Nvidia officials want to bring GPU acceleration to hyperscale environments to help drive the development of machine learning and artificial intelligence.
The company today is unveiling two new Tesla GPU accelerators that enable researchers to more quickly create deep neural networks used in deep learning—also known as "machine learning"—and to deploy these networks across data centers. Nvidia also is rolling out software for hyperscale environments to support the workloads that are processed through the GPUs.
The new products are part of a larger push by Nvidia to become a significant player in the development of neural networks, machine learning and artificial intelligence. At the company's GPU Technology Conference 2015 in March, Nvidia founder and CEO Jen-Hsun Huang introduced a number of products for developers and researchers working on deep learning technologies, and told attendees that deep learning is a central part of the vendor's strategy going forward.
"The topic of deep learning is probably as exciting an issue as any in this industry," Huang said at the show.
The CEO and other executives will be in San Francisco today talking about the company's strategy around artificial intelligence and machine learning. Machine learning—essentially giving compute systems the ability to learn over time by using a growing database of information and neural networks—is being used in a broad range of scenarios, from making voice recognition more accurate to identifying objects in videos and photos to enable faster tagging for searches, driving services that can identify individual interests and responding to voice commands made in a conversational tone.
Nvidia for many years has been making inroads into the high-performance computing (HPC) and scientific computing spaces through the development of its Tesla GPU accelerators, which enable organizations to increase the performance of their systems while holding down the power consumption. This is made possible by offloading some of the workloads from the CPUs to the GPUs, which offer parallel computing capabilities and run hundreds of more cores.
An increasing number of supercomputers on the twice-yearly Top500 list of the world's fastest systems use GPU accelerators from Nvidia or Advanced Micro Devices or x86-based Xeon Phi co-processors from Intel. In the most recent list released in June, there were 90 systems using such accelerators, up from 75 in November 2014. Of those, 52 use Nvidia GPUs. The next Top500 list is due out at the SC 15 supercomputing show, which begins Nov. 15 in Austin, Texas.
Now Nvidia is bringing GPU acceleration to hyperscale data centers, such as those run by Web-scale companies like Facebook, Google and Baidu. The challenge for these environments is the massive amounts of data being created, according to Ian Buck, vice president of accelerated computing at Nvidia.
"Today, there are exabytes of data being created by [hyperscale] companies daily," Buck told eWEEK, noting that much of it is driven by user-created content.
For example, 4 billion videos are viewed every day on Facebook, a 400 percent increase in six months, he said. Baidu handles 6 billion queries a day—10 percent of which use speech—while YouTube users watch 300 hours of videos per minute, half the time on their mobile devices. The challenge is making sense of the data—sharing the millions of images and videos with millions of people, tagging and enhancing each second of video as close to real time as possible, discovering and analyzing the content, and instantly creating relevant advertising.
"This is a huge computational challenge," Buck said.
It also increasingly will involve artificial intelligence, and the new Tesla GPU accelerators are designed to help organizations drive innovation in the area. The Tesla HyperScale Accelerator line will give users a 10-fold improvement in performance, according to CEO Huang.
"Machine learning is unquestionably one of the most important developments in computing today, on the scale of the PC, the internet and cloud computing," he said in a statement. "Industries ranging from consumer cloud services, automotive and health care are being revolutionized as we speak. Machine learning is the grand computational challenge of our generation. [With the new Tesla hyperscale GPUs] the time and cost savings to data centers will be significant."