GPUs: Designed for gaming now crucial to HPC and AI

How did a chip meant for gaming become so vital in enterprise computing? Some people thought outside the box.

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It’s rare to see a processor find great success outside of the area it was intended for, but that’s exactly what has happened to the graphics processing unit (GPU). A chip originally intended to speed up gaming graphics and nothing more now powers everything from Adobe Premier and databases to high-performance computing (HPC) and artificial intelligence (AI).

GPUs are now offered in servers from every major OEM plus off-brand vendors, but they aren’t doing graphics acceleration. That’s because the GPU is in essence a giant math co-processor, now being used to perform computation-intensive work ranging from 3D simulations to medical imaging to financial modeling

GPUS vs. CPUs

Because of their single-purpose design, GPU cores are much smaller than cores for CPUs, so GPUs have thousands of cores whereas CPUs max out at 32. With up to 5,000 cores available for a single task, the design lends itself to massive parallel processing.

Wherever an application was begging for parallel processing, that’s where GPU computing took off, said Jon Peddie, president of Jon Peddie Research, which follows the graphics market.

“In the past, parallel processing was done with huge numbers of processors like an x86, so they were very expensive and difficult to program. The GPU as a dedicated single-purpose processor offered much greater compute density, and it’s been exploited in many math acceleration tasks,” he said.

Applications that support GPUs

GPU use in the data center started with homegrown apps thanks to a language Nvidia developed called CUDA. CUDA uses a C-like syntax to make calls to the GPU instead of the CPU, but instead of doing a call once, it can be done thousands of times in parallel.

As GPU performance improved and the processors proved viable for non-gaming tasks, packaged applications began adding support for them. Desktop apps, like Adobe Premier, jumped on board but so did server-side apps, including SQL databases. The GPU is ideally suited to accelerate the processing of SQL queries because SQL performs the same operation – usually a search – on every row in the set. The GPU can parallelize this process by assigning a row of data to a single core.

Brytlyt, SQream Technologies, MapD, Kinetica, PG-Strom and Blazegraph all offer GPU-accelerated analytics in their databases. Oracle has said it is working with Nvidia but nothing appears firm yet. Microsoft does not support GPU acceleration on SQL Server.

GPUs and high-performance computing (HPC)

GPUs have also found a home in HPC, where many tasks like simulations, financial modeling and 3D rendering also run well in a parallel environment. According to Intersect 360, a market research firm that follows the HPC market, 34 of the 50 most popular HPC application packages offer GPU support, including all of the top 15 HPC apps.

This includes the chemistry apps GROMACS, Gaussian and VASP, ANSYS and OpenFOAM for fluid dynamics, Simulia Abaqus for structural analysis and WRF for weather/environment modeling.

“We believe GPU computing has reached a tipping point in the HPC market that will encourage continued increased in application optimization,” the analysts said in their report.

GPU computing examples

The rapidly emerging market for GPUs is AI and machine learning, which are massively parallel problems. “Lots of enterprises and CIOs are seeing how they can use d

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