  Measurements in Liquid Argon Time Projection Chamber (LArTPC) neutrino
detectors, such as the MicroBooNE detector at Fermilab, feature large, high
fidelity event images. Deep learning techniques have been extremely successful
in classification tasks of photographs, but their application to LArTPC event
images is challenging, due to the large size of the events. Events in these
detectors are typically two orders of magnitude larger than images found in
classical challenges, like recognition of handwritten digits contained in the
MNIST database or object recognition in the ImageNet database. Ideally,
training would occur on many instances of the entire event data, instead of
many instances of cropped regions of interest from the event data. However,
such efforts lead to extremely long training cycles, which slow down the
exploration of new network architectures and hyperparameter scans to improve
the classification performance. We present studies of scaling a LArTPC
classification problem on multiple architectures, spanning multiple nodes. The
studies are carried out on simulated events in the MicroBooNE detector. We
emphasize that it is beyond the scope of this study to optimize networks or
extract the physics from any results here. Institutional computing at Pacific
Northwest National Laboratory and the SummitDev machine at Oak Ridge National
Laboratory's Leadership Computing Facility have been used. To our knowledge,
this is the first use of state-of-the-art Convolutional Neural Networks for
particle physics and their attendant compute techniques onto the DOE Leadership
Class Facilities. We expect benefits to accrue particularly to the Deep
Underground Neutrino Experiment (DUNE) LArTPC program, the flagship US High
Energy Physics (HEP) program for the coming decades.
