  Deploying deep learning (DL) models across multiple compute devices to train
large and complex models continues to grow in importance because of the demand
for faster and more frequent training. Data parallelism (DP) is the most widely
used parallelization strategy, but as the number of devices in data parallel
training grows, so does the communication overhead between devices.
Additionally, a larger aggregate batch size per step leads to statistical
efficiency loss, i.e., a larger number of epochs are required to converge to a
desired accuracy. These factors affect overall training time and beyond a
certain number of devices, the speedup from leveraging DP begins to scale
poorly. In addition to DP, each training step can be accelerated by exploiting
model parallelism (MP). This work explores hybrid parallelization, where each
data parallel worker is comprised of more than one device, across which the
model dataflow graph (DFG) is split using MP. We show that at scale, hybrid
training will be more effective at minimizing end-to-end training time than
exploiting DP alone. We project that for Inception-V3, GNMT, and BigLSTM, the
hybrid strategy provides an end-to-end training speedup of at least 26.5%, 8%,
and 22% respectively compared to what DP alone can achieve at scale.
