Climate Research Pulls Deep Learning Onto Traditional Supercomputers
Over the last year, stories pointing to a bright future for deep neural networks and deep learning in general have proliferated. However, most of what we have seen has been centered on the use of deep learning to power consumer services. Speech and image recognition, video analysis, and other features have spun from deep learning developments, but from the mainstream view, it would seem that scientific computing use cases are still limited.
Deep neural networks present an entirely different way of thinking about a problem set and the data that feeds it. While there are established approaches for images and …
Climate Research Pulls Deep Learning Onto Traditional Supercomputers was written by Nicole Hemsoth at The Next Platform.
The former Scalock reaches GA.
Let’s ignore the data flowing through the network for a moment (though the universal scaling law might provide an interesting way to look at packets or flows per second as transactions), and focus just on the control plane. When we look at the control plane, we find a routing protocol or a centralized controller that accepts information about changes in the network topology (and other data points), and builds a model of the network topology which can be used to forward traffic. Questions we can ask about the state being handled by the control plane include things like: How many changes are there? What is the rate at which this information arrives? How many changes might be present in the system at any given time? How many devices participate in the control plane?