Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

Reprinted with permission from HPCWire. Petaflop per second deep learning training performance on the Cori supercomputer at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) has given climate scientists the ability to use machine learning to identify extreme weather events in huge climate simulation datasets. Predictive accuracies ranging from 89.4% to as high as 99.1% show that trained deep learning neural networks (DNNs) can identify weather fronts, tropical cyclones, and long narrow air flows that transport water vapor from the tropics called atmospheric rivers (Figure 1). Figure 1: Relation between ground truth (green boxes) and classification plus regression results (red boxes) of the DNN trained to recognize atmospheric phenomena. The strong relationship between ground truth and the neural network prediction can be seen in the classification plus regression results reported by Berkeley Lab climate scientist Michael Wehner at the Intel Developer Conference held during SC17 last November in Denver, Colorado. Supercomputers like NERSC's Cori system provide scientists with an extraordinary tool to model climate change significantly faster and far more accurately than was possible on previous generation supercomputers.