In this paper, we present a strong scaling approach to run seismic segmentation models in
parallel. The models used here are deep neural networks, which are increasingly being used by
the Oil and Gas industry. The typical approach to run these models in parallel is to increase the
mini-batch sizes as the number of tasks increases, in what we call weak scaling. This strategy
has worked well for massive labeled training sets. However, for moderately large training sets
this may not be the case. Here we present and evaluate a dierent strategy with a seismic
segmentation model with a real dataset using Power machines.
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