  Recently there has been a surge of research on improving the communication
efficiency of distributed training. However, little work has been done to
systematically understand whether the network is the bottleneck and to what
extent.
  In this paper, we take a first-principles approach to measure and analyze the
network performance of distributed training. As expected, our measurement
confirms that communication is the component that blocks distributed training
from linear scale-out. However, contrary to the common belief, we find that the
network is running at low utilization and that if the network can be fully
utilized, distributed training can achieve a scaling factor of close to one.
Moreover, while many recent proposals on gradient compression advocate over
100x compression ratio, we show that under full network utilization, there is
no need for gradient compression in 100 Gbps network. On the other hand, a
lower speed network like 10 Gbps requires only 2x--5x gradients compression
ratio to achieve almost linear scale-out. Compared to application-level
techniques like gradient compression, network-level optimizations do not
require changes to applications and do not hurt the performance of trained
models. As such, we advocate that the real challenge of distributed training is
for the network community to develop high-performance network transport to
fully utilize the network capacity and achieve linear scale-out.
