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Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

2021-10-11 11:02:50
Martin Uray, Eduard Hirsch, Gerold Katzinger, Michael Gadermayr
arXiv_AI
   
Abstract

Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this up-scaling process, an IT service provider can be hired or in-house personnel can attempt to implement a software stack. The first option can be quite expensive if it is just about connecting several machines. For the latter option often experience is missing with the data science staff in order to navigate through the software jungle. In this technical report, we illustrate the decision process towards an on-premises infrastructure, our implemented system architecture, and the transformation of the software stack towards a scaleable GPU cluster system.

Abstract (translated)

URL

https://arxiv.org/abs/2110.05156

PDF

https://arxiv.org/pdf/2110.05156.pdf

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