Every now and then we get questions on running heavy rendering solutions, computing applications or graphic intensive desktops in a cloud environment.
Every time this discussion starts to emerge, the hunger for fancy toys pops into our mind thanks to the fact Nvidia and their CUDA technology relying on GPU’s saw daylight.
But… as it always goes with cheering moments and pure joy, reality kicks in when you start to build a proposal with real world things in mind like cost of ownership, actual practical usage of GPU’s, performance and not entirely unimportant: flexibility.
VMware and Nvidia have gone a long way in making the GPU dream available for the masses, however we are 2017 now and finally using GPU’s for a variety of applications becomes actually possible for the normal human beings among us.
Do not be fooled: it still costs a dime, but at least the technology is starting to fill in the blanks.
Behold, the virtual desktop!
While many people tend to automatically link a graphics card and the fancy GPU (which is acronym for Graphics Processing Unit) with a ton of memory to games and video, the GPU can do more. Why?
Check out this excellent (and quite entertaining video) to understand the difference between CPU and GPU’s.
True… in a virtual environment, having a graphics card available in a server with tons of graphical memory and with the correct VMware setup, you can start a true VDI (Virtual Desktop Infrastructure) frenzy, but the true path where GPU’s start to shine is when it comes to parallel computing in our humble opinion (think scientific calculations, tons of processing, …).
Therefore we at Kinamo are quite happy to inform you about the differences in GPU enabled cloud environments, why you should care and, why probably not (if you’re just looking for a simple WordPress hosting). Nevertheless, we had you entertained till this point!
However, let’s get down to business… Forget about the parallel space-age computing thing for a while and let’s dive into the standard graphical basics!
And then they invented… 3D acceleration
You probably remember that fancy tick box in the DirectX 3D settings dialog centuries ago: Enable 3D Hardware Acceleration. If you don’t… well it was there, we ensure you.
Truth is… not much has changed, from a practical point of view.
3D Hardware Acceleration was kind of synonym for “Make sure you start using that expensive graphics card I paid for”.
Without the 3D hardware acceleration you still had the joy of falling back to the dreaded “Software rendering” which basically meant your hard working CPU would get even busier rendering your frame rate and drawing your screens.
When working with a virtual desktop on a VMware environment (for example) without all the fancy GPU stuff, it basically is the same: you can enable the Soft 3D acceleration.
You do not require GPU hardware to be available in your ESXi host for this, but this comes with a drawback. It is a simple but effective rendering with limited DirectX or OpenGL capabilities and fair enough for Windows Aero desktop with some limited 3D gizmo’s. Do not expect to start a full blown 3D render application with these settings, most likely you will end up in tears (next to your dying VM).
So… GPU hardware it is?
Ever since virtualization hypervisors started to introduce the ability to use GPU’s effectively, adding a graphics card with decent GPU and graphical memory has become a sexy option (all be it still quite expensive!).
Once you have added a hardware card to the host, a new world opens.
VMware has two additional options: vSGA which allows you to share the GPU’s among your VM’s and each VM requires to have some video memory assigned to it as well. It relies on the SVGA 3D driver and is a feasible option, but in our opinion most applicable for day to day virtual desktops… Again, running Autodesk’s 3D Max is not quite an option.
If you want real joy… there’s vDGA! Basically, with this ability VMware ESXi allows you to pass the hardware card straight to the virtual machine.
Clearly this means instant rendering power, since the card is assigned one-on-one to the virtual machine, but as always: “It comes with a prize”.
Your virtual machine is locked to your host (hence, it is using the hardware card IN the host) so forget fancy failover / high-availability / vMotion and other wonderful assets of the platform.
Also, an additional vm with vDGA? You will need a second card (and so on).
This is a terrific option performance wise, but in our opinion if you go this route you might as well go for a managed dedicated server with a hardware card and use that as a rendering or 3D rig!
Nvidia’s GRID and vGPU technology
Luckily Nvidia invented the vGPU, think of it as a virtualisation layer for GPU’s.
We will not dig into the details here (sorry) but to summarize it: by using vGPU’s each vm get’s a resource assigned from your GPU.
The Nvidia GRID technology (GRID Manager, and yes, it is license based) allows you to pass that little part of the GPU as a native pure hardware GPU to the VM, resulting in true 3D acceleration without the disadvantages of vDGA.
For more information on Nvidia GRID we suggest you head out to the Nvidia website!
Google Compute Engine and GPU’s? AWS and GPU’s?
Both Google Compute Engine and AWS have announced that they will be offering GPU’s in their cloud offering. While this is still in beta (don’t we love new technology!) it certainly opens the abilities of deep learning, setting up true performing rendering applications and effectively addressing GPU’s for scientific computing purposes, calculations, data modelling and so on.
Since we are not married to our own infrastructure we are very excited about Google Compute Engine introducing these aspects. Setting up hybrid cloud environments with no limitations is becoming reality!
For the tinkering fans among us, Nvidia has released an excellent blog post on setting up nvidia-docker, the layer that allows your docker container to be aware of the Nvidia hardware. Read up on deploying Nvidia Docker here and be sure to visit the GitHub Nvidia Docker project!
We hope you enjoyed this article and if you’re interested in learning (no, not deep learning) why we’re so fond of GPU enabled computing, drop us a line!