GTCx, a dazzling preview of the future

IT was not big in terms of attendee numbers, but it was certainly mesmerising from a content perspective. GPU manufacturer, Nvidia, held its first GTCx Australia conference in Melbourne earlier this week.

During the keynote, Dr. David B. Kirk, Nvidia Fellow and the company's former chief scientist, traced the timeline of GPU-accelerated computing from a decade ago to its current applications. In a subsequent interview with DNA, he expounded further on the complexities of GPU programming and the future of the current CMOS manufacturing technology.
 
He was also of the opinion that despite the predicted end of Moore's Law, CMOS-based manufacturing technology will be used for a very long time as it is very well understood. Kirk also dismissed Intel's claims that its Xeon Phi co-processors were faster than Nvidia's GPUs for deep learning.
 
Watch out for DNA's full interview with David Kirk in the near future.
 
One of the other highlights at GTCx Australia was the eerily lifelike baby created entirely through GPU-accelerated computing. The speaker, Mark Sagar, demoed the fourth generation of his creation, BabyX.
 
You can see the third iteration of the virtual baby in the video below.

 
According to Mark Sagar, his company Soul Machine's goal is to make human-computer interaction more human by creating artificial nervous systems which drive virtual characters in real-time. The system underlying the BabyX demo has been designed to integrate realistic computer graphics and a wide range of neuroscience models. It is highly scalable and the company hopes to simulate the entire human body on a cloud-based supercomputer in the near future.

The GTCx Australia conference was co-hosted by Monash University. This is the first major event under the recently-formed Monash-Nvidia partnership. Nvidia showed its recently announced Tesla P4, P40 and P100 for PCIe accelerators which are aimed at the fast growing deep learning sector which uses GPUs to accelerate computing tasks.

Responsiveness is key for services such as interactive speech, visual search and video recommendations. As deep learning models increase in accuracy and complexity, CPUs are no longer capable of delivering an interactive user experience.