The modern graphics processor (GPU) started as an accelerator for Windows video games, but in the last 20 years it has become an enterprise server processor for high-performance computing and artificial intelligence applications.

Now GPUs are at the top of the copy for the performance used in supercomputers, AI training and inferences, drug research, financial modeling and medical imaging. They’ve also been applied to more popular tasks for situations where processors just aren’t fast enough, as with GPUs. relational databases.

As the demand for GPUs grows, so will competition between GPU vendors for servers, and there are only three: Nvidia, AMD and (soon) Intel. Intel has tried and failed twice to come up with an alternative to others’ GPUs, but is making another attempt.

The importance of GPUs in data centers

These three providers recognize the demand for GPUs in data centers as a growing opportunity. This is because GPUs are better suited to processors than many of the computations required by AI and machine learning in enterprise data centers and hyperscaler networks. Processors can do the job; it just takes them longer.

Because GPUs are designed to solve complex mathematical problems in parallel by dividing them into separate tasks that they work on at the same time, they solve them faster. To achieve this, they have multiple cores, much more than general-purpose CPUs. For example, Intel’s Xeon server processors have up to 28 cores, while AMD’s Epyc server processors have up to 64. Unlike Nvidia’s current generation of GPUs, Ampere has 6,912 cores, all running in parallel to do one thing: math, in particular floating-point mathematics.

The performance of GPUs is measured in how many of these math floating point operations they can perform per second or FLOPS. This number sometimes specifies the standardized floating-point format used when making the measure, such as FP64.

Copyright © 2022 IDG Communications, Inc.

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