Supercomputing Gets Neural Network Boost in Quantum Chemistry | The Next Platform

Photo: Nicole Hemsoth

"Just two years ago, supercomputing was thrust into a larger spotlight because of the surge of interest in deep learning." insist Nicole Hemsoth, Co-founder and co-editor. 

Photo: The Next Platform

As we talked about here, the hardware similarities, particularly for training on GPU-accelerated machines and key HPC development approaches, including MPI to scale across a massive number of nodes, brought new attention to the world of scientific and technical computing.

What wasn’t clear then was how traditional supercomputing could benefit from all the framework developments in deep learning. After all, they had many of the same hardware environments and problems that could benefit from prediction, but what they lacked were models that could be mapped to traditional HPC codes. In that short amount of time—mostly in the last year—there has been a big push in many traditional HPC areas to do just that…to find ways to make supercomputing simulations more streamlined by training on datasets to predict properties, filter through noise, and make broad connections that would take power-hungry simulations long periods to chew through.

Also just a few years ago, the real traction in deep learning was focused on image, video, and speech recognition and analysis, often for consumer-facing services. However, as we have described in detail, there is a new wave of applications for neural networks that could upend the way we think about scientific and technical computing—those traditional realms of supercomputing.

One of the emerging areas cited in the above review of recent work in scientific computing areas that are being altered by deep learning is in molecular and materials science. While the work here is still in the early stages, Google Brain researchers are among those making strides in applying deep learning to solve more complex materials science and molecular interaction problems in quantum chemistry. The goal is to build complex machine learning models for chemical prediction that can learn from their own features—saving a great deal of computational time and cost over traditional simulations.
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Source: The Next Platform