Channel and filter parallelism for large-scale CNN training

Accelerating large-scale CNN training is needed to keep training times reasonable as datasets grow larger and models become more complex. Existing frameworks primarily scale using data-parallelism, but this is limited by the mini-batch size, which cannot grow arbitrarily. We introduce three algorithms that partition channel or filter data to exploit parallelism beyond the sample dimension. Further, they partition the parameters of convolutional layers, replacing global all reduces with segmented allreduces---smaller, concurrent allreduces among disjoint processor sets. These algorithms enable strong scaling, reduced communication overhead, and reduced memory pressure, enabling training of very wide CNNs.