GPU implementation is about more than deep learning

GPU implementation
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When you consider a typical GPU implementation, you probably think of some advanced AI application. But that’s not the only place businesses are putting the chips to work.

“[GPUs] are obviously applicable for Google and Facebook and companies doing AI. But for startups like ours that have to justify capital spend in today’s business value, we still want that speed,” said Kyle Hubert, CTO at Simulmedia Inc.

The New York-based advertising technology company is using GPUs to make fairly traditional processes, like data reporting and business intelligence dashboards,work faster. Using a platform from MapD, Simulmedia has built a reporting and data querying tool that lets sales staff and others in the organization visualize how certain television ads are performing and answer any client inquiries as they come in.

Using GPUs for more than deep learning

Kyle Hubert, CTO, Simulmedia
Kyle Hubert

GPU technology is getting lots of attention today, primarily due to how businesses are using it. The chips power the training underlying some of the most advanced AI use cases, like image recognition, natural language translation and self-driving cars. But, of course, they were originally built to power video game graphics. Their main appeal is speedy processing power. And while that may be crucial for enabling neural networks to churn through millions of training examples, there are also other use cases in which the speed that comes from a GPU implementation is beneficial.

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