12.5 Virtualisation

It is possible to share high-performance compute resources amongst multiple users simultaneously by running virtual desktops on a machine that users connect to remotely. There are many reasons why IT professionals favour this solution for sharing expensive resources. However, the important question that must be asked in advance is: how will the GPU(s) be shared between users? Unless the Virtual Desktop Infrastructure (VDI) environment is implemented correctly, users may experience erratic model start up and solve times. There are different sharing mechanisms possible depending on the type of GPU and the choice of operating system. Some key points are:

• Solving the 2D Shallow Water Equations is a computationally intensive task. TUFLOW HPC is a well-optimised engine that will efficiently utilise all of a GPU’s compute resources for hours, sometimes days depending on the size of the model. For optimum performance, it is generally safest to only allow one HPC compute job per GPU device.
• In a VDI environment for modellers, where resources are typically pooled and assigned dynamically, GPU resources should instead be assigned as isolated and independent resources, dedicated to a user session.
• When opting for large NVIDIA GPUs, choosing a GPU model and a hypervisor that support Multi Instance GPU is recommended. Otherwise, select multiple smaller GPUs that can be exclusively assigned to individual user sessions.