New deep learning techniques lead to materials imaging breakthrough

Supercomputers help researchers study the causes and effects--usually in that order--of complex phenomena. However, scientists occasionally need to deduce the origins of scientific phenomena based on observable results. These so-called inverse problems are notoriously difficult to solve, especially when the amount of data that must be analyzed outgrows traditional machine-learning tools. To better understand inverse problems, a team from the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL), NVIDIA, and Uber Technologies developed and demonstrated two new techniques within a widely used communication library called Horovod. Developed by Uber, this platform trains deep neural networks (DNNs) that use algorithms to imitate and harness the decision-making power of the human brain for scientific applications. Because Horovod relies on a single coordinator to provide instructions to many different workers (i.e., GPUs in this case) to complete this process, large-scale deep-learning applications often encounter significant slowdowns during training.