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  • Hi, my name is Alex Hudak, I'm an Offering Manager at IBM, and today I'm going to talk

  • to you about: What is a GPU? So, I get some pretty basic questions on GPUs,

  • and that's what I'm going to go over today. First question is, what is a GPU? What is

  • the difference between a GPU and CPU, so I'm going to represent those here.

  • And then, lastly, why use a GPU? And, is it even important to use a GPU on cloud?

  • So, let's start first with, what is a GPU? GPU stands for "Graphic Processing Unit".

  • But, oftentimes people are more familiar with CPUs.

  • So, CPUs are actually made up of just a few cores. You can think of these cores as the

  • power, the ability of a CPU to do certain calculations or computations.

  • On the other hand, though, GPUs are made up of hundreds of cores. But, what difference

  • does it make? So, the thing with the CPU is that when it

  • does a computation it does so in a serial form. So, it does one computation at a time.

  • But with the GPU, it does it in parallel. So, the importance of these two differences

  • is that with a GPU, you're able to do computations all at once, and very intense computations

  • at that. So, oftentimes when you have app codes, a

  • lot of it's going to be going to the CPU. But then every now and then, you're going

  • to have an application that's going to require quite a bit of compute-intensive support that

  • the CPU just can't do, so it's going to be offloaded to the GPU. So, you can think of

  • a GPU as that extra muscle or that extra brain power that the CPU just can't do on its own.

  • So, there are two main providers of GPUs in industry: NVIDIA and AMD. Both providers manufacture

  • GPUs that are optimized for certain use cases. So, let's jump into that, because a big question

  • I get is: Why do I even need a GPU? In what industries and in what use cases?

  • 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-graphic-intensive 3D CAD program. So, rather than having the server right next to

  • you or right in the field with you, 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 in industry

  • anymore. It's a big piece of it, but now financial services, life sciences, 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're 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 buzz word that's been going around, it

  • stands for "High Performance Computing". While a GPU is not absolutely necessary for HPC,

  • it's 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 cloud. If HPC is such a big piece of that, what else is important about 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 on-prem

  • infrastructure, 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's 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-intense 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 cloud is that you only pay for what you use.

  • So, if a company is using on-prem technology or infrastructure but they're 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're 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 for 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.

Hi, my name is Alex Hudak, I'm an Offering Manager at IBM, and today I'm going to talk


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繪圖晶片(GPUs: Explained)

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    joey joey 發佈於 2021 年 09 月 18 日