As part of our effort to speed the deployment of GPU-accelerated high-performance computing and AI, we've more than tripled the number of containers available from our NVIDIA GPU Cloud (NGC) since launch last year. Users can now take advantage of 35 deep learning, high-performance computing, and visualization containers from NGC, a story we'll be telling in depth at this week's International Supercomputing Conference in Frankfurt. Over the past three years, containers have become a crucial tool in deploying applications on a shared cluster and speeding the work, especially for researchers and data scientists running AI workloads. These containers make deploying deep learning frameworks -- building blocks for designing, training and validating deep neural networks -- faster and easier. Installing frameworks is complicated and time consuming.

Supercomputers used to be a market unto themselves. In their time, giants such as IBM, Control Data Corporation (CDC), Evans & Sutherland (E&S), Silicon Graphics and Cray Computing ruled the supercomputing market. But with Moore's Law restricted to only scaling transistor count, scale-out has taken over a conference series that had epitomized scale-up. Because the nature of supercomputing has changed so much in the past two decades, the market has expanded into a much broader high-performance computing (HPC) market. At this year's SC16 conference, SGI made news by being purchased by Hewlett Packard Enterprise (HPE) and the Cray booth looked a bit forlorn.