Intel Pushing Xeon for Deep Learning
Intel has released a new benchmark test for its Xeon Scalable processors to show why enterprises should be considering them for their deep learning projects.
The report state that when using AWS Sockeye Neural Machine Translation (NMT) with Intel Math Kernel Library and Apache MXNet, results are four times faster using the the company's Xeon Scalable processor than with Nvidia's V100 GPU. Step-by-step instructions are detailed in the report for duplicating the results.
"These results demonstrate the gains of using Intel MKL with Intel Xeon processors. In addition, properly setting the environment variables gives additional performance and provides comparable performance to V100 (22.5 vs 23.2 sentences per second)," the benchmark report stated.
"In addition to these gains, additional optimizations are coming soon that we expect will further improve CPU performance."
The results follow a late 2017 academic report the company released focused on how several universities found using Intel Xeon Scalable processors for their deep learning training.
Nvidia is, of course, also pushing its offerings as the ultimate solution for deep learning, as is Qualcomm and AMD, as well as startups like Cerebras, KnuEdge and Groq.
Becky Nagel is the vice president of Web & Digital Strategy for 1105's Enterprise Computing and Education Groups, where she oversees the front-end Web team and deals with all aspects of digital strategy for the groups. She also serves as executive editor the ECG Web sites, and you'll even find her byline on PureAI.com, the ECG group's newest site for enterprise developers working with AI. She recently gave a talk at a leading technical publishers conference about how changes in Web technology may impact publishers' bottom lines. Follow her on twitter @beckynagel.