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Multi-user predictive rendering on remote multi-GPU clusters

, , , , , , (1)
1
J. Randrianandrasana
  • Function : Author
A. Chanonier
  • Function : Author
H. Deleau
  • Function : Author
T Muller
P. Porral
  • Function : Author
M. Krajecki

Abstract

Many stages of the industry workflow have been benefiting from CAD software applications and real-time computer graphics for decades allowing manufacturers to perform team project reviews and assessments while decreasing the need for expensive physical mockups. However, when it comes to the perceived quality of the final product, more sophisticated physically based engines are often preferred though involving huge computation times. In this context, our work aims at reducing this gap by providing a predictive rendering solution leveraging the computing resources offered by modern multi-GPU supercomputers. To that end, we propose a simple static load balancing approach leveraging the stochastic nature of Monte Carlo rendering. Our solution efficiently exploits the available computing resources and addresses the industry collaboration needs by providing a real-time multiuser web access to the virtual mockup.
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Dates and versions

hal-02977423 , version 1 (24-10-2020)

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J. Randrianandrasana, A. Chanonier, H. Deleau, T Muller, P. Porral, et al.. Multi-user predictive rendering on remote multi-GPU clusters. IEEE VR International Workshop on Collaborative Virtual Environments (3DCVE), 2018, Reutlingen, Germany. pp.1-4, ⟨10.1109/3DCVE.2018.8637114⟩. ⟨hal-02977423⟩
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