Emődi, Márk Benjámin and Kovács, József and Lovas, Róbert and Szénási, Sándor (2021) Evaluation of GPU Virtualisation Approaches for Machine Learning Enhanced Debugging of Cloud Orchestration. In: 15th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2021. Obuda University; IEEE, Budapest, 000425-000430. ISBN 9781728195438 10.1109/SACI51354.2021.9465570

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Abstract

Nowadays, computing demand on General-Purpose Graphics Processing Units (GPGPUs) is steadily increasing due to the great interest in machine learning. The computational time of embarrassingly parallel tasks can be reduced with such GPUs by orders of magnitude compared to CPUs. In this paper, we briefly overview a wide range of GPU virtualisation strategies (including API remoting, para/full virtualisation and hardware based virtualisation) and their related methods. The fundamental details are also discussed to understand the differences between the presented solutions. Finally, the key features are described and are evaluated to help choose an effective baseline framework for a challenging graph-based machine learning method to be applied in the field of debugging of cloud orchestration.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Laboratory of Parallel and Distributed Systems
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	06 Nov 2021 09:41
Last Modified:	06 Nov 2021 09:41
URI:	https://eprints.sztaki.hu/id/eprint/10153

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