Chris Mason from Acceleware presented this talk at GTC 2016. “This session will focus on real life examples including an RF powered contact lens, a wireless capsule endoscopy, and a smart watch. The session will also outline the basics of the subgridding algorithm along with the GPU implementation and the development challenges. Performance results will illustrate the significant reduction in computation times when using a localized subgridded mesh running on an NVIDIA Tesla GPU.”
Nvidia in the Driver’s Seat for Deep Learning
May 17, 2016 by
In this special guest feature, Robert Roe from Scientific Computing World describes why Nvidia is in the driver’s seat for Deep Learning. “Nvidia CEO Jen-Hsun Huang’s theme for the opening keynote was based on “a new computing model.” Huang explained that Nvidia builds computing technologies for the most demanding computer users in the world and that the most demanding applications require GPU acceleration. ‘The computers you need aren’t run of the mill. You need supercharged computing, GPU accelerated computing’ said Huang.”
