  Mapping all the neurons in the brain requires automatic reconstruction of
entire cells from volume electron microscopy data. The flood-filling network
(FFN) architecture has demonstrated leading performance for segmenting
structures from this data. However, the training of the network is
computationally expensive. In order to reduce the training time, we implemented
synchronous and data-parallel distributed training using the Horovod library,
which is different from the asynchronous training scheme used in the published
FFN code. We demonstrated that our distributed training scaled well up to 2048
Intel Knights Landing (KNL) nodes on the Theta supercomputer. Our trained
models achieved similar level of inference performance, but took less training
time compared to previous methods. Our study on the effects of different batch
sizes on FFN training suggests ways to further improve training efficiency. Our
findings on optimal learning rate and batch sizes agree with previous works.
