  We study the factors affecting training time in multi-device deep learning
systems. Given a specification of a convolutional neural network, our goal is
to minimize the time to train this model on a cluster of commodity CPUs and
GPUs. We first focus on the single-node setting and show that by using standard
batching and data-parallel techniques, throughput can be improved by at least
5.5x over state-of-the-art systems on CPUs. This ensures an end-to-end training
speed directly proportional to the throughput of a device regardless of its
underlying hardware, allowing each node in the cluster to be treated as a black
box. Our second contribution is a theoretical and empirical study of the
tradeoffs affecting end-to-end training time in a multiple-device setting. We
identify the degree of asynchronous parallelization as a key factor affecting
both hardware and statistical efficiency. We see that asynchrony can be viewed
as introducing a momentum term. Our results imply that tuning momentum is
critical in asynchronous parallel configurations, and suggest that published
results that have not been fully tuned might report suboptimal performance for
some configurations. For our third contribution, we use our novel understanding
of the interaction between system and optimization dynamics to provide an
efficient hyperparameter optimizer. Our optimizer involves a predictive model
for the total time to convergence and selects an allocation of resources to
minimize that time. We demonstrate that the most popular distributed deep
learning systems fall within our tradeoff space, but do not optimize within the
space. By doing this optimization, our prototype runs 1.9x to 12x faster than
the fastest state-of-the-art systems.
