Evaluation of GPU Virtualisation Approaches for Machine Learning Enhanced Debugging of Cloud Orchestration

Nowadays, computing demand on General-Purpose Graphics Processing Units (GPGPUs) is steadily increasing due to the great interest in machine learning. The computational time or 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 arc described and are evaluated to help choose an effective baseline Framework for a challenging graph-based machine learning method to he applied in the field of debugging of cloud orchestration.

Automated summary

This paper discusses the increasing demand for computing on General-Purpose Graphics Processing Units (GPGPUs) for machine learning. It provides an overview of GPU virtualization strategies and their fundamental details, highlighting the importance of key features and evaluation in choosing an effective baseline framework.