What is GPU and How it works

So, the first we’ll talk about is VDI. VDI stands for ‘Virtual Desktop Infrastructure’. So, GPUs are created to support high-intensive graphic applications. Think about if you are a construction worker, right?

And you’re out in the field, and you need to access a very high graphics-intensive 3D CAD program. So, rather than having the server right next to you or right in the field with you can have a server that’s a country away in a cloud data center and be able to view that 3D graphic as if that server was right with you.

And that’s going to be supported by the GPU because the GPU supports graphic-intensive applications. Another example of this would be movie animation or rendering. So in fact, GPUs actually first got their name mainly with the gaming industry. Oftentimes they were referred to as “gaming processing units” because of this ability to provide end-users with low-latency graphics.

But gaming is no longer the focus of the industry anymore. It’s a big piece of it, but now financial services and even healthcare are starting to get into it with artificial intelligence.

So artificial intelligence has two big pieces to it there’s machine learning and there’s deep learning. So now there are also GPUs that are optimized and created specifically for those applications.

There are some that are created for inferencing for machine learning purposes, and there are some that are created to help data scientists create and train neural networks. In other words, they are trying to create these algorithms that can think like a human brain. That’s something that a CPU can simply not do on its own, and it requires GPU capabilities.

And then, lastly, let’s talk about HPC. HPC is a buzzword that’s been going around, it stands for “High-Performance Computing”. While a GPU is not absolutely necessary for HPC, It is an important part of it.

So, high-performance computing is the company’s ability to spread out their compute-intensive workloads amongst multiple compute nodes (or in the case of cloud servers). Oftentimes, though, these applications are very compute-intensive it could include rendering, it could include AI and that’s where a GPU comes in.

You can add a GPU to these servers that are spread out amongst an HPC application and utilize those in that manner. So, this is a nice little segue into why should use GPUs on the cloud. If HPC is such a big piece of that, what else is important about the cloud?

So, the first part of that is you get high performance you need the cloud for that. The GPUs are great. but not on their own. So, back in the day(and even still today), there are companies that use a lot of no-perm infrastructures, and they utilize that infrastructure for any of their compute-intensive applications.

However, especially in the case of GPUs, the technology is ever-changing. In fact, there’s typically a new GPU coming out almost every single year. So, it is actually very expensive and nearly impractical for companies to keep up with the latest technology at this point.

Cloud providers actually have the ability to continually update their technology and provide GPUs to these companies to utilize them when they need them.

So on a more granular basis though, cloud technology can often be broken down from an infrastructure perspective between bare metal and virtual servers. So, let’s talk about the differences, There are advantages of using a GPU on both types of infrastructure.

If you utilize a GPU on a bare-metal infrastructure, the companies oftentimes have access to the entire server itself, and they can customize the configuration. So this is great for companies that are going to be really utilizing that server and that GPU intensive application on a pretty consistent basis.

But for companies that need a GPU maybe just on a burst workload scenario, the virtual server option might be even better. And the nice thing about virtual is that there are often different pricing models as well, including hourly. And the cool thing about the cloud is that you only pay for what you use.

So if a company is using on-prem technology or infrastructure but they are not utilizing it at the time, that technology is depreciating, and it’s essentially a waste of money for that company. When they offload to the cloud, they only pay for what they are using.

And so it just makes a lot more sense from a cost perspective; and then, because the GPU is so great at performance, it just makes sense from a performance perspective as well. So, companies are able to focus way more on output than they are on keeping up with the latest technology.