What Will GPU Accelerated AI Lend to Traditional Supercomputing?

This week at the International Supercomputing Conference (ISC '16) we are expecting a wave of vendors and high performance computing pros to blur the borders between traditional supercomputing and what is around the corner on the application front--artificial intelligence and machine learning. For some, merging those two areas is a stretch, but for others, particularly GPU maker, Nvidia, which just extended its supercomputing/deep learning roadmap this morning, the story is far more direct since much of the recent deep learning work has hinged on GPUs for training of neural networks and machine learning algorithms. We have written extensively over the last year about how GPUs are being used in both deep learning and in HPC separately, but we might soon arrive at a fuller merger between the two areas, at least from a systems and hardware perspective. "Deep learning is not just an application segment, it's a whole new computing model," Ian Buck, VP of Accelerated Computing, tells The Next Platform. "If you had asked me at the launch of CUDA if GPUs would be in the largest supercomputers or revolutionizing artificial intelligence, I would have said that was a vision or even a pipe dream."