a mobile UE and a MEC server instead of video codecs and this way avoiding video decoding. We demonstrate the ability to address latency bottlenecks on edge computing workloads that transact graphics. We select SPIR-V compatible GPU kernels as the intermediate representation. Our approach requires know-how in GPU architecture and GPU domain-specific languages (DSLs), but compared to video-based edge graphics, it decreases UE device delay by sevenfold. Further, we find that due to low cold-start times on both UEs and MEC servers, application migration can happen in milliseconds. We imply that graphics-based location-aware applications, such as MR, can benefit from this kind of approach.

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ISBN: 978-1-7281-6047-4
ISBN Print: 978-1-7281-6048-1
Pages: 1 - 5
DOI: 10.1109/6GSUMMIT49458.2020.9083751
Host publication: 2020 2nd 6G Wireless Summit (6G SUMMIT)
Conference: 6G Wireless Summit
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: This research is financially supported by the Academy of Finland 6Genesis Flagship (grant 318927) and by the AI Enhanced Mobile Edge Computing project, funded by the Future Makers program of Jane and Aatos Erkko Foundation and Technology Industries of Finland Centennial Foundation. Thanks to Jani Saloranta for reading drafts of this paper.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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