  Communication overhead poses an important obstacle to distributed DNN
training and draws increasing attention in recent years. Despite continuous
efforts, prior solutions such as gradient compression/reduction,
compute/communication overlapping and layer-wise flow scheduling, etc., are
still coarse-grained and insufficient for an efficient distributed training
especially when the network is under pressure. We present DLCP, a novel
solution exploiting the domain-specific properties of deep learning to optimize
communication overhead of DNN training in a fine-grained manner. At its heart,
DLCP comprises of several key innovations beyond prior work: e.g., it exploits
{\em bounded loss tolerance} of SGD-based training to improve tail
communication latency which cannot be avoided purely through gradient
compression. It then performs fine-grained packet-level prioritization and
dropping, as opposed to flow-level scheduling, based on layers and magnitudes
of gradients to further speedup model convergence without affecting accuracy.
In addition, it leverages inter-packet order-independency to perform per-packet
load balancing without causing classical re-ordering issues. DLCP works with
both Parameter Server and collective communication routines. We have
implemented DLCP with commodity switches, integrated it with various training
frameworks including TensorFlow, MXNet and PyTorch, and deployed it in our
small-scale testbed with 10 Nvidia V100 GPUs. Our testbed experiments and
large-scale simulations show that DLCP delivers up to $84.3\%$ additional
training acceleration over the best existing solutions.
