Nvidia Brings Tesla P100 GPU Acceleration to PCIe Servers
The push to expand GPU computing in HPC and the enterprise is working. Nvidia in May announced that its data center business in the first quarter grew 63 percent over the same period in 2015, to $143 million, due in large part to the increasing demand in HPC for deep learning, in which computers can be trained to learn based on experience, much like humans do. "One of the most important areas of high performance computing has been this area called deep learning," Huang said during a conference call in May about the financial numbers, according to a transcript on Seeking Alpha. "Deep learning is a very important field of machine learning, and machine learning is now in the process of revolutionizing artificial intelligence, making machines more and more intelligent and using it to discover insight that, quite frankly, isn't possible otherwise." Also at ISC 16, Nvidia officials introduced upgrades to the vendor's deep learning software. The company offers DIGITS—Deep Learning GPU Training System—to help users design, train and validate deep neural networks, and with DIGITS 4, a new object detection workflow enables scientists to train these networks to find such objects as faces, pedestrians, traffic signs and vehicles from among many other images. This is important for everything from tracking objects from satellites to driver assistance systems. DIGITS 4 release candidate will be available this week from the Nvidia developer program. Version 5.1 of Nvidia's cuDNN, also available immediately, delivers accelerated training of deep neural networks, while the GPU Inference Engine (GIE) optimizes trained deep neural networks for efficient runtime performance.